updates
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@ -13,12 +13,13 @@ from tqdm import tqdm
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from utils.utils import xyxy2xywh, xywh2xyxy
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img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif']
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vid_formats = ['.mov', '.avi', '.mp4']
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class LoadImages: # for inference
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def __init__(self, path, img_size=416):
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self.height = img_size
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img_formats = ['.jpg', '.jpeg', '.png', '.tif']
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vid_formats = ['.mov', '.avi', '.mp4']
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files = []
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if os.path.isdir(path):
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@ -146,11 +147,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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self.augment = augment
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self.image_weights = image_weights
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self.rect = False if image_weights else rect
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self.label_files = [x.replace('images', 'labels').
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replace('.jpeg', '.txt').
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replace('.jpg', '.txt').
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replace('.bmp', '.txt').
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replace('.png', '.txt') for x in self.img_files]
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# Define labels
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self.label_files = [x.replace('images', 'labels') for x in self.img_files]
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for f in img_formats:
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self.label_files = [x.replace(f, '.txt') for x in self.label_files]
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# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
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if self.rect:
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@ -169,9 +170,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# Sort by aspect ratio
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ar = s[:, 1] / s[:, 0] # aspect ratio
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i = ar.argsort()
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ar = ar[i]
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self.img_files = [self.img_files[i] for i in i]
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self.label_files = [self.label_files[i] for i in i]
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ar = ar[i]
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# Set training image shapes
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shapes = [[1, 1]] * nb
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