def main_function(cam_url, image_url, queue_reader, area):
'''
This is main function
'''
print("Cam URL: {}".format(cam_url))
print("Area: {}".format(area))
# Variables for tracking faces
frame_counter = 0
# Variables holding the correlation trackers and the name per faceid
list_of_trackers = TrackersList()
clear_tracking_folder()
# Model for human detection
print('Load YOLO model ...')
options = {
"model": "./cfg/yolo.cfg",
"load": "../models/yolo.weights",
"threshold": 0.5
}
detector = TFNet(options)
# Model for person re-id
body_extractor = BodyExtractor()
if image_url is not None:
imgcv = cv2.imread(image_url)
results = detector.return_predict(imgcv)
print(results)
imgcv = draw_results(imgcv, results)
print('Result drawn as ../test-data/result.jpg')
cv2.imwrite('../test-data/result.jpg', imgcv)
track_results = TrackerResultsDict()
predict_dict = {}
if Config.CALC_FPS:
start_time = time.time()
if args.cam_url is not None:
frame_reader = URLFrameReader(args.cam_url, scale_factor=1)
elif queue_reader is not None:
frame_reader = RabbitFrameReader(rabbit_mq, queue_reader)
else:
print('Empty Image Source')
return -1
video_out_fps, video_out_w, video_out_h, = frame_reader.get_info()
print(video_out_fps, video_out_w, video_out_h)
center = (int(video_out_w / 2), int(video_out_h / 2))
bbox = [
int(center[0] - Config.Frame.ROI_CROP[0] * video_out_w),
int(center[1] - video_out_h * Config.Frame.ROI_CROP[1]),
int(center[0] + Config.Frame.ROI_CROP[2] * video_out_w),
int(center[1] + Config.Frame.ROI_CROP[3] * video_out_h)
]
video_out = None
if Config.Track.TRACKING_VIDEO_OUT:
# new_w = abs(bbox[0] - bbox[2])
# new_h = abs(bbox[1] - bbox[3])
# print(new_w, new_h)
video_out = VideoHandle('../data/tracking_video_out.avi',
video_out_fps, int(video_out_w),
int(video_out_h))
dlib_c_tr_video = cv2.VideoWriter('../data/check_tracking_video.avi',
cv2.VideoWriter_fourcc(*'XVID'),
video_out_fps,
(int(video_out_w), int(video_out_h)))
try:
while True: # frame_reader.has_next():
frame = frame_reader.next_frame()
if frame is None:
print("Waiting for the new image")
trackers_return_dict, predict_trackers_dict = \
list_of_trackers.check_delete_trackers(None, rabbit_mq)
track_results.update_two_dict(trackers_return_dict)
predict_dict.update(predict_trackers_dict)
# list_of_trackers.trackers_history.check_time(None)
# if args.cam_url is not None:
# frame_reader = URLFrameReader(args.cam_url, scale_factor=1)
# elif queue_reader is not None:
# frame_reader = RabbitFrameReader(rabbit_mq, queue_reader)
# else:
# print('Empty Image Source')
# return -1
continue
print("Frame ID: %d" % frame_counter)
# frame = frame[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
if Config.Track.TRACKING_VIDEO_OUT:
video_out.tmp_video_out(frame)
if Config.CALC_FPS:
fps_counter = time.time()
tmpi = list_of_trackers.update_dlib_trackers(frame)
dlib_c_tr_video.write(tmpi)
if frame_counter % Config.Frame.FRAME_INTERVAL == 0:
start_time = time.time()
detected_bbs = []
results = detector.return_predict(frame)
print('Detect time: {}'.format(1 / (time.time() - start_time)))
for result in results:
if result['label'] != 'person':
continue
x1 = result['topleft']['x']
y1 = result['topleft']['y']
x2 = result['bottomright']['x']
y2 = result['bottomright']['y']
origin_bb = [x1, y1, x2, y2]
detected_bbs.append(origin_bb)
print(is_inner_bb(bbox, origin_bb))
if not is_inner_bb(bbox, origin_bb):
continue
if calc_bb_size(origin_bb) < 10000:
continue
bb_size = calc_bb_size(origin_bb)
body_image = frame[origin_bb[1]:origin_bb[3],
origin_bb[0]:origin_bb[2], :]
# body_emb = body_extractor.extract_feature(body_image)
# TODO: refractor matching_detected_face_with_trackers
matched_fid = list_of_trackers.matching_face_with_trackers(
frame, frame_counter, origin_bb, None, body_image,
body_extractor)
# Update list_of_trackers
list_of_trackers.update_trackers_list(
matched_fid, time.time(), origin_bb, body_image,
bb_size, area, frame_counter, None, body_extractor,
rabbit_mq)
# list_of_trackers.update_trackers_by_tracking(body_extractor,
# frame,
# area,
# frame_counter,
# detected_bbs,
# rabbit_mq)
trackers_return_dict, predict_trackers_dict = \
list_of_trackers.check_delete_trackers(None, rabbit_mq)
track_results.update_two_dict(trackers_return_dict)
predict_dict.update(predict_trackers_dict)
# Check extract trackers history time (str(frame_counter) + '_' + str(i))
# list_of_trackers.trackers_history.check_time(None)
frame_counter += 1
if Config.CALC_FPS:
print("FPS: %f" % (1 / (time.time() - fps_counter)))
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
if Config.Track.PREDICT_DICT_OUT:
PickleUtils.save_pickle(Config.PREDICTION_DICT_FILE, predict_dict)
except KeyboardInterrupt:
print('Keyboard Interrupt !!! Release All !!!')
# list_of_trackers.trackers_history.check_time(matcher)
if Config.CALC_FPS:
print('Time elapsed: {}'.format(time.time() - start_time))
print('Avg FPS: {}'.format(
(frame_counter + 1) / (time.time() - start_time)))
frame_reader.release()
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
video_out.release()
if Config.Track.PREDICT_DICT_OUT:
PickleUtils.save_pickle(Config.PREDICTION_DICT_FILE, predict_dict)
def generic_function(cam_url, queue_reader, area, face_extractor_model,
re_source, multi_thread):
'''
This is main function
'''
print("Generic function")
print("Cam URL: {}".format(cam_url))
print("Area: {}".format(area))
# Variables for tracking faces
frame_counter = 0
# Variables holding the correlation trackers and the name per faceid
tracking_dirs = glob.glob(Config.TRACKING_DIR + '/*')
if tracking_dirs == []:
number_of_existing_trackers = 0
else:
lof_int_trackid = [
int(tracking_dir.split('/')[-1]) for tracking_dir in tracking_dirs
]
number_of_existing_trackers = max(lof_int_trackid) + 1
imageid_to_keyid = {}
tracker_manager = TrackerManager(
area,
imageid_to_keyid=imageid_to_keyid,
current_id=number_of_existing_trackers)
if Config.Matcher.CLEAR_SESSION:
clear_session_folder()
global querying_top10_image_ids_queue
# mongodb_faceinfo.remove({})
# reg_dict = PickleUtils.read_pickle(Config.REG_IMAGE_FACE_DICT_FILE)
# if reg_dict is not None:
# for fid in reg_dict:
# mongodb_faceinfo.insert_one({'image_id': fid, 'face_id': reg_dict[fid]})
# print('Saved regdict in mongodb as collection regdict')
matcher = FaissMatcher()
matcher.build(
mongodb_faceinfo, imageid_to_keyid=imageid_to_keyid, use_image_id=True)
svm_matcher = None
if Config.Matcher.CLOSE_SET_SVM:
svm_matcher = SVMMatcher()
svm_matcher.build(mongodb_faceinfo)
track_results = TrackerResultsDict()
if Config.CALC_FPS:
start_time = time.time()
if cam_url is not None:
frame_reader = URLFrameReader(cam_url, scale_factor=1)
elif queue_reader is not None:
frame_reader = RabbitFrameReader(rabbit_mq, queue_reader)
elif args.anno_mode:
frame_reader = URLFrameReader('./nothing.mp4', scale_factor=1)
else:
print('Empty Image Source')
return -1
if not args.anno_mode:
video_out_fps, video_out_w, video_out_h, = frame_reader.get_info()
print(video_out_fps, video_out_w, video_out_h)
bbox = [
int(Config.Frame.ROI_CROP[0] * video_out_w),
int(Config.Frame.ROI_CROP[1] * video_out_h),
int(Config.Frame.ROI_CROP[2] * video_out_w),
int(Config.Frame.ROI_CROP[3] * video_out_h)
]
# bbox = [0, 0, video_out_w, video_out_h]
video_out = None
if Config.Track.TRACKING_VIDEO_OUT:
video_out = VideoHandle(time.time(), video_out_fps, int(video_out_w),
int(video_out_h))
# Turn on querying top 10 from queue
if Config.QUERY_TOP10_MODE:
thread = threading.Thread(target=give_this_id_10_closest_ids)
thread.daemon = True
thread.start()
frame_queue = []
lock = threading.Lock()
if multi_thread:
is_on = [True]
t = threading.Thread(target=(get_frames), args=(frame_queue, frame_reader, re_source, \
cam_url, queue_reader, lock, is_on, ))
t.start()
try:
while True:
ms_msg = rabbit_mq.receive_str(Config.Queues.MERGE)
ms_flag = 'merge'
if ms_msg is None:
ms_msg = rabbit_mq.receive_str(Config.Queues.SPLIT)
ms_flag = 'split'
if ms_msg is not None:
merge_anchor, merge_list = extract_info_from_json(ms_msg)
while merge_list != []:
image_id1 = merge_list.pop()
split_merge_id(ms_flag, image_id1, merge_anchor, matcher,
preprocessor, face_extractor,
tracker_manager, mongodb_dashinfo,
mongodb_faceinfo, mongodb_mslog)
continue
action_msg = rabbit_mq.receive_str(Config.Queues.ACTION)
if action_msg is not None:
return_dict = json.loads(action_msg)
print('Receive: {}'.format(return_dict))
if return_dict['actionType'] == 'getNearest':
lock.acquire()
querying_top10_image_ids_queue.append(return_dict['data'])
lock.release()
continue
if args.anno_mode:
print('Annotation Mode, waiting for new tasks ...')
time.sleep(1)
continue
if multi_thread:
if len(frame_queue) > 0:
lock.acquire()
frame = frame_queue.pop(0)
lock.release()
else:
frame = None
else:
frame = frame_reader.next_frame()
tracker_manager.update_trackers(frame)
#do this before check_and_recognize tracker (sync local matcher vs mongodb)
trackers_return_dict, recognized_results = update_recognition(
self, matcher, svm_matcher, mongodb_faceinfo)
for tracker, matching_result_dict in recognized_results:
handle_results(tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid, \
dump=False)
# trackers_return_dict = tracker_manager.find_and_process_end_track(mongodb_faceinfo)
track_results.merge(trackers_return_dict)
tracker_manager.long_term_history.check_time(
matcher, mongodb_faceinfo)
if frame is None:
print("Waiting for the new image")
# if Config.Track.RECOGNIZE_FULL_TRACK:
# overtime_track_ids = tracker_manager.find_overtime_current_trackers(
# time_last=Config.Track.CURRENT_EXTRACR_TIMER-5,
# find_unrecognized=True
# )
# for overtime_track_id in overtime_track_ids:
# checking_tracker, top_predicted_face_ids, matching_result_dict = \
# tracker_manager.check_and_recognize_tracker(
# matcher,
# overtime_track_id,
# mongodb_faceinfo,
# svm_matcher)
# handle_results(checking_tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid,\
# dump=False)
if re_source and not multi_thread:
print('Trying to connect the stream again ...')
if cam_url is not None:
frame_reader = URLFrameReader(cam_url, scale_factor=1)
elif queue_reader is not None:
frame_reader = RabbitFrameReader(
rabbit_mq, queue_reader)
else:
print('Empty Image Source')
return -1
break
print("Frame ID: %d" % frame_counter)
if "_rotate" in video:
# rotate cw
rotation = int(video.split("_")[-1].split(".")[0])
frame = rotate_image_90(frame, rotation)
if Config.Track.TRACKING_VIDEO_OUT:
video_out.tmp_video_out(frame)
if Config.CALC_FPS:
fps_counter = time.time()
if frame_counter % Config.Frame.FRAME_INTERVAL == 0:
# crop frame
#frame = frame[bbox[1]:bbox[3], bbox[0]:bbox[2],:]
origin_bbs, points = detector.detect_face(frame)
if len(origin_bbs) > 0:
origin_bbs = [origin_bb[:4] for origin_bb in origin_bbs]
display_and_padded_faces = [
CropperUtils.crop_display_face(frame, origin_bb)
for origin_bb in origin_bbs
]
#cropped_faces = [CropperUtils.crop_face(frame, origin_bb) for origin_bb in origin_bbs]
preprocessed_images = [
preprocessor.process(
CropperUtils.crop_face(frame, origin_bb))
for origin_bb in origin_bbs
]
embeddings_array, _ = face_extractor.extract_features_all_at_once(
preprocessed_images)
for i, origin_bb in enumerate(origin_bbs):
display_face, str_padded_bbox = display_and_padded_faces[i]
#cropped_face = CropperUtils.crop_face(frame, origin_bb)
# Calculate embedding
preprocessed_image = preprocessed_images[i]
# preprocessed_image = align_preprocessor.process(frame, points[:,i], aligner, 160)
emb_array = np.asarray([embeddings_array[i]])
face_info = FaceInfo(
#oigin_bb.tolist(),
#emb_array,
frame_counter,
origin_bb,
points[:, i]
#display_face,
#str_padded_bbox,
#points[:,i].tolist()
)
is_good_face = handle_filters(points[:, i], coeff_extractor,
face_info, preprocessed_image)
face_info.is_good = is_good_face
# TODO: refractor matching_detected_face_with_trackers
matched_track_id = tracker_manager.track(face_info)
if not face_info.is_good:
print('BAD FACE')
continue
# Update tracker_manager
tracker_manager.update(matched_track_id, frame, face_info)
checking_tracker = None
# if not Config.Track.RECOGNIZE_FULL_TRACK:
# checking_tracker, top_predicted_face_ids, matching_result_dict = \
# tracker_manager.check_and_recognize_tracker(
# matcher,
# matched_track_id,
# mongodb_faceinfo,
# svm_matcher)
# handle_results(checking_tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid, \
# dump=True)
# if Config.Track.RECOGNIZE_FULL_TRACK:
# overtime_track_ids = tracker_manager.find_overtime_current_trackers(
# time_last=Config.Track.CURRENT_EXTRACR_TIMER-5,
# find_unrecognized=True
# )
# for overtime_track_id in overtime_track_ids:
# checking_tracker, top_predicted_face_ids, matching_result_dict = \
# tracker_manager.check_and_recognize_tracker(
# matcher,
# overtime_track_id,
# mongodb_faceinfo,
# svm_matcher)
# handle_results(checking_tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid, \
# dump=False)
frame_counter += 1
if Config.CALC_FPS:
print("FPS: %f" % (1 / (time.time() - fps_counter)))
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
Config.Track.CURRENT_EXTRACR_TIMER = 0
trackers_return_dict = tracker_manager.find_and_process_end_track(
mongodb_faceinfo)
Config.Track.HISTORY_CHECK_TIMER = 0
Config.Track.HISTORY_EXTRACT_TIMER = 0
tracker_manager.long_term_history.check_time(matcher, mongodb_faceinfo)
except KeyboardInterrupt:
if multi_thread:
is_on[0] = False
t.join()
print('Keyboard Interrupt !!! Release All !!!')
Config.Track.CURRENT_EXTRACR_TIMER = 0
trackers_return_dict = tracker_manager.find_and_process_end_track(
mongodb_faceinfo)
Config.Track.HISTORY_CHECK_TIMER = 0
Config.Track.HISTORY_EXTRACT_TIMER = 0
tracker_manager.long_term_history.check_time(matcher, mongodb_faceinfo)
if Config.CALC_FPS:
print('Time elapsed: {}'.format(time.time() - start_time))
print('Avg FPS: {}'.format(
(frame_counter + 1) / (time.time() - start_time)))
frame_reader.release()
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
video_out.release()
def generic_function(cam_url, queue_reader, area, face_extractor_model,
re_source):
'''
This is main function
'''
print("Generic function")
print("Cam URL: {}".format(cam_url))
print("Area: {}".format(area))
# TODO: init logger, modulize this?
# Variables for tracking faces
frame_counter = 0
if Config.Matcher.CLEAR_SESSION:
clear_session_folder()
if Config.Mode.CALC_FPS:
start_time = time.time()
if cam_url is not None:
frame_reader = URLFrameReader(cam_url)
else:
print('Empty Image Source')
return -1
video_out_fps, video_out_w, video_out_h, = frame_reader.get_info()
print(video_out_fps, video_out_w, video_out_h)
video_out = None
if Config.Track.TRACKING_VIDEO_OUT:
video_out = VideoHandle(time.time(), video_out_fps, int(video_out_w),
int(video_out_h))
db = DashboardDatabase(use_image_id=True)
rabbit_mq = RabbitMQ((Config.Rabbit.USERNAME, Config.Rabbit.PASSWORD),
(Config.Rabbit.IP_ADDRESS, Config.Rabbit.PORT))
matcher = KdTreeMatcher()
matcher.build(db)
# find current track
import glob
tracking_dirs = glob.glob(Config.Dir.TRACKING_DIR + '/*')
if tracking_dirs == []:
number_of_existing_trackers = 0
else:
lof_int_trackid = [
int(tracking_dir.split('/')[-1]) for tracking_dir in tracking_dirs
]
number_of_existing_trackers = max(lof_int_trackid) + 1
mode = 'video' # video, live
'''
# Feature 1: Find Merge Split
splitMerge = pipe.SplitMergeThread(database=db, rabbit_mq=rabbit_mq, matcher=matcher)
splitMerge.daemon = True
splitMerge.start()
# Feature 2: Find similar
findSimilarFaceThread = pipe.FindSimilarFaceThread(database=db, rabbit_mq=rabbit_mq)
findSimilarFaceThread.daemon = True
findSimilarFaceThread.start()
'''
# main program stage
stageDetectFace = pipe.Stage(pipe.FaceDetectWorker, 1)
stagePreprocess = pipe.Stage(pipe.PreprocessDetectedFaceWorker, 1)
stageDistributor = pipe.Stage(pipe.FaceDistributorWorker, 1)
stageExtract = pipe.Stage(pipe.FaceExtractWorker, 1)
stageTrack = pipe.Stage(pipe.FullTrackTrackingWorker,
1,
area=area,
database=db,
matcher=matcher,
init_tracker_id=number_of_existing_trackers)
stageResultToTCH = pipe.Stage(pipe.SendToDashboardWorker,
1,
database=db,
rabbit_mq=rabbit_mq)
stageStorage = pipe.Stage(pipe.DashboardStorageWorker, 1)
stageDatabase = pipe.Stage(pipe.DashboardDatabaseWorker, 1, database=db)
stageDetectFace.link(stagePreprocess)
stagePreprocess.link(stageDistributor)
stageDistributor.link(stageExtract)
stageExtract.link(stageTrack)
stageTrack.link(stageResultToTCH)
stageTrack.link(stageStorage)
stageTrack.link(stageDatabase)
if Config.Track.TRACKING_VIDEO_OUT:
stageVideoOut = pipe.Stage(pipe.VideoWriterWorker,
1,
database=db,
video_out=video_out)
stageTrack.link(stageVideoOut)
pipeline = pipe.Pipeline(stageDetectFace)
print('Begin')
try:
while frame_reader.has_next():
#continue
frame = frame_reader.next_frame()
if frame is None:
if mode == 'video':
print("Wait for executor to finish it jobs")
pipeline.put(None)
break
if mode == 'live':
if re_source:
print('Trying to connect the stream again ...')
if cam_url is not None:
frame_reader = URLFrameReader(cam_url,
scale_factor=1,
should_crop=True)
continue
print('Read frame', frame_counter, frame.shape)
if frame_counter % Config.Frame.FRAME_INTERVAL == 0:
# timer = Timer(frame_counter)
task = pipe.Task(pipe.Task.Frame)
task.package(frame=frame, frame_info=frame_counter)
pipeline.put(task)
# pipeline.put((frame, frame_counter, timer))
frame_counter += 1
print('Time elapsed: {}'.format(time.time() - start_time))
print('Avg FPS: {}'.format(
(frame_counter + 1) / (time.time() - start_time)))
frame_reader.release()
'''
splitMerge.join()
findSimilarFaceThread.join()
'''
except KeyboardInterrupt:
if Config.Track.TRACKING_VIDEO_OUT:
video_out.release_tmp()
pipeline.put(None)
print('Keyboard Interrupt !!! Release All !!!')
print('Time elapsed: {}'.format(time.time() - start_time))
print('Avg FPS: {}'.format(
(frame_counter + 1) / (time.time() - start_time)))
frame_reader.release()
'''