mias_preprocess.py 文件源码

python
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项目:Unsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks 作者: xtarx 项目源码 文件源码
def generate_patches(input_image):
    # print("in generate patchhes")
    global global_counter
    input_image = crop_center(input_image, 384, 384)
    patches = image.extract_patches_2d(input_image, patch_size, max_patches=50,
                                       random_state=rng)
    for counter, i in enumerate(patches):

        if np.any(i):
            matlabimg.imsave('./data/mias_anomalous/' + str(global_counter) + '.png', i, cmap='gray')
            global_counter += 1

#
# convert_pgm_to_png_anomalous()
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