def __preprocess_particle(self, batch_data):
# scale the image to the model input size
#batch_data = tf.image.resize_images(batch_data, self.num_col, self.num_row)
# get the scale tensor shape
batch_data_shape = batch_data.get_shape().as_list()
# uppack the tensor into sub-tensor
batch_data_list = tf.unpack(batch_data)
for i in xrange(batch_data_shape[0]):
# Pass image tensor object to a PIL image
image = Image.fromarray(batch_data_list[i].eval())
# Use PIL or other library of the sort to rotate
random_degree = random.randint(0, 359)
rotated = Image.Image.rotate(image, random_degree)
# Convert rotated image back to tensor
rotated_tensor = tf.convert_to_tensor(np.array(rotated))
#slice_image = tf.slice(batch_data, [i, 0, 0, 0], [1, -1, -1, -1])
#slice_image_reshape = tf.reshape(slice_image, [batch_data_shape[1], batch_data_shape[2], batch_data_shape[3]])
#distorted_image = tf.image.random_flip_up_down(batch_data_list[i], seed = 1234)
#distorted_image = tf.image.random_flip_left_right(distorted_image, seed = 1234)
#distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)
#distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)
# Subtract off the mean and divide by the variance of the pixels.
distorted_image = tf.image.per_image_whitening(rotated_tensor)
batch_data_list[i] = distorted_image
# pack the list of tensor into one tensor
batch_data = tf.pack(batch_data_list)
return batch_data
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