import os
import cv2
import numpy as np
import tensorflow as tf
import wbc.network as network
import wbc.guided_filter as guided_filter
from tqdm import tqdm


def resize_crop(image):
    h, w, c = np.shape(image)
    if min(h, w) > 720:
        if h > w:
            h, w = int(720 * h / w), 720
        else:
            h, w = 720, int(720 * w / h)
    image = cv2.resize(image, (w, h),
                       interpolation=cv2.INTER_AREA)
    h, w = (h // 8) * 8, (w // 8) * 8
    image = image[:h, :w, :]
    return image


def cartoonize(load_folder, save_folder, model_path):
    print(model_path)
    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
    network_out = network.unet_generator(input_photo)
    final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)

    all_vars = tf.trainable_variables()
    gene_vars = [var for var in all_vars if 'generator' in var.name]
    saver = tf.train.Saver(var_list=gene_vars)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)

    sess.run(tf.global_variables_initializer())
    saver.restore(sess, tf.train.latest_checkpoint(model_path))
    name_list = os.listdir(load_folder)
    for name in tqdm(name_list):
        try:
            load_path = os.path.join(load_folder, name)
            save_path = os.path.join(save_folder, name)
            image = cv2.imread(load_path)
            image = resize_crop(image)
            batch_image = image.astype(np.float32) / 127.5 - 1
            batch_image = np.expand_dims(batch_image, axis=0)
            output = sess.run(final_out, feed_dict={input_photo: batch_image})
            output = (np.squeeze(output) + 1) * 127.5
            output = np.clip(output, 0, 255).astype(np.uint8)
            cv2.imwrite(save_path, output)
        except:
            print('cartoonize {} failed'.format(load_path))


class Cartoonize:
    def __init__(self, model_path):
        print(model_path)
        self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
        network_out = network.unet_generator(self.input_photo)
        self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)

        all_vars = tf.trainable_variables()
        gene_vars = [var for var in all_vars if 'generator' in var.name]
        saver = tf.train.Saver(var_list=gene_vars)

        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        self.sess = tf.Session(config=config)

        self.sess.run(tf.global_variables_initializer())
        saver.restore(self.sess, tf.train.latest_checkpoint(model_path))

    def run(self, load_folder, save_folder):
        name_list = os.listdir(load_folder)
        for name in tqdm(name_list):
            try:
                load_path = os.path.join(load_folder, name)
                save_path = os.path.join(save_folder, name)
                image = cv2.imread(load_path)
                image = resize_crop(image)
                batch_image = image.astype(np.float32) / 127.5 - 1
                batch_image = np.expand_dims(batch_image, axis=0)
                output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
                output = (np.squeeze(output) + 1) * 127.5
                output = np.clip(output, 0, 255).astype(np.uint8)
                cv2.imwrite(save_path, output)
            except:
                print('cartoonize {} failed'.format(load_path))

    def run_sigle(self, load_path, save_path):
        try:
            image = cv2.imread(load_path)
            image = resize_crop(image)
            batch_image = image.astype(np.float32) / 127.5 - 1
            batch_image = np.expand_dims(batch_image, axis=0)
            output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
            output = (np.squeeze(output) + 1) * 127.5
            output = np.clip(output, 0, 255).astype(np.uint8)
            cv2.imwrite(save_path, output)
        except:
            print('cartoonize {} failed'.format(load_path))


if __name__ == '__main__':
    model_path = 'saved_models'
    load_folder = 'test_images'
    save_folder = 'cartoonized_images'
    if not os.path.exists(save_folder):
        os.mkdir(save_folder)
    cartoonize(load_folder, save_folder, model_path)