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