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lichen
2022-04-12 09:43:39 +08:00
parent c1d82fafc4
commit bedb982d73
3 changed files with 72 additions and 71 deletions
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137
detect.py
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@ -100,44 +100,44 @@ def detect(opt, save_img=False):
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# Stream results
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path += '.mp4'
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer.write(im0)
# # Write results
# for *xyxy, conf, cls in reversed(det):
# if save_txt: # Write to file
# xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
# line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
# with open(txt_path + '.txt', 'a') as f:
# f.write(('%g ' * len(line)).rstrip() % line + '\n')
#
# if save_img or view_img: # Add bbox to image
# label = f'{names[int(cls)]} {conf:.2f}'
# plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
#
# # Print time (inference + NMS)
# print(f'{s}Done. ({t2 - t1:.3f}s)')
#
# # Stream results
# if view_img:
# cv2.imshow(str(p), im0)
# cv2.waitKey(1) # 1 millisecond
#
# # Save results (image with detections)
# if save_img:
# if dataset.mode == 'image':
# cv2.imwrite(save_path, im0)
# else: # 'video' or 'stream'
# if vid_path != save_path: # new video
# vid_path = save_path
# if isinstance(vid_writer, cv2.VideoWriter):
# vid_writer.release() # release previous video writer
# if vid_cap: # video
# fps = vid_cap.get(cv2.CAP_PROP_FPS)
# w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# else: # stream
# fps, w, h = 30, im0.shape[1], im0.shape[0]
# save_path += '.mp4'
# vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
# vid_writer.write(im0)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
@ -158,33 +158,34 @@ def detect(opt, save_img=False):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
#parser.add_argument('--weights', nargs='+', type=str, default='runs/zhanting/yolov5s_finetune/exp12/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--weights', nargs='+', type=str, default='runs/zhanting/yolov5m_finetune/exp4/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='data/pic4', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='0,1', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
print("opt:",opt)
check_requirements(exclude=('pycocotools', 'thop'))
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect(opt)
strip_optimizer(opt.weights)
else:
detect(opt,True)
pass
# parser = argparse.ArgumentParser()
# #parser.add_argument('--weights', nargs='+', type=str, default='runs/zhanting/yolov5s_finetune/exp12/weights/best.pt', help='model.pt path(s)')
# parser.add_argument('--weights', nargs='+', type=str, default='runs/zhanting/yolov5m_finetune/exp4/weights/best.pt', help='model.pt path(s)')
# parser.add_argument('--source', type=str, default='data/pic4', help='source') # file/folder, 0 for webcam
# parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
# parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
# parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
# parser.add_argument('--device', default='0,1', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
# parser.add_argument('--view-img', action='store_true', help='display results')
# parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
# parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
# parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
# parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
# parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
# parser.add_argument('--augment', action='store_true', help='augmented inference')
# parser.add_argument('--update', action='store_true', help='update all models')
# parser.add_argument('--project', default='runs/detect', help='save results to project/name')
# parser.add_argument('--name', default='exp', help='save results to project/name')
# parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
# opt = parser.parse_args()
# print("opt:",opt)
# check_requirements(exclude=('pycocotools', 'thop'))
#
# with torch.no_grad():
# if opt.update: # update all models (to fix SourceChangeWarning)
# for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
# detect(opt)
# strip_optimizer(opt.weights)
# else:
# detect(opt,True)

4
ieemoo-ai-zhanting.py
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@ -69,9 +69,9 @@ def get_isempty():
print('now_time', now_time)
print('get date use time: {0:.2f}s'.format(getdateend - start))
except:
return repr(pred)
return pred
return repr(pred)
return pred

2
stop.sh
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@ -1,2 +1,2 @@
#!/bin/bash
supervisorctl start ieemoo-ai-zhanting
supervisorctl stop ieemoo-ai-zhanting