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| | | import argparse |
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| | | import cv2 |
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| | | import imgaug as ia |
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| | | from imgaug import augmenters as iaa |
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| | | self.visible = False |
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| | | |
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| | | def main(): |
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| | | def main(args): |
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| | | random.seed() |
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| | | ia.seed(random.randrange(10000)) |
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| | | |
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| | | for i in range(len(class_name_list)): |
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| | | class_ids[class_name_list[i]] = i |
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| | | |
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| | | num_gen = 60000 |
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| | | num_iter = 1 |
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| | | w_gen = 1440 |
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| | | h_gen = 960 |
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| | | |
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| | | for i in range(num_gen): |
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| | | for i in range(args.num_gen): |
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| | | # Arbitrarily select top left and right corners for perspective transformation |
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| | | # Since the training image are generated with random rotation, don't need to skew all four sides |
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| | | skew = [[random.uniform(0, 0.25), 0], [0, 1], [1, 1], |
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| | | [random.uniform(0.75, 1), 0]] |
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| | | generator = ImageGenerator(background.get_random(), class_ids, w_gen, h_gen, skew=skew) |
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| | | generator = ImageGenerator(background.get_random(), class_ids, args.width, args.height, skew=skew) |
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| | | out_name = '' |
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| | | |
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| | | # Use 2 to 5 cards per generator |
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| | |
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| | | card = Card(card_img, card_info, detected_object_list) |
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| | | generator.add_card(card) |
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| | | |
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| | | for j in range(num_iter): |
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| | | for j in range(args.num_iter): |
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| | | seq = iaa.Sequential([ |
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| | | iaa.Multiply((0.8, 1.2)), # darken / brighten the whole image |
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| | | iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(64)), per_channel=0.1, size_px_max=[3, 6], |
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| | | |
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| | | if __name__ == '__main__': |
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| | | main() |
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| | | parser = argparse.ArgumentParser() |
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| | | parser.add_argument('-n', '--num_gen', dest='num_gen', help='Number of training images to generate', |
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| | | type=int, required=True) |
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| | | parser.add_argument('-ni', '--num_iter', dest='num_iter', help='Number of iterations to generate each config', |
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| | | type=int, default=1) |
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| | | parser.add_argument('-w', '--width', dest='width', help='Width of the training image', type=int, default=1440) |
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| | | parser.add_argument('-ht', '--height', dest='height', help='Height of the training image', type=int, default=960) |
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| | | args = parser.parse_args() |
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| | | main(args) |
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