SensorEffectAugmentation/geometric_transformation_module.py at master · alexacarlson/SensorEffectAugmentation · GitHub

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import tensorflow as tf
#import numpy as np
import pdb
"""
--------------------------------------------
Spatial Transformer - based python module
--------------------------------------------
Adapted from:
    .. [1]  Spatial Transformer Networks
            Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu
            Submitted on 5 Jun 2015
    .. [2]  https://github.com/skaae/transformer_network/blob/master/transformerlayer.py
    .. [3]  https://github.com/EderSantana/seya/blob/keras1/seya/layers/attention.py

    from https://github.com/oarriaga/spatial_transformer_networks/blob/master/src/spatial_transformer.py
"""

def perform_proj_transformation(image_batch, Ht, output_size, mask=None):
    ##
    ## Perform the projective warping transformation.
    ##
    aug_images = proj_transform(Ht, image_batch, output_size )
    return aug_images

def perform_aff_transformation(image_batch, Ht, output_size, mask=None):
    ##
    ## Perform the affine warping transformation.
    ##
    aug_images = aff_transform(Ht, image_batch, output_size)
    return aug_images

def _repeat( x, num_repeats):
    ones = tf.ones((1, num_repeats), dtype='int32')
    x = tf.reshape(x, shape=(-1,1))
    x = tf.matmul(x, ones)
    return tf.reshape(x, [-1])

def _interpolate( image, x, y, output_size):
    batch_size = tf.shape(image)[0]
    height = tf.shape(image)[1]
    width = tf.shape(image)[2]
    num_channels = tf.shape(image)[3]

    x = tf.cast(x , dtype='float32')
    y = tf.cast(y , dtype='float32')

    height_float = tf.cast(height, dtype='float32')
    width_float = tf.cast(width, dtype='float32')

    output_height = output_size[0]
    output_width  = output_size[1]

    x = .5*(x + 1.0)*(width_float)
    y = .5*(y + 1.0)*(height_float)

    x0 = tf.cast(tf.floor(x), 'int32')
    x1 = x0 + 1
    y0 = tf.cast(tf.floor(y), 'int32')
    y1 = y0 + 1

    max_y = tf.cast(height - 1, dtype='int32')
    max_x = tf.cast(width - 1,  dtype='int32')
    zero = tf.zeros([], dtype='int32')

    x0 = tf.clip_by_value(x0, zero, max_x)
    x1 = tf.clip_by_value(x1, zero, max_x)
    y0 = tf.clip_by_value(y0, zero, max_y)
    y1 = tf.clip_by_value(y1, zero, max_y)

    flat_image_dimensions = width*height
    pixels_batch = tf.range(batch_size)*flat_image_dimensions
    flat_output_dimensions = output_height*output_width

    base = _repeat(pixels_batch, flat_output_dimensions)

    base_y0 = base + y0*width
    base_y1 = base + y1*width
    indices_a = base_y0 + x0
    indices_b = base_y1 + x0
    indices_c = base_y0 + x1
    indices_d = base_y1 + x1

    flat_image = tf.reshape(image, shape=(-1, num_channels))
    flat_image = tf.cast(flat_image, dtype='float32')
    pixel_values_a = tf.gather(flat_image, indices_a)
    pixel_values_b = tf.gather(flat_image, indices_b)
    pixel_values_c = tf.gather(flat_image, indices_c)
    pixel_values_d = tf.gather(flat_image, indices_d)

    x0 = tf.cast(x0, 'float32')
    x1 = tf.cast(x1, 'float32')
    y0 = tf.cast(y0, 'float32')
    y1 = tf.cast(y1, 'float32')

    area_a = tf.expand_dims(((x1 - x) * (y1 - y)), 1)
    area_b = tf.expand_dims(((x1 - x) * (y - y0)), 1)
    area_c = tf.expand_dims(((x - x0) * (y1 - y)), 1)
    area_d = tf.expand_dims(((x - x0) * (y - y0)), 1)
    output = tf.add_n([area_a*pixel_values_a,
                       area_b*pixel_values_b,
                       area_c*pixel_values_c,
                       area_d*pixel_values_d])
    return output

def _meshgrid( height, width):
    x_linspace = tf.linspace(-1., 1., width)
    y_linspace = tf.linspace(-1., 1., height)
    x_coordinates, y_coordinates = tf.meshgrid(x_linspace, y_linspace)
    x_coordinates = tf.reshape(x_coordinates, [-1])
    y_coordinates = tf.reshape(y_coordinates, [-1])
    ones = tf.ones_like(x_coordinates)
    indices_grid = tf.concat([x_coordinates, y_coordinates, ones], 0)
    return indices_grid

def proj_transform(proj_transformation, input_shape, output_size ):
    #
    # changed to take a projective transform
    #
    batch_size = tf.shape(input_shape)[0]
    height = tf.shape(input_shape)[1]
    width = tf.shape(input_shape)[2]
    num_channels = tf.shape(input_shape)[3]
    #
    proj_transformation = tf.reshape(proj_transformation, shape=(batch_size,3,3))
    #
    proj_transformation = tf.reshape(proj_transformation, (-1, 3, 3))
    proj_transformation = tf.cast(proj_transformation, 'float32')
    #
    width = tf.cast(width, dtype='float32')
    height = tf.cast(height, dtype='float32')
    output_height = output_size[0]
    output_width = output_size[1]
    indices_grid = _meshgrid(output_height, output_width)
    indices_grid = tf.expand_dims(indices_grid, 0)
    indices_grid = tf.reshape(indices_grid, [-1]) # flatten?
    #
    indices_grid = tf.tile(indices_grid, tf.stack([batch_size]))
    indices_grid = tf.reshape(indices_grid, (batch_size, 3, -1))
    #
    transformed_grid = tf.matmul(proj_transformation, indices_grid)
    x_s = tf.slice(transformed_grid, [0, 0, 0], [-1, 1, -1])
    y_s = tf.slice(transformed_grid, [0, 1, 0], [-1, 1, -1])
    x_s_flatten = tf.reshape(x_s, [-1])
    y_s_flatten = tf.reshape(y_s, [-1])
    #
    transformed_image = _interpolate(input_shape, x_s_flatten, y_s_flatten, output_size)
    #
    transformed_image = tf.reshape(transformed_image, shape=(batch_size, output_height, output_width, num_channels))
    #
    return transformed_image

def aff_transform(affine_transformation, input_shape, output_size):
    #
    # original ST affine transformation
    #
    batch_size = tf.shape(input_shape)[0]
    height = tf.shape(input_shape)[1]
    width = tf.shape(input_shape)[2]
    num_channels = tf.shape(input_shape)[3]

    affine_transformation = tf.reshape(affine_transformation, shape=(batch_size,2,3))

    affine_transformation = tf.reshape(affine_transformation, (-1, 2, 3))
    affine_transformation = tf.cast(affine_transformation, 'float32')

    width = tf.cast(width, dtype='float32')
    height = tf.cast(height, dtype='float32')
    output_height = output_size[0]
    output_width = output_size[1]
    indices_grid = _meshgrid(output_height, output_width)
    indices_grid = tf.expand_dims(indices_grid, 0)
    indices_grid = tf.reshape(indices_grid, [-1]) # flatten?

    indices_grid = tf.tile(indices_grid, tf.stack([batch_size]))
    indices_grid = tf.reshape(indices_grid, (batch_size, 3, -1))

    transformed_grid = tf.matmul(affine_transformation, indices_grid)
    x_s = tf.slice(transformed_grid, [0, 0, 0], [-1, 1, -1])
    y_s = tf.slice(transformed_grid, [0, 1, 0], [-1, 1, -1])
    x_s_flatten = tf.reshape(x_s, [-1])
    y_s_flatten = tf.reshape(y_s, [-1])

    transformed_image = _interpolate(input_shape, x_s_flatten, y_s_flatten, output_size)

    transformed_image = tf.reshape(transformed_image, shape=(batch_size, output_height, output_width, num_channels))
    #pdb.set_trace()
    return transformed_image