# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
from spatial_transformer import transformer
from scipy import ndimage
import numpy as np
import matplotlib.pyplot as plt
from tf_utils import conv2d, linear, weight_variable, bias_variable

# %% Create a batch of three images (1600 x 1200)
# %% Image retrieved from https://raw.githubusercontent.com/skaae/transformer_network/master/cat.jpg
im = ndimage.imread('cat.jpg')
im = im / 255.
im = im.reshape(1, 1200, 1600, 3)
im = im.astype('float32')

# %% Simulate batch
batch = np.append(im, im, axis=0)
batch = np.append(batch, im, axis=0)
num_batch = 3

x = tf.placeholder(tf.float32, [None, 1200, 1600, 3])
x = tf.cast(batch,'float32')

# %% Create localisation network and convolutional layer
with tf.variable_scope('spatial_transformer_0'):

    # %% Create a fully-connected layer with 6 output nodes
    n_fc = 6 
    W_fc1 = tf.Variable(tf.zeros([1200 * 1600 * 3, n_fc]), name='W_fc1')

    # %% Zoom into the image
    initial = np.array([[0.5,0, 0],[0,0.5,0]]) 
    initial = initial.astype('float32')
    initial = initial.flatten()

    b_fc1 = tf.Variable(initial_value=initial, name='b_fc1')
    h_fc1 = tf.matmul(tf.zeros([num_batch ,1200 * 1600 * 3]), W_fc1) + b_fc1
    h_trans = transformer(x, h_fc1, downsample_factor=2)

# %% Run session
sess = tf.Session()
sess.run(tf.initialize_all_variables())
y = sess.run(h_trans, feed_dict={x: batch})

# plt.imshow(y[0])