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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)
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