diff --git a/CV/OpenCV/.gitignore b/CV/OpenCV/.gitignore
new file mode 100644
index 0000000..f0cd5a5
--- /dev/null
+++ b/CV/OpenCV/.gitignore
@@ -0,0 +1,2 @@
+.dist
+.DS_Store
diff --git a/CV/OpenCV/Interface/blank_img.py b/CV/OpenCV/Interface/blank_img.py
new file mode 100644
index 0000000..78686ca
--- /dev/null
+++ b/CV/OpenCV/Interface/blank_img.py
@@ -0,0 +1,5 @@
+import cv2
+import numpy as np
+
+img = np.full((500,500,3), 255, dtype=np.uint8)
+cv2.imwrite('./img/blank_500.jpg', img)
diff --git a/CV/OpenCV/Interface/draw_circle.py b/CV/OpenCV/Interface/draw_circle.py
new file mode 100644
index 0000000..11bbde1
--- /dev/null
+++ b/CV/OpenCV/Interface/draw_circle.py
@@ -0,0 +1,30 @@
+import cv2
+
+img = cv2.imread('../img/blank_500.jpg')
+
+
+cv2.circle(img, (150, 150), 100, (255,0,0))
+cv2.circle(img, (300, 150), 70, (0,255,0), 5)
+cv2.circle(img, (400, 150), 50, (0,0,255), -1)
+
+
+cv2.ellipse(img, (50, 300), (50, 50), 0, 0, 360, (0,0,255))
+cv2.ellipse(img, (150, 300), (50, 50), 0, 0, 180, (255,0,0))
+cv2.ellipse(img, (200, 300), (50, 50), 0, 181, 360, (0,0,255))    
+
+
+cv2.ellipse(img, (325, 300), (75, 50), 0, 0, 360, (0,255,0))
+cv2.ellipse(img, (450, 300), (50, 75), 0, 0, 360, (255,0,255))
+
+
+cv2.ellipse(img, (50, 425), (50, 75), 15, 0, 360, (0,0,0))
+cv2.ellipse(img, (200, 425), (50, 75), 45, 0, 360, (0,0,0))
+
+
+cv2.ellipse(img, (350, 425), (50, 75), 45, 0, 180, (0,0,255))
+cv2.ellipse(img, (400, 425), (50, 75), 45, 181, 360, (255,0,0))
+
+
+cv2.imshow('circle', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/draw_line.py b/CV/OpenCV/Interface/draw_line.py
new file mode 100644
index 0000000..cd6f13b
--- /dev/null
+++ b/CV/OpenCV/Interface/draw_line.py
@@ -0,0 +1,24 @@
+import cv2
+
+img = cv2.imread('../img/blank_500.jpg')
+
+cv2.line(img, (50, 50), (150, 50), (255,0,0))
+cv2.line(img, (200, 50), (300, 50), (0,255,0))
+cv2.line(img, (350, 50), (450, 50), (0,0,255))
+
+
+cv2.line(img, (100, 100), (400, 100), (255,255,0), 10)  
+cv2.line(img, (100, 150), (400, 150), (255,0,255), 10)
+cv2.line(img, (100, 200), (400, 200), (0,255,255), 10)
+cv2.line(img, (100, 250), (400, 250), (200,200,200), 10)
+cv2.line(img, (100, 300), (400, 300), (0,0,0), 10)
+
+
+cv2.line(img, (100, 350), (400, 400), (0,0,255), 20, cv2.LINE_4)
+cv2.line(img, (100, 400), (400, 450), (0,0,255), 20, cv2.LINE_8)
+cv2.line(img, (100, 450), (400, 500), (0,0,255), 20, cv2.LINE_AA)
+cv2.line(img, (0,0), (500,500), (0,0,255))                      
+
+cv2.imshow('lines', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/draw_poly.py b/CV/OpenCV/Interface/draw_poly.py
new file mode 100644
index 0000000..35732e2
--- /dev/null
+++ b/CV/OpenCV/Interface/draw_poly.py
@@ -0,0 +1,21 @@
+import cv2
+import numpy as np                          # 좌표 표현을 위한 numpy 모듈  ---①
+
+img = cv2.imread('../img/blank_500.jpg')
+
+
+
+pts1 = np.array([[50,50], [150,150], [100,140],[200,240]], dtype=np.int32)
+pts2 = np.array([[350,50], [250,200], [450,200]], dtype=np.int32)
+pts3 = np.array([[150,300], [50,450], [250,450]], dtype=np.int32)
+pts4 = np.array([[350,250], [450,350], [400,450], [300,450], [250,350]], dtype=np.int32) 
+
+
+cv2.polylines(img, [pts1], False, (255,0,0))
+cv2.polylines(img, [pts2], False, (0,0,0), 10)
+cv2.polylines(img, [pts3], True, (0,0,255), 10)
+cv2.polylines(img, [pts4], True, (0,0,0))
+
+cv2.imshow('polyline', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/draw_rect.py b/CV/OpenCV/Interface/draw_rect.py
new file mode 100644
index 0000000..2799400
--- /dev/null
+++ b/CV/OpenCV/Interface/draw_rect.py
@@ -0,0 +1,11 @@
+import cv2
+
+img = cv2.imread('../img/blank_500.jpg')
+
+cv2.rectangle(img, (50, 50), (150, 150), (255,0,0) )
+cv2.rectangle(img, (300, 300), (100, 100), (0,255,0), 10 )
+cv2.rectangle(img, (450, 200), (200, 450), (0,0,255), -1 )
+
+cv2.imshow('rectangle', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/draw_text.py b/CV/OpenCV/Interface/draw_text.py
new file mode 100644
index 0000000..cbe4a13
--- /dev/null
+++ b/CV/OpenCV/Interface/draw_text.py
@@ -0,0 +1,40 @@
+import cv2
+
+img = cv2.imread('../img/blank_500.jpg')
+
+# sans-serif small
+cv2.putText(img, "Plain", (50, 30), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0,0))            
+# sans-serif normal
+cv2.putText(img, "Simplex", (50, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0,0))        
+# sans-serif bold
+cv2.putText(img, "Duplex", (50, 110), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0,0))         
+# sans-serif normall X2  ---①
+cv2.putText(img, "Simplex", (200, 110), cv2.FONT_HERSHEY_SIMPLEX, 2, (0,0,250)) 
+
+# serif small
+cv2.putText(img, "Complex Small", (50, 180), cv2.FONT_HERSHEY_COMPLEX_SMALL, \
+            1, (0, 0,0))   
+# serif normal
+cv2.putText(img, "Complex", (50, 220), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0,0))
+# serif bold
+cv2.putText(img, "Triplex", (50, 260), cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0,0))               
+# serif normal X2  ---②
+cv2.putText(img, "Complex", (200, 260), cv2.FONT_HERSHEY_TRIPLEX, 2, (0,0,255))               
+
+# hand-wringing sans-serif
+cv2.putText(img, "Script Simplex", (50, 330), cv2.FONT_HERSHEY_SCRIPT_SIMPLEX, \
+            1, (0, 0,0)) 
+# hand-wringing serif
+cv2.putText(img, "Script Complex", (50, 370), cv2.FONT_HERSHEY_SCRIPT_COMPLEX, \
+            1, (0, 0,0)) 
+
+# sans-serif + italic ---③
+cv2.putText(img, "Plain Italic", (50, 430), \
+            cv2.FONT_HERSHEY_PLAIN | cv2.FONT_ITALIC, 1, (0, 0,0)) 
+# sarif + italic
+cv2.putText(img, "Complex Italic", (50, 470), \
+            cv2.FONT_HERSHEY_COMPLEX | cv2.FONT_ITALIC, 1, (0, 0,0)) 
+
+cv2.imshow('draw text', img)
+cv2.waitKey()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/Interface/event_key.py b/CV/OpenCV/Interface/event_key.py
new file mode 100644
index 0000000..7f4b7ac
--- /dev/null
+++ b/CV/OpenCV/Interface/event_key.py
@@ -0,0 +1,27 @@
+import cv2
+
+img_file = "../img/girl.jpg" 
+img = cv2.imread(img_file) 
+title = 'IMG'
+x, y = 100, 100
+
+while True:
+    cv2.imshow(title, img)
+    cv2.moveWindow(title, x, y)
+
+    key = cv2.waitKey(0) & 0xFF
+    print(key, chr(key))
+    
+    if key == ord('h'):
+        x -= 10
+    elif key == ord('j'):
+        y += 10
+    elif key == ord('k'):
+        y -= 10
+    elif key == ord('l'):
+        x += 10
+    elif key == ord('q') or key == 27:
+        break
+        cv2.destroyAllWindows()
+    cv2.moveWindow(title, x, y )
+        
\ No newline at end of file
diff --git a/CV/OpenCV/Interface/event_mouse_circle.py b/CV/OpenCV/Interface/event_mouse_circle.py
new file mode 100644
index 0000000..b513718
--- /dev/null
+++ b/CV/OpenCV/Interface/event_mouse_circle.py
@@ -0,0 +1,21 @@
+import cv2
+
+title = 'mouse event'
+img = cv2.imread('../img/blank_500.jpg')
+cv2.imshow(title, img)
+
+
+def onMouse(event, x, y, flags, param):
+    print(event, x, y, )
+    
+    if event == cv2.EVENT_LBUTTONDOWN:
+        cv2.circle(img, (x,y), 30, (0,0,0), -1)
+        cv2.imshow(title, img)
+
+
+cv2.setMouseCallback(title, onMouse)
+
+while True:
+    if cv2.waitKey(0) & 0xFF == 27:
+        break
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/Interface/event_mouse_circle_flag.py b/CV/OpenCV/Interface/event_mouse_circle_flag.py
new file mode 100644
index 0000000..cbdd37c
--- /dev/null
+++ b/CV/OpenCV/Interface/event_mouse_circle_flag.py
@@ -0,0 +1,38 @@
+import cv2
+
+
+title = 'mouse event'
+img = cv2.imread('../img/blank_500.jpg')
+cv2.imshow(title, img)
+
+
+colors = {'black':(0,0,0),
+         'red' : (0,0,255),
+         'blue':(255,0,0),
+         'green': (0,255,0) }
+
+
+def onMouse(event, x, y, flags, param):
+    print(event, x, y, flags)
+    color = colors['black']
+
+    if event == cv2.EVENT_LBUTTONDOWN:
+        if flags & cv2.EVENT_FLAG_CTRLKEY and flags & cv2.EVENT_FLAG_SHIFTKEY:
+            color = colors['green']
+
+        elif flags & cv2.EVENT_FLAG_SHIFTKEY:
+            color = colors['blue']
+
+        elif flags & cv2.EVENT_FLAG_CTRLKEY:
+            color = colors['red']
+
+        cv2.circle(img, (x,y), 30, color, -1) 
+        cv2.imshow(title, img)
+
+
+cv2.setMouseCallback(title, onMouse)
+
+while True:
+    if cv2.waitKey(0) & 0xFF == 27:
+        break
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/Interface/event_trackbar.py b/CV/OpenCV/Interface/event_trackbar.py
new file mode 100644
index 0000000..02ee828
--- /dev/null
+++ b/CV/OpenCV/Interface/event_trackbar.py
@@ -0,0 +1,29 @@
+import cv2
+import numpy as np
+
+win_name = 'Trackbar'
+
+img = cv2.imread('../img/blank_500.jpg')
+cv2.imshow(win_name,img)
+
+
+def onChange(x):                                        
+    print(x)
+
+    r = cv2.getTrackbarPos('R',win_name)               
+    g = cv2.getTrackbarPos('G',win_name)               
+    b = cv2.getTrackbarPos('B',win_name)               
+    print(r, g, b)
+    img[:] = [b,g,r]
+    cv2.imshow(win_name, img)
+
+
+cv2.createTrackbar('R', win_name, 255, 255, onChange)  
+cv2.createTrackbar('G', win_name, 255, 255, onChange)
+cv2.createTrackbar('B', win_name, 255, 255, onChange)
+
+while True:
+    if cv2.waitKey(1) & 0xFF == 27:
+        break
+
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/img_show.py b/CV/OpenCV/Interface/img_show.py
new file mode 100644
index 0000000..7b621b8
--- /dev/null
+++ b/CV/OpenCV/Interface/img_show.py
@@ -0,0 +1,13 @@
+import cv2
+
+
+img_file = "../img/girl.jpg"
+img = cv2.imread(img_file)
+
+
+if img is not None:
+    cv2.imshow('IMG', img)
+    cv2.waitKey()
+    cv2.destroyAllWindows()
+else:
+    print('No image file.')
diff --git a/CV/OpenCV/Interface/img_show_gray.py b/CV/OpenCV/Interface/img_show_gray.py
new file mode 100644
index 0000000..b5fa2d6
--- /dev/null
+++ b/CV/OpenCV/Interface/img_show_gray.py
@@ -0,0 +1,13 @@
+import cv2
+
+img_file = "../img/girl.jpg"
+
+# read image with gray scaling
+img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
+
+if img is not None:
+    cv2.imshow('IMG', img)
+    cv2.waitKey()
+    cv2.destroyAllWindows()
+else:
+    print('No image file.')
diff --git a/CV/OpenCV/Interface/img_write.py b/CV/OpenCV/Interface/img_write.py
new file mode 100644
index 0000000..d7674fa
--- /dev/null
+++ b/CV/OpenCV/Interface/img_write.py
@@ -0,0 +1,11 @@
+import cv2
+
+img_file = '../img/girl.jpg'
+save_file = '../img/girl_gray.jpg'
+
+
+img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
+cv2.imshow(img_file, img)
+cv2.imwrite(save_file, img)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/video_cam.py b/CV/OpenCV/Interface/video_cam.py
new file mode 100644
index 0000000..f4fa2b4
--- /dev/null
+++ b/CV/OpenCV/Interface/video_cam.py
@@ -0,0 +1,22 @@
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+if cap.isOpened():
+    while True:
+        ret, img = cap.read()
+
+        if ret:
+            cv2.imshow('camera', img)
+            if cv2.waitKey(1) != -1:
+                break
+
+        else:
+            print('no frame')
+            break
+else:
+    print("can't open camera.")
+
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/video_cam_rec.py b/CV/OpenCV/Interface/video_cam_rec.py
new file mode 100644
index 0000000..cbda57f
--- /dev/null
+++ b/CV/OpenCV/Interface/video_cam_rec.py
@@ -0,0 +1,32 @@
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+if cap.isOpened:
+    file_path = './record.avi'
+    fps = 30.0
+    fourcc = cv2.VideoWriter_fourcc(*'DIVX')
+    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    size = (int(width), int(height))
+    out = cv2.VideoWriter(file_path, fourcc, fps, size)
+
+
+    while True:
+        ret, frame = cap.read()
+
+        if ret:
+            cv2.imshow('camera-recording',frame)
+            out.write(frame)
+            if cv2.waitKey(int(1000 / fps)) != -1: 
+                break
+        else:
+            print("no frame!")
+            break
+    out.release()
+
+else:
+    print("can't open camera!")
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/video_cam_resize.py b/CV/OpenCV/Interface/video_cam_resize.py
new file mode 100644
index 0000000..2b69f4b
--- /dev/null
+++ b/CV/OpenCV/Interface/video_cam_resize.py
@@ -0,0 +1,31 @@
+import cv2
+
+
+cap = cv2.VideoCapture(0)
+width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+
+print("Original width: %d, height:%d" % (width, height) ) 
+
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
+width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+
+print("Resized width: %d, height:%d" % (width, height) )
+
+if cap.isOpened():
+    while True:
+        ret, img = cap.read()
+        if ret:
+            cv2.imshow('camera', img)
+            if cv2.waitKey(1) != -1:
+                break
+        else:
+            print('no frame!')
+            break
+else:
+    print("can't open camera!")
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/video_cam_take_pic.py b/CV/OpenCV/Interface/video_cam_take_pic.py
new file mode 100644
index 0000000..820e0b2
--- /dev/null
+++ b/CV/OpenCV/Interface/video_cam_take_pic.py
@@ -0,0 +1,21 @@
+import cv2
+
+
+cap = cv2.VideoCapture(0)
+
+if cap.isOpened() :
+    while True:
+        ret, frame = cap.read()
+        if ret:
+            cv2.imshow('camera',frame)
+
+            if cv2.waitKey(1) != -1:
+                cv2.imwrite('photo.jpg', frame)
+                break
+        else:
+            print('no frame!')
+            break
+else:
+    print('no camera!')
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/video_play.py b/CV/OpenCV/Interface/video_play.py
new file mode 100644
index 0000000..eef7425
--- /dev/null
+++ b/CV/OpenCV/Interface/video_play.py
@@ -0,0 +1,18 @@
+import cv2
+
+
+video_file = "../img/big_buck.avi"
+
+cap = cv2.VideoCapture(video_file)
+if cap.isOpened():
+    while True:
+        ret, img = cap.read()
+        if ret:
+            cv2.imshow(video_file, img)
+            cv2.waitKey(25)
+        else:
+            break
+else:
+    print("can't open video.")
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/video_play_fps.py b/CV/OpenCV/Interface/video_play_fps.py
new file mode 100644
index 0000000..21860e3
--- /dev/null
+++ b/CV/OpenCV/Interface/video_play_fps.py
@@ -0,0 +1,23 @@
+import cv2
+
+
+video_file = "../img/big_buck.avi"
+
+cap = cv2.VideoCapture(video_file)
+
+if cap.isOpened():
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    delay = int(1000/fps)
+    print("FPS: %f, Delay: %dms" %(fps, delay))
+
+    while True:
+        ret, img = cap.read() 
+        if ret:
+            cv2.imshow(video_file, img)
+            cv2.waitKey(delay)
+        else:
+            break
+else:
+    print("can't open video.")
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/Interface/win.py b/CV/OpenCV/Interface/win.py
new file mode 100644
index 0000000..3a0672a
--- /dev/null
+++ b/CV/OpenCV/Interface/win.py
@@ -0,0 +1,24 @@
+import cv2
+
+
+file_path = '../img/girl.jpg'
+img = cv2.imread(file_path)
+img_gray = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
+
+cv2.namedWindow('origin')
+cv2.namedWindow('gray', cv2.WINDOW_NORMAL)
+cv2.imshow('origin', img)
+cv2.imshow('gray', img_gray)
+
+cv2.moveWindow('origin', 0, 0)
+cv2.moveWindow('gray', 100, 100)
+
+cv2.waitKey(0)
+cv2.resizeWindow('origin', 200, 200)
+cv2.resizeWindow('gray', 100, 100)
+
+cv2.waitKey(0)
+cv2.destroyWindow("gray")
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/MachineLearning/README.md b/CV/OpenCV/MachineLearning/README.md
new file mode 100644
index 0000000..0d29553
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/README.md
@@ -0,0 +1,7 @@
+# Machine Learning with OpenCV-Python
+
+Codes in this directory are copied from [here](https://github.com/dltpdn/insightbook.opencv_project_python/tree/master/09.ml).
+
+## Reference
+
+- Sewoo Lee. [insightbook.opencv_project_python](https://github.com/dltpdn/insightbook.opencv_project_python)
diff --git a/CV/OpenCV/MachineLearning/bow_plane_bike_test.py b/CV/OpenCV/MachineLearning/bow_plane_bike_test.py
new file mode 100644
index 0000000..ec20b58
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/bow_plane_bike_test.py
@@ -0,0 +1,40 @@
+import cv2
+import numpy as np
+
+categories =  ['airplanes', 'Motorbikes' ]
+dict_file = './plane_bike_dict.npy'
+#dict_file = './plane_bike_dict_4000.npy'
+svm_model_file = './plane_bike_svm.xml'
+#svm_model_file = './plane_bike_svm_4000.xml'
+
+# 테스트 할 이미지 경로 --- ①
+imgs = ['../img/aircraft.jpg','../img/jetstar.jpg', 
+        '../img/motorcycle.jpg', '../img/motorbike.jpg']
+
+# 특징 추출기(SIFT) 생성 ---②
+detector = cv2.xfeatures2d.SIFT_create()
+# BOW 추출기 생성 및 사전 로딩 ---③
+bowextractor = cv2.BOWImgDescriptorExtractor(detector, \
+                                cv2.BFMatcher(cv2.NORM_L2))
+bowextractor.setVocabulary(np.load(dict_file))
+# 훈련된 모델 읽어서 SVM 객체 생성 --- ④
+svm  = cv2.ml.SVM_load(svm_model_file)
+
+# 4개의 이미지 테스트 
+for i, path in enumerate(imgs):
+    img = cv2.imread(path)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # 테스트 이미지에서 BOW 히스토그램 추출 ---⑤
+    hist = bowextractor.compute(gray, detector.detect(gray))
+    # SVM 예측 ---⑥
+    ret, result = svm.predict(hist)
+    # 결과 표시 
+    name = categories[int(result[0][0])]
+    txt, base = cv2.getTextSize(name, cv2.FONT_HERSHEY_PLAIN, 2, 3)
+    x,y = 10, 50
+    cv2.rectangle(img, (x,y-base-txt[1]), (x+txt[0], y+txt[1]), (30,30,30), -1)
+    cv2.putText(img, name, (x,y), cv2.FONT_HERSHEY_PLAIN, \
+                                 2, (0,255,0), 2, cv2.LINE_AA)
+    cv2.imshow(path, img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/bow_plane_bike_train.py b/CV/OpenCV/MachineLearning/bow_plane_bike_train.py
new file mode 100644
index 0000000..5eae96b
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/bow_plane_bike_train.py
@@ -0,0 +1,89 @@
+import cv2
+import numpy as np
+import os, glob, time
+
+
+# 각종 변수 선언---①
+startT = time.time()                        # 소요시간 측정을 위한 시간 저장
+categories =  ['airplanes', 'Motorbikes' ]  # 카테고리 이름 
+dictionary_size = 50                        # 사전 크기, 클러스터 갯수 
+base_path = "../img/101_ObjectCategories/"  # 학습 이미지 기본 경로 
+dict_file = './plane_bike_dict.npy'         # 사전 객체 저장할 파일 이름 
+svm_model_file = './plane_bike_svm.xml'     # SVM 모델 객체 저장할 파일 이름 
+
+# 추출기와 BOW 객체 생 --- ②
+detector = cv2.xfeatures2d.SIFT_create()    # 추출기로 SIFT 생성 
+matcher = cv2.BFMatcher(cv2.NORM_L2)        # 매칭기로 BF 생성
+bowTrainer = cv2.BOWKMeansTrainer(dictionary_size) # KMeans로 구현된 BWOTrainer 생성
+bowExtractor = cv2.BOWImgDescriptorExtractor(detector, matcher) # 히스토그램 계산할 BOW추출기 생성
+
+# 특징 디스크립터를 KMeansTrainer에 추가---③
+train_paths = []                            # 훈련에 사용할 모든 이미지 경로 
+train_labels = []                           # 학습 데이타 레이블
+print('Adding descriptor to BOWTrainer...')
+for idx, category in enumerate(categories): # 카테고리 순회
+    dir_path = base_path + category          
+    img_paths = glob.glob(dir_path +'/*.jpg') 
+    img_len = len(img_paths)
+    for i, img_path in enumerate(img_paths): # 카테고리 내의 모든 이미지 파일 순회
+        train_paths.append(img_path)        
+        train_labels.append(idx)            # 학습 데이타 레이블, 0 또는 1 
+        img = cv2.imread(img_path)          
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # 특징점과 특징 디스크립터 추출 및 bowTrainer에 추가 ---④
+        kpt, desc= detector.detectAndCompute(gray, None) 
+        bowTrainer.add(desc)                
+        print('\t%s %d/%d(%.2f%%)' \
+              %(category,i+1, img_len, (i+1)/img_len*100), end='\r')
+    print()
+print('Adding descriptor completed...')
+
+# KMeans 클러스터로 군집화하여 시각 사전 생성 및 저장---⑤
+print('Starting Dictionary clustering(%d)... \
+        It will take several time...'%dictionary_size)
+dictionary = bowTrainer.cluster() # 군집화로 시각 사전 생성  
+np.save(dict_file, dictionary)    # 시각 사전 데이타(넘파일)를 파일로 저장
+print('Dictionary Clustering completed...dictionary shape:',dictionary.shape)
+
+# 시각 사전과 모든 이미지의 매칭점으로 히스토그램 계산---⑥
+bowExtractor.setVocabulary(dictionary)      # bowExtractor에 시각 사전 셋팅 
+train_desc = []                             # 학습 데이타 
+for i, path in enumerate(train_paths):      # 모든 학습 대상 이미지 순회
+    img = cv2.imread(path)                  # 이미지 읽기 
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
+    # 매칭점에 대한 히스토그램 계산 --- ⑦
+    hist = bowExtractor.compute(gray, detector.detect(gray)) 
+    train_desc.extend(hist)                 
+    print('Compute histogram training set...(%.2f%%)'\
+                    %((i+1)/len(train_paths)*100),end='\r')
+print("\nsvm items", len(train_desc), len(train_desc[0]))
+
+# 히스토그램을 학습데이타로 SVM 훈련 및 모델 저장---⑧
+print('svm training...')
+svm = cv2.ml.SVM_create()
+svm.trainAuto(np.array(train_desc), cv2.ml.ROW_SAMPLE, np.array(train_labels))
+svm.save(svm_model_file)
+print('svm training completed.')
+print('Training Elapsed: %s'\
+        %time.strftime('%H:%M:%S', time.gmtime(time.time()-startT)))
+
+# 원래의 이미지로 테스트 --- ⑨
+print("Accuracy(Self)")
+for label, dir_name in enumerate(categories):
+    labels = []
+    results = []
+    img_paths = glob.glob(base_path + '/'+dir_name +'/*.*')
+    for img_path in img_paths:
+        labels.append(label)
+        img = cv2.imread(img_path)
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        feature = bowExtractor.compute(gray, detector.detect(gray))
+        ret, result = svm.predict(feature)
+        resp = result[0][0]
+        results.append(resp)
+
+    labels = np.array(labels)
+    results = np.array(results)
+    err = (labels != results)
+    err_mean = err.mean()
+    print('\t%s: %.2f %%' % (dir_name, (1 - err_mean)*100))
diff --git a/CV/OpenCV/MachineLearning/data/haarcascade_eye.xml b/CV/OpenCV/MachineLearning/data/haarcascade_eye.xml
new file mode 100644
index 0000000..b21e3b9
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/data/haarcascade_eye.xml
@@ -0,0 +1,12213 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 20x20 frontal eye detector.
+    Created by Shameem Hameed (http://umich.edu/~shameem)
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <maxWeakCount>93</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount></featureParams>
+  <stageNum>24</stageNum>
+  <stages>
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.4562760591506958e+00</stageThreshold>
+      <weakClassifiers>
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diff --git a/CV/OpenCV/MachineLearning/data/haarcascade_frontalface_alt.xml b/CV/OpenCV/MachineLearning/data/haarcascade_frontalface_alt.xml
new file mode 100644
index 0000000..ade4b21
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/data/haarcascade_frontalface_alt.xml
@@ -0,0 +1,24350 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 20x20 gentle adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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diff --git a/CV/OpenCV/MachineLearning/data/haarcascade_frontalface_default.xml b/CV/OpenCV/MachineLearning/data/haarcascade_frontalface_default.xml
new file mode 100644
index 0000000..cbd1aa8
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/data/haarcascade_frontalface_default.xml
@@ -0,0 +1,33314 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 24x24 discrete(?) adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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diff --git a/CV/OpenCV/MachineLearning/haar_face.py b/CV/OpenCV/MachineLearning/haar_face.py
new file mode 100644
index 0000000..def479f
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/haar_face.py
@@ -0,0 +1,27 @@
+import numpy as np
+import cv2
+
+# 얼굴 검출을 위한 케스케이드 분류기 생성 --- ①
+face_cascade = cv2.CascadeClassifier('./data/haarcascade_frontalface_default.xml')
+# 눈 검출을 위한 케스케이드 분류기 생성 ---②
+eye_cascade = cv2.CascadeClassifier('./data/haarcascade_eye.xml')
+# 검출할 이미지 읽고 그레이 스케일로 변환 ---③
+img = cv2.imread('../img/children.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+# 얼굴 검출 ---④
+faces = face_cascade.detectMultiScale(gray)
+# 검출된 얼굴 순회 ---⑤
+for (x,y,w,h) in faces:
+    # 검출된 얼굴에 사각형 표시 ---⑥
+    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
+    # 얼굴 영역을 ROI로 설정 ---⑦
+    roi = gray[y:y+h, x:x+w]
+    # ROI에서 눈 검출 ---⑧
+    eyes = eye_cascade.detectMultiScale(roi)
+    # 검출된 눈에 사각형 표 ---⑨
+    for (ex,ey,ew,eh) in eyes:
+        cv2.rectangle(img[y:y+h, x:x+w],(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
+# 결과 출력 
+cv2.imshow('img',img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/haar_face_cam.py b/CV/OpenCV/MachineLearning/haar_face_cam.py
new file mode 100644
index 0000000..c99be88
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/haar_face_cam.py
@@ -0,0 +1,31 @@
+import cv2
+
+# 얼굴과  검출을 위한 케스케이드 분류기 생성 
+face_cascade = cv2.CascadeClassifier('../data/haarcascade_frontalface_default.xml')
+eye_cascade = cv2.CascadeClassifier('../data/haarcascade_eye.xml')
+
+# 카메라 캡쳐 활성화
+cap = cv2.VideoCapture(0)
+while cap.isOpened():    
+    ret, img = cap.read()  # 프레임 읽기
+    if ret:
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # 얼굴 검출    
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, \
+                                        minNeighbors=5, minSize=(80,80))
+        for(x,y,w,h) in faces:
+            cv2.rectangle(img, (x,y), (x+w, y+h), (0, 255,0),2)
+            roi = gray[y:y+h, x:x+w]
+            # 눈 검출
+            eyes = eye_cascade.detectMultiScale(roi)
+            for i, (ex, ey, ew, eh) in enumerate(eyes):
+                if i >= 2:
+                    break
+                cv2.rectangle(img[y:y+h, x:x+w], (ex,ey), (ex+ew, ey+eh), \
+                                    (255,0,0),2)
+        cv2.imshow('face detect', img)
+    else:
+        break
+    if cv2.waitKey(5) == 27:
+        break
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/MachineLearning/k-means_color.py b/CV/OpenCV/MachineLearning/k-means_color.py
new file mode 100644
index 0000000..06e1aa7
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/k-means_color.py
@@ -0,0 +1,23 @@
+import numpy as np
+import cv2
+
+K = 16 # 군집화 갯수(16컬러) ---①
+img = cv2.imread('../img/taekwonv1.jpg')
+# 군집화를 위한 데이타 구조와 형식 변환 ---②
+data = img.reshape((-1,3)).astype(np.float32)
+# 반복 중지 요건 ---③
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+# 평균 클러스터링 적용 ---④
+ret,label,center=cv2.kmeans(data,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
+# 중심 값을 정수형으로 변환 ---⑤
+center = np.uint8(center)
+print(center)
+# 각 레이블에 해당하는 중심값으로 픽셀 값 선택 ---⑥
+res = center[label.flatten()]
+# 원본 영상의 형태로 변환 ---⑦
+res = res.reshape((img.shape))
+# 결과 출력 ---⑧
+merged = np.hstack((img, res))
+cv2.imshow('KMeans Color',merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/k-means_handwritten.py b/CV/OpenCV/MachineLearning/k-means_handwritten.py
new file mode 100644
index 0000000..b36c60f
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/k-means_handwritten.py
@@ -0,0 +1,18 @@
+import cv2, numpy as np
+import matplotlib.pyplot as plt
+import mnist
+
+# 공통 모듈로 부터 MINST 전체 이미지 데이타 읽기 ---①
+data, _ = mnist.getData()
+# 중지 요건 
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+# 평균 클러스터링 적용, 10개의 그룹으로 묶음 ---②
+ret,label,center=cv2.kmeans(data,10,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
+# 중앙점 이미지 출력
+for i in range(10):
+    # 각 중앙점 값으로 이미지 생성 ---③
+    cent_img = center[i].reshape(20,20).astype(np.uint8)
+    plt.subplot(2,5, i+1)
+    plt.imshow(cent_img, 'gray')
+    plt.xticks([]);plt.yticks([])
+plt.show()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/k-means_random.py b/CV/OpenCV/MachineLearning/k-means_random.py
new file mode 100644
index 0000000..85cce20
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/k-means_random.py
@@ -0,0 +1,24 @@
+import numpy as np, cv2
+import matplotlib.pyplot as plt
+
+# 0~150 임의의 2수, 25개 ---①
+a = np.random.randint(0,150,(25,2))
+# 128~255 임의의 2수, 25개  ---②
+b = np.random.randint(128, 255,(25,2))
+# a, b를 병합 ---③
+data = np.vstack((a,b)).astype(np.float32)
+# 중지 요건 ---④
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+# 평균 클러스터링 적용 ---⑤
+ret,label,center=cv2.kmeans(data,2,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
+# label에 따라 결과 분류 ---⑥
+red = data[label.ravel()==0]
+blue = data[label.ravel()==1]
+
+# plot에 결과 출력 ---⑦
+plt.scatter(red[:,0],red[:,1], c='r')
+plt.scatter(blue[:,0],blue[:,1], c='b')
+# 각 그룹의 중앙점 출력 ---⑧
+plt.scatter(center[0,0],center[0,1], s=100, c='r', marker='s')
+plt.scatter(center[1,0],center[1,1], s=100, c='b', marker='s')
+plt.show()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/kNN_handwritten.py b/CV/OpenCV/MachineLearning/kNN_handwritten.py
new file mode 100644
index 0000000..30eb221
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/kNN_handwritten.py
@@ -0,0 +1,39 @@
+import numpy as np, cv2
+import mnist
+
+# 훈련 데이타 가져오기 ---①
+train, train_labels = mnist.getData()
+# Knn 객체 생성 및 학습 ---②
+knn = cv2.ml.KNearest_create()
+knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
+
+# 인식시킬 손글씨 이미지 읽기 ---③
+image = cv2.imread('../img/4027.png')
+cv2.imshow("image", image)
+cv2.waitKey(0) 
+
+# 그레이 스케일 변환과 스레시홀드 ---④
+gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (5, 5), 0)
+_, gray = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
+# 최외곽 컨투어만 찾기 ---⑤
+img, contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, \
+                                        cv2.CHAIN_APPROX_SIMPLE)
+# 모든 컨투어 순회 ---⑥
+for c in contours:
+    # 컨투어를 감싸는 외접 사각형으로 숫자 영역 좌표 구하기 ---⑦
+    (x, y, w, h) = cv2.boundingRect(c)    
+    # 외접 사각형의 크기가 너무 작은것은 제외 ---⑧
+    if w >= 5 and h >= 25:
+        # 숫자 영역만 roi로 확보하고 사각형 그리기 ---⑨
+        roi = gray[y:y + h, x:x + w]
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 1)
+        # 테스트 데이타 형식으로 변환 ---⑩
+        data = mnist.digit2data(roi)
+        # 결과 예측해서 이미지에 표시---⑪
+        ret, result, neighbours, dist = knn.findNearest(data, k=1)
+        cv2.putText(image, "%d"%ret, (x , y + 155), \
+                        cv2.FONT_HERSHEY_DUPLEX, 2, (255, 0, 0), 2)
+        cv2.imshow("image", image)
+        cv2.waitKey(0) 
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/kNN_mnist.py b/CV/OpenCV/MachineLearning/kNN_mnist.py
new file mode 100644
index 0000000..2a9345a
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/kNN_mnist.py
@@ -0,0 +1,17 @@
+import numpy as np, cv2
+import mnist
+
+# 훈련 데이타와 테스트 데이타 가져오기 ---①
+train, train_labels = mnist.getTrain()
+test, test_labels = mnist.getTest()
+# kNN 객체 생성 및 훈련 ---②
+knn = cv2.ml.KNearest_create()
+knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
+# k값을 1~10까지 변경하면서 예측 ---③
+for k in range(1, 11):
+    # 결과 예측 ---④
+    ret, result, neighbors, distance = knn.findNearest(test, k=k)
+    # 정확도 계산 및 출력 ---⑤
+    correct = np.sum(result == test_labels)
+    accuracy = correct / result.size * 100.0
+    print("K:%d, Accuracy :%.2f%%(%d/%d)" % (k, accuracy, correct, result.size) )
diff --git a/CV/OpenCV/MachineLearning/kNN_movie.py b/CV/OpenCV/MachineLearning/kNN_movie.py
new file mode 100644
index 0000000..e81c85b
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/kNN_movie.py
@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# 0~99 사이의 랜덤 값 25x2 ---①
+trainData = np.random.randint(0,100,(25,2)).astype(np.float32)
+# trainDatat[0]:kick, trainData[1]:kiss, kick > kiss ? 1 : 0 ---②
+responses = (trainData[:, 0] >trainData[:,1]).astype(np.float32)
+# 0: action : 1romantic ---③
+action = trainData[responses==0]
+romantic = trainData[responses==1]
+# action은 파랑 삼각형, romantic은 빨강색 동그라미로 표시 ---④
+plt.scatter(action[:,0],action[:,1], 80, 'b', '^', label='action')
+plt.scatter(romantic[:,0],romantic[:,1], 80, 'r', 'o',label="romantic")
+# 새로운 데이타 생성, 0~99 랜덤 수 1X2, 초록색 사각형으로 표시 ---⑤
+newcomer = np.random.randint(0,100,(1,2)).astype(np.float32)
+plt.scatter(newcomer[:,0],newcomer[:,1],200,'g','s', label="new")
+
+# Knearest 알고리즘 생성 및 훈련 --- ⑥
+knn = cv2.ml.KNearest_create()
+knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
+# 결과 예측 ---⑦
+ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)#K=3
+print("ret:%s, result:%s, neighbours:%s, dist:%s" \
+            %(ret, results, neighbours, dist))
+# 새로운 결과에 화살표로 표시 ---⑧
+anno_x, anno_y = newcomer.ravel()
+label = "action" if results == 0 else "romantic" 
+plt.annotate(label, xy=(anno_x + 1, anno_y+1), \
+            xytext=(anno_x+5, anno_y+10), arrowprops={'color':'black'})
+plt.xlabel('kiss');plt.ylabel('kick')
+plt.legend(loc="upper right")
+plt.show()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/kNN_random.py b/CV/OpenCV/MachineLearning/kNN_random.py
new file mode 100644
index 0000000..561b1b4
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/kNN_random.py
@@ -0,0 +1,29 @@
+import cv2,  numpy as np,  matplotlib.pyplot as plt
+
+# 0~99 사이의 랜덤한 수 25x2개 데이타 생성 ---①
+trainData = np.random.randint(0,100,(25,2)).astype(np.float32)
+# 0~1 사이의 랜덤 수 25x1개 레이블 생성 ---②
+labels = np.random.randint(0,2,(25,1))
+# 레이블 값 0과 같은 자리는 red, 1과 같은 자리는 blue로 분류해서 표시
+red = trainData[labels.ravel()==0]
+blue = trainData[labels.ravel()==1]
+plt.scatter(red[:,0], red[:,1], 80, 'r', '^') # 빨강색 삼각형
+plt.scatter(blue[:,0], blue[:,1], 80, 'b', 's')# 파랑색 사각형
+
+# 0 ~ 99 사이의 랜덤 수 신규 데이타 생성 ---③
+newcomer = np.random.randint(0,100,(1,2)).astype(np.float32)
+plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o') # 초록색 원
+
+# KNearest 알고리즘 객체 생성 ---④
+knn = cv2.ml.KNearest_create()
+# train, 행 단위 샘플 ---⑤
+knn.train(trainData, cv2.ml.ROW_SAMPLE, labels)
+# 예측 ---⑥
+#ret, results = knn.predict(newcomer)
+ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)#K=3
+# 결과 출력
+print('ret:%s, result:%s, negibours:%s, distance:%s' \
+        %(ret,results, neighbours, dist))
+plt.annotate('red' if ret==0.0 else 'blue', xy=newcomer[0], \
+             xytext=(newcomer[0]+1))
+plt.show()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/lbp_face1_collect.py b/CV/OpenCV/MachineLearning/lbp_face1_collect.py
new file mode 100644
index 0000000..c2e0bcc
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/lbp_face1_collect.py
@@ -0,0 +1,54 @@
+import cv2
+import numpy as np
+import os 
+
+# 변수 설정 ---①
+base_dir = './faces/'   # 사진 저장할 디렉토리 경로
+target_cnt = 400        # 수집할 사진 갯수
+cnt = 0                 # 사진 촬영 수
+
+# 얼굴 검출 분류기 생성 --- ②
+face_classifier = cv2.CascadeClassifier(\
+                    '../data/haarcascade_frontalface_default.xml')
+
+# 사용자 이름과 번호를 입력 받아 디렉토리 생성 ---③
+name = input("Insert User Name(Only Alphabet):")
+id = input("Insert User Id(Non-Duplicate number):")
+dir = os.path.join(base_dir, name+'_'+ id)
+if not os.path.exists(dir):
+    os.mkdir(dir)
+
+# 카메라 캡쳐 
+cap = cv2.VideoCapture(0)
+while cap.isOpened():
+    ret, frame = cap.read()
+    if ret:
+        img = frame.copy()
+        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        # 얼굴 검출 --- ④
+        faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+        if len(faces) == 1:
+            (x,y,w,h) = faces[0]
+            # 얼굴 영역 표시 및 파일 저장 ---⑤
+            cv2.rectangle(frame, (x,y), (x+w, y+h), (0,255,0), 1)
+            face = gray[y:y+h, x:x+w]
+            face = cv2.resize(face, (200, 200))
+            file_name_path = os.path.join(dir,  str(cnt) + '.jpg')
+            cv2.imwrite(file_name_path, face)
+            cv2.putText(frame, str(cnt), (x, y), cv2.FONT_HERSHEY_COMPLEX, \
+                             1, (0,255,0), 2)
+            cnt+=1
+        else:
+            # 얼굴 검출이 없거나 1이상 인 경우 오류 표시 ---⑥
+            if len(faces) == 0 :
+                msg = "no face."
+            elif len(faces) > 1:
+                msg = "too many face."
+            cv2.putText(frame, msg, (10, 50), cv2.FONT_HERSHEY_DUPLEX, \
+                            1, (0,0,255))
+        cv2.imshow('face record', frame)
+        if cv2.waitKey(1) == 27 or cnt == target_cnt: 
+            break
+cap.release()
+cv2.destroyAllWindows()      
+print("Collecting Samples Completed.")
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/lbp_face2_train.py b/CV/OpenCV/MachineLearning/lbp_face2_train.py
new file mode 100644
index 0000000..6e9e613
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/lbp_face2_train.py
@@ -0,0 +1,32 @@
+import cv2
+import numpy as np
+import os, glob
+
+# 변수 설정 --- ①
+base_dir = './faces'
+train_data, train_labels = [], []
+
+
+dirs = [d for d in glob.glob(base_dir+"/*") if os.path.isdir(d)]
+print('Collecting train data set:')
+for dir in dirs:
+    # name_id 형식에서 id를 분리 ---②
+    id = dir.split('_')[1]          
+    files = glob.glob(dir+'/*.jpg')
+    print('\t path:%s, %dfiles'%(dir, len(files)))
+    for file in files:
+        img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
+        # 이미지는 train_data, 아이디는 train_lables에 저장 ---③
+        train_data.append(np.asarray(img, dtype=np.uint8))
+        train_labels.append(int(id))
+
+# NumPy 배열로 변환 ---④
+train_data = np.asarray(train_data)
+train_labels = np.int32(train_labels)
+
+# LBP 얼굴인식기 생성 및 훈련 ---⑤
+print('Starting LBP Model training...')
+model = cv2.face.LBPHFaceRecognizer_create()
+model.train(train_data, train_labels)
+model.write('./faces/all_face.xml')
+print("Model trained successfully!")
diff --git a/CV/OpenCV/MachineLearning/lbp_face3_recognize.py b/CV/OpenCV/MachineLearning/lbp_face3_recognize.py
new file mode 100644
index 0000000..d89540c
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/lbp_face3_recognize.py
@@ -0,0 +1,58 @@
+import cv2
+import numpy as np
+import os, glob
+
+# 변수 설정 ---①
+base_dir = './faces'
+min_accuracy = 85
+
+# LBP 얼굴 인식기 및 케스케이드 얼굴 검출기 생성 및 훈련 모델 읽기 ---②
+face_classifier = cv2.CascadeClassifier(\
+                '../data/haarcascade_frontalface_default.xml')
+model = cv2.face.LBPHFaceRecognizer_create()
+model.read(os.path.join(base_dir, 'all_face.xml'))
+
+# 디렉토리 이름으로 사용자 이름과 아이디 매핑 정보 생성 ---③
+dirs = [d for d in glob.glob(base_dir+"/*") if os.path.isdir(d)]
+names = dict([])
+for dir in dirs:
+    dir = os.path.basename(dir)
+    name, id = dir.split('_')
+    names[int(id)] = name
+
+# 카메라 캡처 장치 준비 
+cap = cv2.VideoCapture(0)
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        print("no frame")
+        break
+    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+    # 얼굴 검출 ---④
+    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+    for (x,y,w,h) in faces:
+        # 얼굴 영역 표시하고 샘플과 같은 크기로 축소 ---⑤
+        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,255),2)
+        face = frame[y:y+h, x:x+w]
+        face = cv2.resize(face, (200, 200))
+        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
+        # LBP 얼굴 인식기로 예측 ---⑥
+        label, confidence = model.predict(face)
+        if confidence < 400:
+            # 정확도 거리를 퍼센트로 변환 ---⑦
+            accuracy = int( 100 * (1 -confidence/400))
+            if accuracy >= min_accuracy:
+                msg =  '%s(%.0f%%)'%(names[label], accuracy)
+            else:
+                msg = 'Unknown'
+        # 사용자 이름과 정확도 결과 출력 ---⑧
+        txt, base = cv2.getTextSize(msg, cv2.FONT_HERSHEY_PLAIN, 1, 3)
+        cv2.rectangle(frame, (x,y-base-txt[1]), (x+txt[0], y+txt[1]), \
+                    (0,255,255), -1)
+        cv2.putText(frame, msg, (x, y), cv2.FONT_HERSHEY_PLAIN, 1, \
+                    (200,200,200), 2,cv2.LINE_AA)
+    cv2.imshow('Face Recognition', frame)
+    if cv2.waitKey(1) == 27: #esc 
+        break
+cap.release()
+cv2.destroyAllWindows()     
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/mnist.py b/CV/OpenCV/MachineLearning/mnist.py
new file mode 100644
index 0000000..dde5782
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/mnist.py
@@ -0,0 +1,72 @@
+import numpy as np, cv2
+
+data = None  # 이미지 데이타 셋 
+k = list(range(10)) # [0,1,2,3,4,5,6,7,8,9] 레이블 셋
+
+# 이미지 데이타 읽어들이는 함수 ---①
+def load():
+    global data
+    # 0~9 각각 500(5x100)개, 총5000(50x100)개, 한 숫자당 400(20x20)픽셀
+    image = cv2.imread('../img/digits.png')
+    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+    # 숫자 한개(20x20)씩 구분하기 위해 행별(50)로 나누고 열별(100)로 나누기
+    cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]
+    # 리스트를 NumPy 배열로  변환 (50 x 100 x 20 x 20 )
+    data = np.array(cells)
+
+# 모든 숫자 데이타 반환 ---②
+def getData(reshape=True):
+    if data is None: load() # 이미지 읽기 확인
+    # 모든 데이타를 N x 400 형태로 변환
+    if reshape:
+        full = data.reshape(-1, 400).astype(np.float32) # 5000x400
+    else:
+        full = data
+    labels = np.repeat(k,500).reshape(-1,1)  # 각 숫자당 500번 반복(10x500)
+    return (full, labels)
+
+# 훈련용 데이타 반환 ---③
+def getTrain(reshape=True):
+    if data is None: load() # 이미지 읽기 확인
+    # 50x100 중에 90열만 훈련 데이타로 사용
+    train = data[:,:90]
+    if reshape:
+        # 훈련 데이타를 N X 400으로 변환
+        train = train.reshape(-1,400).astype(np.float32) # 4500x400
+    # 레이블 생성
+    train_labels = np.repeat(k,450).reshape(-1,1) # 각 숫자당 45번 반복(10x450)
+    return (train, train_labels)
+
+# 테스트용 데이타 반환 ---④
+def getTest(reshape=True):
+    if data is None: load()
+    # 50x100 중에 마지막 10열만 훈련 데이타로 사용
+    test = data[:,90:100]
+    # 테스트 데이타를 N x 400으로 변환
+    if reshape:
+        test = test.reshape(-1,400).astype(np.float32) # 500x400
+    test_labels = np.repeat(k,50).reshape(-1,1)
+    return (test, test_labels)
+
+
+# 손글씨 숫자 한 개를 20x20 로 변환후에 1x400 형태로 변환 ---⑤
+def digit2data(src, reshape=True):
+    h, w = src.shape[:2]
+    square = src
+    # 정사각형 형태로 만들기
+    if h > w:
+        pad = (h - w)//2
+        square = np.zeros((h, h), dtype=np.uint8)
+        square[:, pad:pad+w] = src
+    elif w > h :
+        pad = (w - h)//2
+        square = np.zeros((w, w), dtype=np.uint8)
+        square[pad:pad+h, :] = src
+    # 0으로 채워진 20x20 이미지 생성
+    px20 = np.zeros((20,20), np.uint8)
+    # 원본을 16x16으로 축소해서 테두리 2픽셀 확보
+    px20[2:18, 2:18] = cv2.resize(square, (16,16), interpolation=cv2.INTER_AREA)
+    if reshape:
+        # 1x400형태로 변환
+        px20 = px20.reshape((1,400)).astype(np.float32)
+    return px20
diff --git a/CV/OpenCV/MachineLearning/plane_bike_dict.npy b/CV/OpenCV/MachineLearning/plane_bike_dict.npy
new file mode 100644
index 0000000..e54340f
Binary files /dev/null and b/CV/OpenCV/MachineLearning/plane_bike_dict.npy differ
diff --git a/CV/OpenCV/MachineLearning/plane_bike_dict_4000.npy b/CV/OpenCV/MachineLearning/plane_bike_dict_4000.npy
new file mode 100644
index 0000000..ad85fb9
Binary files /dev/null and b/CV/OpenCV/MachineLearning/plane_bike_dict_4000.npy differ
diff --git a/CV/OpenCV/MachineLearning/plane_bike_svm.xml b/CV/OpenCV/MachineLearning/plane_bike_svm.xml
new file mode 100644
index 0000000..e3437a7
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/plane_bike_svm.xml
@@ -0,0 +1,6175 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<opencv_ml_svm>
+  <format>3</format>
+  <svmType>C_SVC</svmType>
+  <kernel>
+    <type>RBF</type>
+    <gamma>3.3750000000000002e-02</gamma></kernel>
+  <C>3.1250000000000000e+02</C>
+  <term_criteria><epsilon>1.1920928955078125e-07</epsilon>
+    <iterations>1000</iterations></term_criteria>
+  <var_count>50</var_count>
+  <class_count>2</class_count>
+  <class_labels type_id="opencv-matrix">
+    <rows>2</rows>
+    <cols>1</cols>
+    <dt>i</dt>
+    <data>
+      0 1</data></class_labels>
+  <sv_total>439</sv_total>
+  <support_vectors>
+    <_>
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+        203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218
+        219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
+        235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250
+        251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266
+        267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282
+        283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298
+        299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
+        315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330
+        331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346
+        347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362
+        363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378
+        379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394
+        395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410
+        411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426
+        427 428 429 430 431 432 433 434 435 436 437 438</index></_></decision_functions></opencv_ml_svm>
+</opencv_storage>
diff --git a/CV/OpenCV/MachineLearning/plane_bike_svm_4000.xml b/CV/OpenCV/MachineLearning/plane_bike_svm_4000.xml
new file mode 100644
index 0000000..41e1a95
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/plane_bike_svm_4000.xml
@@ -0,0 +1,125065 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<opencv_ml_svm>
+  <format>3</format>
+  <svmType>C_SVC</svmType>
+  <kernel>
+    <type>RBF</type>
+    <gamma>5.0625000000000009e-01</gamma></kernel>
+  <C>3.1250000000000000e+02</C>
+  <term_criteria><epsilon>1.1920928955078125e-07</epsilon>
+    <iterations>1000</iterations></term_criteria>
+  <var_count>4000</var_count>
+  <class_count>2</class_count>
+  <class_labels type_id="opencv-matrix">
+    <rows>2</rows>
+    <cols>1</cols>
+    <dt>i</dt>
+    <data>
+      0 1</data></class_labels>
+  <sv_total>505</sv_total>
+  <support_vectors>
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+        -1.6159475037590883e+02 -1.8397252901687287e+02
+        -2.6007586477855932e+02 -1.5980616565712560e+02
+        -6.0353639199929390e+01 -7.5453129509141291e+01
+        -3.1250000000000000e+02 -1.2883527114591368e+02
+        -1.9234968267468895e+02 -8.0500894413246868e+01
+        -2.0493221797240525e+02 -2.0497292202463177e+02
+        -3.5405660480810601e+00 -3.1250000000000000e+02
+        -3.9833054753733876e+01 -3.1250000000000000e+02
+        -1.0619814948368955e+02 -1.4916125937545749e+01
+        -2.8646719040807710e+01 -7.1699079947842748e+01
+        -1.1000913097503286e+02 -3.0120298670761766e+01
+        -3.1250000000000000e+02 -1.3891582765148146e+02
+        -1.0028176141776352e+02 -1.2978682155248032e+02
+        -8.2777394411550361e+01 -3.1250000000000000e+02
+        -7.4197930136542297e+01 -2.5025349082891282e+01
+        -2.3096974179127326e+01 -8.3927683800319475e+00
+        -1.3130827349506168e+02 -2.8395383221575730e+02
+        -1.1153871429160907e+02 -6.7876556189591355e+00
+        -2.7123380001938472e+01 -3.1250000000000000e+02
+        -3.1250000000000000e+02 -2.6483811189225815e+02
+        -4.2093521951543160e+01 -7.7929410490042642e+01
+        -2.5823171537824390e+01 -5.5776643913601674e+01
+        -1.5153740127219754e+02 -1.3857213734620109e+02
+        -3.0438399017032611e+02 -5.5533276449751071e+01
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+        -3.1250000000000000e+02 -7.7746456092237196e+01
+        -7.3657922074318890e+01 -5.1034064562088375e+01
+        -3.1250000000000000e+02 -7.5050640359024555e+01
+        -5.2155670590421330e+00 -1.7271199267814274e+02
+        -6.1670451821759409e+00 -3.6923460547849629e+01
+        -1.4864850757615548e+02 -1.5812868195068728e+02
+        -3.3840185625751886e+01 -2.1421416824413978e+02
+        -1.8613127718710675e+02 -2.5695849885706636e+01
+        -1.6335093182154256e+02 -5.0383315118900720e+00
+        -2.5374162234945655e+02 -2.6707320625818278e+02
+        -1.7553936410504616e+02 -6.2734726805197248e+01
+        -1.6710250741319635e+02 -1.2923331699082883e+02
+        -1.3699737017003311e+01 -5.1962414618681002e+01
+        -3.7954599902117032e+01 -3.3411809122169124e+01
+        -1.1307895496710195e+02 -2.1257024870639211e+02
+        -7.3178683008844757e+01 -4.0200867446773877e+01
+        -5.3625749873324269e-01 -8.2334975024505866e+01
+        -9.4853893459500654e+01 -1.4688696825600618e+02
+        -7.0805858020039707e+01 -1.0705510891394265e+02
+        -1.3255325883159213e+02 -3.4939628197957070e+01
+        -3.1250000000000000e+02 -2.4129624328034114e+02
+        -1.5604010801445318e+01</alpha>
+      <index>
+        160 161 162 163 152 153 154 155 156 157 158 159 176 177 178 179
+        175 180 181 182 169 164 165 166 167 168 170 171 172 173 174 126
+        124 125 127 128 129 130 131 132 133 116 117 118 119 120 134 121
+        122 123 148 145 146 147 149 150 151 135 136 137 138 139 140 141
+        142 143 144 221 222 223 224 225 212 213 214 215 216 217 218 219
+        220 235 236 237 234 238 239 240 226 227 228 229 230 231 232 211
+        233 190 191 192 193 194 195 196 184 183 197 185 186 187 188 189
+        203 204 205 206 207 208 202 209 210 198 199 200 201 38 39 40 41
+        37 42 43 44 45 32 33 34 35 36 53 54 55 56 57 58 48 46 47 31 49
+        50 51 52 11 5 6 7 8 9 10 12 13 14 15 16 2 0 1 17 3 4 26 24 25 27
+        28 29 30 18 19 20 21 22 23 100 96 97 98 99 101 102 103 89 90 91
+        92 93 94 95 108 109 110 111 112 113 114 115 106 104 105 59 107
+        70 71 72 73 74 75 66 60 61 62 63 64 65 67 68 69 83 76 84 85 86
+        87 88 77 78 79 80 82 81 414 415 416 417 418 419 420 421 410 405
+        406 407 408 409 411 412 413 429 430 431 432 433 434 435 436 437
+        422 423 424 425 426 427 428 384 379 380 381 382 383 385 386 387
+        388 389 390 391 370 371 372 373 374 392 375 376 377 378 398 399
+        400 401 397 402 403 404 393 394 395 396 487 488 486 489 472 473
+        474 475 476 477 478 490 479 480 481 482 483 484 485 498 499 500
+        501 502 503 504 492 491 471 493 494 495 496 497 449 446 447 448
+        445 450 451 452 453 438 439 440 441 442 443 444 464 465 466 467
+        468 469 470 459 454 455 456 457 458 460 461 462 463 281 282 283
+        284 285 280 286 287 288 269 270 271 272 273 274 275 276 277 278
+        279 301 297 298 299 300 302 303 304 305 306 289 290 291 292 293
+        294 295 296 252 253 254 255 251 256 257 258 259 241 242 243 244
+        245 246 247 248 249 250 264 265 266 267 268 260 261 307 262 263
+        349 350 351 352 353 342 343 344 345 346 354 347 348 365 360 361
+        362 363 364 366 367 368 355 356 357 358 359 317 318 319 320 316
+        321 322 323 324 308 309 310 311 312 313 314 315 332 333 334 335
+        336 337 338 339 340 341 327 325 326 369 328 329 330 331</index></_></decision_functions></opencv_ml_svm>
+</opencv_storage>
diff --git a/CV/OpenCV/MachineLearning/svm_handwritten.py b/CV/OpenCV/MachineLearning/svm_handwritten.py
new file mode 100644
index 0000000..967a1f1
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/svm_handwritten.py
@@ -0,0 +1,43 @@
+import cv2
+import numpy as np
+import mnist
+import svm_mnist_hog_train 
+
+
+# 훈련해서 저장한 SVM 객체 읽기 ---① 
+svm = cv2.ml.SVM_load('./svm_mnist.xml')
+# 인식할 손글씨 이미지 읽기 ---②
+image = cv2.imread('../img/4027.png')
+cv2.imshow("image", image)
+cv2.waitKey(0) 
+
+# 인식할 이미지를 그레이 스케일로 변환 및 스레시홀드 적용 ---③
+gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (5, 5), 0)
+_, gray = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
+
+# 최외곽 컨투어만 찾기 ---④
+img, contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, \
+                                        cv2.CHAIN_APPROX_SIMPLE)
+for c in contours:
+    # 컨투어를 감싸는 외접 사각형 구하기 ---⑤
+    (x, y, w, h) = cv2.boundingRect(c)    
+    # 외접 사각형의 크기가 너무 작은것은 제외 ---⑥
+    if w >= 5 and h >= 25:
+        # 숫자 영역만 roi로 확보하고 사각형 그리기 ---⑦
+        roi = gray[y:y + h, x:x + w]
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 1)
+        # 테스트 데이타 형식으로 변환 ---⑧
+        px20 = mnist.digit2data(roi, False)
+        # 기울어진 숫자를 바로 세우기 ---⑨
+        deskewed = svm_mnist_hog_train.deskew(px20)
+        # 인식할 숫자에 대한 HOG 디스크립터 계산 ---⑩
+        hogdata = svm_mnist_hog_train.hogDesc.compute(deskewed)
+        testData = np.float32(hogdata).reshape(-1, hogdata.shape[0])
+        # 결과 예측해서 표시 ---⑪
+        ret, result = svm.predict(testData)
+        cv2.putText(image, "%d"%result[0], (x , y + 155), \
+                        cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)
+        cv2.imshow("image", image)
+        cv2.waitKey(0) 
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/svm_hog_pedestrian.py b/CV/OpenCV/MachineLearning/svm_hog_pedestrian.py
new file mode 100644
index 0000000..1155a6f
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/svm_hog_pedestrian.py
@@ -0,0 +1,38 @@
+import cv2
+
+# default 디덱터를 위한 HOG 객체 생성 및 설정--- ①
+hogdef = cv2.HOGDescriptor()
+hogdef.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+# dailer 디덱터를 위한 HOG 객체 생성 및 설정--- ②
+hogdaim  = cv2.HOGDescriptor((48,96), (16,16), (8,8), (8,8), 9)
+hogdaim.setSVMDetector(cv2.HOGDescriptor_getDaimlerPeopleDetector())
+
+cap = cv2.VideoCapture('../img/walking.avi')
+mode = True  # 모드 변환을 위한 플래그 변수 
+print('Toggle Space-bar to change mode.')
+while cap.isOpened():
+    ret, img = cap.read()
+    if ret :
+        if mode:
+            # default 디텍터로 보행자 검출 --- ③
+            found, _ = hogdef.detectMultiScale(img)
+            for (x,y,w,h) in found:
+                cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,255))
+        else:
+            # daimler 디텍터로 보행자 검출 --- ④
+            found, _ = hogdaim.detectMultiScale(img)
+            for (x,y,w,h) in found:
+                cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0))
+        cv2.putText(img, 'Detector:%s'%('Default' if mode else 'Daimler'), \
+                        (10,50 ), cv2.FONT_HERSHEY_DUPLEX,1, (0,255,0),1)
+        cv2.imshow('frame', img)
+        key = cv2.waitKey(1) 
+        if key == 27:
+            break
+        elif key == ord(' '):
+            mode = not mode
+    else:
+        break
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/svm_mnist.xml b/CV/OpenCV/MachineLearning/svm_mnist.xml
new file mode 100644
index 0000000..35c1182
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/svm_mnist.xml
@@ -0,0 +1,117242 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<opencv_ml_svm>
+  <format>3</format>
+  <svmType>C_SVC</svmType>
+  <kernel>
+    <type>RBF</type>
+    <gamma>5.0625000000000009e-01</gamma></kernel>
+  <C>1.2500000000000000e+01</C>
+  <term_criteria><epsilon>1.1920928955078125e-07</epsilon>
+    <iterations>1000</iterations></term_criteria>
+  <var_count>324</var_count>
+  <class_count>10</class_count>
+  <class_labels type_id="opencv-matrix">
+    <rows>10</rows>
+    <cols>1</cols>
+    <dt>i</dt>
+    <data>
+      0 1 2 3 4 5 6 7 8 9</data></class_labels>
+  <sv_total>1574</sv_total>
+  <support_vectors>
+    <_>
+      5.17262779e-02 9.35632586e-02 5.31439073e-02 1.69259943e-02 0. 0.
+      0. 0. 6.19961927e-03 3.59992117e-01 3.09394479e-01 5.13095222e-02
+      1.12839937e-02 0. 0. 0. 0. 7.30072632e-02 8.04277435e-02
+      2.80755877e-01 3.59992117e-01 2.62215048e-01 1.11114636e-01
+      2.61979597e-03 0. 0. 4.49652597e-03 3.59992117e-01 3.59992117e-01
+      3.59992117e-01 2.14373603e-01 7.10301697e-02 1.57341138e-02
+      1.93040185e-02 6.38094917e-03 1.09329134e-01 3.02126318e-01
+      2.46447340e-01 7.98531994e-03 1.91920542e-03 5.96582424e-04 0. 0.
+      5.34466410e-04 3.98382470e-02 3.02126318e-01 3.02126318e-01
+      2.52071097e-02 1.16777988e-02 8.94873636e-04 0. 1.23945000e-02
+      9.52680558e-02 1.86090901e-01 3.02126318e-01 3.02126318e-01
+      2.91412789e-02 6.57367706e-02 8.21195841e-02 3.62979956e-02
+      4.18007150e-02 2.41332594e-02 1.54525310e-01 3.02126318e-01
+      3.02126318e-01 1.15940154e-01 1.48761347e-01 1.87829688e-01
+      4.48481664e-02 1.27524599e-01 2.07886413e-01 2.81448513e-01
+      2.02211559e-01 9.48665813e-02 8.56801029e-03 5.95997786e-03
+      1.14794908e-04 9.88499960e-04 6.78170994e-02 2.89823532e-01
+      2.38416955e-01 2.09596306e-02 3.76076694e-03 0. 0. 6.88845944e-03
+      3.84770855e-02 1.81789801e-01 2.89823532e-01 1.01254150e-01
+      2.16472462e-01 1.49470747e-01 1.30985215e-01 8.39738026e-02
+      1.61045790e-01 8.28258246e-02 2.89823532e-01 2.89823532e-01
+      2.02814519e-01 1.87059883e-02 1.50430640e-02 2.82767545e-02
+      1.47873294e-02 2.74055362e-01 2.89823532e-01 2.89823532e-01
+      2.89823532e-01 9.14169699e-02 1.20503930e-02 7.06016421e-02
+      3.05881470e-01 2.34838381e-01 2.34727055e-01 1.69401735e-01
+      1.13880076e-01 8.45472142e-03 5.30070218e-04 1.33072436e-01
+      1.91763401e-01 3.05881470e-01 2.03479990e-01 1.35192454e-01
+      1.04248323e-01 8.91254842e-02 8.69417656e-03 2.22473610e-02
+      6.60705031e-04 5.00863278e-03 4.15058173e-02 8.35694149e-02
+      1.72181204e-01 3.05881470e-01 3.05881470e-01 5.82934096e-02
+      2.40316484e-04 2.71109026e-03 3.67260501e-02 1.11456633e-01
+      1.54108256e-01 2.65062332e-01 2.51094490e-01 3.05881470e-01
+      1.55931413e-01 3.92829478e-02 2.03399107e-01 1.05383433e-01
+      5.86817414e-02 1.31478488e-01 2.22521022e-01 1.31941572e-01
+      5.92780113e-02 2.64858231e-02 6.95694983e-02 2.22521022e-01
+      1.30350068e-01 9.62585732e-02 1.65467680e-01 2.22521022e-01
+      1.43465370e-01 1.96845487e-01 2.01288149e-01 2.17014655e-01
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+      7.32518062e-02 1.13484092e-01 2.22521022e-01 2.22521022e-01
+      2.22521022e-01 2.22521022e-01 1.24033920e-01 1.31841689e-01
+      1.85578123e-01 2.22521022e-01 5.38249724e-02 7.21556246e-02
+      9.05193165e-02 8.48638266e-02 2.55839705e-01 1.10263787e-01
+      1.66598722e-01 1.64650738e-01 9.08733681e-02 6.19512377e-03
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+      2.78676212e-01 2.94301897e-01 9.12829861e-02 1.16341524e-02
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+      1.71193674e-01 4.76770103e-02 2.83974782e-02 7.49590173e-02
+      9.08415839e-02 4.36567515e-03 2.42233202e-01 2.94301897e-01
+      2.94301897e-01 2.94301897e-01 9.82369110e-02 1.43608125e-02
+      8.98835342e-03 5.68561081e-04 3.10454075e-03 1.38627045e-04 0.
+      2.35650761e-04 2.82172728e-02 1.61874980e-01 4.03520346e-01
+      8.53189901e-02 1.14973569e-02 1.03941439e-02 1.16650602e-02
+      3.86768095e-02 6.72542751e-02 1.87043428e-01 2.04197571e-01
+      4.03520346e-01 4.03520346e-01 1.50776863e-01 4.37192433e-03 0. 0.
+      0. 6.02462934e-03 1.22673046e-02 9.66792405e-02 3.05892006e-02
+      1.72460359e-02 3.75435054e-02 0. 0. 0. 9.35716741e-03
+      6.73433691e-02 4.03520346e-01 4.03520346e-01 1.57822475e-01
+      1.96698792e-02 1.77780800e-02 4.70523462e-02 9.51531678e-02
+      1.22021012e-01 7.76510686e-02 2.75332123e-01 2.75332123e-01
+      2.08268866e-01 8.98567811e-02 2.75332123e-01 2.11253837e-01
+      7.38927200e-02 4.75456528e-02 2.21131407e-02 9.56306532e-02
+      2.45659322e-01 2.75332123e-01 3.91847715e-02 2.25245705e-04 0. 0.
+      1.77580537e-03 2.79244315e-02 2.57643938e-01 2.75332123e-01
+      2.75332123e-01 1.37730256e-01 2.75332123e-01 1.24858737e-01 0. 0.
+      4.21103602e-03 7.09314048e-02 2.75332123e-01 2.75332123e-01
+      4.30694334e-02 3.40311259e-01 3.40311259e-01 2.10906360e-02
+      1.90740228e-02 2.55251932e-03 0. 4.70086522e-02 8.99481326e-02
+      2.87167798e-03 3.40311259e-01 3.40311259e-01 1.51590466e-01
+      1.37694836e-01 6.88806968e-03 0. 2.20198114e-03 2.64097325e-04
+      8.96941498e-02 3.40311259e-01 3.40311259e-01 3.02789267e-04 0. 0.
+      0. 7.49758929e-02 1.29582256e-01 8.61304346e-03 3.16855431e-01
+      3.40311259e-01 1.21115684e-03 3.89199774e-03 0. 0. 0.
+      6.46541885e-04</_>
+    <_>
+      1.60437617e-02 2.00986445e-01 1.62470296e-01 3.57448347e-02
+      7.62276677e-03 6.69070694e-04 8.11181555e-04 0. 5.32100303e-03
+      1.90829009e-01 3.79341573e-01 1.86168164e-01 9.45135206e-03
+      1.34536647e-03 2.67628185e-03 3.24472506e-03 0. 3.07810046e-02
+      7.56391603e-03 2.21660092e-01 3.79341573e-01 3.79341573e-01
+      1.08124129e-01 6.25733240e-03 3.24472506e-03 0. 1.39663310e-03
+      8.63121375e-02 3.79341573e-01 3.79341573e-01 2.58874774e-01
+      2.01643985e-02 1.30924927e-02 1.29788974e-02 0. 4.38792026e-03
+      2.55094707e-01 3.00259382e-01 3.94118950e-02 0. 0. 6.32901676e-04
+      6.99732499e-03 2.95141712e-02 2.72529215e-01 1.60834029e-01
+      6.60508722e-02 5.45361079e-03 0. 0. 0. 9.42432433e-02
+      3.00259382e-01 3.00259382e-01 1.46001071e-01 3.00259382e-01
+      3.00259382e-01 3.62263769e-02 5.10460231e-04 2.53160577e-03
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+      1.40623301e-01 2.91942637e-02 2.52101407e-03 1.23414341e-02
+      4.83554527e-02 3.00259382e-01 3.00259382e-01 3.00259382e-01
+      2.34705862e-02 0. 0. 0. 0. 0. 1.93038166e-01 3.29466581e-01
+      1.87518999e-01 1.65880902e-03 0. 0. 0. 1.22877629e-02
+      7.20322737e-03 1.70053259e-01 2.86740869e-01 1.64375473e-02
+      7.33302091e-04 0. 0. 0. 6.70199916e-02 1.88034654e-01
+      3.29466581e-01 3.29466581e-01 1.10336110e-01 3.13100580e-04 0. 0.
+      0. 3.29466581e-01 3.29466581e-01 3.29466581e-01 3.29466581e-01
+      1.59630943e-02 4.63147240e-04 2.96306908e-02 3.09810787e-01
+      3.09810787e-01 3.09810787e-01 4.55699228e-02 7.99374015e-04 0. 0.
+      1.05172917e-02 1.23551548e-01 3.09810787e-01 3.09810787e-01
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+</opencv_storage>
diff --git a/CV/OpenCV/MachineLearning/svm_mnist_hog_train.py b/CV/OpenCV/MachineLearning/svm_mnist_hog_train.py
new file mode 100644
index 0000000..22bdbc3
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/svm_mnist_hog_train.py
@@ -0,0 +1,56 @@
+import cv2 
+import numpy as np
+import mnist
+import time
+
+# 기울어진 숫자를 바로 세우기 위한 함수 ---①
+affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
+def deskew(img):
+    m = cv2.moments(img)
+    if abs(m['mu02']) < 1e-2:
+        return img.copy()
+    skew = m['mu11']/m['mu02']
+    M = np.float32([[1, skew, -0.5*20*skew], [0, 1, 0]])
+    img = cv2.warpAffine(img,M,(20, 20),flags=affine_flags)
+    return img
+
+# HOGDescriptor를 위한 파라미터 설정 및 생성---②
+winSize = (20,20)
+blockSize = (10,10)
+blockStride = (5,5)
+cellSize = (5,5)
+nbins = 9
+hogDesc = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins)
+
+if __name__ =='__main__':
+    # MNIST 이미지에서 학습용 이미지와 테스트용 이미지 가져오기 ---③
+    train_data, train_label  = mnist.getTrain(reshape=False)
+    test_data, test_label = mnist.getTest(reshape=False)
+    # 학습 이미지 글씨 바로 세우기 ---④
+    deskewed = [list(map(deskew,row)) for row in train_data]
+    # 학습 이미지 HOG 계산 ---⑤
+    hogdata = [list(map(hogDesc.compute,row)) for row in deskewed]
+    train_data = np.float32(hogdata)
+    print('SVM training started...train data:', train_data.shape)
+    # 학습용 HOG 데이타 재배열  ---⑥
+    train_data = train_data.reshape(-1,train_data.shape[2])
+    # SVM 알고리즘 객체 생성 및 훈련 ---⑦
+    svm = cv2.ml.SVM_create()
+    startT = time.time()
+    svm.trainAuto(train_data, cv2.ml.ROW_SAMPLE, train_label)
+    endT = time.time() - startT
+    print('SVM training complete. %.2f Min'%(endT/60))  
+    # 훈련된  결과 모델 저장 ---⑧
+    svm.save('svm_mnist.xml')
+
+    # 테스트 이미지 글씨 바로 세우기 및 HOG 계산---⑨
+    deskewed = [list(map(deskew,row)) for row in test_data]
+    hogdata = [list(map(hogDesc.compute,row)) for row in deskewed]
+    test_data = np.float32(hogdata)
+    # 테스트용 HOG 데이타 재배열 ---⑩
+    test_data = test_data.reshape(-1,test_data.shape[2])
+    # 테스트 데이타 결과 예측 ---⑪
+    ret, result = svm.predict(test_data)
+    # 예측 결과와 테스트 레이블이 맞은 갯수 합산 및 정확도 출력---⑫
+    correct = (result==test_label).sum()
+    print('Accuracy: %.2f%%'%(correct*100.0/result.size))
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/svm_random.py b/CV/OpenCV/MachineLearning/svm_random.py
new file mode 100644
index 0000000..0b60608
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/svm_random.py
@@ -0,0 +1,38 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+ 
+# 0~158 구간 임의의 수 25 x 2 생성   ---①
+a = np.random.randint(0,158,(25,2))
+# 98~255 구간 임의의 수 25 x 2 생성 ---②
+b = np.random.randint(98, 255,(25,2))
+# a, b를 병합, 50 x 2의 임의의 수 생성 ---③
+trainData = np.vstack((a, b)).astype(np.float32)
+# 0으로 채워진 50개 배열 생성 ---④
+responses = np.zeros((50,1), np.int32)
+# 25 ~ 50 까지 1로 변경 ---⑤
+responses[25:] = 1
+
+# 0과 같은 자리의 학습 데이타는 빨강색 삼각형으로 분류 및 표시 ---⑥
+red = trainData[responses.ravel()==0]
+plt.scatter(red[:,0],red[:,1],80,'r','^')
+# 1과 같은 자리의 학습 데이타는 파랑색 사각형으로 분류 및 표시 ---⑦
+blue = trainData[responses.ravel()==1]
+plt.scatter(blue[:,0],blue[:,1],80,'b','s')
+# 0~255 구간의 새로운 임의의 수 생성 및 초록색 원으로 표시 ---⑧
+newcomer = np.random.randint(0,255,(1,2)).astype(np.float32)
+plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o')
+# SVM 알고리즘 객체 생성 및 훈련---⑨
+svm = cv2.ml.SVM_create()
+
+svm.trainAuto(trainData, cv2.ml.ROW_SAMPLE, responses)
+# svm_random.xml 로 저장 ---⑩
+svm.save('./svm_random.xml')
+# 저장한 모델을 다시 읽기 ---⑪
+svm2  = cv2.ml.SVM_load('./svm_random.xml')
+# 새로운 임의의 수 예측 ---⑫
+ret, results = svm2.predict(newcomer)
+# 결과 표시 ---⑬
+plt.annotate('red' if results[0]==0 else 'blue', xy=newcomer[0], xytext=(newcomer[0]+1))
+print("return:%s, results:%s"%(ret, results))
+plt.show()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/workhop_face_distotion_camera.py b/CV/OpenCV/MachineLearning/workhop_face_distotion_camera.py
new file mode 100644
index 0000000..a2ce957
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/workhop_face_distotion_camera.py
@@ -0,0 +1,81 @@
+import cv2
+import numpy as np
+
+# 얼굴과 눈동자 검출기 생성
+face_cascade = cv2.CascadeClassifier('../data/haarcascade_frontalface_default.xml')
+eye_cascade = cv2.CascadeClassifier('../data/haarcascade_eye.xml')
+
+# 렌즈 왜곡 효과 함수
+def distortedMap(rows, cols, type=0):
+    map_y, map_x = np.indices((rows, cols), dtype=np.float32)
+    # 렌즈 효과
+    ## 렌즈 효과, 중심점 이동
+    map_lenz_x = (2*map_x - cols)/cols
+    map_lenz_y = (2*map_y - rows)/rows
+    ## 렌즈 효과, 극좌표 변환
+    r, theta = cv2.cartToPolar(map_lenz_x, map_lenz_y)
+    if type==0:
+    ## 볼록 렌즈 효과 매핑 좌표 연산
+        r[r< 1] = r[r<1] **3  
+    else:
+    ## 오목 렌즈 효과 매핑 좌표 연산
+        r[r< 1] = r[r<1] **0.5
+    ## 렌즈 효과, 직교 좌표 복원
+    mapx, mapy = cv2.polarToCart(r, theta)
+    ## 렌즈 효과, 좌상단 좌표 복원
+    mapx = ((mapx + 1)*cols)/2
+    mapy = ((mapy + 1)*rows)/2
+    return (mapx, mapy)
+
+# 얼굴 검출 함수
+def findFaces(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray)
+    face_coords = []
+    for (x,y,w,h) in faces:
+        face_coords.append((x, y, w, h))
+    return face_coords
+# 눈 검출 함수
+def findEyes(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray)
+    eyes_coords = []
+    for (x,y,w,h) in faces:
+        roi_gray = gray[y:y+h, x:x+w]
+        eyes = eye_cascade.detectMultiScale(roi_gray )
+        for(ex,ey,ew,eh) in eyes:
+            eyes_coords.append((ex+x,ey+y,ew,eh))
+    return eyes_coords
+
+
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 480)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 320)
+
+while True:
+    ret, frame = cap.read()
+    img1 = frame.copy()
+    img2 = frame.copy()
+    # 얼굴 검출해서 오목/볼록 렌즈 효과로 왜곡 적용
+    faces = findFaces(frame)
+    for face in faces:
+        x,y,w,h = face
+        mapx, mapy = distortedMap(w,h, 1)
+        roi = img1[y:y+h, x:x+w]
+        convex = cv2.remap(roi,mapx,mapy,cv2.INTER_LINEAR)
+        img1[y:y+h, x:x+w] = convex
+    # 눈 영역 검출해서 볼록 렌즈 효과로 왜곡 적용
+    eyes = findEyes(frame)
+    for eye in eyes :
+        x,y,w,h = eye
+        mapx, mapy = distortedMap(w,h)
+        roi = img2[y:y+h, x:x+w]
+        convex = cv2.remap(roi,mapx,mapy,cv2.INTER_LINEAR)
+        img2[y:y+h, x:x+w] = convex
+    # 하나의 이미지로 병합해서 출력
+    merged = np.hstack((frame, img1, img2))
+    cv2.imshow('Face Distortion', merged)
+    if cv2.waitKey(1) == 27:
+        break
+cv2.destroyAllWindows()
+        
diff --git a/CV/OpenCV/MachineLearning/workshop_face_mosaic.py b/CV/OpenCV/MachineLearning/workshop_face_mosaic.py
new file mode 100644
index 0000000..b478734
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/workshop_face_mosaic.py
@@ -0,0 +1,27 @@
+import cv2
+
+rate = 15   # 모자이크에 사용할 축소 비율 (1/rate)
+#하르 케스케이드 검출기 생성 ---             
+face_cascade = cv2.CascadeClassifier('./data/haarcascade_frontalface_default.xml')
+
+cap = cv2.VideoCapture(0)
+while cap.isOpened():
+    ret, frame = cap.read()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5, \
+                                                                minSize=(80,80))
+    for (x,y,w,h) in faces:
+        x = x - 5
+        y = y - 15
+        w = w + 10
+        h = h + 30
+        roi = frame[y:y+h, x:x+w]   # 관심영역 지정
+        roi = cv2.resize(roi, (w//rate, h//rate)) # 1/rate 비율로 축소
+        # 원래 크기로 확대
+        roi = cv2.resize(roi, (w,h), interpolation=cv2.INTER_AREA)  
+        frame[y:y+h, x:x+w] = roi   # 원본 이미지에 적용
+    cv2.imshow('mosaic', frame)
+    if cv2.waitKey(1) == 27:
+        break
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/MachineLearning/workshop_hannibal_mask.py b/CV/OpenCV/MachineLearning/workshop_hannibal_mask.py
new file mode 100644
index 0000000..58f2599
--- /dev/null
+++ b/CV/OpenCV/MachineLearning/workshop_hannibal_mask.py
@@ -0,0 +1,41 @@
+import cv2
+import numpy as np
+
+# 마스크 이미지 읽기 
+face_mask = cv2.imread('../img/mask_hannibal.png')
+h_mask, w_mask = face_mask.shape[:2]
+# 얼굴 검출기 생성
+face_cascade = cv2.CascadeClassifier('../data/haarcascade_frontalface_alt.xml')
+
+cap = cv2.VideoCapture(0)
+while True:
+    ret, frame = cap.read()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+	# 얼굴 영역 검출
+    face_rects = face_cascade.detectMultiScale(gray, 1.3, 5)
+    for (x,y,w,h) in face_rects:
+        if h > 0 and w > 0:
+        		# 마스크 위치 보정
+            x = int(x + 0.1*w)
+            y = int(y + 0.4*h)
+            w = int(0.8 * w)
+            h = int(0.8 * h)
+
+            frame_roi = frame[y:y+h, x:x+w]
+            # 마스크 이미지를 얼굴 크기에 맞게 조정 
+            face_mask_small = cv2.resize(face_mask, (w, h), \
+                                interpolation=cv2.INTER_AREA)
+			# 마스크 이미지 합성
+            gray_mask = cv2.cvtColor(face_mask_small, cv2.COLOR_BGR2GRAY)
+            ret, mask = cv2.threshold(gray_mask, 50, 255, cv2.THRESH_BINARY)
+            mask_inv = cv2.bitwise_not(mask)
+            masked_face = cv2.bitwise_and(face_mask_small, face_mask_small,\
+                                         mask=mask)
+            masked_frame = cv2.bitwise_and(frame_roi, frame_roi, mask=mask_inv)
+            frame[y:y+h, x:x+w] = cv2.add(masked_face, masked_frame)
+
+    cv2.imshow('Hanibal Mask', frame)
+    if cv2.waitKey(1) == 27:
+        break
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/face-detection/README.md b/CV/OpenCV/face-detection/README.md
new file mode 100644
index 0000000..a556c8e
--- /dev/null
+++ b/CV/OpenCV/face-detection/README.md
@@ -0,0 +1,7 @@
+# Face Detection
+
+Credits to Adrian Rosebrock.
+
+## Reference
+
+- Adrian Rosebrock. [OpenCV-Python Face Detection](https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/)
diff --git a/CV/OpenCV/face-detection/additional/face_delauney_triangle.py b/CV/OpenCV/face-detection/additional/face_delauney_triangle.py
new file mode 100644
index 0000000..b8b21c3
--- /dev/null
+++ b/CV/OpenCV/face-detection/additional/face_delauney_triangle.py
@@ -0,0 +1,46 @@
+import cv2
+import numpy as np
+import dlib
+
+# 얼굴 검출기와 랜드마크 검출기 생성 --- ①
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
+
+img = cv2.imread("../img/man_face.jpg")
+h, w = img.shape[:2]
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+# 얼굴 영역 검출 --- ②
+rects = faces = detector(gray)
+
+points = []
+for rect in rects:
+    # 랜드마크 검출 --- ③
+    shape = predictor(gray, rect)
+    for i in range(68):
+        part = shape.part(i)
+        points.append((part.x, part.y))
+        
+
+# 들로네 삼각 분할 객체 생성 --- ④
+x,y,w,h = cv2.boundingRect(np.float32(points))
+subdiv = cv2.Subdiv2D((x,y,x+w,y+h))
+# 랜드마크 좌표 추가 --- ⑤
+subdiv.insert(points)
+# 들로네 삼각형 좌표 계산 --- ⑥
+triangleList = subdiv.getTriangleList()
+# 들로네 삼각형 그리기 --- ⑦
+h, w = img.shape[:2]
+cnt = 0
+for t in triangleList :
+    pts = t.reshape(-1,2).astype(np.int32)
+    # 좌표 중에 이미지 영역을 벗어나는 것을 제외(음수 등) ---⑧
+    if (pts < 0).sum() or (pts[:, 0] > w).sum() or (pts[:, 1] > h).sum():
+        print(pts) 
+        continue
+    cv2.polylines(img, [pts], True, (255, 255,255), 1, cv2.LINE_AA)
+    cnt+=1
+print(cnt)
+
+
+cv2.imshow("Delaunay",img)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/face-detection/additional/face_landmark.py b/CV/OpenCV/face-detection/additional/face_landmark.py
new file mode 100644
index 0000000..8fa7d11
--- /dev/null
+++ b/CV/OpenCV/face-detection/additional/face_landmark.py
@@ -0,0 +1,28 @@
+import cv2
+import dlib
+
+# 얼굴 검출기와 랜드마크 검출기 생성 --- ①
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
+
+img = cv2.imread("../img/man_face.jpg")
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+# 얼굴 영역 검출 --- ②
+faces = detector(gray)
+for rect in faces:
+    # 얼굴 영역을 좌표로 변환 후 사각형 표시 --- ③
+    x,y = rect.left(), rect.top()
+    w,h = rect.right()-x, rect.bottom()-y
+    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
+
+    # 얼굴 랜드마크 검출 --- ④
+    shape = predictor(gray, rect)
+    for i in range(68):
+        # 부위별 좌표 추출 및 표시 --- ⑤
+        part = shape.part(i)
+        cv2.circle(img, (part.x, part.y), 2, (0, 0, 255), -1)
+        cv2.putText(img, str(i), (part.x, part.y), cv2.FONT_HERSHEY_PLAIN, \
+                                         0.5,(255,255,255), 1, cv2.LINE_AA)
+
+cv2.imshow("face landmark", img)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/face-detection/additional/face_landmark_cam.py b/CV/OpenCV/face-detection/additional/face_landmark_cam.py
new file mode 100644
index 0000000..ebcec66
--- /dev/null
+++ b/CV/OpenCV/face-detection/additional/face_landmark_cam.py
@@ -0,0 +1,36 @@
+import cv2
+import dlib
+
+# 얼굴 검출기와 랜드마크 검출기 생성 --- ①
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
+
+cap = cv2.VideoCapture(0)
+#cap.set(cv2.cv2.CAP_PROP_FRAME_WIDTH, 480)
+#cap.set(cv2.cv2.CAP_PROP_FRAME_HEIGHT, 320)
+
+while cap.isOpened():
+    ret, img = cap.read()
+    if not ret:
+        print('no frame.');break
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # 얼굴 영역 검출 --- ②
+    faces = detector(gray)
+    for rect in faces:
+        # 얼굴 영역을 좌표로 변환 후 사각형 표시 --- ③
+        x,y = rect.left(), rect.top()
+        w,h = rect.right()-x, rect.bottom()-y
+        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
+    
+        # 얼굴 랜드마크 검출 --- ④
+        shape = predictor(gray, rect)
+        for i in range(68):
+            # 부위별 좌표 추출 및 표시 --- ⑤
+            part = shape.part(i)
+            cv2.circle(img, (part.x, part.y), 2, (0, 0, 255), -1)
+            #cv2.putText(img, str(i), (part.x, part.y), cv2.FONT_HERSHEY_PLAIN, 0.5,(255,255,255), 1, cv2.LINE_AA)
+    
+    cv2.imshow("face landmark", img)
+    if cv2.waitKey(1)== 27:
+        break
+cap.release()
diff --git a/CV/OpenCV/face-detection/additional/face_swap.py b/CV/OpenCV/face-detection/additional/face_swap.py
new file mode 100644
index 0000000..fcd0b3a
--- /dev/null
+++ b/CV/OpenCV/face-detection/additional/face_swap.py
@@ -0,0 +1,100 @@
+import cv2
+import numpy as np
+import dlib
+import sys
+
+# 얼굴 검출기와 랜드마크 검출기 생성 --- ①
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
+
+# 얼굴 및 랜드마크 검출해서 좌표 반환하는 함수 ---② 
+def getPoints(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    rects = detector(gray)
+    points = []
+    for rect in rects:
+        shape = predictor(gray, rect)
+        for i in range(68):
+            part = shape.part(i)
+            points.append((part.x, part.y))
+    return points    
+# 랜드마크 좌표로 들로네 삼각형 반환 ---③
+def getTriangles(img, points):
+    w,h = img2.shape[:2]
+    subdiv = cv2.Subdiv2D((0,0,w,h));
+    subdiv.insert(points) 
+    triangleList = subdiv.getTriangleList();
+    triangles = []
+    for t in triangleList:        
+        pt = t.reshape(-1,2)
+        if not (pt < 0).sum() and not (pt[:, 0] > w).sum() \
+                              and not (pt[:, 1] > h).sum(): 
+            indice = []
+            for i in range(0, 3):
+                for j in range(0, len(points)):                    
+                    if(abs(pt[i][0] - points[j][0]) < 1.0 \
+                        and abs(pt[i][1] - points[j][1]) < 1.0):
+                        indice.append(j)    
+            if len(indice) == 3:                                                
+                triangles.append(indice)
+    return triangles
+
+# 삼각형 어핀 변환 함수 ---④
+def warpTriangle(img1, img2, pts1, pts2):
+    x1,y1,w1,h1 = cv2.boundingRect(np.float32([pts1]))
+    x2,y2,w2,h2 = cv2.boundingRect(np.float32([pts2]))
+    
+    roi1 = img1[y1:y1+h1, x1:x1+w1]
+    roi2 = img2[y2:y2+h2, x2:x2+w2]
+    
+    offset1 = np.zeros((3,2), dtype=np.float32)
+    offset2 = np.zeros((3,2), dtype=np.float32)
+    for i in range(3):
+        offset1[i][0], offset1[i][1] = pts1[i][0]-x1, pts1[i][1]-y1
+        offset2[i][0], offset2[i][1] = pts2[i][0]-x2, pts2[i][1]-y2
+    
+    mtrx = cv2.getAffineTransform(offset1, offset2)
+    warped = cv2.warpAffine( roi1, mtrx, (w2, h2), None, \
+                        cv2.INTER_LINEAR, cv2.BORDER_REFLECT_101 )
+    
+    mask = np.zeros((h2, w2), dtype = np.uint8)
+    cv2.fillConvexPoly(mask, np.int32(offset2), (255))
+    
+    warped_masked = cv2.bitwise_and(warped, warped, mask=mask)
+    roi2_masked = cv2.bitwise_and(roi2, roi2, mask=cv2.bitwise_not(mask))
+    roi2_masked = roi2_masked + warped_masked
+    img2[y2:y2+h2, x2:x2+w2] = roi2_masked
+
+if __name__ == '__main__' :
+    # 이미지 읽기 ---⑤
+    img1 = cv2.imread('../img/boy_face.jpg')
+    img2 = cv2.imread('../img/girl_face.jpg')
+    cv2.imshow('img1', img1)
+    cv2.imshow('img2', img2)
+    img_draw = img2.copy()
+    # 각 이미지에서 얼굴 랜드마크 좌표 구하기--- ⑥ 
+    points1 = getPoints(img1)
+    points2 = getPoints(img2)
+    # 랜드마크 좌표로 볼록 선체 구하기 --- ⑦
+    hullIndex = cv2.convexHull(np.array(points2), returnPoints = False)
+    hull1 = [points1[int(idx)] for idx in hullIndex]
+    hull2 = [points2[int(idx)] for idx in hullIndex]
+    # 볼록 선체 안 들로네 삼각형 좌표 구하기 ---⑧ 
+    triangles = getTriangles(img2, hull2)
+    
+    # 각 삼각형 좌표로 삼각형 어핀 변환 ---⑨    
+    for i in range(0, len(triangles)):
+        t1 = [hull1[triangles[i][j]] for j in range(3)]
+        t2 = [hull2[triangles[i][j]] for j in range(3)]
+        warpTriangle(img1, img_draw, t1, t2)
+    # 볼록선체를 마스크로 써서 얼굴 합성 ---⑩
+    mask = np.zeros(img2.shape, dtype = img2.dtype)  
+    cv2.fillConvexPoly(mask, np.int32(hull2), (255, 255, 255))
+    r = cv2.boundingRect(np.float32([hull2]))    
+    center = ((r[0]+int(r[2]/2), r[1]+int(r[3]/2)))
+    output = cv2.seamlessClone(np.uint8(img_draw), img2, mask, center, \
+                                cv2.NORMAL_CLONE)
+    
+    cv2.imshow("Face Swapped", output)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
diff --git a/CV/OpenCV/face-detection/deploy.prototxt.txt b/CV/OpenCV/face-detection/deploy.prototxt.txt
new file mode 100644
index 0000000..905580e
--- /dev/null
+++ b/CV/OpenCV/face-detection/deploy.prototxt.txt
@@ -0,0 +1,1789 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 300
+  dim: 300
+}
+
+layer {
+  name: "data_bn"
+  type: "BatchNorm"
+  bottom: "data"
+  top: "data_bn"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "data_scale"
+  type: "Scale"
+  bottom: "data_bn"
+  top: "data_bn"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_h"
+  type: "Convolution"
+  bottom: "data_bn"
+  top: "conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "msra"
+      variance_norm: FAN_OUT
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "conv1_bn_h"
+  type: "BatchNorm"
+  bottom: "conv1_h"
+  top: "conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "conv1_scale_h"
+  type: "Scale"
+  bottom: "conv1_h"
+  top: "conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_relu"
+  type: "ReLU"
+  bottom: "conv1_h"
+  top: "conv1_h"
+}
+layer {
+  name: "conv1_pool"
+  type: "Pooling"
+  bottom: "conv1_h"
+  top: "conv1_pool"
+  pooling_param {
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "layer_64_1_conv1_h"
+  type: "Convolution"
+  bottom: "conv1_pool"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_64_1_bn2_h"
+  type: "BatchNorm"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_64_1_scale2_h"
+  type: "Scale"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_64_1_relu2"
+  type: "ReLU"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+}
+layer {
+  name: "layer_64_1_conv2_h"
+  type: "Convolution"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv2_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_64_1_sum"
+  type: "Eltwise"
+  bottom: "layer_64_1_conv2_h"
+  bottom: "conv1_pool"
+  top: "layer_64_1_sum"
+}
+layer {
+  name: "layer_128_1_bn1_h"
+  type: "BatchNorm"
+  bottom: "layer_64_1_sum"
+  top: "layer_128_1_bn1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_128_1_scale1_h"
+  type: "Scale"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_bn1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_128_1_relu1"
+  type: "ReLU"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_bn1_h"
+}
+layer {
+  name: "layer_128_1_conv1_h"
+  type: "Convolution"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_bn2"
+  type: "BatchNorm"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_128_1_scale2"
+  type: "Scale"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_128_1_relu2"
+  type: "ReLU"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+}
+layer {
+  name: "layer_128_1_conv2"
+  type: "Convolution"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_conv_expand_h"
+  type: "Convolution"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_conv_expand_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_sum"
+  type: "Eltwise"
+  bottom: "layer_128_1_conv2"
+  bottom: "layer_128_1_conv_expand_h"
+  top: "layer_128_1_sum"
+}
+layer {
+  name: "layer_256_1_bn1"
+  type: "BatchNorm"
+  bottom: "layer_128_1_sum"
+  top: "layer_256_1_bn1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_256_1_scale1"
+  type: "Scale"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_bn1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_256_1_relu1"
+  type: "ReLU"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_bn1"
+}
+layer {
+  name: "layer_256_1_conv1"
+  type: "Convolution"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_bn2"
+  type: "BatchNorm"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_256_1_scale2"
+  type: "Scale"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_256_1_relu2"
+  type: "ReLU"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+}
+layer {
+  name: "layer_256_1_conv2"
+  type: "Convolution"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_conv_expand"
+  type: "Convolution"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_conv_expand"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_sum"
+  type: "Eltwise"
+  bottom: "layer_256_1_conv2"
+  bottom: "layer_256_1_conv_expand"
+  top: "layer_256_1_sum"
+}
+layer {
+  name: "layer_512_1_bn1"
+  type: "BatchNorm"
+  bottom: "layer_256_1_sum"
+  top: "layer_512_1_bn1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_512_1_scale1"
+  type: "Scale"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_bn1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_512_1_relu1"
+  type: "ReLU"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_bn1"
+}
+layer {
+  name: "layer_512_1_conv1_h"
+  type: "Convolution"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1 # 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_bn2_h"
+  type: "BatchNorm"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_512_1_scale2_h"
+  type: "Scale"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_512_1_relu2"
+  type: "ReLU"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+}
+layer {
+  name: "layer_512_1_conv2_h"
+  type: "Convolution"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv2_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 2 # 1
+    kernel_size: 3
+    stride: 1
+    dilation: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_conv_expand_h"
+  type: "Convolution"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_conv_expand_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1 # 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_sum"
+  type: "Eltwise"
+  bottom: "layer_512_1_conv2_h"
+  bottom: "layer_512_1_conv_expand_h"
+  top: "layer_512_1_sum"
+}
+layer {
+  name: "last_bn_h"
+  type: "BatchNorm"
+  bottom: "layer_512_1_sum"
+  top: "layer_512_1_sum"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "last_scale_h"
+  type: "Scale"
+  bottom: "layer_512_1_sum"
+  top: "layer_512_1_sum"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "last_relu"
+  type: "ReLU"
+  bottom: "layer_512_1_sum"
+  top: "fc7"
+}
+
+layer {
+  name: "conv6_1_h"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "conv6_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_1_relu"
+  type: "ReLU"
+  bottom: "conv6_1_h"
+  top: "conv6_1_h"
+}
+layer {
+  name: "conv6_2_h"
+  type: "Convolution"
+  bottom: "conv6_1_h"
+  top: "conv6_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_relu"
+  type: "ReLU"
+  bottom: "conv6_2_h"
+  top: "conv6_2_h"
+}
+layer {
+  name: "conv7_1_h"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv7_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_1_relu"
+  type: "ReLU"
+  bottom: "conv7_1_h"
+  top: "conv7_1_h"
+}
+layer {
+  name: "conv7_2_h"
+  type: "Convolution"
+  bottom: "conv7_1_h"
+  top: "conv7_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_relu"
+  type: "ReLU"
+  bottom: "conv7_2_h"
+  top: "conv7_2_h"
+}
+layer {
+  name: "conv8_1_h"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv8_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_1_relu"
+  type: "ReLU"
+  bottom: "conv8_1_h"
+  top: "conv8_1_h"
+}
+layer {
+  name: "conv8_2_h"
+  type: "Convolution"
+  bottom: "conv8_1_h"
+  top: "conv8_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_relu"
+  type: "ReLU"
+  bottom: "conv8_2_h"
+  top: "conv8_2_h"
+}
+layer {
+  name: "conv9_1_h"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv9_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_1_relu"
+  type: "ReLU"
+  bottom: "conv9_1_h"
+  top: "conv9_1_h"
+}
+layer {
+  name: "conv9_2_h"
+  type: "Convolution"
+  bottom: "conv9_1_h"
+  top: "conv9_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_relu"
+  type: "ReLU"
+  bottom: "conv9_2_h"
+  top: "conv9_2_h"
+}
+layer {
+  name: "conv4_3_norm"
+  type: "Normalize"
+  bottom: "layer_256_1_bn1"
+  top: "conv4_3_norm"
+  norm_param {
+    across_spatial: false
+    scale_filler {
+      type: "constant"
+      value: 20
+    }
+    channel_shared: false
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_loc"
+  top: "conv4_3_norm_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_loc_perm"
+  top: "conv4_3_norm_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_conf"
+  top: "conv4_3_norm_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_conf_perm"
+  top: "conv4_3_norm_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv4_3_norm"
+  bottom: "data"
+  top: "conv4_3_norm_mbox_priorbox"
+  prior_box_param {
+    min_size: 30.0
+    max_size: 60.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 8
+    offset: 0.5
+  }
+}
+layer {
+  name: "fc7_mbox_loc"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "fc7_mbox_loc_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_loc"
+  top: "fc7_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_loc_perm"
+  top: "fc7_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "fc7_mbox_conf_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_conf"
+  top: "fc7_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_conf_perm"
+  top: "fc7_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "fc7"
+  bottom: "data"
+  top: "fc7_mbox_priorbox"
+  prior_box_param {
+    min_size: 60.0
+    max_size: 111.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 16
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv6_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_loc"
+  top: "conv6_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_loc_perm"
+  top: "conv6_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv6_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_conf"
+  top: "conv6_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_conf_perm"
+  top: "conv6_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv6_2_h"
+  bottom: "data"
+  top: "conv6_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 111.0
+    max_size: 162.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 32
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv7_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv7_2_mbox_loc"
+  top: "conv7_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv7_2_mbox_loc_perm"
+  top: "conv7_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv7_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv7_2_mbox_conf"
+  top: "conv7_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv7_2_mbox_conf_perm"
+  top: "conv7_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv7_2_h"
+  bottom: "data"
+  top: "conv7_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 162.0
+    max_size: 213.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 64
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv8_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv8_2_mbox_loc"
+  top: "conv8_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv8_2_mbox_loc_perm"
+  top: "conv8_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv8_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv8_2_mbox_conf"
+  top: "conv8_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv8_2_mbox_conf_perm"
+  top: "conv8_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv8_2_h"
+  bottom: "data"
+  top: "conv8_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 213.0
+    max_size: 264.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 100
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv9_2_h"
+  top: "conv9_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_loc"
+  top: "conv9_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_loc_perm"
+  top: "conv9_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv9_2_h"
+  top: "conv9_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_conf"
+  top: "conv9_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_conf_perm"
+  top: "conv9_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv9_2_h"
+  bottom: "data"
+  top: "conv9_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 264.0
+    max_size: 315.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 300
+    offset: 0.5
+  }
+}
+layer {
+  name: "mbox_loc"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_loc_flat"
+  bottom: "fc7_mbox_loc_flat"
+  bottom: "conv6_2_mbox_loc_flat"
+  bottom: "conv7_2_mbox_loc_flat"
+  bottom: "conv8_2_mbox_loc_flat"
+  bottom: "conv9_2_mbox_loc_flat"
+  top: "mbox_loc"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_conf"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_conf_flat"
+  bottom: "fc7_mbox_conf_flat"
+  bottom: "conv6_2_mbox_conf_flat"
+  bottom: "conv7_2_mbox_conf_flat"
+  bottom: "conv8_2_mbox_conf_flat"
+  bottom: "conv9_2_mbox_conf_flat"
+  top: "mbox_conf"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_priorbox"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_priorbox"
+  bottom: "fc7_mbox_priorbox"
+  bottom: "conv6_2_mbox_priorbox"
+  bottom: "conv7_2_mbox_priorbox"
+  bottom: "conv8_2_mbox_priorbox"
+  bottom: "conv9_2_mbox_priorbox"
+  top: "mbox_priorbox"
+  concat_param {
+    axis: 2
+  }
+}
+
+layer {
+  name: "mbox_conf_reshape"
+  type: "Reshape"
+  bottom: "mbox_conf"
+  top: "mbox_conf_reshape"
+  reshape_param {
+    shape {
+      dim: 0
+      dim: -1
+      dim: 2
+    }
+  }
+}
+layer {
+  name: "mbox_conf_softmax"
+  type: "Softmax"
+  bottom: "mbox_conf_reshape"
+  top: "mbox_conf_softmax"
+  softmax_param {
+    axis: 2
+  }
+}
+layer {
+  name: "mbox_conf_flatten"
+  type: "Flatten"
+  bottom: "mbox_conf_softmax"
+  top: "mbox_conf_flatten"
+  flatten_param {
+    axis: 1
+  }
+}
+
+layer {
+  name: "detection_out"
+  type: "DetectionOutput"
+  bottom: "mbox_loc"
+  bottom: "mbox_conf_flatten"
+  bottom: "mbox_priorbox"
+  top: "detection_out"
+  include {
+    phase: TEST
+  }
+  detection_output_param {
+    num_classes: 2
+    share_location: true
+    background_label_id: 0
+    nms_param {
+      nms_threshold: 0.45
+      top_k: 400
+    }
+    code_type: CENTER_SIZE
+    keep_top_k: 200
+    confidence_threshold: 0.01
+  }
+}
diff --git a/CV/OpenCV/face-detection/detect_faces.py b/CV/OpenCV/face-detection/detect_faces.py
new file mode 100644
index 0000000..cf52f09
--- /dev/null
+++ b/CV/OpenCV/face-detection/detect_faces.py
@@ -0,0 +1,63 @@
+# USAGE
+# python detect_faces.py --image rooster.jpg --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel
+
+# import the necessary packages
+import numpy as np
+import argparse
+import cv2
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required=True,
+	help="path to input image")
+ap.add_argument("-p", "--prototxt", required=True,
+	help="path to Caffe 'deploy' prototxt file")
+ap.add_argument("-m", "--model", required=True,
+	help="path to Caffe pre-trained model")
+ap.add_argument("-c", "--confidence", type=float, default=0.5,
+	help="minimum probability to filter weak detections")
+args = vars(ap.parse_args())
+
+# load our serialized model from disk
+print("[INFO] loading model...")
+net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
+
+# load the input image and construct an input blob for the image
+# by resizing to a fixed 300x300 pixels and then normalizing it
+image = cv2.imread(args["image"])
+(h, w) = image.shape[:2]
+blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
+	(300, 300), (104.0, 177.0, 123.0))
+
+# pass the blob through the network and obtain the detections and
+# predictions
+print("[INFO] computing object detections...")
+net.setInput(blob)
+detections = net.forward()
+
+# loop over the detections
+for i in range(0, detections.shape[2]):
+	# extract the confidence (i.e., probability) associated with the
+	# prediction
+	confidence = detections[0, 0, i, 2]
+
+	# filter out weak detections by ensuring the `confidence` is
+	# greater than the minimum confidence
+	if confidence > args["confidence"]:
+		# compute the (x, y)-coordinates of the bounding box for the
+		# object
+		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+		(startX, startY, endX, endY) = box.astype("int")
+ 
+		# draw the bounding box of the face along with the associated
+		# probability
+		text = "{:.2f}%".format(confidence * 100)
+		y = startY - 10 if startY - 10 > 10 else startY + 10
+		cv2.rectangle(image, (startX, startY), (endX, endY),
+			(0, 0, 255), 2)
+		cv2.putText(image, text, (startX, y),
+			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+
+# show the output image
+cv2.imshow("Output", image)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/face-detection/detect_faces_video.py b/CV/OpenCV/face-detection/detect_faces_video.py
new file mode 100644
index 0000000..b1f645f
--- /dev/null
+++ b/CV/OpenCV/face-detection/detect_faces_video.py
@@ -0,0 +1,83 @@
+# USAGE
+# python detect_faces_video.py --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel
+
+# import the necessary packages
+from imutils.video import VideoStream
+import numpy as np
+import argparse
+import imutils
+import time
+import cv2
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-p", "--prototxt", required=True,
+	help="path to Caffe 'deploy' prototxt file")
+ap.add_argument("-m", "--model", required=True,
+	help="path to Caffe pre-trained model")
+ap.add_argument("-c", "--confidence", type=float, default=0.5,
+	help="minimum probability to filter weak detections")
+args = vars(ap.parse_args())
+
+# load our serialized model from disk
+print("[INFO] loading model...")
+net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
+
+# initialize the video stream and allow the cammera sensor to warmup
+print("[INFO] starting video stream...")
+vs = VideoStream(src=0).start()
+time.sleep(2.0)
+
+# loop over the frames from the video stream
+while True:
+	# grab the frame from the threaded video stream and resize it
+	# to have a maximum width of 400 pixels
+	frame = vs.read()
+	frame = imutils.resize(frame, width=400)
+ 
+	# grab the frame dimensions and convert it to a blob
+	(h, w) = frame.shape[:2]
+	blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
+		(300, 300), (104.0, 177.0, 123.0))
+ 
+	# pass the blob through the network and obtain the detections and
+	# predictions
+	net.setInput(blob)
+	detections = net.forward()
+
+	# loop over the detections
+	for i in range(0, detections.shape[2]):
+		# extract the confidence (i.e., probability) associated with the
+		# prediction
+		confidence = detections[0, 0, i, 2]
+
+		# filter out weak detections by ensuring the `confidence` is
+		# greater than the minimum confidence
+		if confidence < args["confidence"]:
+			continue
+
+		# compute the (x, y)-coordinates of the bounding box for the
+		# object
+		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+		(startX, startY, endX, endY) = box.astype("int")
+ 
+		# draw the bounding box of the face along with the associated
+		# probability
+		text = "{:.2f}%".format(confidence * 100)
+		y = startY - 10 if startY - 10 > 10 else startY + 10
+		cv2.rectangle(frame, (startX, startY), (endX, endY),
+			(0, 0, 255), 2)
+		cv2.putText(frame, text, (startX, y),
+			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+
+	# show the output frame
+	cv2.imshow("Frame", frame)
+	key = cv2.waitKey(1) & 0xFF
+ 
+	# if the `q` key was pressed, break from the loop
+	if key == ord("q"):
+		break
+
+# do a bit of cleanup
+cv2.destroyAllWindows()
+vs.stop()
\ No newline at end of file
diff --git a/CV/OpenCV/face-detection/res10_300x300_ssd_iter_140000.caffemodel b/CV/OpenCV/face-detection/res10_300x300_ssd_iter_140000.caffemodel
new file mode 100644
index 0000000..809dfd7
Binary files /dev/null and b/CV/OpenCV/face-detection/res10_300x300_ssd_iter_140000.caffemodel differ
diff --git a/CV/OpenCV/filter/blur_avg_api.py b/CV/OpenCV/filter/blur_avg_api.py
new file mode 100644
index 0000000..e6ef07d
--- /dev/null
+++ b/CV/OpenCV/filter/blur_avg_api.py
@@ -0,0 +1,17 @@
+import cv2
+import numpy as np
+
+file_name = '../img/taekwonv1.jpg'
+img = cv2.imread(file_name)
+
+# bluring with blur()
+blur1 = cv2.blur(img, (10,10))
+
+# bluring with boxFilter()
+blur2 = cv2.boxFilter(img, -1, (10,10))
+
+# 결과 출력
+merged = np.hstack( (img, blur1, blur2))
+cv2.imshow('blur', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/blur_avg_kernel.py b/CV/OpenCV/filter/blur_avg_kernel.py
new file mode 100644
index 0000000..9a0d776
--- /dev/null
+++ b/CV/OpenCV/filter/blur_avg_kernel.py
@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/girl.jpg')
+
+# generate 5x5 size filter
+kernel = np.ones((5,5))/5**2
+
+# apply filter
+blured = cv2.filter2D(img, -1, kernel)
+
+# show result images
+cv2.imshow('origin', img)
+cv2.imshow('avrg blur', blured) 
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/blur_bilateral.py b/CV/OpenCV/filter/blur_bilateral.py
new file mode 100644
index 0000000..9111de5
--- /dev/null
+++ b/CV/OpenCV/filter/blur_bilateral.py
@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/gaussian_noise.jpg")
+
+# apply Gaussian Blur filter
+blur1 = cv2.GaussianBlur(img, (5,5), 0)
+
+# apply bilateral filter
+blur2 = cv2.bilateralFilter(img, 5, 75, 75)
+
+# show result images
+merged = np.hstack((img, blur1, blur2))
+cv2.imshow('bilateral', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/blur_gaussian.py b/CV/OpenCV/filter/blur_gaussian.py
new file mode 100644
index 0000000..b9b0edf
--- /dev/null
+++ b/CV/OpenCV/filter/blur_gaussian.py
@@ -0,0 +1,25 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/gaussian_noise.jpg')
+
+# blurring by generating Gaussian kernel
+k1 = np.array([[1, 2, 1],
+                   [2, 4, 2],
+                   [1, 2, 1]]) *(1/16)
+blur1 = cv2.filter2D(img, -1, k1)
+
+# Use Gaussian kernel API
+k2 = cv2.getGaussianKernel(3, 0)
+blur2 = cv2.filter2D(img, -1, k2*k2.T)
+
+# blurring with Gaussian kernell
+blur3 = cv2.GaussianBlur(img, (3, 3), 0)
+
+# show result images
+print('k1:', k1)
+print('k2:', k2*k2.T)
+merged = np.hstack((img, blur1, blur2, blur3))
+cv2.imshow('gaussian blur', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/blur_median.py b/CV/OpenCV/filter/blur_median.py
new file mode 100644
index 0000000..68a4c7f
--- /dev/null
+++ b/CV/OpenCV/filter/blur_median.py
@@ -0,0 +1,13 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/salt_pepper_noise.jpg")
+
+# apply median blur
+blur = cv2.medianBlur(img, 5)
+
+# show results
+merged = np.hstack((img,blur))
+cv2.imshow('media', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/edge_canny.py b/CV/OpenCV/filter/edge_canny.py
new file mode 100644
index 0000000..f808cb1
--- /dev/null
+++ b/CV/OpenCV/filter/edge_canny.py
@@ -0,0 +1,13 @@
+import cv2, time
+import numpy as np
+
+img = cv2.imread("../img/sudoku.jpg")
+
+# apply Canny edge
+edges = cv2.Canny(img,100,200)
+
+# show result images
+cv2.imshow('Original', img)
+cv2.imshow('Canny', edges)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/edge_differential.py b/CV/OpenCV/filter/edge_differential.py
new file mode 100644
index 0000000..afea5f0
--- /dev/null
+++ b/CV/OpenCV/filter/edge_differential.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/sudoku.jpg")
+
+#  generate gradient kernel
+gx_kernel = np.array([[ -1, 1]])
+gy_kernel = np.array([[ -1],[ 1]])
+
+# apply filters
+edge_gx = cv2.filter2D(img, -1, gx_kernel)
+edge_gy = cv2.filter2D(img, -1, gy_kernel)
+
+# show result image
+merged = np.hstack((img, edge_gx, edge_gy))
+cv2.imshow('edge', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/edge_laplacian.py b/CV/OpenCV/filter/edge_laplacian.py
new file mode 100644
index 0000000..79b9bb2
--- /dev/null
+++ b/CV/OpenCV/filter/edge_laplacian.py
@@ -0,0 +1,13 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/sudoku.jpg")
+
+# apply Laplacian filter
+edge = cv2.Laplacian(img, -1)
+
+# show image
+merged = np.hstack((img, edge))
+cv2.imshow('Laplacian', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/edge_prewitt.py b/CV/OpenCV/filter/edge_prewitt.py
new file mode 100644
index 0000000..92267cc
--- /dev/null
+++ b/CV/OpenCV/filter/edge_prewitt.py
@@ -0,0 +1,19 @@
+import cv2
+import numpy as np
+
+file_name = "../img/sudoku.jpg"
+img = cv2.imread(file_name)
+
+# generate prewitt kernel
+gx_k = np.array([[-1,0,1], [-1,0,1],[-1,0,1]])
+gy_k = np.array([[-1,-1,-1],[0,0,0], [1,1,1]])
+
+# apply prewitt kernel
+edge_gx = cv2.filter2D(img, -1, gx_k)
+edge_gy = cv2.filter2D(img, -1, gy_k)
+
+# show result images
+merged = np.hstack((img, edge_gx, edge_gy, edge_gx+edge_gy))
+cv2.imshow('prewitt', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/edge_roberts.py b/CV/OpenCV/filter/edge_roberts.py
new file mode 100644
index 0000000..d0af1be
--- /dev/null
+++ b/CV/OpenCV/filter/edge_roberts.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/sudoku.jpg")
+
+# generate Roberts kernel
+gx_kernel = np.array([[1,0], [0,-1]])
+gy_kernel = np.array([[0, 1],[-1,0]])
+
+# apply kernel
+edge_gx = cv2.filter2D(img, -1, gx_kernel)
+edge_gy = cv2.filter2D(img, -1, gy_kernel)
+
+# show result image
+merged = np.hstack((img, edge_gx, edge_gy, edge_gx+edge_gy))
+cv2.imshow('roberts cross', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/edge_scharr.py b/CV/OpenCV/filter/edge_scharr.py
new file mode 100644
index 0000000..0948cf6
--- /dev/null
+++ b/CV/OpenCV/filter/edge_scharr.py
@@ -0,0 +1,22 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/sudoku.jpg")
+
+# generate scharr kernel
+gx_k = np.array([[-3,0,3], [-10,0,10],[-3,0,3]])
+gy_k = np.array([[-3,-10,-3],[0,0,0], [3,10,3]])
+edge_gx = cv2.filter2D(img, -1, gx_k)
+edge_gy = cv2.filter2D(img, -1, gy_k)
+
+# detect edges by using Scharr kernel
+scharrx = cv2.Scharr(img, -1, 1, 0)
+scharry = cv2.Scharr(img, -1, 0, 1)
+
+# show result image
+merged1 = np.hstack((img, edge_gx, edge_gy))
+merged2 = np.hstack((img, scharrx, scharry))
+merged = np.vstack((merged1, merged2))
+cv2.imshow('Scharr', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/edge_sobel.py b/CV/OpenCV/filter/edge_sobel.py
new file mode 100644
index 0000000..b25d126
--- /dev/null
+++ b/CV/OpenCV/filter/edge_sobel.py
@@ -0,0 +1,24 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("../img/sudoku.jpg")
+
+# generate Sobel kernel
+gx_k = np.array([[-1,0,1], [-2,0,2],[-1,0,1]])
+gy_k = np.array([[-1,-2,-1],[0,0,0], [1,2,1]])
+
+# apply Sobel kernel
+edge_gx = cv2.filter2D(img, -1, gx_k)
+edge_gy = cv2.filter2D(img, -1, gy_k)
+
+# detect edges by using Sobel kernel
+sobelx = cv2.Sobel(img, -1, 1, 0, ksize=3)
+sobely = cv2.Sobel(img, -1, 0, 1, ksize=3) 
+
+# show result image
+merged1 = np.hstack((img, edge_gx, edge_gy, edge_gx+edge_gy))
+merged2 = np.hstack((img, sobelx, sobely, sobelx+sobely))
+merged = np.vstack((merged1, merged2))
+cv2.imshow('sobel', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/morph_dilate.py b/CV/OpenCV/filter/morph_dilate.py
new file mode 100644
index 0000000..f551f29
--- /dev/null
+++ b/CV/OpenCV/filter/morph_dilate.py
@@ -0,0 +1,12 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/morph_hole.png')
+
+k = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
+dst = cv2.dilate(img, k)
+
+merged = np.hstack((img, dst))
+cv2.imshow('Dilation', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/morph_erode.py b/CV/OpenCV/filter/morph_erode.py
new file mode 100644
index 0000000..db585ac
--- /dev/null
+++ b/CV/OpenCV/filter/morph_erode.py
@@ -0,0 +1,13 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/morph_dot.png')
+
+k = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
+
+erosion = cv2.erode(img, k)
+
+merged = np.hstack((img, erosion))
+cv2.imshow('Erode', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/morph_gradient.py b/CV/OpenCV/filter/morph_gradient.py
new file mode 100644
index 0000000..5fb6c05
--- /dev/null
+++ b/CV/OpenCV/filter/morph_gradient.py
@@ -0,0 +1,12 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/morphological.png')
+
+k = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
+gradient = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, k)
+
+merged = np.hstack((img, gradient))
+cv2.imshow('gradient', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/morph_hat.py b/CV/OpenCV/filter/morph_hat.py
new file mode 100644
index 0000000..d6cb20a
--- /dev/null
+++ b/CV/OpenCV/filter/morph_hat.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/moon_gray.jpg')
+
+k = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
+
+# apply tophat filter
+tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, k)
+
+# apply blackhat filter
+blackhat = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, k)
+
+# show result image
+merged = np.hstack((img, tophat, blackhat))
+cv2.imshow('tophat blackhat', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/morph_open_close.py b/CV/OpenCV/filter/morph_open_close.py
new file mode 100644
index 0000000..2ecf2fd
--- /dev/null
+++ b/CV/OpenCV/filter/morph_open_close.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+img1 = cv2.imread('../img/morph_dot.png', cv2.IMREAD_GRAYSCALE)
+img2 = cv2.imread('../img/morph_hole.png', cv2.IMREAD_GRAYSCALE)    
+
+k = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
+
+opening = cv2.morphologyEx(img1, cv2.MORPH_OPEN, k)
+closing = cv2.morphologyEx(img2, cv2.MORPH_CLOSE, k)
+
+
+merged1 = np.hstack((img1, opening))
+merged2 = np.hstack((img2, closing))
+merged3 = np.vstack((merged1, merged2))
+cv2.imshow('opening, closing', merged3)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/pyramid_gaussian.py b/CV/OpenCV/filter/pyramid_gaussian.py
new file mode 100644
index 0000000..79ebdbe
--- /dev/null
+++ b/CV/OpenCV/filter/pyramid_gaussian.py
@@ -0,0 +1,16 @@
+import cv2
+
+img = cv2.imread('../img/girl.jpg')
+
+# reduce image with Gaussian image pyramid
+smaller = cv2.pyrDown(img) # img x 1/4
+
+# expand image with Gaussian image pyramid
+bigger = cv2.pyrUp(img) # img x 4
+
+# 결과 출력
+cv2.imshow('img', img)
+cv2.imshow('pyrDown', smaller)
+cv2.imshow('pyrUp', bigger)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/pyramid_laplacian.py b/CV/OpenCV/filter/pyramid_laplacian.py
new file mode 100644
index 0000000..9df7778
--- /dev/null
+++ b/CV/OpenCV/filter/pyramid_laplacian.py
@@ -0,0 +1,22 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/taekwonv1.jpg')
+
+# reduce with Gaussian image pyramid
+smaller = cv2.pyrDown(img)
+
+# expand the reduced image by using Gaussian image pyramid
+bigger = cv2.pyrUp(smaller)
+
+# subtract the expanded image from original image
+laplacian = cv2.subtract(img, bigger)
+
+# restore the image by adding expanded image to laplacian image
+restored = bigger + laplacian
+
+# show all images
+merged = np.hstack((img, laplacian, bigger, restored))
+cv2.imshow('Laplacian Pyramid', merged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/filter/workshop_mosic2.py b/CV/OpenCV/filter/workshop_mosic2.py
new file mode 100644
index 0000000..a1b78fd
--- /dev/null
+++ b/CV/OpenCV/filter/workshop_mosic2.py
@@ -0,0 +1,18 @@
+import cv2
+
+ksize = 30
+win_title = 'mosaic'
+img = cv2.imread('../img/taekwonv1.jpg')
+
+while True:
+    x,y,w,h = cv2.selectROI(win_title, img, False)
+
+    if w > 0 and h > 0:
+        roi = img[y : y + h, x : x + w]
+        roi = cv2.blur(roi, (ksize, ksize))
+        img[y : y + h, x : x + w] = roi
+        cv2.imshow(win_title, img)
+    else:
+        break
+
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/filter/workshop_painting_cam.py b/CV/OpenCV/filter/workshop_painting_cam.py
new file mode 100644
index 0000000..f5c394b
--- /dev/null
+++ b/CV/OpenCV/filter/workshop_painting_cam.py
@@ -0,0 +1,46 @@
+import cv2
+import numpy as np
+
+# connect camera
+cap = cv2.VideoCapture(0)   
+
+while cap.isOpened():
+    # read frame
+    ret, frame = cap.read()
+    # reduce the frame size to increase the processing speed
+    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
+
+    # terminate by pressing esc key
+    if cv2.waitKey(1) == 27:
+        break
+
+    # convert to gray scaling
+    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # applying Gaussian blur to remove noises
+    img_gray = cv2.GaussianBlur(img_gray, (9,9), 0)
+
+    # detect edges by using Laplacian kernel
+    edges = cv2.Laplacian(img_gray, -1, None, 5)
+    ret, sketch = cv2.threshold(edges, 70, 255, cv2.THRESH_BINARY_INV)
+    
+    # Expansion operation to emphasize borders
+    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
+    sketch = cv2.erode(sketch, kernel)
+
+    # Apply a medium blur filter to smooth the border
+    sketch = cv2.medianBlur(sketch, 5)
+    # convert gray scaling to color image
+    img_sketch = cv2.cvtColor(sketch, cv2.COLOR_GRAY2BGR)
+
+    # apply average blur filter
+    img_paint = cv2.blur(frame, (10,10) )
+    # compose color image and sketch image
+    img_paint = cv2.bitwise_and(img_paint, img_paint, mask=sketch)
+    
+    # show result image
+    merged = np.hstack((img_sketch, img_paint))
+    cv2.imshow('Sketch Camera', merged)
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/getAffine.py b/CV/OpenCV/geometric_transform/getAffine.py
new file mode 100644
index 0000000..2c84f8f
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/getAffine.py
@@ -0,0 +1,24 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+file_name = '../img/fish.jpg'
+img = cv2.imread(file_name)
+rows, cols = img.shape[:2]
+
+pts1 = np.float32([[100, 50], [200, 50], [100, 200]])
+pts2 = np.float32([[80, 70], [210, 60], [250, 120]])
+
+cv2.circle(img, (100,50), 5, (255,0), -1)
+cv2.circle(img, (200,50), 5, (0,255,0), -1)
+cv2.circle(img, (100,200), 5, (0,0,255), -1)
+
+mtrx = cv2.getAffineTransform(pts1, pts2)
+dst = cv2.warpAffine(img, mtrx, (int(cols*1.5), rows))
+
+
+cv2.imshow('origin',img)
+cv2.imshow('affin', dst)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/perspective.py b/CV/OpenCV/geometric_transform/perspective.py
new file mode 100644
index 0000000..c4d7d26
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/perspective.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np
+
+
+file_name = "../img/fish.jpg"
+img = cv2.imread(file_name)
+rows, cols = img.shape[:2]
+
+pts1 = np.float32([[0,0], [0,rows], [cols, 0], [cols,rows]])
+pts2 = np.float32([[100,50], [10,rows-50], [cols-100, 50], [cols-10,rows-50]])
+
+cv2.circle(img, (0,0), 10, (255,0,0), -1)
+cv2.circle(img, (0,rows), 10, (0,255,0), -1)
+cv2.circle(img, (cols,0), 10, (0,0,255), -1)
+cv2.circle(img, (cols,rows), 10, (0,255,255), -1)
+
+mtrx = cv2.getPerspectiveTransform(pts1, pts2)
+dst = cv2.warpPerspective(img, mtrx, (cols, rows))
+
+cv2.imshow("origin", img)
+cv2.imshow('perspective', dst)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/geometric_transform/perspective_scan.py b/CV/OpenCV/geometric_transform/perspective_scan.py
new file mode 100644
index 0000000..693e375
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/perspective_scan.py
@@ -0,0 +1,49 @@
+import cv2
+import numpy as np
+
+win_name = "scanning"
+img = cv2.imread("../img/paper.jpg")
+rows, cols = img.shape[:2]
+draw = img.copy()
+pts_cnt = 0
+pts = np.zeros((4,2), dtype=np.float32)
+
+def onMouse(event, x, y, flags, param):
+    global  pts_cnt
+
+    if event == cv2.EVENT_LBUTTONDOWN:  
+        cv2.circle(draw, (x,y), 10, (0,255,0), -1)
+        cv2.imshow(win_name, draw)
+
+        pts[pts_cnt] = [x,y]
+        pts_cnt += 1
+
+        if pts_cnt == 4:
+            sm = pts.sum(axis=1)
+            diff = np.diff(pts, axis = 1)
+
+            topLeft = pts[np.argmin(sm)]
+            bottomRight = pts[np.argmax(sm)]
+            topRight = pts[np.argmin(diff)]
+            bottomLeft = pts[np.argmax(diff)]
+
+            pts1 = np.float32([topLeft, topRight, bottomRight , bottomLeft])
+
+            w1 = abs(bottomRight[0] - bottomLeft[0])
+            w2 = abs(topRight[0] - topLeft[0])
+            h1 = abs(topRight[1] - bottomRight[1])
+            h2 = abs(topLeft[1] - bottomLeft[1])
+
+            width = max([w1, w2])
+            height = max([h1, h2])
+            
+            pts2 = np.float32([[0,0], [width-1,0], [width-1,height-1], [0,height-1]])
+
+            mtrx = cv2.getPerspectiveTransform(pts1, pts2)
+            result = cv2.warpPerspective(img, mtrx, (width, height))
+            cv2.imshow('scanned', result)
+
+cv2.imshow(win_name, img)
+cv2.setMouseCallback(win_name, onMouse)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/remap_barrel.py b/CV/OpenCV/geometric_transform/remap_barrel.py
new file mode 100644
index 0000000..0ae70e2
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/remap_barrel.py
@@ -0,0 +1,29 @@
+import cv2
+import numpy as np
+
+
+k1, k2, k3 = 0.5, 0.2, 0.0  # Barrel Distortion
+#k1, k2, k3 = -0.3, 0, 0    # Pincushion Distortion
+
+img = cv2.imread('../img/girl.jpg')
+rows, cols = img.shape[:2]
+
+
+mapy, mapx = np.indices((rows, cols),dtype=np.float32)
+
+mapx = 2*mapx/(cols-1)-1
+mapy = 2*mapy/(rows-1)-1
+r, theta = cv2.cartToPolar(mapx, mapy)
+
+ru = r*(1+k1*(r**2) + k2*(r**4) + k3*(r**6)) 
+
+mapx, mapy = cv2.polarToCart(ru, theta)
+mapx = ((mapx + 1)*cols-1)/2
+mapy = ((mapy + 1)*rows-1)/2
+
+distored = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
+
+cv2.imshow('original', img)
+cv2.imshow('distorted', distored)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/remap_flip.py b/CV/OpenCV/geometric_transform/remap_flip.py
new file mode 100644
index 0000000..bc71f93
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/remap_flip.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+import time
+
+
+img = cv2.imread('../img/girl.jpg')
+rows, cols = img.shape[:2]
+
+st = time.time()
+mflip = np.float32([ [-1, 0, cols-1],[0, -1, rows-1]])
+fliped1 = cv2.warpAffine(img, mflip, (cols, rows))
+print('matrix:', time.time()-st)
+
+
+st2 = time.time()
+mapy, mapx = np.indices((rows, cols),dtype=np.float32)
+mapx = cols - mapx - 1
+mapy = rows - mapy - 1
+
+fliped2 = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
+print('remap:', time.time() - st2)
+
+cv2.imshow('origin', img)
+cv2.imshow('fliped1',fliped1)
+cv2.imshow('fliped2',fliped2)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/remap_lens.py b/CV/OpenCV/geometric_transform/remap_lens.py
new file mode 100644
index 0000000..a1e11e3
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/remap_lens.py
@@ -0,0 +1,35 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/taekwonv1.jpg')
+print(img.shape)
+rows, cols = img.shape[:2]
+
+exp = 2
+scale = 1
+
+
+mapy, mapx = np.indices((rows, cols),dtype=np.float32)
+
+
+mapx = 2*mapx/(cols-1)-1
+mapy = 2*mapy/(rows-1)-1
+
+
+r, theta = cv2.cartToPolar(mapx, mapy)
+
+r[r< scale] = r[r<scale] **exp  
+
+mapx, mapy = cv2.polarToCart(r, theta)
+
+
+mapx = ((mapx + 1)*cols-1)/2
+mapy = ((mapy + 1)*rows-1)/2
+
+distorted = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
+
+cv2.imshow('origin', img)
+cv2.imshow('distorted', distorted)
+cv2.waitKey()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/geometric_transform/remap_sin_cos.py b/CV/OpenCV/geometric_transform/remap_sin_cos.py
new file mode 100644
index 0000000..d35b3c7
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/remap_sin_cos.py
@@ -0,0 +1,28 @@
+import cv2
+import numpy as np
+
+
+l = 20      # wave length
+amp = 15    # amplitude
+
+img = cv2.imread('../img/taekwonv1.jpg')
+rows, cols = img.shape[:2]
+
+mapy, mapx = np.indices((rows, cols),dtype=np.float32)
+
+sinx = mapx + amp * np.sin(mapy/l)  
+cosy = mapy + amp * np.cos(mapx/l)
+
+
+img_sinx=cv2.remap(img, sinx, mapy, cv2.INTER_LINEAR)
+img_cosy=cv2.remap(img, mapx, cosy, cv2.INTER_LINEAR)
+
+img_both=cv2.remap(img, sinx, cosy, cv2.INTER_LINEAR, None, cv2.BORDER_REPLICATE)
+
+cv2.imshow('origin', img)
+cv2.imshow('sin x', img_sinx)
+cv2.imshow('cos y', img_cosy)
+cv2.imshow('sin cos', img_both)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/rotate_getmatrix.py b/CV/OpenCV/geometric_transform/rotate_getmatrix.py
new file mode 100644
index 0000000..10cb441
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/rotate_getmatrix.py
@@ -0,0 +1,17 @@
+import cv2
+
+
+img = cv2.imread('../img/fish.jpg')
+rows,cols = img.shape[0:2]
+
+m45 = cv2.getRotationMatrix2D((cols/2,rows/2),45,0.5) 
+m90 = cv2.getRotationMatrix2D((cols/2,rows/2),90,1.5) 
+
+img45 = cv2.warpAffine(img, m45,(cols, rows))
+img90 = cv2.warpAffine(img, m90,(cols, rows))
+
+cv2.imshow('origin',img)
+cv2.imshow("45", img45)
+cv2.imshow("90", img90)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/rotate_matrix.py b/CV/OpenCV/geometric_transform/rotate_matrix.py
new file mode 100644
index 0000000..3d2a73d
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/rotate_matrix.py
@@ -0,0 +1,26 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/fish.jpg')
+rows,cols = img.shape[0:2]
+
+d45 = 45.0 * np.pi / 180
+d90 = 90.0 * np.pi / 180
+
+
+m45 = np.float32( [[ np.cos(d45), -1* np.sin(d45), rows//2],
+                    [np.sin(d45), np.cos(d45), -1*cols//4]])
+m90 = np.float32( [[ np.cos(d90), -1* np.sin(d90), rows],
+                    [np.sin(d90), np.cos(d90), 0]])
+
+
+r45 = cv2.warpAffine(img,m45,(cols,rows))
+r90 = cv2.warpAffine(img,m90,(rows,cols))
+
+
+cv2.imshow("origin", img)
+cv2.imshow("45", r45)
+cv2.imshow("90", r90)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/scale_matrix.py b/CV/OpenCV/geometric_transform/scale_matrix.py
new file mode 100644
index 0000000..ae64c62
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/scale_matrix.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/fish.jpg')
+height, width = img.shape[:2]
+
+
+m_small = np.float32([[0.5, 0, 0], [0, 0.5,0]])  
+m_big = np.float32([[2, 0, 0], [0, 2, 0]])  
+
+
+dst1 = cv2.warpAffine(img, m_small, (int(height*0.5), int(width*0.5)))
+dst2 = cv2.warpAffine(img, m_big, (int(height*2), int(width*2)))
+
+
+dst3 = cv2.warpAffine(img, m_small, (int(height*0.5), int(width*0.5)), None, cv2.INTER_AREA)
+dst4 = cv2.warpAffine(img, m_big, (int(height*2), int(width*2)), None, cv2.INTER_CUBIC)
+
+
+cv2.imshow("original", img)
+cv2.imshow("small", dst1)
+cv2.imshow("big", dst2)
+cv2.imshow("small INTER_AREA", dst3)
+cv2.imshow("big INTER_CUBIC", dst4)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/scale_resize.py b/CV/OpenCV/geometric_transform/scale_resize.py
new file mode 100644
index 0000000..496f86f
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/scale_resize.py
@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/fish.jpg')
+height, width = img.shape[:2]
+
+
+dst1 = cv2.resize(img, (int(width*0.5), int(height*0.5)), interpolation=cv2.INTER_AREA)
+
+dst2 = cv2.resize(img, None,  None, 2, 2, cv2.INTER_CUBIC)
+
+cv2.imshow("original", img)
+cv2.imshow("small", dst1)
+cv2.imshow("big", dst2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/translate.py b/CV/OpenCV/geometric_transform/translate.py
new file mode 100644
index 0000000..43290d4
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/translate.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/fish.jpg')
+rows,cols = img.shape[0:2]
+
+dx, dy = 100, 50
+
+mtrx = np.float32([[1, 0, dx], [0, 1, dy]])  
+
+dst = cv2.warpAffine(img, mtrx, (cols+dx, rows+dy))   
+
+dst2 = cv2.warpAffine(img, mtrx, (cols+dx, rows+dy), None, cv2.INTER_LINEAR, cv2.BORDER_CONSTANT, (255,0,0) )
+
+dst3 = cv2.warpAffine(img, mtrx, (cols+dx, rows+dy), None, cv2.INTER_LINEAR, cv2.BORDER_REFLECT)
+
+cv2.imshow('original', img)
+cv2.imshow('trans',dst)
+cv2.imshow('BORDER_CONSTATNT', dst2)
+cv2.imshow('BORDER_FEFLECT', dst3)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/triangle_affine.py b/CV/OpenCV/geometric_transform/triangle_affine.py
new file mode 100644
index 0000000..36070aa
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/triangle_affine.py
@@ -0,0 +1,44 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread("../img/taekwonv1.jpg")
+img2 = img.copy()
+draw = img.copy()
+
+pts1 = np.float32([[188,14], [85,202], [294,216]])
+pts2 = np.float32([[128,40], [85,307], [306,167]])
+
+x1,y1,w1,h1 = cv2.boundingRect(pts1)
+x2,y2,w2,h2 = cv2.boundingRect(pts2)
+
+roi1 = img[y1:y1+h1, x1:x1+w1]
+roi2 = img2[y2:y2+h2, x2:x2+w2]
+
+offset1 = np.zeros((3,2), dtype=np.float32)
+offset2 = np.zeros((3,2), dtype=np.float32)
+
+for i in range(3):
+    offset1[i][0], offset1[i][1] = pts1[i][0]-x1, pts1[i][1]-y1
+    offset2[i][0], offset2[i][1] = pts2[i][0]-x2, pts2[i][1]-y2
+
+
+mtrx = cv2.getAffineTransform(offset1, offset2)
+warped = cv2.warpAffine(roi1, mtrx, (w2, h2), None, cv2.INTER_LINEAR, cv2.BORDER_REFLECT_101)
+
+mask = np.zeros((h2, w2), dtype = np.uint8)
+cv2.fillConvexPoly(mask, np.int32(offset2), (255))
+
+warped_masked = cv2.bitwise_and(warped, warped, mask=mask)
+roi2_masked = cv2.bitwise_and(roi2, roi2, mask=cv2.bitwise_not(mask))
+roi2_masked = roi2_masked + warped_masked
+img2[y2:y2+h2, x2:x2+w2] = roi2_masked
+
+
+cv2.rectangle(draw, (x1, y1), (x1+w1, y1+h1), (0,255,0), 1)
+cv2.polylines(draw, [pts1.astype(np.int32)], True, (255,0,0), 1)
+cv2.rectangle(img2, (x2, y2), (x2+w2, y2+h2), (0,255,0), 1)
+cv2.imshow('origin', draw)
+cv2.imshow('warped triangle', img2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/undistort_barrel.py b/CV/OpenCV/geometric_transform/undistort_barrel.py
new file mode 100644
index 0000000..54b4d49
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/undistort_barrel.py
@@ -0,0 +1,27 @@
+import numpy as np
+import cv2
+
+
+img = np.full((300,400,3), 255, np.uint8)
+img[::10, :, :] = 0
+img[:, ::10, :] = 0
+width  = img.shape[1]
+height = img.shape[0]
+
+
+k1, k2, p1, p2 = 0.001, 0, 0, 0     # Barrel distortion
+#k1, k2, p1, p2 = -0.0005, 0, 0, 0  # Pincushion distortion
+distCoeff = np.float64([k1, k2, p1, p2])
+
+
+fx, fy = 10, 10
+cx, cy = width/2, height/2
+camMtx = np.float32([[fx,0, cx], [0, fy, cy], [0 ,0 ,1]])
+
+
+dst = cv2.undistort(img,camMtx,distCoeff)
+
+cv2.imshow('original', img)
+cv2.imshow('dst',dst)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/workhop_distotion_camera.py b/CV/OpenCV/geometric_transform/workhop_distotion_camera.py
new file mode 100644
index 0000000..a12fe0d
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/workhop_distotion_camera.py
@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
+rows, cols = 240, 320
+map_y, map_x = np.indices((rows, cols), dtype=np.float32)
+
+
+map_mirrorh_x,map_mirrorh_y = map_x.copy(), map_y.copy() 
+map_mirrorv_x,map_mirrorv_y = map_x.copy(), map_y.copy()    
+
+map_mirrorh_x[: , cols//2:] = cols - map_mirrorh_x[:, cols//2:]-1
+
+map_mirrorv_y[rows//2:, :] = rows - map_mirrorv_y[rows//2:, :]-1
+
+
+map_wave_x, map_wave_y = map_x.copy(), map_y.copy()
+map_wave_x = map_wave_x + 15*np.sin(map_y/20)
+map_wave_y = map_wave_y + 15*np.sin(map_x/20)    
+
+
+
+map_lenz_x = 2*map_x/(cols-1)-1
+map_lenz_y = 2*map_y/(rows-1)-1
+
+r, theta = cv2.cartToPolar(map_lenz_x, map_lenz_y)
+r_convex = r.copy()
+r_concave = r
+
+r_convex[r< 1] = r_convex[r<1] **2  
+print(r.shape, r_convex[r<1].shape)
+
+r_concave[r< 1] = r_concave[r<1] **0.5
+
+map_convex_x, map_convex_y = cv2.polarToCart(r_convex, theta)
+map_concave_x, map_concave_y = cv2.polarToCart(r_concave, theta)
+
+
+map_convex_x = ((map_convex_x + 1)*cols-1)/2
+map_convex_y = ((map_convex_y + 1)*rows-1)/2
+map_concave_x = ((map_concave_x + 1)*cols-1)/2
+map_concave_y = ((map_concave_y + 1)*rows-1)/2
+
+
+while True:
+    ret, frame = cap.read()
+    
+    mirrorh=cv2.remap(frame,map_mirrorh_x,map_mirrorh_y,cv2.INTER_LINEAR)
+    mirrorv=cv2.remap(frame,map_mirrorv_x,map_mirrorv_y,cv2.INTER_LINEAR)
+    wave = cv2.remap(frame,map_wave_x,map_wave_y,cv2.INTER_LINEAR, None, cv2.BORDER_REPLICATE)
+
+    convex = cv2.remap(frame,map_convex_x,map_convex_y,cv2.INTER_LINEAR)
+    concave = cv2.remap(frame,map_concave_x,map_concave_y,cv2.INTER_LINEAR)
+    
+    r1 = np.hstack(( frame, mirrorh, mirrorv))
+    r2 = np.hstack(( wave, convex, concave))
+    merged = np.vstack((r1, r2))
+
+    cv2.imshow('distorted',merged)
+    if cv2.waitKey(1) & 0xFF== 27:
+        break
+
+cap.release
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/workshop_liquify_tool.py b/CV/OpenCV/geometric_transform/workshop_liquify_tool.py
new file mode 100644
index 0000000..f1decd1
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/workshop_liquify_tool.py
@@ -0,0 +1,77 @@
+import cv2
+import numpy as np
+
+
+win_title = 'Liquify'
+half = 50
+isDragging = False
+
+
+def liquify(img, cx1,cy1, cx2,cy2) :
+    x, y, w, h = cx1-half, cy1-half, half*2, half*2
+    roi = img[y:y+h, x:x+w].copy()
+    out = roi.copy()
+
+    offset_cx1,offset_cy1 = cx1-x, cy1-y
+    offset_cx2,offset_cy2 = cx2-x, cy2-y
+    
+    tri1 = [[ (0,0), (w, 0), (offset_cx1, offset_cy1)], # top
+            [ [0,0], [0, h], [offset_cx1, offset_cy1]], # left
+            [ [w, 0], [offset_cx1, offset_cy1], [w, h]], # right
+            [ [0, h], [offset_cx1, offset_cy1], [w, h]]] # bottom
+
+    tri2 = [[ [0,0], [w,0], [offset_cx2, offset_cy2]], # top
+            [ [0,0], [0, h], [offset_cx2, offset_cy2]], # left
+            [ [w,0], [offset_cx2, offset_cy2], [w, h]], # right
+            [ [0,h], [offset_cx2, offset_cy2], [w, h]]] # bottom
+
+    
+    for i in range(4):
+        matrix = cv2.getAffineTransform( np.float32(tri1[i]), np.float32(tri2[i]))
+        warped = cv2.warpAffine( roi.copy(), matrix, (w, h), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
+        
+        mask = np.zeros((h, w), dtype = np.uint8)
+        cv2.fillConvexPoly(mask, np.int32(tri2[i]), (255,255,255))
+        
+        warped = cv2.bitwise_and(warped, warped, mask=mask)
+        out = cv2.bitwise_and(out, out, mask=cv2.bitwise_not(mask))
+        out = out + warped
+
+
+    img[y:y+h, x:x+w] = out
+    return img 
+
+
+
+def onMouse(event,x,y,flags,param):     
+    global cx1, cy1, isDragging, img
+    
+    if event == cv2.EVENT_MOUSEMOVE:  
+        if not isDragging :
+            img_draw = img.copy()
+            cv2.rectangle(img_draw, (x-half, y-half), (x+half, y+half), (0,255,0)) 
+            cv2.imshow(win_title, img_draw)
+            
+    elif event == cv2.EVENT_LBUTTONDOWN :   
+        isDragging = True
+        cx1, cy1 = x, y
+        
+    elif event == cv2.EVENT_LBUTTONUP :
+        if isDragging:
+            isDragging = False
+            
+            liquify(img, cx1, cy1, x, y)    
+            cv2.imshow(win_title, img)
+
+if __name__ == '__main__' :
+    img = cv2.imread("../img/man_face.jpg")
+    h, w = img.shape[:2]
+
+    cv2.namedWindow(win_title)
+    cv2.setMouseCallback(win_title, onMouse) 
+    cv2.imshow(win_title, img)
+    while True:
+        key = cv2.waitKey(1)
+        if key & 0xFF == 27:
+            break
+    cv2.destroyAllWindows()
diff --git a/CV/OpenCV/geometric_transform/workshop_mosaic.py b/CV/OpenCV/geometric_transform/workshop_mosaic.py
new file mode 100644
index 0000000..5767100
--- /dev/null
+++ b/CV/OpenCV/geometric_transform/workshop_mosaic.py
@@ -0,0 +1,20 @@
+import cv2
+
+rate = 15
+win_title = 'mosaic'
+img = cv2.imread('../img/taekwonv1.jpg')
+
+while True:
+    x,y,w,h = cv2.selectROI(win_title, img, False)
+    if w and h:
+        roi = img[y:y+h, x:x+w]
+        roi = cv2.resize(roi, (w//rate, h//rate))
+        
+        
+        roi = cv2.resize(roi, (w,h), interpolation=cv2.INTER_AREA)  
+        img[y:y+h, x:x+w] = roi
+        cv2.imshow(win_title, img)
+    else:
+        break
+
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/addition_rgba_mask.py b/CV/OpenCV/image_processing/addition_rgba_mask.py
new file mode 100644
index 0000000..7b02a14
--- /dev/null
+++ b/CV/OpenCV/image_processing/addition_rgba_mask.py
@@ -0,0 +1,32 @@
+import cv2
+import numpy as np
+
+
+img_fg = cv2.imread('../img/opencv_logo.png', cv2.IMREAD_UNCHANGED)
+img_bg = cv2.imread('../img/girl.jpg')
+
+
+_, mask = cv2.threshold(img_fg[:,:,3], 1, 255, cv2.THRESH_BINARY)
+mask_inv = cv2.bitwise_not(mask)
+
+
+img_fg = cv2.cvtColor(img_fg, cv2.COLOR_BGRA2BGR)
+h, w = img_fg.shape[:2]
+roi = img_bg[10:10+h, 10:10+w ]
+
+
+masked_fg = cv2.bitwise_and(img_fg, img_fg, mask=mask)
+masked_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
+
+
+added = masked_fg + masked_bg
+img_bg[10:10+h, 10:10+w] = added
+
+cv2.imshow('mask', mask)
+cv2.imshow('mask_inv', mask_inv)
+cv2.imshow('masked_fg', masked_fg)
+cv2.imshow('masked_bg', masked_bg)
+cv2.imshow('added', added)
+cv2.imshow('result', img_bg)
+cv2.waitKey()
+cv2.destroyAllWindows() 
diff --git a/CV/OpenCV/image_processing/arithmatic.py b/CV/OpenCV/image_processing/arithmatic.py
new file mode 100644
index 0000000..48d93e8
--- /dev/null
+++ b/CV/OpenCV/image_processing/arithmatic.py
@@ -0,0 +1,24 @@
+import cv2
+import numpy as np
+
+
+a = np.uint8([[200, 50]]) 
+b = np.uint8([[100, 100]])
+
+
+add1 = a + b
+sub1 = a - b
+mult1 = a * 2
+div1 = a / 3
+
+
+add2 = cv2.add(a, b)
+sub2 = cv2.subtract(a, b)
+mult2 = cv2.multiply(a , 2)
+div2 = cv2.divide(a, 3)
+
+
+print(add1, add2)
+print(sub1, sub2)
+print(mult1, mult2)
+print(div1, div2)
diff --git a/CV/OpenCV/image_processing/arithmatic_mask.py b/CV/OpenCV/image_processing/arithmatic_mask.py
new file mode 100644
index 0000000..4cf9667
--- /dev/null
+++ b/CV/OpenCV/image_processing/arithmatic_mask.py
@@ -0,0 +1,13 @@
+import cv2
+import numpy as np
+
+
+a = np.array([[1, 2]], dtype=np.uint8)
+b = np.array([[10, 20]], dtype=np.uint8)
+mask = np.array([[1, 0]], dtype=np.uint8)
+
+
+c1 = cv2.add( a, b , None, mask)
+print(c1)
+c2 = cv2.add( a, b , b.copy(), mask)
+print(c2, b)
diff --git a/CV/OpenCV/image_processing/bgr2gray.py b/CV/OpenCV/image_processing/bgr2gray.py
new file mode 100644
index 0000000..bc0aec9
--- /dev/null
+++ b/CV/OpenCV/image_processing/bgr2gray.py
@@ -0,0 +1,17 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/girl.jpg')
+
+img2 = img.astype(np.uint16)
+b,g,r = cv2.split(img2)
+
+gray1 = ((b + g + r)/3).astype(np.uint8)
+
+gray2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+cv2.imshow('original', img)
+cv2.imshow('gray1', gray1)
+cv2.imshow('gray2', gray2)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/bgr2hsv.py b/CV/OpenCV/image_processing/bgr2hsv.py
new file mode 100644
index 0000000..6fb1992
--- /dev/null
+++ b/CV/OpenCV/image_processing/bgr2hsv.py
@@ -0,0 +1,21 @@
+import cv2
+import numpy as np
+
+
+
+red_bgr = np.array([[[0,0,255]]], dtype=np.uint8)
+green_bgr = np.array([[[0,255,0]]], dtype=np.uint8)
+blue_bgr = np.array([[[255,0,0]]], dtype=np.uint8)
+yellow_bgr = np.array([[[0,255,255]]], dtype=np.uint8)
+
+
+red_hsv = cv2.cvtColor(red_bgr, cv2.COLOR_BGR2HSV);
+green_hsv = cv2.cvtColor(green_bgr, cv2.COLOR_BGR2HSV);
+blue_hsv = cv2.cvtColor(blue_bgr, cv2.COLOR_BGR2HSV);
+yellow_hsv = cv2.cvtColor(yellow_bgr, cv2.COLOR_BGR2HSV);
+
+
+print("red:",red_hsv)
+print("green:", green_hsv)
+print("blue", blue_hsv)
+print("yellow", yellow_hsv)
diff --git a/CV/OpenCV/image_processing/bgr2yuv.py b/CV/OpenCV/image_processing/bgr2yuv.py
new file mode 100644
index 0000000..02f094f
--- /dev/null
+++ b/CV/OpenCV/image_processing/bgr2yuv.py
@@ -0,0 +1,15 @@
+import cv2
+import numpy as np
+
+
+dark = np.array([[[0,0,0]]], dtype=np.uint8)
+middle = np.array([[[127,127,127]]], dtype=np.uint8)
+bright = np.array([[[255,255,255]]], dtype=np.uint8)
+
+dark_yuv = cv2.cvtColor(dark, cv2.COLOR_BGR2YUV)
+middle_yuv = cv2.cvtColor(middle, cv2.COLOR_BGR2YUV)
+bright_yuv = cv2.cvtColor(bright, cv2.COLOR_BGR2YUV)
+
+print("dark:",dark_yuv)
+print("middle:", middle_yuv)
+print("bright", bright_yuv)
diff --git a/CV/OpenCV/image_processing/bitwise.py b/CV/OpenCV/image_processing/bitwise.py
new file mode 100644
index 0000000..8ef7742
--- /dev/null
+++ b/CV/OpenCV/image_processing/bitwise.py
@@ -0,0 +1,25 @@
+import numpy as np, cv2
+import matplotlib.pylab as plt
+
+
+
+img1 = np.zeros( ( 200,400), dtype=np.uint8)
+img2 = np.zeros( ( 200,400), dtype=np.uint8)
+img1[:, :200] = 255
+img2[100:200, :] = 255
+
+bitAnd = cv2.bitwise_and(img1, img2)
+bitOr = cv2.bitwise_or(img1, img2)
+bitXor = cv2.bitwise_xor(img1, img2)
+bitNot = cv2.bitwise_not(img1)
+
+imgs = {'img1':img1, 'img2':img2, 'and':bitAnd, 
+          'or':bitOr, 'xor':bitXor, 'not(img1)':bitNot}
+
+for i, (title, img) in enumerate(imgs.items()):
+    plt.subplot(3,2,i+1)
+    plt.title(title)
+    plt.imshow(img, 'gray')
+    plt.xticks([]); plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/bitwise_masking.py b/CV/OpenCV/image_processing/bitwise_masking.py
new file mode 100644
index 0000000..ec07f1b
--- /dev/null
+++ b/CV/OpenCV/image_processing/bitwise_masking.py
@@ -0,0 +1,19 @@
+import numpy as np, cv2
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/girl.jpg')
+
+
+mask = np.zeros_like(img)
+cv2.circle(mask, (150,140), 100, (255,255,255), -1)
+
+
+masked = cv2.bitwise_and(img, mask)
+
+
+cv2.imshow('original', img)
+cv2.imshow('mask', mask)
+cv2.imshow('masked', masked)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/bitwise_masking2.py b/CV/OpenCV/image_processing/bitwise_masking2.py
new file mode 100644
index 0000000..ab7a1a7
--- /dev/null
+++ b/CV/OpenCV/image_processing/bitwise_masking2.py
@@ -0,0 +1,18 @@
+import numpy as np, cv2
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/girl.jpg')
+
+
+mask = np.zeros(img.shape[:2], dtype=np.uint8)
+cv2.circle(mask, (150,140), 100, (255), -1)
+
+
+masked = cv2.bitwise_and(img, img, mask=mask)
+
+cv2.imshow('original', img)
+cv2.imshow('mask', mask)
+cv2.imshow('masked', masked)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/blending_alpha.py b/CV/OpenCV/image_processing/blending_alpha.py
new file mode 100644
index 0000000..ef53fbb
--- /dev/null
+++ b/CV/OpenCV/image_processing/blending_alpha.py
@@ -0,0 +1,17 @@
+import cv2
+import numpy as np
+
+alpha = 0.5
+
+img1 = cv2.imread('../img/wing_wall.jpg')
+img2 = cv2.imread('../img/yate.jpg')
+
+blended = img1 * alpha + img2 * (1-alpha)
+blended = blended.astype(np.uint8)
+cv2.imshow('img1 * alpha + img2 * (1-alpha)', blended)
+
+dst = cv2.addWeighted(img1, alpha, img2, (1-alpha), 0) 
+cv2.imshow('cv2.addWeighted', dst)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/CV/OpenCV/image_processing/blending_alpha_trackbar.py b/CV/OpenCV/image_processing/blending_alpha_trackbar.py
new file mode 100644
index 0000000..3d222d9
--- /dev/null
+++ b/CV/OpenCV/image_processing/blending_alpha_trackbar.py
@@ -0,0 +1,21 @@
+import cv2
+import numpy as np
+
+
+win_name = 'Alpha blending'
+trackbar_name = 'fade'
+
+def onChange(x):
+    alpha = x/100
+    dst = cv2.addWeighted(img1, 1-alpha, img2, alpha, 0) 
+    cv2.imshow(win_name, dst)
+
+
+img1 = cv2.imread('../img/man_face.jpg')
+img2 = cv2.imread('../img/lion_face.jpg')
+
+cv2.imshow(win_name, img1)
+cv2.createTrackbar(trackbar_name, win_name, 0, 100, onChange)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/blending_simple.py b/CV/OpenCV/image_processing/blending_simple.py
new file mode 100644
index 0000000..7933146
--- /dev/null
+++ b/CV/OpenCV/image_processing/blending_simple.py
@@ -0,0 +1,21 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img1 = cv2.imread('../img/wing_wall.jpg')
+img2 = cv2.imread('../img/yate.jpg')
+
+
+img3 = img1 + img2
+img4 = cv2.add(img1, img2)
+
+imgs = {'img1':img1, 'img2':img2, 'img1+img2': img3, 'cv.add(img1, img2)': img4}
+
+for i, (k, v) in enumerate(imgs.items()):
+    plt.subplot(2,2, i + 1)
+    plt.imshow(v[:,:,::-1])
+    plt.title(k)
+    plt.xticks([]); plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/chromakey.py b/CV/OpenCV/image_processing/chromakey.py
new file mode 100644
index 0000000..5c89599
--- /dev/null
+++ b/CV/OpenCV/image_processing/chromakey.py
@@ -0,0 +1,42 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+
+img1 = cv2.imread('../img/man_chromakey.jpg')
+img2 = cv2.imread('../img/street.jpg')
+
+height1, width1 = img1.shape[:2]
+height2, width2 = img2.shape[:2]
+x = (width2 - width1)//2
+y = height2 - height1
+w = x + width1
+h = y + height1
+
+
+chromakey = img1[:10, :10, :]
+offset = 20
+
+
+hsv_chroma = cv2.cvtColor(chromakey, cv2.COLOR_BGR2HSV)
+hsv_img = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
+
+
+chroma_h = hsv_chroma[:,:,0]
+lower = np.array([chroma_h.min()-offset, 100, 100])
+upper = np.array([chroma_h.max()+offset, 255, 255])
+
+
+mask = cv2.inRange(hsv_img, lower, upper)
+mask_inv = cv2.bitwise_not(mask)
+roi = img2[y:h, x:w]
+fg = cv2.bitwise_and(img1, img1, mask=mask_inv)
+bg = cv2.bitwise_and(roi, roi, mask=mask)
+img2[y:h, x:w] = fg + bg
+
+
+cv2.imshow('chromakey', img1)
+cv2.imshow('added', img2)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/diff_absolute.py b/CV/OpenCV/image_processing/diff_absolute.py
new file mode 100644
index 0000000..eb7dcfa
--- /dev/null
+++ b/CV/OpenCV/image_processing/diff_absolute.py
@@ -0,0 +1,23 @@
+import numpy as np, cv2
+
+
+
+img1 = cv2.imread('../img/robot_arm1.jpg')
+img2 = cv2.imread('../img/robot_arm2.jpg')
+img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+diff = cv2.absdiff(img1_gray, img2_gray)
+
+_, diff = cv2.threshold(diff, 1, 255, cv2.THRESH_BINARY)
+diff_red = cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)
+diff_red[:,:,2] = 0
+
+spot = cv2.bitwise_xor(img2, diff_red)
+
+cv2.imshow('img1', img1)
+cv2.imshow('img2', img2)
+cv2.imshow('diff', diff)
+cv2.imshow('spot', spot)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/histo_2d.py b/CV/OpenCV/image_processing/histo_2d.py
new file mode 100644
index 0000000..4a7be05
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_2d.py
@@ -0,0 +1,27 @@
+import cv2
+import matplotlib.pylab as plt
+
+
+plt.style.use('classic')
+img = cv2.imread('../img/mountain.jpg')
+
+plt.subplot(131)
+hist = cv2.calcHist([img], [0,1], None, [32,32], [0,256,0,256])
+p = plt.imshow(hist)
+plt.title('Blue and Green')
+plt.colorbar(p)
+
+
+plt.subplot(132)
+hist = cv2.calcHist([img], [1,2], None, [32,32], [0,256,0,256])
+p = plt.imshow(hist)
+plt.title('Green and Red')
+plt.colorbar(p)
+
+plt.subplot(133)
+hist = cv2.calcHist([img], [0,2], None, [32,32], [0,256,0,256])
+p = plt.imshow(hist)
+plt.title('Blue and Red')
+plt.colorbar(p)
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/histo_backproject.py b/CV/OpenCV/image_processing/histo_backproject.py
new file mode 100644
index 0000000..00c361f
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_backproject.py
@@ -0,0 +1,53 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+win_name = 'back_projection'
+img = cv2.imread('../img/pump_horse.jpg')
+hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+draw = img.copy()
+
+
+def masking(bp, win_name):
+    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
+    cv2.filter2D(bp,-1,disc,bp)
+    _, mask = cv2.threshold(bp, 1, 255, cv2.THRESH_BINARY)
+    result = cv2.bitwise_and(img, img, mask=mask)
+    cv2.imshow(win_name, result)
+
+
+def backProject_manual(hist_roi):
+    hist_img = cv2.calcHist([hsv_img], [0,1], None,[180,256], [0,180,0,256])
+    hist_rate = hist_roi/ (hist_img + 1)
+    
+    h,s,v = cv2.split(hsv_img)
+    bp = hist_rate[h.ravel(), s.ravel()]
+    bp = np.minimum(bp, 1)
+    bp = bp.reshape(hsv_img.shape[:2])
+
+    cv2.normalize(bp,bp, 0, 255, cv2.NORM_MINMAX)
+    bp = bp.astype(np.uint8)
+    
+    masking(bp,'result_manual')
+
+
+def backProject_cv(hist_roi):
+    bp = cv2.calcBackProject([hsv_img], [0, 1], hist_roi,  [0, 180, 0, 256], 1)
+    masking(bp,'result_cv')
+
+
+
+(x,y,w,h) = cv2.selectROI(win_name, img, False)
+
+if w > 0 and h > 0:
+    roi = draw[y:y+h, x:x+w]
+    cv2.rectangle(draw, (x, y), (x+w, y+h), (0,0,255), 2)
+    hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
+    hist_roi = cv2.calcHist([hsv_roi],[0, 1], None, [180, 256], [0, 180, 0, 256] )
+    backProject_manual(hist_roi)
+    backProject_cv(hist_roi)
+
+cv2.imshow(win_name, draw)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/histo_clahe.py b/CV/OpenCV/image_processing/histo_clahe.py
new file mode 100644
index 0000000..baf77dc
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_clahe.py
@@ -0,0 +1,22 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/bright.jpg')
+img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
+
+img_eq = img_yuv.copy()
+img_eq[:,:,0] = cv2.equalizeHist(img_eq[:,:,0])
+img_eq = cv2.cvtColor(img_eq, cv2.COLOR_YUV2BGR)
+
+img_clahe = img_yuv.copy()
+clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
+img_clahe[:,:,0] = clahe.apply(img_clahe[:,:,0])
+img_clahe = cv2.cvtColor(img_clahe, cv2.COLOR_YUV2BGR)
+
+cv2.imshow('Before', img)
+cv2.imshow('CLAHE', img_clahe)
+cv2.imshow('equalizeHist', img_eq)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/histo_compare.py b/CV/OpenCV/image_processing/histo_compare.py
new file mode 100644
index 0000000..1b8001f
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_compare.py
@@ -0,0 +1,42 @@
+import cv2, numpy as np
+import matplotlib.pylab as plt
+
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/taekwonv2.jpg')
+img3 = cv2.imread('../img/taekwonv3.jpg')
+img4 = cv2.imread('../img/dr_ochanomizu.jpg')
+
+cv2.imshow('query', img1)
+imgs = [img1, img2, img3, img4]
+hists = []
+
+for i, img in enumerate(imgs) :
+    plt.subplot(1,len(imgs),i+1)
+    plt.title('img%d'% (i+1))
+    plt.axis('off') 
+    plt.imshow(img[:,:,::-1])
+    
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    
+    hist = cv2.calcHist([hsv], [0,1], None, [180,256], [0,180,0, 256])
+    
+    cv2.normalize(hist, hist, 0, 1, cv2.NORM_MINMAX)
+    hists.append(hist)
+
+
+query = hists[0]
+methods = {'CORREL' :cv2.HISTCMP_CORREL, 'CHISQR':cv2.HISTCMP_CHISQR, 
+           'INTERSECT':cv2.HISTCMP_INTERSECT,
+           'BHATTACHARYYA':cv2.HISTCMP_BHATTACHARYYA}
+
+for j, (name, flag) in enumerate(methods.items()):
+    print('%-10s'%name, end='\t')
+    for i, (hist, img) in enumerate(zip(hists, imgs)):
+        ret = cv2.compareHist(query, hist, flag)
+        if flag == cv2.HISTCMP_INTERSECT:
+            ret = ret/np.sum(query)
+        print("img%d:%7.2f"% (i+1 , ret), end='\t')
+    print()
+plt.show()
diff --git a/CV/OpenCV/image_processing/histo_equalize.py b/CV/OpenCV/image_processing/histo_equalize.py
new file mode 100644
index 0000000..2846614
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_equalize.py
@@ -0,0 +1,36 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+
+img = cv2.imread('../img/yate.jpg', cv2.IMREAD_GRAYSCALE)
+rows, cols = img.shape[:2]
+
+
+hist = cv2.calcHist([img], [0], None, [256], [0, 256])
+cdf = hist.cumsum()
+cdf_m = np.ma.masked_equal(cdf, 0)
+cdf_m = (cdf_m - cdf_m.min()) / (rows * cols) * 255
+cdf = np.ma.filled(cdf_m,0).astype('uint8')
+print(cdf.shape)
+img2 = cdf[img]
+
+
+img3 = cv2.equalizeHist(img)
+
+
+hist2 = cv2.calcHist([img2], [0], None, [256], [0, 256])
+hist3 = cv2.calcHist([img3], [0], None, [256], [0, 256])
+
+
+cv2.imshow('Before', img)
+cv2.imshow('Manual', img2)
+cv2.imshow('cv2.equalizeHist()', img3)
+hists = {'Before':hist, 'Manual':hist2, 'cv2.equalizeHist()': hist3}
+
+for i, (k, v) in enumerate(hists.items()):
+    plt.subplot(1,3,i+1)
+    plt.title(k)
+    plt.plot(v)
+plt.show()
diff --git a/CV/OpenCV/image_processing/histo_equalize_yuv.py b/CV/OpenCV/image_processing/histo_equalize_yuv.py
new file mode 100644
index 0000000..33798d3
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_equalize_yuv.py
@@ -0,0 +1,13 @@
+import numpy as np, cv2
+
+
+img = cv2.imread('../img/yate.jpg') #이미지 읽기, BGR 스케일
+
+img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
+img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0]) 
+img2 = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR) 
+
+cv2.imshow('Before', img)
+cv2.imshow('After', img2)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/histo_gray.py b/CV/OpenCV/image_processing/histo_gray.py
new file mode 100644
index 0000000..551e36b
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_gray.py
@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/mountain.jpg', cv2.IMREAD_GRAYSCALE)
+cv2.imshow('img', img)
+
+hist = cv2.calcHist([img], [0], None, [256], [0,255])
+plt.plot(hist)
+
+print("hist.shape:", hist.shape)
+print("hist.sum():", hist.sum(), "img.shape:",img.shape)
+plt.show()
diff --git a/CV/OpenCV/image_processing/histo_normalize.py b/CV/OpenCV/image_processing/histo_normalize.py
new file mode 100644
index 0000000..5b2a4b0
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_normalize.py
@@ -0,0 +1,29 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/abnormal.jpg', cv2.IMREAD_GRAYSCALE)
+
+img_f = img.astype(np.float32)
+img_norm = ((img_f - img_f.min()) * (255) / (img_f.max() - img_f.min()))
+img_norm = img_norm.astype(np.uint8)
+
+img_norm2 = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX)
+
+hist = cv2.calcHist([img], [0], None, [256], [0, 255])
+hist_norm = cv2.calcHist([img_norm], [0], None, [256], [0, 255])
+hist_norm2 = cv2.calcHist([img_norm2], [0], None, [256], [0, 255])
+
+cv2.imshow('Before', img)
+cv2.imshow('Manual', img_norm)
+cv2.imshow('cv2.normalize()', img_norm2)
+
+hists = {'Before' : hist, 'Manual':hist_norm, 'cv2.normalize()':hist_norm2}
+
+for i, (k, v) in enumerate(hists.items()):
+    plt.subplot(1,3,i+1)
+    plt.title(k)
+    plt.plot(v)
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/histo_rgb.py b/CV/OpenCV/image_processing/histo_rgb.py
new file mode 100644
index 0000000..4e8f7a8
--- /dev/null
+++ b/CV/OpenCV/image_processing/histo_rgb.py
@@ -0,0 +1,15 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/mountain.jpg')
+cv2.imshow('img', img)
+
+channels = cv2.split(img)
+colors = ('b', 'g', 'r')
+
+for (ch, color) in zip (channels, colors):
+    hist = cv2.calcHist([ch], [0], None, [256], [0, 255])
+    plt.plot(hist, color = color)
+plt.show()
diff --git a/CV/OpenCV/image_processing/hsv_color_mask.py b/CV/OpenCV/image_processing/hsv_color_mask.py
new file mode 100644
index 0000000..d8380a9
--- /dev/null
+++ b/CV/OpenCV/image_processing/hsv_color_mask.py
@@ -0,0 +1,44 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread("../img/cube.jpg")
+hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+
+blue1 = np.array([90, 50, 50])
+blue2 = np.array([120, 255,255])
+green1 = np.array([45, 50,50])
+green2 = np.array([75, 255,255])
+red1 = np.array([0, 50,50])
+red2 = np.array([15, 255,255])
+red3 = np.array([165, 50,50])
+red4 = np.array([180, 255,255])
+yellow1 = np.array([20, 50,50])
+yellow2 = np.array([35, 255,255])
+
+
+mask_blue = cv2.inRange(hsv, blue1, blue2)
+mask_green = cv2.inRange(hsv, green1, green2)
+mask_red = cv2.inRange(hsv, red1, red2)
+mask_red2 = cv2.inRange(hsv, red3, red4)
+mask_yellow = cv2.inRange(hsv, yellow1, yellow2)
+
+
+res_blue = cv2.bitwise_and(img, img, mask=mask_blue)
+res_green = cv2.bitwise_and(img, img, mask=mask_green)
+res_red1 = cv2.bitwise_and(img, img, mask=mask_red)
+res_red2 = cv2.bitwise_and(img, img, mask=mask_red2)
+res_red = cv2.bitwise_or(res_red1, res_red2)
+res_yellow = cv2.bitwise_and(img, img, mask=mask_yellow)
+
+
+imgs = {'original': img, 'blue':res_blue, 'green':res_green, 
+                            'red':res_red, 'yellow':res_yellow}
+for i, (k, v) in enumerate(imgs.items()):
+    plt.subplot(2,3, i+1)
+    plt.title(k)
+    plt.imshow(v[:,:,::-1])
+    plt.xticks([]); plt.yticks([])
+plt.show()
diff --git a/CV/OpenCV/image_processing/rgba.py b/CV/OpenCV/image_processing/rgba.py
new file mode 100644
index 0000000..3299572
--- /dev/null
+++ b/CV/OpenCV/image_processing/rgba.py
@@ -0,0 +1,15 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/opencv_logo.png')               
+bgr = cv2.imread('../img/opencv_logo.png', cv2.IMREAD_COLOR)    
+bgra = cv2.imread('../img/opencv_logo.png', cv2.IMREAD_UNCHANGED) 
+
+print("default", img.shape, "color", bgr.shape, "unchanged", bgra.shape) 
+
+cv2.imshow('bgr', bgr)
+cv2.imshow('bgra', bgra)
+cv2.imshow('alpha', bgra[:,:,3])
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/roi.py b/CV/OpenCV/image_processing/roi.py
new file mode 100644
index 0000000..ad6b7d9
--- /dev/null
+++ b/CV/OpenCV/image_processing/roi.py
@@ -0,0 +1,17 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('./img/sunset.jpg')
+
+# RoI setting
+x=320; y=150; w=50; h=50
+roi = img[y:y+h, x:x+w]
+
+print(roi.shape)                # roi shape, (50,50,3)
+cv2.rectangle(roi, (0,0), (h-1, w-1), (0,255,0)) # draw rectangle to the entire roi
+cv2.imshow("img", img)
+
+key = cv2.waitKey(0)
+print(key)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/roi_copy.py b/CV/OpenCV/image_processing/roi_copy.py
new file mode 100644
index 0000000..b8ae387
--- /dev/null
+++ b/CV/OpenCV/image_processing/roi_copy.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/sunset.jpg')
+
+x=320; y=150; w=50; h=50
+roi = img[y:y+h, x:x+w]
+img2 = roi.copy()
+
+img[y : y + h, x + w: x + w + w] = roi
+cv2.rectangle(img, (x,y), (x+w+w, y+h), (0,255,0))
+
+cv2.imshow("img", img)
+cv2.imshow("roi", img2)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/roi_crop_mouse.py b/CV/OpenCV/image_processing/roi_crop_mouse.py
new file mode 100644
index 0000000..905dc34
--- /dev/null
+++ b/CV/OpenCV/image_processing/roi_crop_mouse.py
@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+
+
+isDragging = False
+x0, y0, w, h = -1,-1,-1,-1
+blue, red = (255,0,0),(0,0,255)
+
+def onMouse(event,x,y,flags,param):
+    global isDragging, x0, y0, img
+
+    if event == cv2.EVENT_LBUTTONDOWN:
+        isDragging = True
+        x0 = x
+        y0 = y
+
+    elif event == cv2.EVENT_MOUSEMOVE:
+        if isDragging:
+            img_draw = img.copy()
+            cv2.rectangle(img_draw, (x0, y0), (x, y), blue, 2)
+            cv2.imshow('img', img_draw)
+
+    elif event == cv2.EVENT_LBUTTONUP:
+        if isDragging:
+            isDragging = False          
+            w = x - x0
+            h = y - y0
+            print("x:%d, y:%d, w:%d, h:%d" % (x0, y0, w, h))
+
+            if w > 0 and h > 0:
+                img_draw = img.copy()
+                
+                cv2.rectangle(img_draw, (x0, y0), (x, y), red, 2) 
+                cv2.imshow('img', img_draw)
+                roi = img[y0:y0+h, x0:x0+w]
+                cv2.imshow('cropped', roi)
+                cv2.moveWindow('cropped', 0, 0)
+                cv2.imwrite('./cropped.jpg', roi)
+                print("croped.")
+
+            else:
+                cv2.imshow('img', img)
+                print("좌측 상단에서 우측 하단으로 영역을 드래그 하세요.")
+
+
+img = cv2.imread('../img/sunset.jpg')
+cv2.imshow('img', img)
+cv2.setMouseCallback('img', onMouse)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/roi_select_img.py b/CV/OpenCV/image_processing/roi_select_img.py
new file mode 100644
index 0000000..e326a06
--- /dev/null
+++ b/CV/OpenCV/image_processing/roi_select_img.py
@@ -0,0 +1,15 @@
+import cv2,  numpy as np
+
+
+img = cv2.imread('../img/sunset.jpg')
+
+x,y,w,h	=	cv2.selectROI('img', img, False)
+if w and h:
+    roi = img[y:y+h, x:x+w]
+    cv2.imshow('cropped', roi)
+    cv2.moveWindow('cropped', 0, 0)
+    cv2.imwrite('./cropped2.jpg', roi)
+
+cv2.imshow('img', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/seamlessclone.py b/CV/OpenCV/image_processing/seamlessclone.py
new file mode 100644
index 0000000..67962e8
--- /dev/null
+++ b/CV/OpenCV/image_processing/seamlessclone.py
@@ -0,0 +1,20 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img1 = cv2.imread("../img/drawing.jpg")
+img2= cv2.imread("../img/my_hand.jpg")
+
+mask = np.full_like(img1, 255)
+
+height, width = img2.shape[:2]
+center = (width//2, height//2)
+
+normal = cv2.seamlessClone(img1, img2, mask, center, cv2.NORMAL_CLONE)
+mixed = cv2.seamlessClone(img1, img2, mask, center, cv2.MIXED_CLONE)
+
+cv2.imshow('normal', normal)
+cv2.imshow('mixed', mixed)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/image_processing/threshold.py b/CV/OpenCV/image_processing/threshold.py
new file mode 100644
index 0000000..c1aca13
--- /dev/null
+++ b/CV/OpenCV/image_processing/threshold.py
@@ -0,0 +1,22 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/gray_gradient.jpg', cv2.IMREAD_GRAYSCALE)
+
+
+thresh_np = np.zeros_like(img)
+thresh_np[ img > 127] = 255
+
+ret, thresh_cv = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) 
+print(ret)
+
+imgs = {'Original': img, 'NumPy API':thresh_np, 'cv2.threshold': thresh_cv}
+for i , (key, value) in enumerate(imgs.items()):
+    plt.subplot(1, 3, i+1)
+    plt.title(key)
+    plt.imshow(value, cmap='gray')
+    plt.xticks([]); plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/threshold_adpted.py b/CV/OpenCV/image_processing/threshold_adpted.py
new file mode 100644
index 0000000..bcb25b9
--- /dev/null
+++ b/CV/OpenCV/image_processing/threshold_adpted.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np 
+import matplotlib.pyplot as plt 
+
+
+blk_size = 9
+C = 5
+img = cv2.imread('../img/sudoku.png', cv2.IMREAD_GRAYSCALE)
+
+ret, th1 = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+th2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, blk_size, C)
+th3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blk_size, C)
+
+imgs = {'Original': img, 'Global-Otsu:%d'%ret:th1, 'Adapted-Mean':th2, 'Adapted-Gaussian': th3}
+
+for i, (k, v) in enumerate(imgs.items()):
+    plt.subplot(2,2,i+1)
+    plt.title(k)
+    plt.imshow(v,'gray')
+    plt.xticks([]),plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/threshold_flag.py b/CV/OpenCV/image_processing/threshold_flag.py
new file mode 100644
index 0000000..f9420f2
--- /dev/null
+++ b/CV/OpenCV/image_processing/threshold_flag.py
@@ -0,0 +1,22 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/gray_gradient.jpg', cv2.IMREAD_GRAYSCALE)
+
+_, t_bin = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
+_, t_bininv = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
+_, t_truc = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
+_, t_2zr = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
+_, t_2zrinv = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)
+
+imgs = {'origin':img, 'BINARY':t_bin, 'BINARY_INV':t_bininv, \
+        'TRUNC':t_truc, 'TOZERO':t_2zr, 'TOZERO_INV':t_2zrinv}
+for i, (key, value) in enumerate(imgs.items()):
+    plt.subplot(2,3, i+1)
+    plt.title(key)
+    plt.imshow(value, cmap='gray')
+    plt.xticks([]);    plt.yticks([])
+    
+plt.show()
diff --git a/CV/OpenCV/image_processing/threshold_otsu.py b/CV/OpenCV/image_processing/threshold_otsu.py
new file mode 100644
index 0000000..e8b192b
--- /dev/null
+++ b/CV/OpenCV/image_processing/threshold_otsu.py
@@ -0,0 +1,19 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+
+img = cv2.imread('../img/scaned_paper.jpg', cv2.IMREAD_GRAYSCALE) 
+_, t_130 = cv2.threshold(img, 130, 255, cv2.THRESH_BINARY)        
+
+t, t_otsu = cv2.threshold(img, -1, 255,  cv2.THRESH_BINARY | cv2.THRESH_OTSU) 
+print('otsu threshold:', t)
+
+imgs = {'Original': img, 't:130':t_130, 'otsu:%d'%t: t_otsu}
+for i , (key, value) in enumerate(imgs.items()):
+    plt.subplot(1, 3, i+1)
+    plt.title(key)
+    plt.imshow(value, cmap='gray')
+    plt.xticks([]); plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/thresholds.py b/CV/OpenCV/image_processing/thresholds.py
new file mode 100644
index 0000000..b9cf107
--- /dev/null
+++ b/CV/OpenCV/image_processing/thresholds.py
@@ -0,0 +1,19 @@
+import cv2
+import numpy as np
+import matplotlib.pylab as plt
+
+img = cv2.imread('../img/scaned_paper.jpg', cv2.IMREAD_GRAYSCALE) #이미지를 그레이 스케일로 읽기
+thresholds = [80, 100, 120, 140, 150, 170, 190]
+imgs = {'Original' : img}
+
+for t in thresholds:
+    _, t_img = cv2.threshold(img, t, 255, cv2.THRESH_BINARY) 
+    imgs['t:%d'%t] = t_img
+
+for i , (key, value) in enumerate(imgs.items()):
+    plt.subplot(2, 4, i+1)
+    plt.title(key)
+    plt.imshow(value, cmap='gray')
+    plt.xticks([]); plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/image_processing/workshop_cctv_motion_sensor.py b/CV/OpenCV/image_processing/workshop_cctv_motion_sensor.py
new file mode 100644
index 0000000..7d19927
--- /dev/null
+++ b/CV/OpenCV/image_processing/workshop_cctv_motion_sensor.py
@@ -0,0 +1,61 @@
+import cv2
+import numpy as np
+
+
+thresh = 25
+max_diff = 5
+
+
+a, b, c = None, None, None
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 480)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 320)
+
+if cap.isOpened():
+    ret, a = cap.read()
+    ret, b = cap.read()
+
+    while ret:
+        ret, c = cap.read()
+        draw = c.copy()
+        if not ret:
+            break
+        
+        
+        a_gray = cv2.cvtColor(a, cv2.COLOR_BGR2GRAY)
+        b_gray = cv2.cvtColor(b, cv2.COLOR_BGR2GRAY)
+        c_gray = cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)
+
+
+        diff1 = cv2.absdiff(a_gray, b_gray)
+        diff2 = cv2.absdiff(b_gray, c_gray)
+
+
+        ret, diff1_t = cv2.threshold(diff1, thresh, 255, cv2.THRESH_BINARY)
+        ret, diff2_t = cv2.threshold(diff2, thresh, 255, cv2.THRESH_BINARY)
+
+
+        diff = cv2.bitwise_and(diff1_t, diff2_t)
+
+
+        k = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
+        diff = cv2.morphologyEx(diff, cv2.MORPH_OPEN, k)
+
+
+        diff_cnt = cv2.countNonZero(diff)
+        if diff_cnt > max_diff:
+            nzero = np.nonzero(diff)
+
+            cv2.rectangle(draw, (min(nzero[1]), min(nzero[0])), (max(nzero[1]), max(nzero[0])), (0,255,0), 2)
+            cv2.putText(draw, "Motion Detected", (10,30), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0,0,255))
+        
+        
+        stacked = np.hstack((draw, cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)))
+        cv2.imshow('motion sensor',stacked )
+
+
+        a = b
+        b = c
+        
+        if cv2.waitKey(1) & 0xFF == 27:
+            break
diff --git a/CV/OpenCV/image_processing/workshop_two_face.py b/CV/OpenCV/image_processing/workshop_two_face.py
new file mode 100644
index 0000000..c62d323
--- /dev/null
+++ b/CV/OpenCV/image_processing/workshop_two_face.py
@@ -0,0 +1,34 @@
+import cv2
+import numpy as np
+
+
+alpha_width_rate = 15
+
+img_face = cv2.imread('../img/man_face.jpg')
+img_skull = cv2.imread('../img/skull.jpg')
+
+img_comp = np.zeros_like(img_face)
+
+
+height, width = img_face.shape[:2]
+middle = width // 2
+alpha_width = width * alpha_width_rate // 100
+start = middle - alpha_width // 2
+step = 100/alpha_width
+
+
+img_comp[:, :middle, : ] = img_face[:, :middle, :].copy()
+img_comp[:, middle:, :] = img_skull[:, middle:, :].copy()
+cv2.imshow('half', img_comp)
+
+
+for i in range(alpha_width+1 ):
+    alpha = (100 - step * i) / 100
+    beta = 1 - alpha
+    
+    img_comp[:, start+i] = img_face[:, start+i] * alpha + img_skull[:, start+i] * beta
+    print(i, alpha, beta)
+    
+cv2.imshow('half skull', img_comp)
+cv2.waitKey()
+cv2.destroyAllWindows()
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diff --git a/CV/OpenCV/match_track/avg_hash.py b/CV/OpenCV/match_track/avg_hash.py
new file mode 100644
index 0000000..99c78a4
--- /dev/null
+++ b/CV/OpenCV/match_track/avg_hash.py
@@ -0,0 +1,23 @@
+import cv2
+
+
+img = cv2.imread('../img/pistol.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+gray = cv2.resize(gray, (16,16))
+avg = gray.mean()
+
+bin = 1 * (gray > avg)
+print(bin)
+
+
+dhash = []
+for row in bin.tolist():
+    s = ''.join([str(i) for i in row])
+    dhash.append('%02x'%(int(s,2)))
+dhash = ''.join(dhash)
+print(dhash)
+
+cv2.namedWindow('pistol', cv2.WINDOW_GUI_NORMAL)
+cv2.imshow('pistol', img)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/match_track/avg_hash_matching.py b/CV/OpenCV/match_track/avg_hash_matching.py
new file mode 100644
index 0000000..fc8e531
--- /dev/null
+++ b/CV/OpenCV/match_track/avg_hash_matching.py
@@ -0,0 +1,44 @@
+import cv2
+import numpy as np
+import glob
+
+
+img = cv2.imread('../img/pistol.jpg')
+cv2.imshow('query', img)
+
+search_dir = '../img/101_ObjectCategories'
+
+
+def img2hash(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    gray = cv2.resize(gray, (16, 16))
+    avg = gray.mean()
+    bi = 1 * (gray > avg)
+    return bi
+
+
+def hamming_distance(a, b):
+    a = a.reshape(1,-1)
+    b = b.reshape(1,-1)
+    distance = (a !=b).sum()
+    return distance
+
+
+query_hash = img2hash(img)
+
+img_path = glob.glob(search_dir+'/**/*.jpg')
+for path in img_path:
+    img = cv2.imread(path)
+    cv2.imshow('searching...', img)
+    cv2.waitKey(5)
+
+    a_hash = img2hash(img)
+    dst = hamming_distance(query_hash, a_hash)
+
+    if dst/256 < 0.25:
+        print(path, dst/256)
+        cv2.imshow(path, img)
+
+cv2.destroyWindow('searching...')
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/corner_goodFeature.py b/CV/OpenCV/match_track/corner_goodFeature.py
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index 0000000..ed1c9e6
--- /dev/null
+++ b/CV/OpenCV/match_track/corner_goodFeature.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/house.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+corners = cv2.goodFeaturesToTrack(gray, 80, 0.01, 10)
+corners = np.int32(corners)
+
+
+for corner in corners:
+    x, y = corner[0]
+    cv2.circle(img, (x, y), 5, (0,0,255), 1, cv2.LINE_AA)
+
+cv2.imshow('Corners', img)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/corner_harris.py b/CV/OpenCV/match_track/corner_harris.py
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index 0000000..86cda58
--- /dev/null
+++ b/CV/OpenCV/match_track/corner_harris.py
@@ -0,0 +1,26 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/house.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+# detect harris corner
+corner = cv2.cornerHarris(gray, 2, 3, 0.04)
+
+# Find the coordinates of 10 % or more of the maximum value of the change result
+coord = np.where(corner > 0.1* corner.max())
+coord = np.stack((coord[1], coord[0]), axis=-1)
+
+# draw circle to the target coordinate
+for x, y in coord:
+    cv2.circle(img, (x,y), 5, (0,0,255), 1, cv2.LINE_AA)
+
+# normalization
+corner_norm = cv2.normalize(corner, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+
+# show result image
+corner_norm = cv2.cvtColor(corner_norm, cv2.COLOR_GRAY2BGR)
+merged = np.hstack((corner_norm, img))
+cv2.imshow('Harris Corner', merged)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/desc_orb.py b/CV/OpenCV/match_track/desc_orb.py
new file mode 100644
index 0000000..9ece040
--- /dev/null
+++ b/CV/OpenCV/match_track/desc_orb.py
@@ -0,0 +1,15 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/house.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+
+orb = cv2.ORB_create()
+keypoints, descriptor = orb.detectAndCompute(img, None)
+img_draw = cv2.drawKeypoints(img, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+
+cv2.imshow('ORB', img_draw)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/desc_sift.py b/CV/OpenCV/match_track/desc_sift.py
new file mode 100644
index 0000000..f91c07a
--- /dev/null
+++ b/CV/OpenCV/match_track/desc_sift.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/house.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+sift = cv2.xfeatures2d.SIFT_create()
+keypoints, descriptor = sift.detectAndCompute(gray, None)
+print('keypoint:',len(keypoints), 'descriptor:', descriptor.shape)
+print(descriptor)
+
+
+img_draw = cv2.drawKeypoints(img, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+
+cv2.imshow('SIFT', img_draw)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/desc_surf.py b/CV/OpenCV/match_track/desc_surf.py
new file mode 100644
index 0000000..4ae72c8
--- /dev/null
+++ b/CV/OpenCV/match_track/desc_surf.py
@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/house.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+surf = cv2.xfeatures2d.SURF_create(1000, 3, True, True)
+keypoints, desc = surf.detectAndCompute(gray, None)
+print(desc.shape, desc)
+
+img_draw = cv2.drawKeypoints(img, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+
+cv2.imshow('SURF', img_draw)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/kpt_blob.py b/CV/OpenCV/match_track/kpt_blob.py
new file mode 100644
index 0000000..5d2d29b
--- /dev/null
+++ b/CV/OpenCV/match_track/kpt_blob.py
@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread("../img/house.jpg")
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.SimpleBlobDetector_create()
+keypoints = detector.detect(gray)
+img = cv2.drawKeypoints(img, keypoints, None, (0,0,255),\
+                flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+ 
+cv2.imshow("Blob", img)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/match_track/kpt_blob_param.py b/CV/OpenCV/match_track/kpt_blob_param.py
new file mode 100644
index 0000000..12b927a
--- /dev/null
+++ b/CV/OpenCV/match_track/kpt_blob_param.py
@@ -0,0 +1,29 @@
+import cv2
+import numpy as np
+ 
+
+img = cv2.imread("../img/house.jpg")
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+params = cv2.SimpleBlobDetector_Params()
+
+
+params.minThreshold = 10
+params.maxThreshold = 240
+params.thresholdStep = 5
+
+params.filterByArea = True
+params.minArea = 200
+
+params.filterByColor = False
+params.filterByConvexity = False
+params.filterByInertia = False
+params.filterByCircularity = False 
+
+detector = cv2.SimpleBlobDetector_create(params)
+keypoints = detector.detect(gray)
+img_draw = cv2.drawKeypoints(img, keypoints, None, None, cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+
+
+cv2.imshow("Blob with Params", img_draw)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/match_track/kpt_fast.py b/CV/OpenCV/match_track/kpt_fast.py
new file mode 100644
index 0000000..1629f94
--- /dev/null
+++ b/CV/OpenCV/match_track/kpt_fast.py
@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/house.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+fast = cv2.FastFeatureDetector_create(50)
+keypoints = fast.detect(gray, None)
+img = cv2.drawKeypoints(img, keypoints, None)
+
+cv2.imshow('FAST', img)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/kpt_gftt.py b/CV/OpenCV/match_track/kpt_gftt.py
new file mode 100644
index 0000000..42e0dbf
--- /dev/null
+++ b/CV/OpenCV/match_track/kpt_gftt.py
@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+ 
+
+img = cv2.imread("../img/house.jpg")
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+gftt = cv2.GFTTDetector_create()
+keypoints = gftt.detect(gray, None)
+img_draw = cv2.drawKeypoints(img, keypoints, None)
+
+cv2.imshow('GFTTDectector', img_draw)
+cv2.waitKey(0)
+cv2.destrolyAllWindows()
diff --git a/CV/OpenCV/match_track/match_bf_orb.py b/CV/OpenCV/match_track/match_bf_orb.py
new file mode 100644
index 0000000..f223657
--- /dev/null
+++ b/CV/OpenCV/match_track/match_bf_orb.py
@@ -0,0 +1,20 @@
+import cv2, numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+
+detector = cv2.ORB_create()
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+matches = matcher.match(desc1, desc2)
+res = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('BFMatcher + ORB', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_bf_sift.py b/CV/OpenCV/match_track/match_bf_sift.py
new file mode 100644
index 0000000..14664d1
--- /dev/null
+++ b/CV/OpenCV/match_track/match_bf_sift.py
@@ -0,0 +1,22 @@
+import cv2, numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.xfeatures2d.SIFT_create()
+
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+
+matcher = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True)
+matches = matcher.match(desc1, desc2)
+
+res = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('BFMatcher + SIFT', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_bf_surf.py b/CV/OpenCV/match_track/match_bf_surf.py
new file mode 100644
index 0000000..5f481db
--- /dev/null
+++ b/CV/OpenCV/match_track/match_bf_surf.py
@@ -0,0 +1,20 @@
+import cv2
+import numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.xfeatures2d.SURF_create()
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
+matches = matcher.match(desc1, desc2)
+res = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('BF + SURF', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_camera.py b/CV/OpenCV/match_track/match_camera.py
new file mode 100644
index 0000000..da018c7
--- /dev/null
+++ b/CV/OpenCV/match_track/match_camera.py
@@ -0,0 +1,81 @@
+import cv2
+import numpy as np
+
+
+img1 = None
+win_name = 'Camera Matching'
+MIN_MATCH = 10
+
+# generate ORB detector instance
+detector = cv2.ORB_create(1000)
+
+# generate Flann extractor
+FLANN_INDEX_LSH = 6
+
+index_params= dict(algorithm = FLANN_INDEX_LSH,
+                   table_number = 6,
+                   key_size = 12,
+                   multi_probe_level = 1)
+search_params=dict(checks=32)
+matcher = cv2.FlannBasedMatcher(index_params, search_params)
+
+cap = cv2.VideoCapture(0)              
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+
+
+while cap.isOpened():       
+    ret, frame = cap.read()
+
+    if img1 is None:
+        res = frame
+    else:
+        img2 = frame
+        gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+        gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+        kp1, desc1 = detector.detectAndCompute(gray1, None)
+        kp2, desc2 = detector.detectAndCompute(gray2, None)
+        
+        matches = matcher.knnMatch(desc1, desc2, 2)
+        
+        ratio = 0.75
+        good_matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * ratio]
+
+        print('good matches:%d/%d' %(len(good_matches),len(matches)))
+        matchesMask = np.zeros(len(good_matches)).tolist()
+        
+        if len(good_matches) > MIN_MATCH: 
+            src_pts = np.float32([ kp1[m.queryIdx].pt for m in good_matches ])
+            dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good_matches ])
+
+            mtrx, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+            accuracy=float(mask.sum()) / mask.size
+            print("accuracy: %d/%d(%.2f%%)"% (mask.sum(), mask.size, accuracy))
+
+            if mask.sum() > MIN_MATCH:
+                matchesMask = mask.ravel().tolist()
+                h,w, = img1.shape[:2]
+                pts = np.float32([ [[0,0]],[[0,h-1]],[[w-1,h-1]],[[w-1,0]] ])
+                dst = cv2.perspectiveTransform(pts,mtrx)
+                img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
+
+        res = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, \
+                            matchesMask=matchesMask,
+                            flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+    
+    cv2.imshow(win_name, res)
+    key = cv2.waitKey(1)
+
+    if key == 27:
+            break          
+    elif key == ord(' '):
+        x,y,w,h = cv2.selectROI(win_name, frame, False)
+        if w and h:
+            img1 = frame[y:y+h, x:x+w]
+
+else:
+    print("can't open camera.")
+
+cap.release()                          
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_flann_orb.py b/CV/OpenCV/match_track/match_flann_orb.py
new file mode 100644
index 0000000..343c831
--- /dev/null
+++ b/CV/OpenCV/match_track/match_flann_orb.py
@@ -0,0 +1,32 @@
+import cv2
+import numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.ORB_create()
+
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+
+FLANN_INDEX_LSH = 6
+index_params= dict(algorithm = FLANN_INDEX_LSH,
+                   table_number = 6,
+                   key_size = 12,
+                   multi_probe_level = 1)
+search_params=dict(checks=32)
+matcher = cv2.FlannBasedMatcher(index_params, search_params)
+
+matches = matcher.match(desc1, desc2)
+
+res = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, \
+            flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+        
+cv2.imshow('Flann + ORB', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_flann_sift.py b/CV/OpenCV/match_track/match_flann_sift.py
new file mode 100644
index 0000000..cc87707
--- /dev/null
+++ b/CV/OpenCV/match_track/match_flann_sift.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+
+detector = cv2.xfeatures2d.SIFT_create()
+
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+
+FLANN_INDEX_KDTREE = 1
+index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+search_params = dict(checks=50)
+
+matcher = cv2.FlannBasedMatcher(index_params, search_params)
+matches = matcher.match(desc1, desc2)
+res = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, \
+                flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('Flann + SIFT', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_flann_surf.py b/CV/OpenCV/match_track/match_flann_surf.py
new file mode 100644
index 0000000..b0db996
--- /dev/null
+++ b/CV/OpenCV/match_track/match_flann_surf.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.xfeatures2d.SURF_create()
+
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+FLANN_INDEX_KDTREE = 1
+index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+search_params = dict(checks=50)
+
+matcher = cv2.FlannBasedMatcher(index_params, search_params)
+matches = matcher.match(desc1, desc2)
+
+res = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, \
+                flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('Flann + SURF', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_good.py b/CV/OpenCV/match_track/match_good.py
new file mode 100644
index 0000000..272a347
--- /dev/null
+++ b/CV/OpenCV/match_track/match_good.py
@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.ORB_create()
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+
+matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+matches = matcher.match(desc1, desc2)
+
+matches = sorted(matches, key=lambda x:x.distance)
+min_dist, max_dist = matches[0].distance, matches[-1].distance
+
+ratio = 0.2
+good_thresh = (max_dist - min_dist) * ratio + min_dist
+
+good_matches = [m for m in matches if m.distance < good_thresh]
+print('matches:%d/%d, min:%.2f, max:%.2f, thresh:%.2f' \
+        %(len(good_matches),len(matches), min_dist, max_dist, good_thresh))
+
+res = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, \
+                flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('Good Match', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_good_knn.py b/CV/OpenCV/match_track/match_good_knn.py
new file mode 100644
index 0000000..ab6b988
--- /dev/null
+++ b/CV/OpenCV/match_track/match_good_knn.py
@@ -0,0 +1,31 @@
+import cv2
+import numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+
+detector = cv2.ORB_create()
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+matcher = cv2.BFMatcher(cv2.NORM_HAMMING2)
+
+matches = matcher.knnMatch(desc1, desc2, 2)
+
+
+ratio = 0.75
+good_matches = [first for first,second in matches \
+                    if first.distance < second.distance * ratio]
+print('matches:%d/%d' %(len(good_matches),len(matches)))
+
+
+res = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, \
+                    flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+cv2.imshow('Matching', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_homography.py b/CV/OpenCV/match_track/match_homography.py
new file mode 100644
index 0000000..09b1bd9
--- /dev/null
+++ b/CV/OpenCV/match_track/match_homography.py
@@ -0,0 +1,37 @@
+import cv2
+import numpy as np
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+
+detector = cv2.ORB_create()
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+matcher = cv2.BFMatcher(cv2.NORM_HAMMING2)
+matches = matcher.knnMatch(desc1, desc2, 2)
+
+
+ratio = 0.75
+good_matches = [first for first,second in matches \
+                    if first.distance < second.distance * ratio]
+print('good matches:%d/%d' %(len(good_matches),len(matches)))
+
+
+src_pts = np.float32([ kp1[m.queryIdx].pt for m in good_matches ])
+dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good_matches ])
+mtrx, mask = cv2.findHomography(src_pts, dst_pts)
+
+h,w, = img1.shape[:2]
+pts = np.float32([ [[0,0]],[[0,h-1]],[[w-1,h-1]],[[w-1,0]] ])
+
+dst = cv2.perspectiveTransform(pts,mtrx)
+img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
+
+res = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, \
+                    flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+cv2.imshow('Matching Homography', res)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/match_homography_accuracy.py b/CV/OpenCV/match_track/match_homography_accuracy.py
new file mode 100644
index 0000000..12bb715
--- /dev/null
+++ b/CV/OpenCV/match_track/match_homography_accuracy.py
@@ -0,0 +1,42 @@
+import cv2, numpy as np
+
+
+img1 = cv2.imread('../img/taekwonv1.jpg')
+img2 = cv2.imread('../img/figures2.jpg')
+gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+detector = cv2.ORB_create()
+kp1, desc1 = detector.detectAndCompute(gray1, None)
+kp2, desc2 = detector.detectAndCompute(gray2, None)
+
+matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+matches = matcher.match(desc1, desc2)
+matches = sorted(matches, key=lambda x:x.distance)
+
+res1 = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, \
+                    flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+
+src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ])
+dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ])
+
+mtrx, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+h,w = img1.shape[:2]
+pts = np.float32([ [[0,0]],[[0,h-1]],[[w-1,h-1]],[[w-1,0]] ])
+dst = cv2.perspectiveTransform(pts,mtrx)
+img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
+
+
+matchesMask = mask.ravel().tolist()
+res2 = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, \
+                    matchesMask = matchesMask,
+                    flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+
+accuracy=float(mask.sum()) / mask.size
+print("accuracy: %d/%d(%.2f%%)"% (mask.sum(), mask.size, accuracy))
+
+cv2.imshow('Matching-All', res1)
+cv2.imshow('Matching-Inlier ', res2)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/template_matching.py b/CV/OpenCV/match_track/template_matching.py
new file mode 100644
index 0000000..0809aaa
--- /dev/null
+++ b/CV/OpenCV/match_track/template_matching.py
@@ -0,0 +1,36 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/figures.jpg')
+template = cv2.imread('../img/taekwonv1.jpg')
+th, tw = template.shape[:2]
+cv2.imshow('template', template)
+
+methods = ['cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR_NORMED', \
+                                     'cv2.TM_SQDIFF_NORMED']
+for i, method_name in enumerate(methods):
+    img_draw = img.copy()
+    method = eval(method_name)
+    res = cv2.matchTemplate(img, template, method)
+
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(method_name, min_val, max_val, min_loc, max_loc)
+
+
+    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
+        top_left = min_loc
+        match_val = min_val
+    else:
+        top_left = max_loc
+        match_val = max_val
+
+    bottom_right = (top_left[0] + tw, top_left[1] + th)
+    cv2.rectangle(img_draw, top_left, bottom_right, (0,0,255),2)
+
+    cv2.putText(img_draw, str(match_val), top_left, \
+                cv2.FONT_HERSHEY_PLAIN, 2,(0,255,0), 1, cv2.LINE_AA)
+    cv2.imshow(method_name, img_draw)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/track_bgsub_mog.py b/CV/OpenCV/match_track/track_bgsub_mog.py
new file mode 100644
index 0000000..a51a0e1
--- /dev/null
+++ b/CV/OpenCV/match_track/track_bgsub_mog.py
@@ -0,0 +1,22 @@
+import numpy as np
+import cv2
+
+
+cap = cv2.VideoCapture('../img/walking.avi')
+fps = cap.get(cv2.CAP_PROP_FPS)
+delay = int(1000/fps)
+
+fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        break
+    
+    fgmask = fgbg.apply(frame)
+    cv2.imshow('frame',frame)
+    cv2.imshow('bgsub',fgmask)
+    if cv2.waitKey(1) & 0xff == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/track_bgsub_mog2.py b/CV/OpenCV/match_track/track_bgsub_mog2.py
new file mode 100644
index 0000000..53ff5d1
--- /dev/null
+++ b/CV/OpenCV/match_track/track_bgsub_mog2.py
@@ -0,0 +1,23 @@
+import numpy as np
+import cv2
+
+
+cap = cv2.VideoCapture('../img/walking.avi')
+fps = cap.get(cv2.CAP_PROP_FPS)
+delay = int(1000/fps)
+
+fgbg = cv2.createBackgroundSubtractorMOG2()
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        break
+    
+    fgmask = fgbg.apply(frame)
+    cv2.imshow('frame',frame)
+    cv2.imshow('bgsub',fgmask)
+    if cv2.waitKey(delay) & 0xff == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/track_camsifht_cam.py b/CV/OpenCV/match_track/track_camsifht_cam.py
new file mode 100644
index 0000000..d8ada92
--- /dev/null
+++ b/CV/OpenCV/match_track/track_camsifht_cam.py
@@ -0,0 +1,51 @@
+import numpy as np
+import cv2
+
+roi_hist = None
+win_name = 'Camshift Tracking'
+termination =  (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
+
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+
+while cap.isOpened():
+    ret, frame = cap.read()    
+    img_draw = frame.copy()
+    
+    if roi_hist is not None:
+        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+        dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)
+        ret, (x,y,w,h) = cv2.CamShift(dst, (x,y,w,h), termination)
+        cv2.rectangle(img_draw, (x,y), (x+w, y+h), (0,255,0), 2)
+        result = np.hstack((img_draw, cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)))
+
+    else:
+        cv2.putText(img_draw, "Hit the Space to set target to track", \
+                (10,30),cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 1, cv2.LINE_AA)
+        result = img_draw
+
+    cv2.imshow(win_name, result)
+    key = cv2.waitKey(1) & 0xff
+
+    # check if esc key has been pressed
+    if  key == 27:
+        break
+
+    elif key == ord(' '):
+        x,y,w,h = cv2.selectROI(win_name, frame, False)
+
+        if w and h :
+            roi = frame[y : y + h, x : x + w]
+            roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
+            mask = None
+            roi_hist = cv2.calcHist([roi], [0], mask, [180], [0,180])
+            cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
+
+        else:
+            roi_hist = None
+else:
+    print('no camera!')
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/track_meanshift_cam.py b/CV/OpenCV/match_track/track_meanshift_cam.py
new file mode 100644
index 0000000..d107807
--- /dev/null
+++ b/CV/OpenCV/match_track/track_meanshift_cam.py
@@ -0,0 +1,57 @@
+import numpy as np
+import cv2
+
+
+roi_hist = None
+win_name = 'MeanShift Tracking'
+termination =  (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
+
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+
+while cap.isOpened():
+    ret, frame = cap.read()    
+    img_draw = frame.copy()
+    
+    if roi_hist is not None:
+        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+        dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)
+        ret, (x,y,w,h) = cv2.meanShift(dst, (x,y,w,h), termination)
+        cv2.rectangle(img_draw, (x,y), (x+w, y+h), (0,255,0), 2)
+        result = np.hstack((img_draw, cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)))
+
+    else:
+        cv2.putText(img_draw, "Hit the Space to set target to track", \
+                 (10,30),cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 1, cv2.LINE_AA)
+        result = img_draw
+
+    cv2.imshow(win_name, result)
+    key = cv2.waitKey(1) & 0xff
+
+    # check if the esc key has been pressed
+    if  key == 27:
+        break
+
+    # check if the space-bar has been pressed  -  setting RoI
+    elif key == ord(' '):
+        x,y,w,h = cv2.selectROI(win_name, frame, False)
+
+        # RoI has been setted well
+        if w and h :
+            roi = frame[y:y+h, x:x+w]
+            # convert RoI to HSV color space
+            roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
+            mask = None
+            
+            # calculate the histogram for RoI
+            roi_hist = cv2.calcHist([roi], [0], mask, [180], [0,180])
+            cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
+
+        else:
+            roi_hist = None
+else:
+    print('no camera!')
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/track_opticalLK.py b/CV/OpenCV/match_track/track_opticalLK.py
new file mode 100644
index 0000000..fe5e60b
--- /dev/null
+++ b/CV/OpenCV/match_track/track_opticalLK.py
@@ -0,0 +1,53 @@
+import numpy as np
+import cv2
+
+
+cap = cv2.VideoCapture('../img/walking.avi')
+fps = cap.get(cv2.CAP_PROP_FPS) # 프레임 수 구하기
+delay = int(1000/fps)
+
+color = np.random.randint(0,255,(200,3))
+lines = None
+prevImg = None
+
+termcriteria =  (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)
+
+while cap.isOpened():
+    ret,frame = cap.read()
+    if not ret:
+        break
+    img_draw = frame.copy()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    if prevImg is None:
+        prevImg = gray
+        lines = np.zeros_like(frame)
+        prevPt = cv2.goodFeaturesToTrack(prevImg, 200, 0.01, 10)
+    
+    else:
+        nextImg = gray    
+        nextPt, status, err = cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPt, None, criteria=termcriteria)
+        
+        prevMv = prevPt[status==1]
+        nextMv = nextPt[status==1]
+        for i,(p, n) in enumerate(zip(prevMv, nextMv)):
+            px,py = p.ravel()
+            nx,ny = n.ravel()
+            
+            cv2.line(lines, (px, py), (nx,ny), color[i].tolist(), 2)
+            cv2.circle(img_draw, (nx,ny), 2, color[i].tolist(), -1)
+            
+        img_draw = cv2.add(img_draw, lines)
+        
+        prevImg = nextImg
+        prevPt = nextMv.reshape(-1,1,2)
+
+    cv2.imshow('OpticalFlow-LK', img_draw)
+    key = cv2.waitKey(delay)
+
+    if key == 27:
+        break
+    elif key == 8:
+        prevImg = None
+cv2.destroyAllWindows()
+cap.release()
\ No newline at end of file
diff --git a/CV/OpenCV/match_track/track_optical_farneback.py b/CV/OpenCV/match_track/track_optical_farneback.py
new file mode 100644
index 0000000..c57d3fb
--- /dev/null
+++ b/CV/OpenCV/match_track/track_optical_farneback.py
@@ -0,0 +1,40 @@
+import cv2
+import numpy as np
+
+
+def drawFlow(img,flow,step=16):
+  h,w = img.shape[:2]
+  idx_y,idx_x = np.mgrid[step/2:h:step,step/2:w:step].astype(np.int)
+  indices =  np.stack( (idx_x,idx_y), axis =-1).reshape(-1,2)
+  
+  for x,y in indices:
+    cv2.circle(img, (x,y), 1, (0,255,0), -1)
+    dx,dy = flow[y, x].astype(np.int)
+    cv2.line(img, (x,y), (x+dx, y+dy), (0,255, 0),2, cv2.LINE_AA )
+
+
+prev = None
+
+cap = cv2.VideoCapture('../img/walking.avi')
+fps = cap.get(cv2.CAP_PROP_FPS)
+delay = int(1000/fps)
+
+while cap.isOpened():
+  ret,frame = cap.read()
+  if not ret: break
+  gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+
+
+  if prev is None: 
+    prev = gray
+  else:
+    flow = cv2.calcOpticalFlowFarneback(prev, gray, None, 0.5, 3, 15, 3, 5, 1.1, cv2.OPTFLOW_FARNEBACK_GAUSSIAN)
+    drawFlow(frame,flow)
+
+    prev = gray
+  
+  cv2.imshow('OpticalFlow-Farneback', frame)
+  if cv2.waitKey(delay) == 27:
+      break
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/track_trackingAPI.py b/CV/OpenCV/match_track/track_trackingAPI.py
new file mode 100644
index 0000000..0fed77e
--- /dev/null
+++ b/CV/OpenCV/match_track/track_trackingAPI.py
@@ -0,0 +1,68 @@
+import cv2
+
+
+
+trackers = [cv2.TrackerBoosting_create,
+            cv2.TrackerMIL_create,
+            cv2.TrackerKCF_create,
+            cv2.TrackerTLD_create,
+            cv2.TrackerMedianFlow_create,
+            cv2.TrackerGOTURN_create,
+            cv2.TrackerCSRT_create,
+            cv2.TrackerMOSSE_create]
+trackerIdx = 0
+tracker = None
+isFirst = True
+
+video_src = 0
+#video_src = "../img/highway.mp4"
+
+cap = cv2.VideoCapture(video_src)
+fps = cap.get(cv2.CAP_PROP_FPS)
+delay = int(1000/fps)
+win_name = 'Tracking APIs'
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        print('Cannot read video file')
+        break
+    img_draw = frame.copy()
+
+    if tracker is None:
+        cv2.putText(img_draw, "Press the Space to set ROI!!", \
+            (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2,cv2.LINE_AA)
+    else:
+        ok, bbox = tracker.update(frame)
+        (x,y,w,h) = bbox
+
+        if ok:
+            cv2.rectangle(img_draw, (int(x), int(y)), (int(x + w), int(y + h)), \
+                          (0,255,0), 2, 1)
+        else :
+            cv2.putText(img_draw, "Tracking fail.", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2,cv2.LINE_AA)
+    
+    trackerName = tracker.__class__.__name__
+    cv2.putText(img_draw, str(trackerIdx) + ":"+trackerName , (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0,255,0),2,cv2.LINE_AA)
+
+    cv2.imshow(win_name, img_draw)
+    key = cv2.waitKey(delay) & 0xff
+    
+    if key == ord(' ') or (video_src != 0 and isFirst): 
+        isFirst = False
+        roi = cv2.selectROI(win_name, frame, False)
+        if roi[2] and roi[3]:
+            tracker = trackers[trackerIdx]()
+            isInit = tracker.init(frame, roi)
+    elif key in range(48, 56):
+        trackerIdx = key-48
+        if bbox is not None:
+            tracker = trackers[trackerIdx]()
+            isInit = tracker.init(frame, bbox)
+    elif key == 27 : 
+        break
+else:
+    print( "Could not open video")
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/workshop_booksearcher.py b/CV/OpenCV/match_track/workshop_booksearcher.py
new file mode 100644
index 0000000..6ebf480
--- /dev/null
+++ b/CV/OpenCV/match_track/workshop_booksearcher.py
@@ -0,0 +1,98 @@
+import cv2
+import glob
+import numpy as np
+
+
+ratio = 0.7
+MIN_MATCH = 10
+
+detector = cv2.ORB_create()
+
+FLANN_INDEX_LSH = 6
+index_params= dict(algorithm = FLANN_INDEX_LSH,
+                   table_number = 6,
+                   key_size = 12,
+                   multi_probe_level = 1)
+search_params=dict(checks=32)
+matcher = cv2.FlannBasedMatcher(index_params, search_params)
+
+
+def serch(img):
+    gray1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    kp1, desc1 = detector.detectAndCompute(gray1, None)
+    
+    results = {}
+    
+    cover_paths = glob.glob('../img/books/*.*')
+    for cover_path in cover_paths:
+        cover = cv2.imread(cover_path)
+        cv2.imshow('Searching...', cover)
+        cv2.waitKey(5)
+        
+        gray2 = cv2.cvtColor(cover, cv2.COLOR_BGR2GRAY)
+        kp2, desc2 = detector.detectAndCompute(gray2, None)
+        matches = matcher.knnMatch(desc1, desc2, 2)
+        
+        good_matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * ratio]
+        
+        if len(good_matches) > MIN_MATCH: 
+            src_pts = np.float32([ kp1[m.queryIdx].pt for m in good_matches ])
+            
+            dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good_matches ])
+            mtrx, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+            
+            accuracy=float(mask.sum()) / mask.size
+            results[cover_path] = accuracy
+
+    cv2.destroyWindow('Searching...')
+    if len(results) > 0:
+        results = sorted([(v,k) for (k,v) in results.items() if v > 0], reverse=True)
+    return results
+
+cap = cv2.VideoCapture(0)
+qImg = None
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        print('No Frame!')
+        break
+    h, w = frame.shape[:2]
+    
+    left = w // 3
+    right = (w // 3) * 2
+    top = (h // 2) - (h // 3)
+    bottom = (h // 2) + (h // 3)
+    cv2.rectangle(frame, (left,top), (right,bottom), (255,255,255), 3)
+    
+    
+    flip = cv2.flip(frame,1)
+    cv2.imshow('Book Searcher', flip)
+    key = cv2.waitKey(10)
+
+    if key == ord(' '):
+        qImg = frame[top:bottom , left:right]
+        cv2.imshow('query', qImg)
+        break
+    elif key == 27:
+        break
+else:
+    print('No Camera!!')
+cap.release()
+
+
+if qImg is not None:
+    gray = cv2.cvtColor(qImg, cv2.COLOR_BGR2GRAY)
+    results = serch(qImg)
+    if len(results) == 0 :
+        print("No matched book cover found.")
+    else:
+        for( i, (accuracy, cover_path)) in enumerate(results):
+            print(i, cover_path, accuracy)
+            if i==0:
+                cover = cv2.imread(cover_path)
+                cv2.putText(cover, ("Accuracy:%.2f%%"%(accuracy*100)), (10,100), \
+                             cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2, cv2.LINE_AA)
+    cv2.imshow('Result', cover)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/match_track/workshop_panorama.py b/CV/OpenCV/match_track/workshop_panorama.py
new file mode 100644
index 0000000..877ad18
--- /dev/null
+++ b/CV/OpenCV/match_track/workshop_panorama.py
@@ -0,0 +1,42 @@
+import cv2
+import numpy as np
+
+
+
+imgL = cv2.imread('../img/restaurant1.jpg')
+imgR = cv2.imread('../img/restaurant2.jpg')
+
+hl, wl = imgL.shape[:2]
+hr, wr = imgR.shape[:2]
+
+grayL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
+grayR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)
+
+
+descriptor = cv2.xfeatures2d.SIFT_create()
+(kpsL, featuresL) = descriptor.detectAndCompute(imgL, None)
+(kpsR, featuresR) = descriptor.detectAndCompute(imgR, None)
+
+matcher = cv2.DescriptorMatcher_create("BruteForce")
+matches = matcher.knnMatch(featuresR, featuresL, 2)
+
+
+good_matches = []
+for m in matches:
+    if len(m) == 2 and m[0].distance < m[1].distance * 0.75:
+        good_matches.append(( m[0].trainIdx, m[0].queryIdx))
+
+
+if len(good_matches) > 4:
+    ptsL = np.float32([kpsL[i].pt for (i, _) in good_matches])
+    ptsR = np.float32([kpsR[i].pt for (_, i) in good_matches])
+    mtrx, status = cv2.findHomography(ptsR,ptsL, cv2.RANSAC, 4.0)
+    panorama = cv2.warpPerspective(imgR, mtrx, (wr + wl, hr))
+    panorama[0:hl, 0:wl] = imgL
+else:
+    panorama = imgL
+
+cv2.imshow("Image Left", imgL)
+cv2.imshow("Image Right", imgR)
+cv2.imshow("Panorama", panorama)
+cv2.waitKey(0)
diff --git a/CV/OpenCV/numpy_matplotlib/np_bgr.py b/CV/OpenCV/numpy_matplotlib/np_bgr.py
new file mode 100644
index 0000000..b28fec8
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_bgr.py
@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+
+
+img = np.zeros((120,120, 3), dtype=np.uint8)
+img[25:35, :] = [255,0,0]
+img[55:65, :] = [0, 255, 0]
+img[85:95, :] = [0,0,255]
+img[:, 35:45] = [255,255,0]
+img[:, 75:85] = [255,0,255]
+cv2.imshow('BGR', img)
+
+if cv2.waitKey(0) & 0xFF == 27:
+    cv2.destroyAllWindows()
diff --git a/CV/OpenCV/numpy_matplotlib/np_broadcat.py b/CV/OpenCV/numpy_matplotlib/np_broadcat.py
new file mode 100644
index 0000000..aabea29
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_broadcat.py
@@ -0,0 +1,8 @@
+import numpy as np
+
+mylist = list(range(10))
+print(mylist)
+
+for i in range(len(mylist)):
+    mylist[i] = mylist[i] + 1
+print(mylist)
\ No newline at end of file
diff --git a/CV/OpenCV/numpy_matplotlib/np_create_arange.py b/CV/OpenCV/numpy_matplotlib/np_create_arange.py
new file mode 100644
index 0000000..b140959
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_create_arange.py
@@ -0,0 +1,8 @@
+import numpy as np
+
+a = np.arange(5)
+print(a.shape)
+print(a.dtype)
+
+b = np.arange(3.0)
+print(b.dtype)
diff --git a/CV/OpenCV/numpy_matplotlib/np_create_array.py b/CV/OpenCV/numpy_matplotlib/np_create_array.py
new file mode 100644
index 0000000..ddf7812
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_create_array.py
@@ -0,0 +1,11 @@
+import numpy as np
+
+a = np.array([1,2,3,4])
+b = np.array([[1,2,3,4], [5,6,7,8]])
+c = np.array([1,2,3.14,4])
+d = np.array([1,2,3,4], dtype=np.float64)
+
+print(a, a.dtype, a.shape)  # --- a
+print(b, b.dtype, b.shape)  # --- b
+print(c, c.dtype, c.shape)  # --- c
+print(d, d.dtype, d.shape)  # --- d
diff --git a/CV/OpenCV/numpy_matplotlib/np_create_like.py b/CV/OpenCV/numpy_matplotlib/np_create_like.py
new file mode 100644
index 0000000..73e38e5
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_create_like.py
@@ -0,0 +1,10 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('./img/girl.jpg')
+a = np.empty_like(img)
+b = np.zeros_like(img)
+c = np.ones_like(img)
+d = np.full_like(img, 255)
+
+print(a, a.shape, a.dtype)
\ No newline at end of file
diff --git a/CV/OpenCV/numpy_matplotlib/np_create_size.py b/CV/OpenCV/numpy_matplotlib/np_create_size.py
new file mode 100644
index 0000000..f8f70bc
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_create_size.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+a = np.empty( (2,3))
+b = np.empty( (2,3), dtype=np.int16)
+c = np.zeros( (2,3))
+d = np.ones( (2,3), dtype=np.float32)
+e = np.full( (2,3,4), 255, dtype=np.uint8)
+print(a, a.dtype, a.shape)
+print(b, b.dtype, b.shape)
+print(c, c.dtype, c.shape)
+print(d, d.dtype, d.shape)
+print(e, e.dtype, e.shape)
\ No newline at end of file
diff --git a/CV/OpenCV/numpy_matplotlib/np_dtype.py b/CV/OpenCV/numpy_matplotlib/np_dtype.py
new file mode 100644
index 0000000..e35cad3
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_dtype.py
@@ -0,0 +1,13 @@
+import numpy as np
+
+a = np.arange(10)
+print(a, a.dtype)
+
+b = a.astype('float32')
+print(b, b.dtype)
+
+c = np.uint8(b) 
+print(c, c.dtype)
+
+d = c.astype(np.float64)
+print(d, d.dtype)
\ No newline at end of file
diff --git a/CV/OpenCV/numpy_matplotlib/np_gray.py b/CV/OpenCV/numpy_matplotlib/np_gray.py
new file mode 100644
index 0000000..01e2252
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_gray.py
@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+
+
+img = np.zeros((120,120), dtype=np.uint8)
+img[25:35, :] = 45
+img[55:65, :] = 115
+img[85:95, :] = 160
+img[:, 35:45] = 205
+img[:, 75:85] = 255
+cv2.imshow('Gray', img)
+
+if cv2.waitKey(0) & 0xFF == 27:
+    cv2.destroyAllWindows()
diff --git a/CV/OpenCV/numpy_matplotlib/np_img.py b/CV/OpenCV/numpy_matplotlib/np_img.py
new file mode 100644
index 0000000..49855e1
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_img.py
@@ -0,0 +1,9 @@
+import cv2
+
+img = cv2.imread('../img/blank_500.jpg')
+print(type(img))
+print(img.ndim)
+print( img.shape)
+print(img.size)
+print( img.dtype)
+print(img.itemsize)
diff --git a/CV/OpenCV/numpy_matplotlib/np_index.py b/CV/OpenCV/numpy_matplotlib/np_index.py
new file mode 100644
index 0000000..a778676
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_index.py
@@ -0,0 +1,5 @@
+import numpy as np
+
+a = np.arange(10) # [0,1,2,3,4,5,6,7,8,9]
+a[5] # 5
+b = np.arange(12).reshape(4,3)
diff --git a/CV/OpenCV/numpy_matplotlib/np_reshape.py b/CV/OpenCV/numpy_matplotlib/np_reshape.py
new file mode 100644
index 0000000..68343af
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_reshape.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+
+a = np.arange(6)
+b = a.reshape(2,3)
+
+c = np.arange(24).reshape(2,3,4)
+
+d = np.arange(100).reshape(2, -1)
+e = np.arange(100).reshape(-1, 5)
+
+
+f = np.ravel(c)
+
+g = np.arange(10).reshape(2,-1)
+
+print(a, a.shape)
+print(b, b.shape)
+print(c, c.shape)
+print(d, d.shape)
+print(e, e.shape)
+print(f, f.shape)
+print(g.T)
diff --git a/CV/OpenCV/numpy_matplotlib/np_scalar_operation.py b/CV/OpenCV/numpy_matplotlib/np_scalar_operation.py
new file mode 100644
index 0000000..f5d4a14
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/np_scalar_operation.py
@@ -0,0 +1,21 @@
+import numpy as np
+
+a = np.arange(5)
+
+b = a + 5
+c = a - 2
+d = a * 2
+e = a / 2
+f = b ** 2
+g = b > 2
+
+print(a)
+print(b)
+print(c)
+print(d)
+print(e)
+print(f)
+print(g)
+print(h)
+print(i)
+print(j)
\ No newline at end of file
diff --git a/CV/OpenCV/numpy_matplotlib/plt_color.py b/CV/OpenCV/numpy_matplotlib/plt_color.py
new file mode 100644
index 0000000..503d2fe
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_color.py
@@ -0,0 +1,8 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.arange(10) # 0,1,2,3,4,5,6,7,8,9
+y = x ** 2         # 0,1,4,9,16,25,36,49,64,81
+
+plt.plot(x,y, 'r')     # generate plot
+plt.show()        # show plot
diff --git a/CV/OpenCV/numpy_matplotlib/plt_imgshow_rgb.py b/CV/OpenCV/numpy_matplotlib/plt_imgshow_rgb.py
new file mode 100644
index 0000000..89f602e
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_imgshow_rgb.py
@@ -0,0 +1,9 @@
+import cv2
+from matplotlib import pyplot as plt
+
+img = cv2.imread('../img/girl.jpg')
+
+plt.imshow(img[:,:,::-1])
+plt.xticks([])                  # remove ticks for X axis   
+plt.yticks([])                  # remove ticks for Y axis
+plt.show()
diff --git a/CV/OpenCV/numpy_matplotlib/plt_imshow.py b/CV/OpenCV/numpy_matplotlib/plt_imshow.py
new file mode 100644
index 0000000..7fba58f
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_imshow.py
@@ -0,0 +1,7 @@
+import cv2
+from matplotlib import pyplot as plt
+
+img = cv2.imread('../img/girl.jpg')
+
+plt.imshow(img)
+plt.show()
diff --git a/CV/OpenCV/numpy_matplotlib/plt_imshow_subplot.py b/CV/OpenCV/numpy_matplotlib/plt_imshow_subplot.py
new file mode 100644
index 0000000..f6c6a10
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_imshow_subplot.py
@@ -0,0 +1,22 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cv2
+
+img1 = cv2.imread('../img/model.jpg')
+img2 = cv2.imread('../img/model2.jpg')
+img3 = cv2.imread('../img/model3.jpg')
+
+
+plt.subplot(1,3,1)
+plt.imshow(img1[:,:,(2,1,0)])
+plt.xticks([]); plt.yticks([])
+
+plt.subplot(1,3,2)
+plt.imshow(img2[:,:,(2,1,0)])
+plt.xticks([]); plt.yticks([])
+
+plt.subplot(1,3,3)
+plt.imshow(img3[:,:,(2,1,0)])
+plt.xticks([]); plt.yticks([])
+
+plt.show()
diff --git a/CV/OpenCV/numpy_matplotlib/plt_plot.py b/CV/OpenCV/numpy_matplotlib/plt_plot.py
new file mode 100644
index 0000000..1c207f6
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_plot.py
@@ -0,0 +1,6 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+a = np.array([2,6,7,3,12,8,4,5])
+plt.plot(a)
+plt.show()
diff --git a/CV/OpenCV/numpy_matplotlib/plt_simple.py b/CV/OpenCV/numpy_matplotlib/plt_simple.py
new file mode 100644
index 0000000..b2f6baa
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_simple.py
@@ -0,0 +1,7 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.arange(10) # 0,1,2,3,4,5,6,7,8,9
+y = x**2         # 0,1,4,9,16,25,36,49,64,81
+plt.plot(x,y)     # generate plot
+plt.show()
diff --git a/CV/OpenCV/numpy_matplotlib/plt_style.py b/CV/OpenCV/numpy_matplotlib/plt_style.py
new file mode 100644
index 0000000..244b681
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_style.py
@@ -0,0 +1,13 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.arange(10)
+f1 = x * 5
+f2 = x **2
+f3 = x **2 + x*2
+
+plt.plot(x,'r--')
+plt.plot(f1, 'g.')
+plt.plot(f2, 'bv')
+plt.plot(f3, 'ks' )
+plt.show()
diff --git a/CV/OpenCV/numpy_matplotlib/plt_subplot.py b/CV/OpenCV/numpy_matplotlib/plt_subplot.py
new file mode 100644
index 0000000..5cd8b5f
--- /dev/null
+++ b/CV/OpenCV/numpy_matplotlib/plt_subplot.py
@@ -0,0 +1,18 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.arange(10)
+
+plt.subplot(2,2,1)
+plt.plot(x,x**2)
+
+plt.subplot(2,2,2)
+plt.plot(x,x*5)
+
+plt.subplot(223)
+plt.plot(x, np.sin(x))
+
+plt.subplot(224)
+plt.plot(x,np.cos(x))
+
+plt.show()
diff --git a/CV/OpenCV/segmentation/cntr_approximate.py b/CV/OpenCV/segmentation/cntr_approximate.py
new file mode 100644
index 0000000..70bffda
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_approximate.py
@@ -0,0 +1,28 @@
+import cv2
+import numpy as np
+
+img = cv2.imread('../img/bad_rect.png')
+img2 = img.copy()
+
+
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
+ret, th = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY)
+
+# finding contours
+temp, contours, hierachy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+contour = contours[0]
+
+# set 5% of entire round as error range
+epsilon = 0.05 * cv2.arcLength(contour, True)
+# calculate approximate contour
+approx = cv2.approxPolyDP(contour, epsilon, True)
+
+# draw contour lines
+cv2.drawContours(img, [contour], -1, (0,255,0), 3)
+cv2.drawContours(img2, [approx], -1, (0,255,0), 3)
+
+# show images
+cv2.imshow('contour', img)
+cv2.imshow('approx', img2)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/cntr_bound_fit.py b/CV/OpenCV/segmentation/cntr_bound_fit.py
new file mode 100644
index 0000000..5ca6b85
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_bound_fit.py
@@ -0,0 +1,41 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread("../img/lightning.png")
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ret, th = cv2.threshold(imgray, 127,255,cv2.THRESH_BINARY_INV)
+
+
+im, contours, hr = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+contr = contours[0]
+
+x,y,w,h = cv2.boundingRect(contr)
+cv2.rectangle(img, (x,y), (x+w, y+h), (0,0,0), 3)
+
+rect = cv2.minAreaRect(contr)
+box = cv2.boxPoints(rect)
+box = np.int0(box)
+cv2.drawContours(img, [box], -1, (0,255,0), 3)
+
+
+(x,y), radius = cv2.minEnclosingCircle(contr)
+cv2.circle(img, (int(x), int(y)), int(radius), (255,0,0), 2)
+
+
+ret, tri = cv2.minEnclosingTriangle(contr)
+cv2.polylines(img, [np.int32(tri)], True, (255,0,255), 2)
+
+
+ellipse = cv2.fitEllipse(contr)
+cv2.ellipse(img, ellipse, (0,255,255), 3)
+
+
+[vx,vy,x,y] = cv2.fitLine(contr, cv2.DIST_L2,0,0.01,0.01)
+cols,rows = img.shape[:2]
+cv2.line(img,(0, 0-x*(vy/vx) + y), (cols-1, (cols-x)*(vy/vx) + y), (0,0,255),2)
+
+
+cv2.imshow('Bound Fit shapes', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/cntr_convexhull.py b/CV/OpenCV/segmentation/cntr_convexhull.py
new file mode 100644
index 0000000..1457904
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_convexhull.py
@@ -0,0 +1,37 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/hand.jpg')
+img2 = img.copy()
+
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ret, th = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
+
+
+temp, contours, heiarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+cntr = contours[0]
+cv2.drawContours(img, [cntr], -1, (0, 255,0), 1)
+
+
+hull = cv2.convexHull(cntr)
+cv2.drawContours(img2, [hull], -1, (0,255,0), 1)
+
+print(cv2.isContourConvex(cntr), cv2.isContourConvex(hull))
+
+
+hull2 = cv2.convexHull(cntr, returnPoints=False)
+defects = cv2.convexityDefects(cntr, hull2)
+
+for i in range(defects.shape[0]):
+    startP, endP, farthestP, distance = defects[i, 0]
+    farthest = tuple(cntr[farthestP][0])
+    dist = distance/256.0
+    if dist > 1 :
+        cv2.circle(img2, farthest, 3, (0,0,255), -1)
+
+
+cv2.imshow('contour', img)
+cv2.imshow('convex hull', img2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/cntr_find.py b/CV/OpenCV/segmentation/cntr_find.py
new file mode 100644
index 0000000..106a4d5
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_find.py
@@ -0,0 +1,36 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/shapes.png')
+img2 = img.copy()
+
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ret, imthres = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY_INV)
+
+
+im2, contour, hierarchy = cv2.findContours(imthres, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+im2, contour2, hierarchy = cv2.findContours(imthres, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+
+print('도형의 갯수: %d(%d)'% (len(contour), len(contour2)))
+
+
+cv2.drawContours(img, contour, -1, (0,255,0), 4)
+cv2.drawContours(img2, contour2, -1, (0,255,0), 4)
+
+
+for i in contour:
+    for j in i:
+        cv2.circle(img, tuple(j[0]), 1, (255,0,0), -1) 
+
+
+for i in contour2:
+    for j in i:
+        cv2.circle(img2, tuple(j[0]), 1, (255,0,0), -1) 
+
+
+cv2.imshow('CHAIN_APPROX_NONE', img)
+cv2.imshow('CHAIN_APPROX_SIMPLE', img2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/cntr_hierarchy.py b/CV/OpenCV/segmentation/cntr_hierarchy.py
new file mode 100644
index 0000000..5159515
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_hierarchy.py
@@ -0,0 +1,34 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/shapes_donut.png')
+img2 = img.copy()
+
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ret, imthres = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY_INV)
+
+
+im2, contour, hierarchy = cv2.findContours(imthres, cv2.RETR_EXTERNAL, \
+                                                cv2.CHAIN_APPROX_NONE)
+                                                
+print(len(contour), hierarchy)
+
+im2, contour2, hierarchy = cv2.findContours(imthres, cv2.RETR_TREE, \
+                                            cv2.CHAIN_APPROX_SIMPLE)
+
+print(len(contour2), hierarchy)
+
+
+cv2.drawContours(img, contour, -1, (0,255,0), 3)
+
+for idx, cont in enumerate(contour2):
+    color = [int(i) for i in np.random.randint(0,255, 3)]
+    cv2.drawContours(img2, contour2, idx, color, 3)
+    cv2.putText(img2, str(idx), tuple(cont[0][0]), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255))
+
+
+cv2.imshow('RETR_EXTERNAL', img)
+cv2.imshow('RETR_TREE', img2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/cntr_matchShape.py b/CV/OpenCV/segmentation/cntr_matchShape.py
new file mode 100644
index 0000000..cc6a6ba
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_matchShape.py
@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+
+
+target = cv2.imread('../img/4star.jpg')
+shapes = cv2.imread('../img/shapestomatch.jpg')
+
+targetGray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
+shapesGray = cv2.cvtColor(shapes, cv2.COLOR_BGR2GRAY)
+
+ret, targetTh = cv2.threshold(targetGray, 127, 255, cv2.THRESH_BINARY_INV)
+ret, shapesTh = cv2.threshold(shapesGray, 127, 255, cv2.THRESH_BINARY_INV)
+
+_, cntrs_target, _ = cv2.findContours(targetTh, cv2.RETR_EXTERNAL, \
+                                            cv2.CHAIN_APPROX_SIMPLE)
+_, cntrs_shapes, _ = cv2.findContours(shapesTh, cv2.RETR_EXTERNAL, \
+                                            cv2.CHAIN_APPROX_SIMPLE)
+
+
+matchs = []
+for contr in cntrs_shapes:
+    match = cv2.matchShapes(cntrs_target[0], contr, cv2.CONTOURS_MATCH_I2, 0.0)
+    matchs.append( (match, contr) )
+    cv2.putText(shapes, '%.2f'%match, tuple(contr[0][0]), cv2.FONT_HERSHEY_PLAIN, 1,(0,0,255),1 )
+
+
+matchs.sort(key=lambda x : x[0])
+
+cv2.drawContours(shapes, [matchs[0][1]], -1, (0,255,0), 3)
+cv2.imshow('target', target)
+cv2.imshow('Match Shape', shapes)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/cntr_moment.py b/CV/OpenCV/segmentation/cntr_moment.py
new file mode 100644
index 0000000..fd47f71
--- /dev/null
+++ b/CV/OpenCV/segmentation/cntr_moment.py
@@ -0,0 +1,37 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread("../img/shapes.png")
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ret, th = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY_INV)
+img2, contours, hierachy = cv2.findContours(th, cv2.RETR_EXTERNAL, \
+                                            cv2.CHAIN_APPROX_SIMPLE)
+
+
+for c in contours:
+    # calculate moment
+    mmt = cv2.moments(c)
+    # m10/m00, m01/m00  -  calculate center point
+    cx = int(mmt['m10']/mmt['m00'])
+    cy = int(mmt['m01']/mmt['m00'])
+
+    # calcualte region area
+    a = mmt['m00']
+    # Area outline length
+    l = cv2.arcLength(c, True)
+    # Draw yellow dot at center point
+    cv2.circle(img, (cx, cy), 5, (0, 255, 255), -1)
+    # Draw area near center point
+    cv2.putText(img, "A:%.0f"%a, (cx, cy+20) , cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255))
+
+    # Draw length at contour start
+    cv2.putText(img, "L:%.2f"%l, tuple(c[0][0]), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0))
+    # Calculate and print the contour width with a function
+    print("area:%.2f"%cv2.contourArea(c, False))
+
+
+
+cv2.imshow('center', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/connected_label.py b/CV/OpenCV/segmentation/connected_label.py
new file mode 100644
index 0000000..d3c2623
--- /dev/null
+++ b/CV/OpenCV/segmentation/connected_label.py
@@ -0,0 +1,20 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/shapes_donut.png')
+img2 = np.zeros_like(img)
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+_, th = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+
+
+cnt, labels = cv2.connectedComponents(th)
+
+
+for i in range(cnt):
+    img2[labels==i] =  [int(j) for j in np.random.randint(0,255, 3)]
+
+cv2.imshow('origin', img)
+cv2.imshow('labeled', img2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/distanceTrans.py b/CV/OpenCV/segmentation/distanceTrans.py
new file mode 100644
index 0000000..cea7e9f
--- /dev/null
+++ b/CV/OpenCV/segmentation/distanceTrans.py
@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/full_body.jpg', cv2.IMREAD_GRAYSCALE)
+_, biimg = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
+
+dst = cv2.distanceTransform(biimg, cv2.DIST_L2, 5)
+dst = (dst/(dst.max()-dst.min()) * 255).astype(np.uint8)
+skeleton = cv2.adaptiveThreshold(dst, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 7, -3)
+
+cv2.imshow('origin', img)
+cv2.imshow('dist', dst)
+cv2.imshow('skel', skeleton)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/flood_fill.py b/CV/OpenCV/segmentation/flood_fill.py
new file mode 100644
index 0000000..259f4e8
--- /dev/null
+++ b/CV/OpenCV/segmentation/flood_fill.py
@@ -0,0 +1,24 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/taekwonv1.jpg')
+rows, cols = img.shape[:2]
+
+mask = np.zeros((rows+2, cols+2), np.uint8)
+newVal = (255,255,255)
+loDiff, upDiff = (10,10,10), (10,10,10)
+
+
+def onMouse(event, x, y, flags, param):
+    global mask, img
+    if event == cv2.EVENT_LBUTTONDOWN:
+        seed = (x,y)
+        retval = cv2.floodFill(img, mask, seed, newVal, loDiff, upDiff)
+        cv2.imshow('img', img)
+
+
+cv2.imshow('img', img)
+cv2.setMouseCallback('img', onMouse)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/grabcut.py b/CV/OpenCV/segmentation/grabcut.py
new file mode 100644
index 0000000..857a026
--- /dev/null
+++ b/CV/OpenCV/segmentation/grabcut.py
@@ -0,0 +1,61 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/taekwonv1.jpg')
+img_draw = img.copy()
+mask = np.zeros(img.shape[:2], dtype=np.uint8)
+rect = [0,0,0,0]
+mode = cv2.GC_EVAL
+
+bgdmodel = np.zeros((1,65),np.float64)
+fgdmodel = np.zeros((1,65),np.float64)
+
+
+def onMouse(event, x, y, flags, param):
+    global mouse_mode, rect, mask, mode
+    if event == cv2.EVENT_LBUTTONDOWN :
+        if flags <= 1:
+            mode = cv2.GC_INIT_WITH_RECT
+            rect[:2] = x, y
+    
+    elif event == cv2.EVENT_MOUSEMOVE and flags & cv2.EVENT_FLAG_LBUTTON :
+        if mode == cv2.GC_INIT_WITH_RECT: # 드래그 진행 중 ---②
+            img_temp = img.copy()
+            
+            cv2.rectangle(img_temp, (rect[0], rect[1]), (x, y), (0,255,0), 2)
+            cv2.imshow('img', img_temp)
+
+        elif flags > 1:
+            mode = cv2.GC_INIT_WITH_MASK
+            if flags & cv2.EVENT_FLAG_CTRLKEY :
+                cv2.circle(img_draw,(x,y),3, (255,255,255),-1)
+                cv2.circle(mask,(x,y),3, cv2.GC_FGD,-1)
+
+            if flags & cv2.EVENT_FLAG_SHIFTKEY :
+                cv2.circle(img_draw,(x,y),3, (0,0,0),-1)
+                cv2.circle(mask,(x,y),3, cv2.GC_BGD,-1)
+
+            cv2.imshow('img', img_draw)
+
+    elif event == cv2.EVENT_LBUTTONUP:
+        if mode == cv2.GC_INIT_WITH_RECT :
+            rect[2:] = x, y
+            
+            cv2.rectangle(img_draw, (rect[0], rect[1]), (x, y), (255,0,0), 2)
+            cv2.imshow('img', img_draw)
+
+        cv2.grabCut(img, mask, tuple(rect), bgdmodel, fgdmodel, 1, mode)
+        img2 = img.copy()
+        
+        img2[(mask==cv2.GC_BGD) | (mask==cv2.GC_PR_BGD)] = 0
+        cv2.imshow('grabcut', img2)
+        mode = cv2.GC_EVAL
+
+
+cv2.imshow('img', img)
+cv2.setMouseCallback('img', onMouse)
+while True:    
+    if cv2.waitKey(0) & 0xFF == 27 : # esc
+        break
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/hough_circle.py b/CV/OpenCV/segmentation/hough_circle.py
new file mode 100644
index 0000000..c0223a0
--- /dev/null
+++ b/CV/OpenCV/segmentation/hough_circle.py
@@ -0,0 +1,20 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/coins_spread1.jpg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+blur = cv2.GaussianBlur(gray, (3,3), 0)
+
+circles = cv2.HoughCircles(blur, cv2.HOUGH_GRADIENT, 1.5, 30, None, 200)
+
+if circles is not None:
+    circles = np.uint16(np.around(circles))
+    for i in circles[0,:]:
+        cv2.circle(img,(i[0], i[1]), i[2], (0, 255, 0), 2)
+        cv2.circle(img, (i[0], i[1]), 2, (0,0,255), 5)
+
+
+cv2.imshow('hough circle', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/hough_line.py b/CV/OpenCV/segmentation/hough_line.py
new file mode 100644
index 0000000..31fd41f
--- /dev/null
+++ b/CV/OpenCV/segmentation/hough_line.py
@@ -0,0 +1,28 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/sudoku.jpg')
+img2 = img.copy()
+h, w = img.shape[:2]
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+edges = cv2.Canny(imgray, 100, 200 )
+
+lines = cv2.HoughLines(edges, 1, np.pi/180, 130)
+for line in lines:
+    r,theta = line[0]
+    tx, ty = np.cos(theta), np.sin(theta)
+    x0, y0 = tx * r, ty * r
+    
+    cv2.circle(img2, (abs(x0), abs(y0)), 3, (0,0,255), -1)
+    
+    x1, y1 = int(x0 + w*(-ty)), int(y0 + h * tx)
+    x2, y2 = int(x0 - w*(-ty)), int(y0 - h * tx)
+    
+    cv2.line(img2, (x1, y1), (x2, y2), (0,255,0), 1)
+
+
+merged = np.hstack((img, img2))
+cv2.imshow('hough line', merged)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/hough_lineP.py b/CV/OpenCV/segmentation/hough_lineP.py
new file mode 100644
index 0000000..c6875a2
--- /dev/null
+++ b/CV/OpenCV/segmentation/hough_lineP.py
@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/sudoku.jpg')
+img2 = img.copy()
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+edges = cv2.Canny(imgray, 50, 200 )
+
+lines = cv2.HoughLinesP(edges, 1, np.pi/180, 10, None, 20, 2)
+for line in lines:
+    x1, y1, x2, y2 = line[0]
+    cv2.line(img2, (x1,y1), (x2, y2), (0,255,0), 1)
+
+merged = np.hstack((img, img2))
+cv2.imshow('Probability hough line', merged)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/mean_shift.py b/CV/OpenCV/segmentation/mean_shift.py
new file mode 100644
index 0000000..bb10699
--- /dev/null
+++ b/CV/OpenCV/segmentation/mean_shift.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/taekwonv1.jpg')
+
+
+def onChange(x):
+    sp = cv2.getTrackbarPos('sp', 'img')
+    sr = cv2.getTrackbarPos('sr', 'img')
+    lv = cv2.getTrackbarPos('lv', 'img')
+
+    mean = cv2.pyrMeanShiftFiltering(img, sp, sr, None, lv)
+    cv2.imshow('img', np.hstack((img, mean)))
+
+
+cv2.imshow('img', np.hstack((img, img)))
+
+cv2.createTrackbar('sp', 'img', 0,100, onChange)
+cv2.createTrackbar('sr', 'img', 0,100, onChange)
+cv2.createTrackbar('lv', 'img', 0,5, onChange)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/watershed.py b/CV/OpenCV/segmentation/watershed.py
new file mode 100644
index 0000000..2a6e5e0
--- /dev/null
+++ b/CV/OpenCV/segmentation/watershed.py
@@ -0,0 +1,44 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/taekwonv1.jpg')
+rows, cols = img.shape[:2]
+img_draw = img.copy()
+
+
+marker = np.zeros((rows, cols), np.int32)
+markerId = 1
+colors = []
+isDragging = False
+
+
+def onMouse(event, x, y, flags, param):
+    global img_draw, marker, markerId, isDragging
+    if event == cv2.EVENT_LBUTTONDOWN:
+        isDragging = True
+        colors.append((markerId, img[y,x]))
+
+    elif event == cv2.EVENT_MOUSEMOVE:
+        if isDragging:
+            marker[y,x] = markerId
+            cv2.circle(img_draw, (x,y), 3, (0,0,255), -1)
+            cv2.imshow('watershed', img_draw)
+
+    elif event == cv2.EVENT_LBUTTONUP:
+        if isDragging:          
+            isDragging = False
+            markerId += 1
+
+    elif event == cv2.EVENT_RBUTTONDOWN:
+            cv2.watershed(img, marker)
+            img_draw[marker == -1] = (0,255,0)
+            for mid, color in colors:
+                img_draw[marker==mid] = color
+            cv2.imshow('watershed', img_draw)
+
+
+cv2.imshow('watershed', img)
+cv2.setMouseCallback('watershed', onMouse)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/workshop_coin_count.py b/CV/OpenCV/segmentation/workshop_coin_count.py
new file mode 100644
index 0000000..8c120f4
--- /dev/null
+++ b/CV/OpenCV/segmentation/workshop_coin_count.py
@@ -0,0 +1,117 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread('../img/coins_connected.jpg')
+rows, cols = img.shape[:2]
+cv2.imshow('original', img)
+
+
+mean = cv2.pyrMeanShiftFiltering(img, 20, 50)
+cv2.imshow('mean', mean)
+
+gray = cv2.cvtColor(mean, cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (3,3), 0)
+
+_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+cv2.imshow('thresh', thresh)
+
+dst = cv2.distanceTransform(thresh, cv2.DIST_L2, 3)
+dst = ( dst / (dst.max() - dst.min()) * 255 ).astype(np.uint8)
+cv2.imshow('dst', dst)
+
+
+localMx = cv2.dilate(dst, np.ones((50,50), np.uint8))
+lm = np.zeros((rows, cols), np.uint8)
+lm[(localMx==dst) & (dst != 0)] = 255
+cv2.imshow('localMx', lm)
+
+
+seeds = np.where(lm ==255)
+seed = np.stack( (seeds[1], seeds[0]), axis=-1)
+fill_mask = np.zeros((rows+2, cols+2), np.uint8)
+for x,y in seed:
+    ret = cv2.floodFill(mean, fill_mask, (x,y), (255,255,255), (10,10,10), (10,10,10))
+cv2.imshow('floodFill', mean)
+
+
+gray = cv2.cvtColor(mean, cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (5,5), 0)
+
+ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+dst = cv2.distanceTransform(thresh, cv2.DIST_L2, 5)
+dst = ( (dst / (dst.max() - dst.min())) * 255 ).astype(np.uint8)
+cv2.imshow('dst2', dst)
+
+ret, sure_fg = cv2.threshold(dst, 0.5*dst.max(), 255,0)
+cv2.imshow('sure_fg', sure_fg)
+
+_, bg_th = cv2.threshold(dst, 0.3*dst.max(),  255, cv2.THRESH_BINARY_INV)
+bg_dst = cv2.distanceTransform(bg_th, cv2.DIST_L2, 5)
+bg_dst = ( (bg_dst / (bg_dst.max() - bg_dst.min())) * 255 ).astype(np.uint8)
+ret, sure_bg = cv2.threshold(bg_dst, 0.3*bg_dst.max(), 255,cv2.THRESH_BINARY)
+cv2.imshow('sure_bg', sure_bg)
+
+
+ret, inv_sure_bg = cv2.threshold(sure_bg, 127, 255,cv2.THRESH_BINARY_INV)
+unkown = cv2.subtract(inv_sure_bg, sure_fg)
+cv2.imshow('unkown', unkown)
+
+_, markers = cv2.connectedComponents(sure_fg)
+
+markers = markers+1
+markers[unkown ==255] = 0
+print("Before watershed:", np.unique(markers))
+
+colors = []
+marker_show = np.zeros_like(img)
+for mid in np.unique(markers):
+    color = [int(j) for j in np.random.randint(0,255, 3)]
+    colors.append((mid, color))
+    marker_show[markers==mid] = color
+    coords = np.where(markers==mid)
+    x, y = coords[1][0], coords[0][0]
+    cv2.putText(marker_show, str(mid), (x+20, y+20), cv2.FONT_HERSHEY_PLAIN, 2, (255,255,255))
+cv2.imshow('before', marker_show)
+
+
+markers = cv2.watershed(img, markers)
+print("After watershed:", np.unique(markers))
+
+
+for mid, color in colors:
+    marker_show[markers == mid] = color
+    coords = np.where(markers == mid)
+
+    if coords[0].size <= 0 : 
+        continue
+    x, y = coords[1][0], coords[0][0]
+    cv2.putText(marker_show, str(mid), (x+20, y+20), cv2.FONT_HERSHEY_PLAIN, 2, (255,255,255))
+
+marker_show[markers==-1] = (0,255,0)
+cv2.imshow('watershed marker', marker_show)
+
+img[markers == -1] = (0,255,0)
+cv2.imshow('watershed', img)
+
+
+mask = np.zeros((rows, cols), np.uint8)
+
+mask[markers!=1] = 255
+nobg = cv2.bitwise_and(img, img, mask=mask)
+coin_label = [l for l in np.unique(markers) if (l != 1 and l !=-1)]
+
+for i, label in enumerate(coin_label):
+    mask[:,:] = 0
+    mask[markers ==label] = 255
+
+    coins = cv2.bitwise_and(img, img, mask=mask)
+    _, contour, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+    x,y,w,h = cv2.boundingRect(contour[0])
+
+    coin = coins[y:y+h, x:x+w]
+    cv2.imshow('coin%d'%(i+1), coin)
+    cv2.imwrite('../img/coin_test/coin%d.jpg'%(i+1), coin)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/workshop_paper_scan.py b/CV/OpenCV/segmentation/workshop_paper_scan.py
new file mode 100644
index 0000000..d4744ba
--- /dev/null
+++ b/CV/OpenCV/segmentation/workshop_paper_scan.py
@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+
+
+win_name = 'scan'
+img = cv2.imread("../img/paper.jpg")
+cv2.imshow('original', img)
+cv2.waitKey(0)
+draw = img.copy()
+
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (3, 3), 0)
+edged = cv2.Canny(gray, 75, 200)
+cv2.imshow(win_name, edged)
+cv2.waitKey(0)
+
+(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+cv2.drawContours(draw, cnts, -1, (0,255,0))
+cv2.imshow(win_name, draw)
+cv2.waitKey(0)
+
+
+cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
+for c in cnts:
+    peri = cv2.arcLength(c, True)
+    vertices = cv2.approxPolyDP(c, 0.02 * peri, True) 
+    if len(vertices) == 4:
+        break
+
+pts = vertices.reshape(4, 2)
+for x,y in pts:
+    cv2.circle(draw, (x,y), 10, (0,255,0), -1)
+cv2.imshow(win_name, draw)
+cv2.waitKey(0)
+merged = np.hstack((img, draw))
+
+
+sm = pts.sum(axis=1)
+diff = np.diff(pts, axis = 1)
+
+topLeft = pts[np.argmin(sm)]
+bottomRight = pts[np.argmax(sm)]
+topRight = pts[np.argmin(diff)]
+bottomLeft = pts[np.argmax(diff)]
+
+
+pts1 = np.float32([topLeft, topRight, bottomRight , bottomLeft])
+
+
+w1 = abs(bottomRight[0] - bottomLeft[0])
+w2 = abs(topRight[0] - topLeft[0])
+h1 = abs(topRight[1] - bottomRight[1])
+h2 = abs(topLeft[1] - bottomLeft[1])
+width = max([w1, w2])
+height = max([h1, h2])
+
+pts2 = np.float32([[0,0], [width-1,0], [width-1,height-1], [0,height-1]])
+
+
+mtrx = cv2.getPerspectiveTransform(pts1, pts2)
+
+result = cv2.warpPerspective(img, mtrx, (width, height))
+cv2.imshow(win_name, result)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/CV/OpenCV/segmentation/workshop_shape.py b/CV/OpenCV/segmentation/workshop_shape.py
new file mode 100644
index 0000000..f3f78e8
--- /dev/null
+++ b/CV/OpenCV/segmentation/workshop_shape.py
@@ -0,0 +1,51 @@
+import cv2
+import numpy as np
+
+
+img = cv2.imread("../img/5shapes.jpg")
+img2 = img.copy()
+imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ret, th = cv2.threshold(imgray, 127, 255, cv2.THRESH_BINARY_INV)
+
+
+_, contours, _ = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+
+for contour in contours:
+    approx = cv2.approxPolyDP(contour, 0.01*cv2.arcLength(contour, True), True)
+    vertices = len(approx)
+    print("vertices:", vertices)
+    
+    
+    mmt = cv2.moments(contour)
+    cx,cy = int(mmt['m10']/mmt['m00']), int(mmt['m01']/mmt['m00'])
+    
+    name = "Unkown"
+    if vertices == 3:
+        name = "Triangle"
+        color = (0,255,0)
+
+    elif vertices == 4:
+        x,y,w,h = cv2.boundingRect(contour)
+        if abs(w-h) <= 3:
+            name = 'Square'
+            color = (0,125,255)
+        else:
+            name = 'Rectangle'
+            color = (0,0,255)
+
+    elif vertices == 10:
+        name = 'Star'
+        color = (255,255,0)
+
+    elif vertices >= 15:
+        name = 'Circle'
+        color = (0,255,255)
+    
+    cv2.drawContours(img2, [contour], -1, color, -1)
+    cv2.putText(img2, name, (cx-50, cy), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (100,100,100), 1)
+
+cv2.imshow('Input Shapes', img)
+cv2.imshow('Recognizing Shapes', img2)
+cv2.waitKey(0)
+cv2.destroyAllWindows()