python minimize_Python实现AI绘画大师
【源码分享】python实现照片转卡通，不再局限于只是人脸转卡通了。
效果如下：
1、图片卡通
test_code文件夹中运行python cartoonize.py
生成图片在cartoonized_images文件夹里
'''Source code for CVPR 2020 paper'Learning to Cartoonize Using White-Box Cartoon Representations'by Xinrui Wang and Jinze yu'''import tensorflow as tfimport tensorflow.contrib.slim as slimimport utilsimport osimport numpy as npimport argparseimport network from tqdm import tqdmos.environ["CUDA_VISIBLE_DEVICES"]="0"def arg_parser(): parser = argparse.ArgumentParser() parser.add_argument("--patch_size", default = 256, type = int) parser.add_argument("--batch_size", default = 16, type = int) parser.add_argument("--total_iter", default = 50000, type = int) parser.add_argument("--adv_train_lr", default = 2e-4, type = float) parser.add_argument("--gpu_fraction", default = 0.5, type = float) parser.add_argument("--save_dir", default = 'pretrain') args = parser.parse_args() return argsdef train(args): input_photo = tf.placeholder(tf.float32, [args.batch_size, args.patch_size, args.patch_size, 3]) output = network.unet_generator(input_photo) recon_loss = tf.reduce_mean(tf.losses.absolute_difference(input_photo, output)) all_vars = tf.trainable_variables() gene_vars = [var for var in all_vars if 'gene' in var.name] update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): optim = tf.train.AdamOptimizer(args.adv_train_lr, beta1=0.5, beta2=0.99)\ .minimize(recon_loss, var_list=gene_vars) ''' config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) ''' gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_fraction) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) saver = tf.train.Saver(var_list=gene_vars, max_to_keep=20) with tf.device('/device:GPU:0'): sess.run(tf.global_variables_initializer()) face_photo_dir = 'dataset/photo_face' face_photo_list = utils.load_image_list(face_photo_dir) scenery_photo_dir = 'dataset/photo_scenery' scenery_photo_list = utils.load_image_list(scenery_photo_dir) for total_iter in tqdm(range(args.total_iter)): if np.mod(total_iter, 5) == 0: photo_batch = utils.next_batch(face_photo_list, args.batch_size) else: photo_batch = utils.next_batch(scenery_photo_list, args.batch_size) _, r_loss = sess.run([optim, recon_loss], feed_dict={input_photo: photo_batch}) if np.mod(total_iter+1, 50) == 0: print('pretrain, iter: {}, recon_loss: {}'.format(total_iter, r_loss)) if np.mod(total_iter+1, 500 ) == 0: saver.save(sess, args.save_dir+'save_models/model', write_meta_graph=False, global_step=total_iter) photo_face = utils.next_batch(face_photo_list, args.batch_size) photo_scenery = utils.next_batch(scenery_photo_list, args.batch_size) result_face = sess.run(output, feed_dict={input_photo: photo_face}) result_scenery = sess.run(output, feed_dict={input_photo: photo_scenery}) utils.write_batch_image(result_face, args.save_dir+'/images', str(total_iter)+'_face_result.jpg', 4) utils.write_batch_image(photo_face, args.save_dir+'/images', str(total_iter)+'_face_photo.jpg', 4) utils.write_batch_image(result_scenery, args.save_dir+'/images', str(total_iter)+'_scenery_result.jpg', 4) utils.write_batch_image(photo_scenery, args.save_dir+'/images', str(total_iter)+'_scenery_photo.jpg', 4) if __name__ == '__main__': args = arg_parser() train(args) 完整代码：
链接：https://pan.baidu.com/s/10YklnSRIw_mc6W4ovlP3uw
提取码：pluq