def __init__(self, args):
self.args = args
# if args.display:
# cv2.namedWindow("test", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("test", args.display_width, args.display_height)
self.open_video()
#self.yolo3 = YOLOv3(args.yolo_cfg, args.yolo_weights, args.yolo_names,use_cuda=args.use_cuda, is_xywh=True, conf_thresh=args.conf_thresh, nms_thresh=args.nms_thresh)
self.command_type = args.mot_type
threshold = np.array([0.7, 0.8, 0.9])
crop_size = [112, 112]
if self.command_type == 'face':
self.mtcnn = MtcnnDetector(threshold, crop_size, args.detect_model)
elif self.command_type == 'person':
self.person_detect = RetinanetDetector(args)
self.deepsort = DeepSort(args.feature_model,
args.face_load_num,
use_cuda=args.use_cuda,
mot_type=self.command_type)
self.kf = KalmanFilter()
self.meanes_track = []
self.convariances_track = []
self.id_cnt_dict = dict()
self.moveTrack = MoveTrackerRun(self.kf)
self.img_clarity = BlurDetection()
self.score = 60.0
def __init__(self, args):
self.args = args
self.open_video()
#self.yolo3 = YOLOv3(args.yolo_cfg, args.yolo_weights, args.yolo_names,use_cuda=args.ctx, is_xywh=True, conf_thresh=args.conf_thresh, nms_thresh=args.nms_thresh)
self.command_type = args.mot_type
threshold = np.array([0.7, 0.8, 0.9])
crop_size = [112, 112]
if self.command_type == 'face':
self.mtcnn = MtcnnDetector(threshold, crop_size, args.detect_model)
elif self.command_type == 'person':
self.person_detect = RetinanetDetector(args)
elif self.command_type == 'head':
self.head_detect = HeadDetect(args)
self.kf = KalmanFilter()
self.deepsort = DeepSort(args.feature_model,
args.face_load_num,
mot_type=args.mot_type)
self.meanes_track = []
self.convariances_track = []
self.id_cnt_dict = dict()
self.tracker_run = TrackerRun(args.tracker_type)
self.moveTrack = MoveTrackerRun(self.kf)
self.img_clarity = BlurDetection()
self.score = 60.0
self.in_num = 0
self.out_num = 0
class MOTTracker(object):
def __init__(self, args):
self.args = args
# if args.display:
# cv2.namedWindow("test", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("test", args.display_width, args.display_height)
self.open_video()
#self.yolo3 = YOLOv3(args.yolo_cfg, args.yolo_weights, args.yolo_names,use_cuda=args.use_cuda, is_xywh=True, conf_thresh=args.conf_thresh, nms_thresh=args.nms_thresh)
self.command_type = args.mot_type
threshold = np.array([0.7, 0.8, 0.9])
crop_size = [112, 112]
if self.command_type == 'face':
self.mtcnn = MtcnnDetector(threshold, crop_size, args.detect_model)
elif self.command_type == 'person':
self.person_detect = RetinanetDetector(args)
self.deepsort = DeepSort(args.feature_model,
args.face_load_num,
use_cuda=args.use_cuda,
mot_type=self.command_type)
self.kf = KalmanFilter()
self.meanes_track = []
self.convariances_track = []
self.id_cnt_dict = dict()
self.moveTrack = MoveTrackerRun(self.kf)
self.img_clarity = BlurDetection()
self.score = 60.0
def open_video(self):
if not os.path.isfile(self.args.VIDEO_PATH):
raise Exception("Error:input video path is not exist")
self.vdo = cv2.VideoCapture(self.args.VIDEO_PATH)
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
if self.args.save_dir:
if not os.path.exists(self.args.save_dir):
os.makedirs(self.args.save_dir)
#fourcc = cv2.VideoWriter_fourcc(*'MJPG')
#self.output = cv2.VideoWriter(self.args.save_path, fourcc, 20, (self.im_width,self.im_height))
if not self.vdo.isOpened():
raise Exception('open video failed')
def xcycah2xcyc(self, xyah):
xyah = np.array(xyah)
xyah = xyah[:, :4]
w = xyah[:, 2] * xyah[:, 3]
h = xyah[:, 3]
xc = xyah[:, 0] #+ w/2
yc = xyah[:, 1] #+ h/2
return np.vstack([xc, yc, w, h]).T
def xcycah2xyxy(self, xcycah):
xcycah = np.array(xcycah)
xcycah = xcycah[:, :4]
w = xcycah[:, 2] * xcycah[:, 3]
h = xcycah[:, 3]
x2 = xcycah[:, 0] + w / 2
y2 = xcycah[:, 1] + h / 2
x1 = xcycah[:, 0] - w / 2
y1 = xcycah[:, 1] - h / 2
return np.vstack([x1, y1, x2, y2]).T
def xyxy2xcyc(self, xywh):
w = xywh[:, 2] - xywh[:, 0]
h = xywh[:, 3] - xywh[:, 1]
xc = xywh[:, 0] + w / 2
yc = xywh[:, 1] + h / 2
return np.vstack([xc, yc, w, h]).T
def xyxy2xywh(self, xywh):
w = xywh[:, 2] - xywh[:, 0]
h = xywh[:, 3] - xywh[:, 1]
return np.vstack([xywh[:, 0], xywh[:, 1], w, h]).T
def xywh2xcycwh(self, xywh):
xywh = np.array(xywh)
xc = xywh[:, 0] + xywh[:, 2] / 2
yc = xywh[:, 1] + xywh[:, 3] / 2
return np.vstack([xc, yc, xywh[:, 2], xywh[:, 3]]).T
def xywh2xyxy(self, xywh):
xywh = np.array(xywh)
x2 = xywh[:, 0] + xywh[:, 2]
y2 = xywh[:, 1] + xywh[:, 3]
return np.vstack([xywh[:, 0], xywh[:, 1], x2, y2]).T
def xcyc2xcycah(self, bbox_xcycwh):
bbox_xcycwh = np.array(bbox_xcycwh, dtype=np.float32)
xc = bbox_xcycwh[:, 0] #- bbox_xcycwh[:,2]/2
yc = bbox_xcycwh[:, 1] #- bbox_xcycwh[:,3]/2
a = bbox_xcycwh[:, 2] / bbox_xcycwh[:, 3]
return np.vstack([xc, yc, a, bbox_xcycwh[:, 3]]).T
def widerbox(self, boxes):
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
boxw = x2 - x1
boxh = y2 - y1
x1 = np.maximum(0, x1 - 0.3 * boxw)
y1 = np.maximum(0, y1 - 0.3 * boxh)
x2 = np.minimum(self.im_width, x2 + 0.3 * boxw)
y2 = np.minimum(self.im_height, y2 + 0.3 * boxh)
return np.vstack([x1, y1, x2, y2]).T
def save_track_results(self, bbox_xyxy, img, identities, offset=[0, 0]):
for i, box in enumerate(bbox_xyxy):
x1, y1, x2, y2 = [int(i) for i in box]
x1 += offset[0]
x2 += offset[0]
y1 += offset[1]
y2 += offset[1]
x1 = min(max(x1, 0), self.im_width - 1)
y1 = min(max(y1, 0), self.im_height - 1)
x2 = min(max(x2, 0), self.im_width - 1)
y2 = min(max(y2, 0), self.im_height - 1)
# box text and bar
id = str(identities[i]) if identities is not None else '0'
crop_img = img[y1:y2, x1:x2, :]
if self.img_clarity._blurrDetection(crop_img) > self.score:
tmp_cnt = self.id_cnt_dict.setdefault(id, 0)
self.id_cnt_dict[id] = tmp_cnt + 1
save_dir = os.path.join(self.args.save_dir, id)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir,
id + '_' + str(tmp_cnt) + '.jpg')
cv2.imwrite(save_path, crop_img)
else:
continue
def detect(self):
cnt = 0
update_fg = True
detect_fg = True
total_time = 0
outputs = []
while self.vdo.isOpened():
start = time.time()
_, ori_im = self.vdo.read()
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
im = np.array([im])
if cnt % 5 == 0 or detect_fg:
# bbox_xcycwh, cls_conf, cls_ids = self.yolo3(im)
# mask = cls_ids==0
# bbox_xcycwh = bbox_xcycwh[mask]
# bbox_xcycwh[:,3:] *= 1.2
# cls_conf = cls_conf[mask]
if self.command_type == 'face':
rectangles = self.mtcnn.detectFace(im, True)
rectangles = rectangles[0]
if len(rectangles) < 1:
continue
bboxes = rectangles[:, :4]
bboxes = self.widerbox(bboxes)
#
bbox_xcycwh = self.xyxy2xcyc(bboxes)
cls_conf = rectangles[:, 4]
elif self.command_type == 'person':
bboxes, cls_conf = self.person_detect.test_img_org(ori_im)
if len(bboxes) == 0:
continue
bbox_xcycwh = self.xywh2xcycwh(bboxes)
#outputs = bboxes #self.xywh2xyxy(bboxes)
update_fg = True
box_xcycah = self.xcyc2xcycah(bbox_xcycwh)
self.moveTrack.track_init(box_xcycah)
self.moveTrack.track_predict()
self.moveTrack.track_update(box_xcycah)
# detect_xywh = self.xyxy2xywh(bboxes) if self.command_type=='face' else bboxes
# self.tracker_run.init(ori_im,detect_xywh.tolist())
detect_fg = False
else:
if len(bbox_xcycwh) > 0:
start1 = time.time()
self.moveTrack.track_predict()
bbox_xcycwh = self.xcycah2xcyc(self.moveTrack.means_track)
#outputs = self.xcycah2xyxy(self.moveTrack.means_track)
# boxes_tmp = self.tracker_run.update(ori_im)
# bbox_xcycwh = self.xywh2xcycwh(boxes_tmp)
end1 = time.time()
print('only tracker time consume:', end1 - start1)
#outputs = self.xywh2xyxy(boxes_tmp)
update_fg = False
detect_fg = False
else:
detect_fg = True
if len(bbox_xcycwh) > 0:
outputs = self.deepsort.update(bbox_xcycwh, cls_conf, ori_im,
update_fg)
end = time.time()
consume = end - start
if len(outputs) > 0:
#outputs = rectangles
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1] #np.zeros(outputs.shape[0])
ori_im = draw_bboxes(ori_im, bbox_xyxy, identities)
#self.save_track_results(bbox_xyxy,ori_im,identities)
print("frame: {} time: {}s, fps: {}".format(
cnt, consume, 1 / (end - start)))
cnt += 1
cv2.imshow("test", ori_im)
c = cv2.waitKey(1) & 0xFF
if c == 27 or c == ord('q'):
break
#if self.args.save_path:
# self.output.write(ori_im)
total_time += consume
self.vdo.release()
cv2.destroyAllWindows()
print("video ave fps and total_time: ", cnt / total_time, total_time)
class MOTTracker(object):
def __init__(self, args):
self.args = args
self.open_video()
#self.yolo3 = YOLOv3(args.yolo_cfg, args.yolo_weights, args.yolo_names,use_cuda=args.ctx, is_xywh=True, conf_thresh=args.conf_thresh, nms_thresh=args.nms_thresh)
self.command_type = args.mot_type
threshold = np.array([0.7, 0.8, 0.9])
crop_size = [112, 112]
if self.command_type == 'face':
self.mtcnn = MtcnnDetector(threshold, crop_size, args.detect_model)
elif self.command_type == 'person':
self.person_detect = RetinanetDetector(args)
elif self.command_type == 'head':
self.head_detect = HeadDetect(args)
self.kf = KalmanFilter()
self.deepsort = DeepSort(args.feature_model,
args.face_load_num,
mot_type=args.mot_type)
self.meanes_track = []
self.convariances_track = []
self.id_cnt_dict = dict()
self.tracker_run = TrackerRun(args.tracker_type)
self.moveTrack = MoveTrackerRun(self.kf)
self.img_clarity = BlurDetection()
self.score = 60.0
self.in_num = 0
self.out_num = 0
def open_video(self):
if not os.path.isfile(self.args.VIDEO_PATH):
raise Exception("Error:input video path is not exist")
self.vdo = cv2.VideoCapture(self.args.VIDEO_PATH)
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
if self.args.save_dir:
if not os.path.exists(self.args.save_dir):
os.makedirs(self.args.save_dir)
#fourcc = cv2.VideoWriter_fourcc(*'MJPG')
#self.output = cv2.VideoWriter(self.args.save_path, fourcc, 20, (self.im_width,self.im_height))
if not self.vdo.isOpened():
raise Exception('open video failed')
def xyxy2xcyc(self, xywh):
w = xywh[:, 2] - xywh[:, 0]
h = xywh[:, 3] - xywh[:, 1]
xc = xywh[:, 0] + w / 2
yc = xywh[:, 1] + h / 2
return np.vstack([xc, yc, w, h]).T
def xyxy2xywh(self, xywh):
w = xywh[:, 2] - xywh[:, 0]
h = xywh[:, 3] - xywh[:, 1]
return np.vstack([xywh[:, 0], xywh[:, 1], w, h]).T
def xywh2xcycwh(self, xywh):
xywh = np.array(xywh)
xc = xywh[:, 0] + xywh[:, 2] / 2
yc = xywh[:, 1] + xywh[:, 3] / 2
return np.vstack([xc, yc, xywh[:, 2], xywh[:, 3]]).T
def widerbox(self, boxes):
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
boxw = x2 - x1
boxh = y2 - y1
x1 = np.maximum(0, x1 - 0.3 * boxw)
y1 = np.maximum(0, y1 - 0.3 * boxh)
x2 = np.minimum(self.im_width, x2 + 0.3 * boxw)
y2 = np.minimum(self.im_height, y2 + 0.3 * boxh)
return np.vstack([x1, y1, x2, y2]).T
def save_track_results(self, bbox_xyxy, img, identities, offset=[0, 0]):
for i, box in enumerate(bbox_xyxy):
x1, y1, x2, y2 = [int(i) for i in box]
x1 += offset[0]
x2 += offset[0]
y1 += offset[1]
y2 += offset[1]
x1 = min(max(x1, 0), self.im_width - 1)
y1 = min(max(y1, 0), self.im_height - 1)
x2 = min(max(x2, 0), self.im_width - 1)
y2 = min(max(y2, 0), self.im_height - 1)
# box text and bar
id = str(identities[i]) if identities is not None else '0'
crop_img = img[y1:y2, x1:x2, :]
if self.img_clarity._blurrDetection(crop_img) > self.score:
tmp_cnt = self.id_cnt_dict.setdefault(id, 0)
self.id_cnt_dict[id] = tmp_cnt + 1
save_dir = os.path.join(self.args.save_dir, id)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir,
id + '_' + str(tmp_cnt) + '.jpg')
cv2.imwrite(save_path, crop_img)
else:
continue
def show_bboxs(self, img, bbox, identities=None, fgs=[]):
imh, imw = img.shape[:2]
for i, box in enumerate(bbox):
box = list(map(int, box))
x1, y1, x2, y2 = box
h = y2 - y1
tm_fg = fgs[i]
if tm_fg == 0:
self.out_num += 1
elif tm_fg == 1:
self.in_num += 1
# box text and bar
id = int(identities[i]) if identities is not None else 0
color = COLORS_10[id % len(COLORS_10)]
label = '{}{:d}'.format("", id)
#t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 2 , 2)[0]
cv2.rectangle(img, (x1, y1), (x2, y2), color, 4)
#cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
#cv2.putText(img,label,(x1,y1+t_size[1]+4), 0, 1e-2*h, [255,255,255], 1)
cv2.putText(img, label, (x1, y1 + 4), 0, 1e-2 * h, [255, 255, 255],
4)
txt = "in:{} out:{}".format(self.in_num, self.out_num)
cv2.putText(img, txt, (imw - 100, 20), 0, 0.5, [0, 0, 255], 2)
cv2.line(img, (0, 150), (imw - 1, 150), (255, 0, 0), 2, 4)
return img
def detect(self):
cnt = 0
update_fg = True
detect_fg = True
total_time = 0
outputs = []
while self.vdo.isOpened():
start = time.time()
_, ori_im = self.vdo.read()
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
im = np.array([im])
if cnt % 20 == 0 or detect_fg:
# bbox_xcycwh, cls_conf, cls_ids = self.yolo3(im)
# mask = cls_ids==0
# bbox_xcycwh = bbox_xcycwh[mask]
# bbox_xcycwh[:,3:] *= 1.2
# cls_conf = cls_conf[mask]
if self.command_type == 'face':
rectangles = self.mtcnn.detectFace(im, True)
rectangles = rectangles[0]
if len(rectangles) < 1:
continue
bboxes = rectangles[:, :4]
bboxes = self.widerbox(bboxes)
#
bbox_xcycwh = self.xyxy2xcyc(bboxes)
cls_conf = rectangles[:, 4]
elif self.command_type == 'person':
bboxes, cls_conf = self.person_detect.test_img_org(ori_im)
if len(bboxes) == 0:
continue
bbox_xcycwh = self.xywh2xcycwh(bboxes)
elif self.command_type == 'head':
bboxes, cls_conf = self.head_detect.inference_img(ori_im)
if len(bboxes) == 0:
continue
bbox_xcycwh = self.xywh2xcycwh(bboxes)
#outputs = bboxes #self.xywh2xyxy(bboxes)
update_fg = True
# box_xcycah = self.xcyc2xcycah(bbox_xcycwh)
# self.moveTrack.track_init(box_xcycah)
# self.moveTrack.track_predict()
# self.moveTrack.track_update(box_xcycah)
detect_xywh = self.xyxy2xywh(
bboxes) if self.command_type == 'face' else bboxes
self.tracker_run.init(ori_im, detect_xywh.tolist())
detect_fg = False
else:
#print('************here')
if len(bbox_xcycwh) > 0:
#self.moveTrack.track_predict()
#bbox_xcycwh = self.xcycah2xcyc(self.means_track)
#outputs = self.xcycah2xyxy(self.moveTrack.means_track)
start1 = time.time()
boxes_tmp = self.tracker_run.update(ori_im)
bbox_xcycwh = self.xywh2xcycwh(boxes_tmp)
end1 = time.time()
print('only tracker time consume:', end1 - start1)
#outputs = self.xywh2xyxy(boxes_tmp)
update_fg = False
detect_fg = False
else:
detect_fg = True
if len(bbox_xcycwh) > 0:
outputs = self.deepsort.update(bbox_xcycwh, cls_conf, ori_im,
update_fg)
end = time.time()
consume = end - start
if len(outputs) > 0:
#outputs = rectangles
bbox_xyxy = outputs[:, :4]
identities = outputs[:, 4] #np.zeros(outputs.shape[0])
fgs = outputs[:, -1]
#ori_im = draw_bboxes(ori_im, bbox_xyxy, identities)
ori_im = self.show_bboxs(ori_im, bbox_xyxy, identities, fgs)
#self.save_track_results(bbox_xyxy,ori_im,identities)
print("frame: {} time: {}s, fps: {}".format(
cnt, consume, 1 / (end - start)))
cnt += 1
cv2.imshow("test", ori_im)
c = cv2.waitKey(1) & 0xFF
if c == 27 or c == ord('q'):
break
#if self.args.save_path:
# self.output.write(ori_im)
total_time += consume
self.vdo.release()
cv2.destroyAllWindows()
print("video ave fps and total_time: ", cnt / total_time, total_time)