def video_feature():
val = request.json
value = val['images_placeholder']
np.set_printoptions(suppress=True, precision=20)
scaled_reshape = np.array(value)
sess = tf.Session()
with sess.as_default():
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]
embedding_arr = np.zeros((1, embedding_size))
feed_dict = {
images_placeholder: scaled_reshape,
phase_train_placeholder: False
}
embedding_arr[0, :] = sess.run(embeddings, feed_dict=feed_dict)
img_data = facenet.check_features(feature_list, embedding_arr[0], {
"name": "",
"cos_sim": 0
}, 0)
print("name : ", img_data["name"], "\nsimilarity : ", img_data["cos_sim"])
if img_data["cos_sim"] != 0:
img_data["cos_sim"] = img_data["cos_sim"][0]
return jsonify(img_data)
def video_feature():
val = request.json
value = val['images_placeholder']
np.set_printoptions(suppress=True, precision=20)
scaled_reshape = np.array(value)
global feat, feature_arr
embedding_arr = np.zeros((1, embedding_size))
feed_dict = {
images_placeholder: scaled_reshape,
phase_train_placeholder: False
}
embedding_arr[0, :] = sess.run(embeddings, feed_dict=feed_dict)
#사람이 등록되었을 경우와 등록 안되어있는 경우를 조건을 다르게 처리함
if feat:
img_data = facenet.check_features(feature_arr[0], embedding_arr[0])
else:
img_data = {"name": "", "cos_sim": 0}
print("name : ", img_data["name"], "\nsimilarity : ", img_data["cos_sim"])
return jsonify(img_data)
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
}
print("scaled_reshape type: ", type(scaled_reshape[i]))
print("scaled_reshape shape", scaled_reshape[i].shape)
print(scaled_reshape[i])
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
print("emb_array type:", type(emb_array))
img_data = facenet.check_features(feature_list, emb_array[0], {
"name": "",
"cos_sim": 0
}, 0)
print("name : ", img_data["name"], "\nsimilarity : ",
img_data["cos_sim"])
# predictions = model.predict_proba(emb_array)
# best_class_indices = np.argmax(predictions, axis=1)
# best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
# print(best_class_indices,' with accuracy ',best_class_probabilities)
# if best_class_probabilities>0.60:#ÀÌÁ¤µµ´Â µÇ¾î¾ß º»ÀÎÀ̶ó°í »ý°¢µÊ 0.53Àº ³Ê¹« ³·¾Æ
if img_data["cos_sim"] >= 0.5:
if button_flag[button_name.index(img_data["name"])] % 2 == 0:
cv2.rectangle(frame, (bb[i][0], bb[i][1]),
(bb[i][2], bb[i][3]), (0, 255, 0),
def show_frame():
print(button_flag)
_, cv2image = cap.read()
bounding_boxes, cv2image = detector.run_mtcnn(cv2image)
nrof_faces = bounding_boxes.shape[0]
print('Detected_FaceNum: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(cv2image.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]
#여기부터 add ------------>
face = cv2image[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2]]
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(
cv2image[0]) or bb[i][3] >= len(cv2image):
print('Face is very close! 0:', bb[i][0], ' 1:', bb[i][1],
' 2:', bb[i][2], ' 3:', bb[i][3])
continue
cropped.append(cv2image[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
}
#서버로 넘김.
#URL = "http://127.0.0.1:5000/video"
#tolist_img = scaled_reshape[i].tolist()
#json_feed = {'images_placeholder': tolist_img}
#response = requests.post(URL, data = json_feed)
#확인
#print("scaled_reshape type: ",type(scaled_reshape[i]))
#print("scaled_reshape shape", scaled_reshape[i].shape)
#print(scaled_reshape[i])
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
#print("emb_array type:", type(emb_array))
img_data = facenet.check_features(feature_list, emb_array[0], {
"name": "",
"cos_sim": 0
}, 0)
print("name : ", img_data["name"], "\nsimilarity : ",
img_data["cos_sim"])
##########################################################################################################
# #
# 현재 GUI에서 button부분이랑 연결이 안되서 우선 이렇게 밖으로 뺴서 얼굴부분은 모두 모자이크 처리하도록 했으요 #
# #
##########################################################################################################
cv2image[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2]] = cv2.blur(
cv2image[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2]], (23, 23))
if img_data["cos_sim"] >= 0.5:
if button_flag[button_name.index(img_data["name"])] % 2 == 0:
cv2.rectangle(cv2image, (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
cv2.putText(cv2image,
img_data["name"], (text_x, text_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255),
thickness=1,
lineType=2)
else:
cv2image[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2]] = cv2.blur(
cv2image[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2]],
(23, 23))
else:
cv2image[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2]]
cv2image = cv2.cvtColor(cv2image, cv2.COLOR_BGR2RGBA)
#cv2image = cv2.flip(cv2image, 1)
#face = cv2.flip(face, 1)
#face = cv2.cvtColor(face, cv2.COLOR_BGR2RGBA)
webcam_img = ImageTk.PhotoImage(image=Image.fromarray(cv2image))
mv_label.imgtk = webcam_img
mv_label.configure(image=webcam_img)
mv_label.after(10, show_frame)