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- import argparse
- import time
- from pathlib import Path
-
- import cv2
- import torch
- import torch.backends.cudnn as cudnn
- from numpy import random
-
- from models.experimental import attempt_load
- from utils.datasets import LoadStreams, LoadImages
- from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
- scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
- from utils.plots import colors, plot_one_box
- from utils.torch_utils import select_device, load_classifier, time_synchronized
-
-
- def detect(opt):
- source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
- save_img = not opt.nosave and not source.endswith('.txt') # save inference images
- webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
- ('rtsp://', 'rtmp://', 'http://', 'https://'))
-
- # Directories
- save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Initialize
- set_logging()
- device = select_device(opt.device)
- half = device.type != 'cpu' # half precision only supported on CUDA
-
- # Load model
- model = attempt_load(weights, map_location=device) # load FP32 model
- stride = int(model.stride.max()) # model stride
- imgsz = check_img_size(imgsz, s=stride) # check img_size
- names = model.module.names if hasattr(model, 'module') else model.names # get class names
- if half:
- model.half() # to FP16
-
- # Second-stage classifier
- classify = False
- if classify:
- modelc = load_classifier(name='resnet101', n=2) # initialize
- modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
-
- # Set Dataloader
- vid_path, vid_writer = None, None
- if webcam:
- view_img = check_imshow()
- cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz, stride=stride)
- else:
- dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
- # Run inference
- if device.type != 'cpu':
- model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
- t0 = time.time()
- 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 = time_synchronized()
- pred = model(img, augment=opt.augment)[0]
-
- # Apply NMS
- pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
- t2 = 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, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
- else:
- p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
-
- p = Path(p) # to Path
- save_path = str(save_dir / p.name) # img.jpg
- txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
- s += '%gx%g ' % img.shape[2:] # print string
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- if 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 += 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 opt.save_crop or view_img: # Add bbox to image
- c = int(cls) # integer class
- label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
-
- plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
- if opt.save_crop:
- save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
-
- # 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 ''
- print(f"Results saved to {save_dir}{s}")
-
- print(f'Done. ({time.time() - t0:.3f}s)')
-
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
- parser.add_argument('--source', type=str, default='data/images', 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=0.25, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
- 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('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
- 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')
- parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
- parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
- parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
- opt = parser.parse_args()
- print(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=opt)
- strip_optimizer(opt.weights)
- else:
- detect(opt=opt)
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