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- import argparse
-
- from utils.datasets import *
- from utils.utils import *
-
-
- def detect(save_img=False):
- out, source, weights, view_img, save_txt, imgsz = \
- opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
- webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
-
- # Initialize
- device = torch_utils.select_device(opt.device)
- if os.path.exists(out):
- shutil.rmtree(out) # delete output folder
- os.makedirs(out) # make new output folder
- half = device.type != 'cpu' # half precision only supported on CUDA
-
- # Load model
- google_utils.attempt_download(weights)
- model = torch.load(weights, map_location=device)['model'].float() # load to FP32
- # torch.save(torch.load(weights, map_location=device), weights) # update model if SourceChangeWarning
- # model.fuse()
- model.to(device).eval()
- if half:
- model.half() # to FP16
-
- # Second-stage classifier
- classify = False
- if classify:
- modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
- modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
- modelc.to(device).eval()
-
- # Set Dataloader
- vid_path, vid_writer = None, None
- if webcam:
- view_img = True
- torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz)
- else:
- save_img = True
- dataset = LoadImages(source, img_size=imgsz)
-
- # Get names and colors
- names = model.names if hasattr(model, 'names') else model.modules.names
- colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
-
- # Run inference
- t0 = time.time()
- img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
- _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
- for path, img, im0s, vid_cap in dataset:
- img = torch.from_numpy(img).to(device)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- if img.ndimension() == 3:
- img = img.unsqueeze(0)
-
- # Inference
- t1 = torch_utils.time_synchronized()
- pred = model(img, augment=opt.augment)[0]
-
- # Apply NMS
- pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
- fast=True, classes=opt.classes, agnostic=opt.agnostic_nms)
- t2 = torch_utils.time_synchronized()
-
- # Apply Classifier
- if classify:
- pred = apply_classifier(pred, modelc, img, im0s)
-
- # Process detections
- for i, det in enumerate(pred): # detections per image
- if webcam: # batch_size >= 1
- p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
- else:
- p, s, im0 = path, '', im0s
-
- save_path = str(Path(out) / Path(p).name)
- s += '%gx%g ' % img.shape[2:] # print string
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- if det is not None and len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
- # Print results
- for c in det[:, -1].unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += '%g %ss, ' % (n, names[int(c)]) # add to string
-
- # Write results
- for *xyxy, conf, cls in det:
- if save_txt: # Write to file
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
- file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
-
- if save_img or view_img: # Add bbox to image
- label = '%s %.2f' % (names[int(cls)], conf)
- plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
-
- # Print time (inference + NMS)
- print('%sDone. (%.3fs)' % (s, t2 - t1))
-
- # Stream results
- if view_img:
- cv2.imshow(p, im0)
- if cv2.waitKey(1) == ord('q'): # q to quit
- raise StopIteration
-
- # Save results (image with detections)
- if save_img:
- if dataset.mode == 'images':
- cv2.imwrite(save_path, im0)
- else:
- if vid_path != save_path: # new video
- vid_path = save_path
- if isinstance(vid_writer, cv2.VideoWriter):
- vid_writer.release() # release previous video writer
-
- 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))
- vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
- vid_writer.write(im0)
-
- if save_txt or save_img:
- print('Results saved to %s' % os.getcwd() + os.sep + out)
- if platform == 'darwin': # MacOS
- os.system('open ' + save_path)
-
- print('Done. (%.3fs)' % (time.time() - t0))
-
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='model.pt path')
- parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
- parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder
- parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
- parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
- parser.add_argument('--device', default='', 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('--classes', nargs='+', type=int, help='filter by class')
- parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- opt = parser.parse_args()
- opt.img_size = check_img_size(opt.img_size)
- print(opt)
-
- with torch.no_grad():
- detect()
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