def run_tensorflow2(): ## running for our dataset Images = [] dataset_folder_path = 'train_1/' for filename in os.listdir(dataset_folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): Images.append(filename) # print (Images) np.random.shuffle(Images) # print (Images) Style_Images, Content_Images = Images[: round(0.50 * len(Images))], Images[round(0.50 * len(Images)):] no_images = 100; with tf.device("/cpu:0"): i = 0; for content_path, style_path in zip(Style_Images, Content_Images): print("Path", content_path, style_path) content_path = dataset_folder_path + "/" + content_path; content_map_path = content_path.split('.')[0] + "_sem.jpg"; style_path = dataset_folder_path + "/" + style_path; style_map_path = style_path.split('.')[0] + "_sem.jpg"; best, best_loss = run_style_transfer(content_path, style_path,content_map_path=content_map_path,style_map_path=style_map_path, num_iterations=25) show_results(results, best, content_path, style_path) if i == no_images: break i += 1
def run_tensorflow(): with tf.device("/gpu:0"): imshow(deprocess_img(load_and_process_img(content_path)), squeze=False) plt.show() best, best_loss = run_style_transfer(content_path, style_path, style_map_path, content_map_path, num_iterations=15) show_results(results, best, content_path, style_path)
def run_tensorflow(self, content_path, style_path): with tf.device(self.device_name): best, best_loss = self.run_style_transfer( content_path, style_path, style_map_path, content_map_path, num_iterations=self.iterations) show_results(self.results, best, content_path, style_path)
def run_tensorflow(self, content_path, style_path): with tf.device(self.device_name): content_map_path = content_path.split('.')[0] + "_color_mask.png" style_map_path = style_path.split('.')[0] + "__color_mask.png" best, best_loss = self.run_style_transfer( content_path, style_path, style_map_path, content_map_path, num_iterations=self.iterations) show_results(self.results, best, content_path, style_path)
def run_tensorflow2(self): ## running for our dataset Images = [] sImages = [] dataset_folder_path = 'final_content' sdataset_folder_path = 'style' for filename in os.listdir(dataset_folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): Images.append(filename) for filename in os.listdir(sdataset_folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): sImages.append(filename) # print (Images) np.random.shuffle(Images) np.random.shuffle(sImages) # print (Images) Style_Images, Content_Images = sImages, Images no_images = 100 with tf.device(self.device_name): i = 0 for style_path, content_path in zip(Style_Images, Content_Images): print("Path", content_path, style_path) content_path = dataset_folder_path + "/" + content_path style_path = sdataset_folder_path + "/" + style_path content_map_path = content_path.split( '.')[0] + "_color_mask.jpg" style_map_path = style_path.split('.')[0] + "_color_mask.jpg" best, best_loss = self.run_style_transfer( content_path, style_path, content_map_path=content_map_path, style_map_path=style_map_path, num_iterations=self.iterations) show_results(self.results, best, content_path, style_path) if i == no_images: break i += 1
def run_tensorflow2(self): ## running for our dataset ## running for our dataset Images = [] sImages = [] dataset_folder_path = 'final_content' sdataset_folder_path = 'style' for filename in os.listdir(dataset_folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): Images.append(filename) for filename in os.listdir(sdataset_folder_path): if filename.endswith(".jpg") or filename.endswith(".png"): sImages.append(filename) print (Images) np.random.shuffle(Images) np.random.shuffle(sImages) print (sImages) Style_Images, Content_Images = sImages , Images no_images=100; with tf.device(self.device_name): i=0; print("fadsf") for style_path ,content_path in zip(Style_Images, Content_Images): print(content_path, style_path) content_path = dataset_folder_path + "/" + content_path; style_path = sdataset_folder_path + "/" + style_path; best, best_loss = self.run_style_transfer(content_path, style_path, num_iterations=self.iterations) show_results(self.results,best, content_path, style_path) if i==no_images: break; i+=1