updates
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@ -444,6 +444,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + padh
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labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + padh
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if self.augment:
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if self.augment:
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# Augment colorspace
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augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v'])
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# Augment imagespace
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# Augment imagespace
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g = 0.0 if mosaic else 1.0 # do not augment mosaics
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g = 0.0 if mosaic else 1.0 # do not augment mosaics
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hyp = self.hyp
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hyp = self.hyp
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@ -511,11 +514,6 @@ def load_image(self, index):
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if self.augment and r < 1: # if training (NOT testing), downsize to inference shape
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if self.augment and r < 1: # if training (NOT testing), downsize to inference shape
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h, w, _ = img.shape
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h, w, _ = img.shape
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img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
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img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
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# Augment colorspace
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if self.augment:
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augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v'])
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return img
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return img
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