random_crop

gistfile1.txt

def random_crop(img, random_crop_size):
    # Note: image_data_format is 'channel_last'
    assert img.shape[2] == 3
    height, width = img.shape[0], img.shape[1]
    dy, dx = random_crop_size
    x = np.random.randint(0, width - dx + 1)
    y = np.random.randint(0, height - dy + 1)
    return img[y:(y+dy), x:(x+dx), :]


def crop_generator(batches, crop_length):
    """Take as input a Keras ImageGen (Iterator) and generate random
    crops from the image batches generated by the original iterator.
    """
    while True:
        batch_x, batch_y = next(batches)
        batch_crops = np.zeros((batch_x.shape[0], crop_length, crop_length, 3))
        for i in range(batch_x.shape[0]):
            batch_crops[i] = random_crop(batch_x[i], (crop_length, crop_length))
        yield (batch_crops, batch_y)

# TensorFlow. 'x' = A placeholder for an image.
original_size = [height, width, channels]
x = tf.placeholder(dtype = tf.float32, shape = original_size)

# Use the following commands to perform random crops
crop_size = [new_height, new_width, channels]
seed = np.random.randint(1234)
x = tf.random_crop(x, size = crop_size, seed = seed)
output = tf.images.resize_images(x, size = original_size)