Alert (no detections) (#3984)
* `Detections()` class `print()` overload * Update common.py
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@@ -307,7 +307,7 @@ class Detections:
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def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
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def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
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for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
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for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
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str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
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str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
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if pred is not None:
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if pred.shape[0]:
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for c in pred[:, -1].unique():
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for c in pred[:, -1].unique():
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n = (pred[:, -1] == c).sum() # detections per class
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n = (pred[:, -1] == c).sum() # detections per class
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str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
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str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
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@@ -318,6 +318,8 @@ class Detections:
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save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
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save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
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else: # all others
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else: # all others
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plot_one_box(box, im, label=label, color=colors(cls))
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plot_one_box(box, im, label=label, color=colors(cls))
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else:
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str += '(no detections)'
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im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
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im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
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if pprint:
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if pprint:
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