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class Detections: |
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class Detections: |
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# YOLOv5 detections class for inference results |
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# YOLOv5 detections class for inference results |
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def __init__(self, imgs, pred, files, times=None, names=None, shape=None): |
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def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None): |
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super().__init__() |
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super().__init__() |
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d = pred[0].device # device |
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d = pred[0].device # device |
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gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations |
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gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations |
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) |
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) |
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self.names = names # class names |
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self.names = names # class names |
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self.files = files # image filenames |
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self.files = files # image filenames |
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self.times = times # profiling times |
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self.xyxy = pred # xyxy pixels |
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self.xyxy = pred # xyxy pixels |
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels |
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels |
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized |
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized |
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def tolist(self): |
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def tolist(self): |
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# return a list of Detections objects, i.e. 'for result in results.tolist():' |
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# return a list of Detections objects, i.e. 'for result in results.tolist():' |
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x = [Detections([self.imgs[i]], [self.pred[i]], names=self.names, shape=self.s) for i in range(self.n)] |
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for d in x: |
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for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: |
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setattr(d, k, getattr(d, k)[0]) # pop out of list |
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r = range(self.n) # iterable |
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x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] |
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# for d in x: |
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# for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: |
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# setattr(d, k, getattr(d, k)[0]) # pop out of list |
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return x |
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return x |
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def __len__(self): |
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def __len__(self): |