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test_image.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle
import sys
sys.path.append('..')
import time
import cv2
import numpy as np
import tensorflow as tf
from scipy import misc
from packages import facenet, detect_face
sample_folder='testing_Images_data'
prediction="prediction_output"
modeldir = 'model'
classifier_filename = 'class/classifier.pkl'
npy='packages'
train_img="train_img"
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, npy)
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
frame_interval = 3
batch_size = 1000
image_size = 182
input_image_size = 160
HumanNames = os.listdir(train_img)
HumanNames.sort()
print('Loading feature extraction model')
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
classifier_filename_exp = os.path.expanduser(classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
# video_capture = cv2.VideoCapture("akshay_mov.mp4")
c = 0
print('Start Recognition!')
prevTime = 0
# ret, frame = video_capture.read()
i=0
ratio=0.0
for folder in os.listdir(sample_folder):
try:
os.mkdir(os.path.join(prediction,folder))
print ('creating folder at',os.path.join(prediction,folder))
except:
pass
length_of_img=float(len(os.listdir(os.path.join(sample_folder,folder))))
pred_len=0.0
for img_path in os.listdir(os.path.join(sample_folder,folder)):
#print ('imagepath:=>',img_path)
name=os.path.basename(img_path)
frame = cv2.imread(os.path.join(sample_folder,folder,img_path),0)
frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5) #resize frame (optional)
curTime = time.time()+1 # calc fps
timeF = frame_interval
if (c % timeF == 0):
find_results = []
if frame.ndim == 2:
frame = facenet.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
#print('Face Detected: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces,4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i][3] >= len(frame):
print('face is too close')
length_of_img=length_of_img-1
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size), interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = {images_placeholder: scaled_reshape[i], phase_train_placeholder: False}
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
#print(predictions)
best_class_indices = np.argmax(predictions, axis=1)
if HumanNames[best_class_indices[0]]==folder:
pred_len+=1
# print(best_class_indices)
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
#print(best_class_probabilities)
cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 255, 0), 2) #boxing face
#plot result idx under box
text_x = bb[i][0]
text_y = bb[i][3] + 20
#print('Result Indices: ', best_class_indices[0])
#print(HumanNames)
for H_i in HumanNames:
# print(H_i)
if HumanNames[best_class_indices[0]] == H_i:
result_names = HumanNames[best_class_indices[0]]
cv2.putText(frame, result_names, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255), thickness=1, lineType=2)
else:
print('Unable to align')
try:
cv2.imwrite(os.path.join(prediction,folder,name), frame)
except:
pass
print ('Accuracy for class '+folder+' is:',(pred_len/length_of_img))
ratio=ratio+(pred_len/length_of_img)
print ('final accuracy',ratio/4.0)