updates
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@ -187,6 +187,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# Preload labels (required for weighted CE training)
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self.imgs = [None] * n
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self.labels = [None] * n
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preload_labels = False
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if preload_labels:
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self.labels = [np.zeros((0, 5))] * n
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iter = tqdm(self.label_files, desc='Reading labels') if n > 10 else self.label_files
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extract_bounding_boxes = False
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@ -206,7 +209,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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img = cv2.imread(str(p))
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h, w, _ = img.shape
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for j, x in enumerate(l):
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f = '%s%sclassification%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
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f = '%s%sclassification%s%g_%g_%s' % (
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p.parent.parent, os.sep, os.sep, x[0], j, p.name)
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if not os.path.exists(Path(f).parent):
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os.makedirs(Path(f).parent) # make new output folder
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box = xywh2xyxy(x[1:].reshape(-1, 4)).ravel()
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@ -215,7 +219,6 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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int(box[0] * w):int(box[2] * w)])
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if not result:
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print('stop')
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except:
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pass # print('Warning: missing labels for %s' % self.img_files[i]) # missing label file
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assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.'
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@ -274,9 +277,12 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# Load labels
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labels = []
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if os.path.isfile(label_path):
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# with open(label_path, 'r') as f:
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# x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
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x = self.labels[index]
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if x is None: # labels not preloaded
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with open(label_path, 'r') as f:
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x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
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self.labels[index] = x # save for next time
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if x.size > 0:
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# Normalized xywh to pixel xyxy format
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labels = x.copy()
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