def extract_tracked_frames():
gpu_id = 1
ctx = mx.gpu(gpu_id)
video_path = '../../dataset/OptFace/CompressedVideo'
detector = Detector('../../model/faster_rcnn/mxnet-face-fr50', ctx)
img_id_map = {}
img_cnt = 0
cls_cnt = 0
for video_file in os.listdir(video_path):
frames_dir = os.path.join('../../dataset/OptFace/tracked_frames',
video_file)
if not os.path.exists(frames_dir):
os.mkdir(frames_dir)
print('processing video: %s' % video_file)
video_dir = os.path.join(video_path, video_file)
video_capture = cv2.VideoCapture(video_dir)
fps = video_capture.get(cv2.CAP_PROP_FPS)
size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), \
int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
num_frame = 0
tracker = sort.Sort()
while video_capture.grab():
flag, frame = video_capture.retrieve()
det = detector.detect_face(frame)
ids = tracker.update(det)
# if ids.shape[0] > 0:
# print(ids)
sticked_img, crops = Function.stick_boxes(frame, ids)
for i in range(ids.shape[0]):
cur_id = int(ids[i][4])
if img_id_map.get(cur_id) is None:
img_id_map[cur_id] = cls_cnt
cls_cnt += 1
dump_crop(frames_dir, crops[i], img_id_map[cur_id], img_cnt)
img_cnt += 1
"""
recognizer.load_train_data(train_data, label, reverse_cls_map)
img_id_map = {}
img_cnt = 0
cls_cnt = 0
video_list = [l.strip() for l in open(video_list)]
for video_file in video_list[0:1]:
print('processing video: %s' % video_file)
video_dir = os.path.join(video_path, video_file)
video_capture = cv2.VideoCapture(video_dir)
fps = video_capture.get(cv2.CAP_PROP_FPS)
size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), \
int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
num_frame = 0
tracker = sort.Sort()
while video_capture.grab():
flag, frame = video_capture.retrieve()
det = detector.detect_face(frame)
ids = tracker.update(det)
sticked_img, crops = Function.stick_boxes(frame, ids)
for crop in crops:
crop_name = recognizer.predict(crop)
print('crop name: %s' % (crop_name))
# sys.exit()
cv2.imshow('a', sticked_img)
if cv2.waitKey(5) == ord('q'):
break