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hand_wo_socket_smootiong copy.py
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import socket
import sys
import cv2
import cv2 as cv
import copy
import csv
import mediapipe as mp
import numpy as np
import struct
import matplotlib.pyplot as plt
import itertools
from pandas import DataFrame
from collections import Counter
from collections import deque
from utils import CvFpsCalc
from model import KeyPointClassifier
from model import PointHistoryClassifier
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
Wr = 0
M0 = 9
M3 = 12
Wr = 0
firstF = [1,2,3,4]
secondF = [5,6,7,8]
middleF = [9,10,11,12]
ringF = [13,14,15,16]
pinkyF= [17,18,19,20]
fingerList = [firstF,secondF,middleF,ringF,pinkyF]
Queue = []
CONF_THRESHOLD = 0.6
Q_NUM =5
pre_List =[]
HOST="192.168.0.110" # 192.168.0.11: 기숙사, 192.168.0.40: 연구실
PORT=8888
successframe = 0 # success한 frame 개수가 몇개인지 판단.
preMatrix = np.empty((12,3))
curMatrix = np.empty((12,3))
moveMatrix = np.zeros((12,3))
confidence = np.zeros((2))
keypoint_classifier = KeyPointClassifier()
point_history_classifier = PointHistoryClassifier()
# Read labels ###########################################################
with open('model/keypoint_classifier/keypoint_classifier_label.csv',
encoding='utf-8-sig') as f:
keypoint_classifier_labels = csv.reader(f)
keypoint_classifier_labels = [
row[0] for row in keypoint_classifier_labels
]
with open(
'model/point_history_classifier/point_history_classifier_label.csv',
encoding='utf-8-sig') as f:
point_history_classifier_labels = csv.reader(f)
point_history_classifier_labels = [
row[0] for row in point_history_classifier_labels
]
# FPS Measurement ########################################################
cvFpsCalc = CvFpsCalc(buffer_len=10)
# Coordinate history #################################################################
history_length = 16
point_history = deque(maxlen=history_length)
# Finger gesture history ################################################
finger_gesture_history = deque(maxlen=history_length)
# ########################################################################
mode = 0
hand_sign_id = -1
isStart = False
def reset():
isStart = False
Queue = []
successframe = 0 # success한 frame 개수가 몇개인지 판단.
preMatrix = np.empty((12,3))
curMatrix = np.empty((12,3))
moveMatrix = np.zeros((12,3))
confidence = np.zeros((2))
point_history = deque(maxlen=history_length)
hand_sign_id = -1
finger_gesture_history = deque(maxlen=history_length)
def n_vector(list1, list2):
fvector = list1-list2
x,y,z = fvector
norm = (x**2+y**2+z**2)**(1/2)
return [x/norm, y/norm, z/norm]
def vector_movement(moveMatrix, successframe, preMatrix, curMatrix):
curmove = abs(curMatrix - preMatrix)
moveMatrix = moveMatrix + ((curmove-moveMatrix)/successframe)
return moveMatrix
def calculate_final(moveMatrix,preMatrix,curMatrix,confidence,successframe):
# single point prediction update
posMatrix = np.zeros((12,3))
for j, pPoint in enumerate(zip(preMatrix,curMatrix)):
for i in range(3):
if pPoint[1][i] >pPoint[0][i] :
posMatrix[j][i] = 1
else:
posMatrix[j][i] = -1
# position = np.array([ 1 if i >j else -1 for cjoint, pjoint in enumerate(zip(preMatrix,curMatrix)) for i, j in enumerate(zip(cjoint, pjoint))])
predict = moveMatrix*posMatrix + preMatrix
# Compare current vector and predict vector and update
curMatrix = (predict * confidence[0] + curMatrix * confidence[1]) / (confidence[0]+confidence[1])
moveMatrix = vector_movement(moveMatrix,successframe,predict,curMatrix) # Single movement vector update
confidence[0] = (confidence[1]*confidence[0])/(confidence[1] + confidence[0])
preMatrix = curMatrix
def make_numpy(hand_handedness):
fingerlist = [1,2,3,4,5,6,7,8,9,10,11,12]
point = []
for i in fingerlist:
fingerPoint = [hand_handedness[i].x,hand_handedness[i].y,hand_handedness[i].z]
point.append(fingerPoint)
return np.array(point)
def calc_bounding_rect(image, landmarks):
image_width, image_height = image.shape[1], image.shape[0]
landmark_array = np.empty((0, 2), int)
for _, landmark in enumerate(landmarks.landmark):
landmark_x = min(int(landmark.x * image_width), image_width - 1)
landmark_y = min(int(landmark.y * image_height), image_height - 1)
landmark_point = [np.array((landmark_x, landmark_y))]
landmark_array = np.append(landmark_array, landmark_point, axis=0)
x, y, w, h = cv.boundingRect(landmark_array)
return [x, y, x + w, y + h]
def calc_landmark_list(image, landmarks):
image_width, image_height = image.shape[1], image.shape[0]
landmark_point = []
# Keypoint
for _, landmark in enumerate(landmarks.landmark):
landmark_x = min(int(landmark.x * image_width), image_width - 1)
landmark_y = min(int(landmark.y * image_height), image_height - 1)
# landmark_z = landmark.z
landmark_point.append([landmark_x, landmark_y])
return landmark_point
def pre_process_landmark(landmark_list):
temp_landmark_list = copy.deepcopy(landmark_list)
# Convert to relative coordinates
base_x, base_y = 0, 0
for index, landmark_point in enumerate(temp_landmark_list):
if index == 0:
base_x, base_y = landmark_point[0], landmark_point[1]
temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x
temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y
# Convert to a one-dimensional list
temp_landmark_list = list(
itertools.chain.from_iterable(temp_landmark_list))
# Normalization
max_value = max(list(map(abs, temp_landmark_list)))
def normalize_(n):
return n / max_value
temp_landmark_list = list(map(normalize_, temp_landmark_list))
return temp_landmark_list
def pre_process_point_history(image, point_history):
image_width, image_height = image.shape[1], image.shape[0]
temp_point_history = copy.deepcopy(point_history)
# Convert to relative coordinates
base_x, base_y = 0, 0
for index, point in enumerate(temp_point_history):
if index == 0:
base_x, base_y = point[0], point[1]
temp_point_history[index][0] = (temp_point_history[index][0] -
base_x) / image_width
temp_point_history[index][1] = (temp_point_history[index][1] -
base_y) / image_height
# Convert to a one-dimensional list
temp_point_history = list(
itertools.chain.from_iterable(temp_point_history))
return temp_point_history
def draw_landmarks(image, landmark_point):
if len(landmark_point) > 0:
# Thumb
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]),
(255, 255, 255), 2)
# Index finger
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]),
(255, 255, 255), 2)
# Middle finger
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]),
(255, 255, 255), 2)
# Ring finger
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]),
(255, 255, 255), 2)
# Little finger
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]),
(255, 255, 255), 2)
# Palm
cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]),
(255, 255, 255), 2)
# Key Points
for index, landmark in enumerate(landmark_point):
if index == 0: # 手首1
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 1: # 手首2
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 2: # 親指：付け根
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 3: # 親指：第1関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 4: # 親指：指先
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 5: # 人差指：付け根
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 6: # 人差指：第2関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 7: # 人差指：第1関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 8: # 人差指：指先
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 9: # 中指：付け根
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 10: # 中指：第2関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 11: # 中指：第1関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 12: # 中指：指先
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 13: # 薬指：付け根
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 14: # 薬指：第2関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 15: # 薬指：第1関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 16: # 薬指：指先
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 17: # 小指：付け根
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 18: # 小指：第2関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 19: # 小指：第1関節
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 20: # 小指：指先
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
return image
def draw_bounding_rect(use_brect, image):
return image
def draw_info_text(image, brect, handedness, hand_sign_text,
finger_gesture_text):
cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[1] - 22),
(0, 0, 0), -1)
info_text = handedness.classification[0].label[0:]
if hand_sign_text != "":
info_text = info_text + ':' + hand_sign_text
cv.putText(image, info_text, (brect[0] + 5, brect[1] - 4),
cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, cv.LINE_AA)
if finger_gesture_text != "":
cv.putText(image, "Finger Gesture:" + finger_gesture_text, (10, 60),
cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 4, cv.LINE_AA)
cv.putText(image, "Finger Gesture:" + finger_gesture_text, (10, 60),
cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 2,
cv.LINE_AA)
return image
def draw_point_history(image, point_history):
for index, point in enumerate(point_history):
if point[0] != 0 and point[1] != 0:
cv.circle(image, (point[0], point[1]), 1 + int(index / 2),
(152, 251, 152), 2)
return image
with mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
framenum = 0
isFirst = True
cap = cv2.VideoCapture(0)
isStart = True
while cap.isOpened():
framenum = framenum+1
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
gimage = cv.flip(image, 1)
if (results.multi_hand_landmarks and results.multi_hand_world_landmarks):
# print(len(results.multi_handedness))
Queue.append([results.multi_handedness, results.multi_hand_landmarks, results.multi_hand_world_landmarks])
else:
Queue.append(None)
if len(Queue) > Q_NUM:
q = Queue.pop(0)
if(isFirst == True):
pre_List.append(q)
pre_List.append(q)
pre_q = pre_List[0]
move = pre_List[1]
if(q is None or len(q)==0):
for i in Queue:
isFail = False
if(i is not None):
isFail = True
break
if(isFail):
q = pre_q
if q and pre_q is not None and len(q)!=0:
for i, (hand_handedness, hand_landmarks,hand_world_landmarks) in enumerate(zip(q[0],q[1],q[2])):
if len(q[0])==1 and hand_handedness.classification[0].score < CONF_THRESHOLD:
if pre_q is not None and len(list(pre_q))==1:
pre_mlh, pre_hand_landmarks,pre_hand_world_landmarks = pre_q
if pre_mlh.classification[0].score >= CONF_THRESHOLD:
hand_landmarks = pre_hand_landmarks
hand_world_landmarks = pre_hand_world_landmarks
if(hand_handedness.classification[0].label == "Left"):
# Bounding box calculation
brect = calc_bounding_rect(gimage, hand_world_landmarks)
# Landmark calculation
landmark_list = calc_landmark_list(gimage, hand_world_landmarks)
# Conversion to relative coordinates / normalized coordinates
pre_processed_landmark_list = pre_process_landmark(
landmark_list)
pre_processed_point_history_list = pre_process_point_history(
image, point_history)
# Hand sign classification
hand_sign_id = keypoint_classifier(pre_processed_landmark_list)
if(hand_sign_id == 0 and isStart == False):
print(hand_sign_id , isStart)
Queue = []
successframe = 0 # success한 frame 개수가 몇개인지 판단.
preMatrix = np.empty((12,3))
curMatrix = np.empty((12,3))
moveMatrix = np.zeros((12,3))
confidence = np.zeros((2))
point_history = deque(maxlen=history_length)
hand_sign_id = -1
finger_gesture_history = deque(maxlen=history_length)
isStart = True
if isStart == False:
# print(hand_sign_id , isStart)
break
if(hand_sign_id == 1):
isStart = False
break
print(hand_sign_id , isStart)
if hand_sign_id == 6: # Point gesture
point_history.append(landmark_list[8])
else:
point_history.append([0, 0])
# Finger gesture classification
finger_gesture_id = 0
point_history_len = len(pre_processed_point_history_list)
if point_history_len == (history_length * 2):
finger_gesture_id = point_history_classifier(
pre_processed_point_history_list)
# Calculates the gesture IDs in the latest detection
finger_gesture_history.append(finger_gesture_id)
most_common_fg_id = Counter(
finger_gesture_history).most_common()
# Drawing part
gimage = draw_bounding_rect(True, gimage)
gimage = draw_landmarks(gimage, landmark_list)
gimage = draw_info_text(
gimage,
brect,
hand_handedness,
keypoint_classifier_labels[hand_sign_id],
point_history_classifier_labels[most_common_fg_id[0][0]],
)
gimage = draw_point_history(gimage, point_history)
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
if isStart == False:
break
if(hand_handedness.classification[0].label == "Right"):
successframe = successframe+1
if successframe == 1 :
preMatrix = make_numpy(hand_world_landmarks.landmark)
continue
curMatrix = make_numpy(hand_world_landmarks.landmark)
if successframe == 2:
moveMatrix = vector_movement(moveMatrix, successframe-1, preMatrix, curMatrix)
confidence[0] = hand_handedness.classification[0].score
preMatrix = curMatrix
continue
confidence[1] = hand_handedness.classification[0].score
calculate_final(moveMatrix,preMatrix,curMatrix,confidence,successframe-1)
preMatrix = curMatrix
lefthandvector = n_vector(curMatrix[0],curMatrix[3])
headvector = n_vector(curMatrix[4],curMatrix[7])
righthandvector = n_vector(curMatrix[8],curMatrix[11])
cv2.putText(image, text="righthand "+str(righthandvector), org=(50,50), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255, 255, 255), thickness=2)
cv2.putText(image, text="head "+str(headvector), org=(100,100), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255, 255, 255), thickness=2)
cv2.putText(image, text="lefthand "+str(lefthandvector), org=(150,150), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255, 255, 255), thickness=2)
# data = struct.pack('<3f',*righthandvector)
# print(data.decode("utf-8"))
# conn.send(str(righthandvector[0]).encode()+str(righthandvector[1]).encode()+str(righthandvector[2]).encode())
# if not results.multi_hand_world_landmarks:
# if(framenum>100):
# pre_List[1] = q
# framenum =0
pre_List[0] = q
pre_List[1] = q
# Flip the image horizontally for a selfie-view display.
if cv2.waitKey(5) & 0xFF == 27:
# df = DataFrame.from_dict(data)
# if not os.path.exists('data2.csv'):
# df.to_csv('data2.csv', index=False, mode='w')
# else:
# df.to_csv('datapoint.csv', index=False, mode='w', header=False)
break
cap.release()
cv.destroyAllWindows()