updates
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6d8e82c175
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10
detect.py
10
detect.py
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@ -6,7 +6,7 @@ from utils.datasets import *
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from utils.utils import *
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def detect(save_txt=False, save_img=False, stream_img=False):
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def detect(save_txt=False, save_img=False, view_img=False):
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img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
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out, source, weights, half = opt.output, opt.source, opt.weights, opt.half
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webcam = source == '0' or source.startswith('rtsp') or source.startswith('http')
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@ -47,11 +47,11 @@ def detect(save_txt=False, save_img=False, stream_img=False):
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# Set Dataloader
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vid_path, vid_writer = None, None
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if streams:
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stream_img = False
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view_img = False
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torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=img_size, half=half)
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elif webcam:
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stream_img = True
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view_img = True
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dataset = LoadWebcam(source, img_size=img_size, half=half)
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else:
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save_img = True
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@ -95,14 +95,14 @@ def detect(save_txt=False, save_img=False, stream_img=False):
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with open(save_path + '.txt', 'a') as file:
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file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
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if save_img or stream_img: # Add bbox to image
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if save_img or view_img: # Add bbox to image
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label = '%s %.2f' % (classes[int(cls)], conf)
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
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print('%sDone. (%.3fs)' % (s, time.time() - t))
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# Stream results
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if stream_img:
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if view_img:
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cv2.imshow(p, im0)
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# Save results (image with detections)
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