diff --git a/detect.py b/detect.py index a249448..1da7335 100755 --- a/detect.py +++ b/detect.py @@ -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) diff --git a/ieemoo-ai-zhanting.py b/ieemoo-ai-zhanting.py index a1c70e3..171740e 100755 --- a/ieemoo-ai-zhanting.py +++ b/ieemoo-ai-zhanting.py @@ -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 diff --git a/stop.sh b/stop.sh index 4ecf0e6..b45e20d 100644 --- a/stop.sh +++ b/stop.sh @@ -1,2 +1,2 @@ #!/bin/bash -supervisorctl start ieemoo-ai-zhanting +supervisorctl stop ieemoo-ai-zhanting