def detect(self):
frame_cnt = -1
while self.vdo.grab():
frame_cnt += 1
start = time.time()
_, ori_im = self.vdo.retrieve()
# im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
im = ori_im
t1_begin = time.time()
bbox_xxyy, cls_conf, cls_ids = self.yolo3.predict(im)
t1_end = time.time()
t2_begin = time.time()
if bbox_xxyy is not None:
# select class cow
# mask = cls_ids == 0
# bbox_xxyy = bbox_xxyy[mask]
# bbox_xxyy[:, 3:] *= 1.2
# cls_conf = cls_conf[mask]
# bbox_xcycwh = bbox_xxyy
# print(" "*10, bbox_xcycwh.shape, cls_conf.shape)
detections = []
for i in range(len(bbox_xxyy)):
# print(bbox_xxyy[i][0].item(), bbox_xxyy[i][1].item(),
# bbox_xxyy[i][2].item(), bbox_xxyy[i][3].item(),
# cls_conf[i].tolist())
detections.append([
bbox_xxyy[i][0].item(), bbox_xxyy[i][1].item(),
bbox_xxyy[i][2].item(), bbox_xxyy[i][3].item(),
cls_conf[i].tolist()
])
# detections.append([*bbox_xcycwh[i].tolist(), cls_conf[i].tolist()])
# print("=" * 30, [*bbox_xcycwh[i], cls_conf[i]])
# print('-'*30, detections)
detections = torch.tensor(detections)
outputs = self.mot_tracker_sort.update(detections)
if len(outputs) > 0:
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
ori_im = draw_bboxes(ori_im, bbox_xyxy, identities)
t2_end = time.time()
end = time.time()
print(
"frame:%d|det:%.4f|sort:%.4f|total:%.4f|det p:%.2f%%|fps:%.2f"
% (frame_cnt, (t1_end - t1_begin), (t2_end - t2_begin),
(end - start), ((t1_end - t1_begin) * 100 /
((end - start))), (1 / (end - start))))
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
if self.args.save_path:
self.output.write(ori_im)
def detect(self):
frame_cnt = -1
while self.vdo.grab():
frame_cnt += 1
# skip frames every 3 frames
if frame_cnt % 3 == 0:
continue
start = time.time()
_, ori_im = self.vdo.retrieve()
# im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
im = ori_im
t1_begin = time.time()
bbox_xxyy, cls_conf, cls_ids = self.yolo3.predict(im)
t1_end = time.time()
t2_begin = time.time()
if bbox_xxyy is not None:
# select class cow
# mask = cls_ids == 0
# bbox_xxyy = bbox_xxyy[mask]
# bbox_xxyy[:, 3:] *= 1.2
# cls_conf = cls_conf[mask]
bbox_xcycwh = xyxy2xywh(bbox_xxyy)
outputs = self.deepsort.update(bbox_xcycwh, cls_conf, im)
if len(outputs) > 0:
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
ori_im = draw_bboxes(ori_im, bbox_xyxy, identities)
t2_end = time.time()
end = time.time()
print(
"frame:%d|det:%.4f|sort:%.4f|total:%.4f|det p:%.2f%%|fps:%.2f"
% (frame_cnt, (t1_end - t1_begin), (t2_end - t2_begin),
(end - start), ((t1_end - t1_begin) * 100 /
((end - start))), (1 / (end - start))))
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
if self.args.save_path:
self.output.write(ori_im)
def detect(self, outfile=None):
frame_cnt = -1
if outfile is not None:
f = open(outfile, 'w')
print("begin....")
while self.vdo.grab():
frame_cnt += 1
if frame_cnt % 3 == 0:
continue
start = time.time()
_, ori_im = self.vdo.retrieve()
im = ori_im
t1_begin = time.time()
bbox_xxyy, cls_conf, cls_ids = self.yolo3.predict(im)
t1_end = time.time()
t2_begin = time.time()
if bbox_xxyy is not None:
# select class
# mask = cls_ids == 0
# bbox_xxyy = bbox_xxyy[mask]
# bbox_xxyy[:, 3:] *= 1.2
# cls_conf = cls_conf[mask]
bbox_xcycwh = xyxy2xywh(bbox_xxyy)
outputs = self.deepsort.update(bbox_xcycwh, cls_conf, im)
if len(outputs) > 0:
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
# 画框
ori_im = draw_bboxes(ori_im, bbox_xyxy, identities)
# frame, id, tlwh(%.2f),1,-1,-1,-1
if outfile is not None:
box_xywh = xyxy2tlwh(bbox_xyxy)
for i in range(len(box_xywh)):
write_line = "%d,%d,%d,%d,%d,%d,1,-1,-1,-1\n" % (
frame_cnt + 1, outputs[i, -1],
int(box_xywh[i][0]), int(box_xywh[i][1]),
int(box_xywh[i][2]), int(box_xywh[i][3]))
f.write(write_line)
t2_end = time.time()
end = time.time()
print(
"frame:%d|det:%.4f|sort:%.4f|total:%.4f|det p:%.2f%%|fps:%.2f"
% (frame_cnt, (t1_end - t1_begin), (t2_end - t2_begin),
(end - start), ((t1_end - t1_begin) * 100 /
((end - start))), (1 / (end - start))))
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
if self.args.save_path:
self.output.write(ori_im)
if outfile is not None:
f.close()
def detect(self):
frame_no = -1
# skip_no = 2
if self.output_file:
f = open(output_file, "w")
while self.vidCap.grab():
frame_no += 1
# skip frames every n frames
# if frame_no % skip_no != 0:
# continue
# start time
total_begin = time.time()
_, img = self.vidCap.retrieve()
# yolov3部分
yolo_begin = time.time()
bbox_xyxy, cls_conf, cls_ids = self.yolov3.predict(img)
# [x1,y1,x2,y2]
yolo_end = time.time()
# deepsort部分
ds_begin = time.time()
if bbox_xyxy is not None:
bbox_cxcywh = xyxy2xywh(bbox_xyxy)
outputs = self.deepsort.update(bbox_cxcywh, cls_conf, img)
if len(outputs) > 0:
# [x1,y1,x2,y2] id
bbox_xyxy = outputs[:, :4]
ids = outputs[:, -1]
img = draw_bboxes(img, bbox_xyxy, ids)
# frame,id,tlwh,1,-1,-1,-1
if self.output_file:
bbox_tlwh = xyxy2xywh(bbox_xyxy)
for i in range(len(bbox_tlwh)):
write_line = "%d,%d,%d,%d,%d,%d,1,-1,-1,-1\n" % (
frame_no + 1, outputs[i, -1],
int(bbox_tlwh[i][0]), int(bbox_tlwh[i][1]),
int(bbox_tlwh[i][2]), int(bbox_tlwh[i][3]))
f.write(write_line)
ds_end = time.time()
total_end = time.time()
if frame_no % 500 == 0:
print("frame:%04d|det:%.4f|deep sort:%.4f|total:%.4f|det p:%.2f%%|fps:%.2f" % (frame_no,
(yolo_end - yolo_begin),
(ds_end - ds_begin),
(total_end - total_begin),
((yolo_end - yolo_begin) * 100 / (
total_end - total_begin)),
(1 / (total_end - total_begin))))
if self.display is True:
cv2.imshow("Test", img)
cv2.waitKey(1)
if self.save_path:
self.output.write(img)
if self.output_file:
f.close()
