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- import argparse
- from sys import platform
-
- from models import * # set ONNX_EXPORT in models.py
- from utils.datasets import *
- from utils.utils import *
-
-
- def detect(save_txt=False, save_img=False):
- img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
- out, source, weights, half, view_img = opt.output, opt.source, opt.weights, opt.half, opt.view_img
- webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
-
- # Initialize
- device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
- if os.path.exists(out):
- shutil.rmtree(out) # delete output folder
- os.makedirs(out) # make new output folder
-
- # Initialize model
- model = Darknet(opt.cfg, img_size)
-
- # Load weights
- attempt_download(weights)
- if weights.endswith('.pt'): # pytorch format
- model.load_state_dict(torch.load(weights, map_location=device)['model'])
- else: # darknet format
- _ = load_darknet_weights(model, weights)
-
- # Fuse Conv2d + BatchNorm2d layers
- # model.fuse()
-
- # Eval mode
- model.to(device).eval()
-
- # Export mode
- if ONNX_EXPORT:
- img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192)
- torch.onnx.export(model, img, 'weights/export.onnx', verbose=True)
- return
-
- # Half precision
- half = half and device.type != 'cpu' # half precision only supported on CUDA
- if half:
- model.half()
-
- # 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=img_size, half=half)
- else:
- save_img = True
- dataset = LoadImages(source, img_size=img_size, half=half)
-
- # Get classes and colors
- classes = load_classes(parse_data_cfg(opt.data)['names'])
- colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
-
- # Run inference
- t0 = time.time()
- for path, img, im0s, vid_cap in dataset:
- t = time.time()
-
- # Get detections
- img = torch.from_numpy(img).to(device)
- if img.ndimension() == 3:
- img = img.unsqueeze(0)
- pred, _ = model(img)
-
- if opt.half:
- pred = pred.float()
-
- for i, det in enumerate(non_max_suppression(pred, opt.conf_thres, opt.nms_thres)): # detections per image
- if webcam: # batch_size >= 1
- p, s, im0 = path[i], '%g: ' % i, im0s[i]
- else:
- p, s, im0 = path, '', im0s
-
- save_path = str(Path(out) / Path(p).name)
- s += '%gx%g ' % img.shape[2:] # print string
- 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, classes[int(c)]) # add to string
-
- # Write results
- for *xyxy, conf, _, cls in det:
- if save_txt: # Write to file
- with open(save_path + '.txt', 'a') as file:
- file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
-
- if save_img or view_img: # Add bbox to image
- label = '%s %.2f' % (classes[int(cls)], conf)
- plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
-
- print('%sDone. (%.3fs)' % (s, time.time() - t))
-
- # Stream results
- if view_img:
- cv2.imshow(p, im0)
-
- # 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 ' + out + ' ' + save_path)
-
- print('Done. (%.3fs)' % (time.time() - t0))
-
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
- parser.add_argument('--data', type=str, default='data/coco.data', help='coco.data file path')
- parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file')
- parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
- parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
- parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
- parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
- parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
- parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
- parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
- parser.add_argument('--view-img', action='store_true', help='display results')
- opt = parser.parse_args()
- print(opt)
-
- with torch.no_grad():
- detect()
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