def realse(stack):
print('Begin to get frame......')
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
detectors = net('onet')
mtcnnDetector = MtcnnDetector(detectors=detectors,
min_face_size=24,
threshold=[0.9, 0.6, 0.7])
while True:
if len(stack) > 30:
image = stack.pop()
image = cv2.resize(image,
(int(image.shape[1]), int(image.shape[0])))
image = np.array(image)
boxes_c, _ = mtcnnDetector.detect_video(image)
for bbox in boxes_c:
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
cv2.putText(image,
str(np.round(bbox[4], 2)),
(int(bbox[0]), int(bbox[1])),
cv2.FONT_HERSHEY_TRIPLEX,
0.3,
color=(0, 255, 0))
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255))
# cut = image[y1:y2, x1:x2]
# for i in range(len(boxes_c)):
# cv2.imwrite(str(i) + '.jpg', cut)
print('deteced face: ({},{}), ({},{})'.format(x1, y1, x2, y2))
cv2.imshow("Detected", image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
def recognize(stack):
logging.info("[INFO]:Starting video stream...")
# os.environ["CUDA_VISIBLE_DEVICES"] = '1'
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# session = tf.Session(config = config)
data = pickle.loads(
open('/home/lxz/project/faceid/alignment.pickle', "rb").read())
detectors = net('onet')
mtcnnDetector = MtcnnDetector(detectors=detectors,
min_face_size=60,
threshold=[0.9, 0.6, 0.7])
logging.info('MTCNN/KNN Model load sucessed !!!!')
while True:
if len(stack) > 20:
boxes = []
frame = stack.pop()
image = np.array(frame)
allBoxes, _ = mtcnnDetector.detect_video(image)
for box in allBoxes:
x_1 = int(box[0])
y_1 = int(box[1])
x_2 = int(box[2])
y_2 = int(box[3])
boxes.append((y_1 - 10, x_2 + 12, y_2 + 10, x_1 - 12))
logging.debug(boxes)
start = time.time()
# num_jitters（re-sample人脸的次数）参数的设定，数值越大精度相对会高，但是速度会慢；
encodings = face_recognition.face_encodings(frame,
boxes,
num_jitters=6)
end = time.time()
logging.info('[INFO]:Encoding face costed: {} s'.format(end -
start))
print('encode time is {}ms'.format((end - start) * 1000))
names = []
for encoding in encodings:
# distance between faces to consider it a match, optimize is 0.6
matches = face_recognition.compare_faces(data['encodings'],
encoding,
tolerance=0.35)
name = 'Stranger'
if True in matches:
matchesidx = [i for (i, b) in enumerate(matches) if b]
counts = {}
for i in matchesidx:
name = data['names'][i]
counts[name] = counts.get(name, 0) + 1
name = max(counts, key=counts.get)
logging.debug(name)
names.append(name)
# 绘制检测框 + 人脸识别结果
for ((top, right, bottom, left), name) in zip(boxes, names):
# print(name)
y1 = int(top)
x1 = int(right)
y2 = int(bottom)
x2 = int(left)
cv2.rectangle(frame, (x2, y1), (x1, y2), (0, 0, 255), 2)
if name == 'Stranger':
cv2.putText(frame, name, (x2, y1 - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
print(name)
cv2.putText(frame, name, (x2, y1 - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
cv2.imshow('Recognize-no-alignment', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
