/* Python source code - Rory McHenry

import tensorflow as tf
import numpy as np
from PIL import Image
import os

learning_rate = 0.1
training_iters = 50
batch_size = 1
display_step = 5
W = 799
H = 449

im = Image.open('min.png')
target = Image.open('mout.png')

x = np.ones( [1, H,W,4] )
x[0,:,:,0:3] = np.array(im)[:,:,0:3].astype(np.float32)/255
y = np.ones( [1, H,W,4] )
y[0,:,:,0:3] = np.array(target)[:,:,0:3].astype(np.float32)/255

x=tf.constant(x,dtype=tf.float32,shape=[1, H,W,4])
y=tf.constant(y,dtype=tf.float32,shape=[1, H,W,4])

keep_prob = tf.placeholder(tf.float32)

def conv2d(x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.tanh(x)

def shaderNet(x, weights, biases, dropout):

    conv1 = conv2d(x    , weights['wc1'], biases['bc1'])
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
    conv4 = conv2d(conv3, weights['wc4'], biases['bc4'])
    
    return x + conv2d(conv4, weights['out'], biases['out']);



weights = {
    'wc1': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
    'wc2': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
    'wc3': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
    'wc4': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
    'out': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1))
}

biases = {
    'bc1': tf.Variable(tf.random_normal([4], stddev=.01)),
    'bc2': tf.Variable(tf.random_normal([4], stddev=.01)),
    'bc3': tf.Variable(tf.random_normal([4], stddev=.01)),
    'bc4': tf.Variable(tf.random_normal([4], stddev=.01)),
    'out': tf.Variable(tf.random_normal([4], stddev=.01))
}

pred = shaderNet(x, weights, biases, keep_prob)


cost = tf.reduce_mean(tf.contrib.losses.mean_squared_error(pred , y ))
optimizer = tf.train.ProximalGradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

init = tf.global_variables_initializer()

saver = tf.train.Saver()

with tf.Session() as sess:

    
    saver.restore(sess, 'C:/Users/rory/py/modelaa.ckpt')
    
    #sess.run(init)
    
    step = 1
    while step * batch_size < training_iters:
        sess.run(optimizer)
        if step % display_step == 0:
            loss, acc = sess.run([cost, accuracy])
            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
            
            img = np.clip(sess.run(pred),0,1)
            img = Image.fromarray((img[0,:,:,0:3]*255).astype(np.uint8), "RGB")
            img.save(os.path.join(os.getcwd(),"out.png"))
           
        step += 1
    print("Optimization Finished!")
    print("Testing Accuracy:", \
        sess.run(accuracy))
    
    save_path = saver.save(sess, 'C:/Users/rory/py/modelaa.ckpt')

    w = sess.run(weights['wc1'])
    b = sess.run(biases['bc1'])

    print( 'vec4 conv3x3(vec2 fragCoord) {')
    print( 'vec4 res = vec4(0);')
    for x in range(-1,2):
        for y in range(-1,2):
            print('res += im((fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
    print( 'return res;')
    print( '}')

    print( 'vec4 ReLU(vec4 i) {')
    print('    return max(vec4(0),i);')
    print('}')

    print('vec4 sigmoid(vec4 i) {')
    print('    return 1./(1.+exp(-i));')
    print('}')

    
    print('vec4 tanh(vec4 i) {')
    print('    return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
    print('}')

    print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
    print('{')
    print('    fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
    print('}')
    w = sess.run(weights['wc2'])
    b = sess.run(biases['bc2'])

    print( 'vec4 conv3x3(vec2 fragCoord) {')
    print( 'vec4 res = vec4(0);')
    for x in range(-1,2):
        for y in range(-1,2):
            print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
    print( 'return res;')
    print( '}')

    print( 'vec4 ReLU(vec4 i) {')
    print('    return max(vec4(0),i);')
    print('}')

    print('vec4 sigmoid(vec4 i) {')
    print('    return 1./(1.+exp(-i));')
    print('}')

    
    print('vec4 tanh(vec4 i) {')
    print('    return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
    print('}')

    print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
    print('{')
    print('    fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
    print('}')
    w = sess.run(weights['wc3'])
    b = sess.run(biases['bc3'])

    print( 'vec4 conv3x3(vec2 fragCoord) {')
    print( 'vec4 res = vec4(0);')
    for x in range(-1,2):
        for y in range(-1,2):
            print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
    print( 'return res;')
    print( '}')

    print( 'vec4 ReLU(vec4 i) {')
    print('    return max(vec4(0),i);')
    print('}')

    print('vec4 sigmoid(vec4 i) {')
    print('    return 1./(1.+exp(-i));')
    print('}')

    
    print('vec4 tanh(vec4 i) {')
    print('    return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
    print('}')

    print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
    print('{')
    print('    fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
    print('}')
    w = sess.run(weights['wc4'])
    b = sess.run(biases['bc4'])

    print( 'vec4 conv3x3(vec2 fragCoord) {')
    print( 'vec4 res = vec4(0);')
    for x in range(-1,2):
        for y in range(-1,2):
            print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
    print( 'return res;')
    print( '}')

    print( 'vec4 ReLU(vec4 i) {')
    print('    return max(vec4(0),i);')
    print('}')

    print('vec4 sigmoid(vec4 i) {')
    print('    return 1./(1.+exp(-i));')
    print('}')

    
    print('vec4 tanh(vec4 i) {')
    print('    return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
    print('}')

    print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
    print('{')
    print('    fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
    print('}')
    
    w = sess.run(weights['out'])
    b = sess.run(biases['out'])

    print( 'vec4 conv3x3(vec2 fragCoord) {')
    print( 'vec4 res = vec4(0);')
    for x in range(-1,2):
        for y in range(-1,2):
            print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
    print( 'return res;')
    print( '}')

    print( 'vec4 ReLU(vec4 i) {')
    print('    return max(vec4(0),i);')
    print('}')

    print('vec4 sigmoid(vec4 i) {')
    print('    return 1./(1.+exp(-i));')
    print('}')

    
    print('vec4 tanh(vec4 i) {')
    print('    return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
    print('}')

    print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
    print('{')
    print('    fragColor = im(uv);')
    print('    if(fragCoord.x>iMouse.x){')
    print('        fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
    print('    }')
    print('}')
*/