In [1]:

    

!git clone https://github.com/titu1994/Neural-Style-Transfer.git


    

    




Cloning into 'Neural-Style-Transfer'...
remote: Counting objects: 1315, done.
remote: Compressing objects: 100% (27/27), done.
remote: Total 1315 (delta 12), reused 26 (delta 8), pack-reused 1280
Receiving objects: 100% (1315/1315), 64.99 MiB | 3.55 MiB/s, done.
Resolving deltas: 100% (770/770), done.

In [0]:

    

dir_path = "Neural-Style-Transfer"


    

In [0]:

    

NETWORK = 'INetwork' + '.py'


    

In [4]:

    

# List all the arguments that can be supplied to Network.py
!python {dir_path}/{NETWORK} -h


    

    




/usr/local/lib/python3.6/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
usage: INetwork.py [-h] [--style_masks STYLE_MASKS [STYLE_MASKS ...]]
                   [--content_mask CONTENT_MASK] [--color_mask COLOR_MASK]
                   [--image_size IMG_SIZE] [--content_weight CONTENT_WEIGHT]
                   [--style_weight STYLE_WEIGHT [STYLE_WEIGHT ...]]
                   [--style_scale STYLE_SCALE]
                   [--total_variation_weight TV_WEIGHT] [--num_iter NUM_ITER]
                   [--model MODEL] [--content_loss_type CONTENT_LOSS_TYPE]
                   [--rescale_image RESCALE_IMAGE]
                   [--rescale_method RESCALE_METHOD]
                   [--maintain_aspect_ratio MAINTAIN_ASPECT_RATIO]
                   [--content_layer CONTENT_LAYER] [--init_image INIT_IMAGE]
                   [--pool_type POOL] [--preserve_color COLOR]
                   [--min_improvement MIN_IMPROVEMENT]
                   base ref [ref ...] res_prefix

Neural style transfer with Keras.

positional arguments:
  base                  Path to the image to transform.
  ref                   Path to the style reference image.
  res_prefix            Prefix for the saved results.

optional arguments:
  -h, --help            show this help message and exit
  --style_masks STYLE_MASKS [STYLE_MASKS ...]
                        Masks for style images
  --content_mask CONTENT_MASK
                        Masks for the content image
  --color_mask COLOR_MASK
                        Mask for color preservation
  --image_size IMG_SIZE
                        Minimum image size
  --content_weight CONTENT_WEIGHT
                        Weight of content
  --style_weight STYLE_WEIGHT [STYLE_WEIGHT ...]
                        Weight of style, can be multiple for multiple styles
  --style_scale STYLE_SCALE
                        Scale the weighing of the style
  --total_variation_weight TV_WEIGHT
                        Total Variation weight
  --num_iter NUM_ITER   Number of iterations
  --model MODEL         Choices are 'vgg16' and 'vgg19'
  --content_loss_type CONTENT_LOSS_TYPE
                        Can be one of 0, 1 or 2. Readme contains the required
                        information of each mode.
  --rescale_image RESCALE_IMAGE
                        Rescale image after execution to original dimentions
  --rescale_method RESCALE_METHOD
                        Rescale image algorithm
  --maintain_aspect_ratio MAINTAIN_ASPECT_RATIO
                        Maintain aspect ratio of loaded images
  --content_layer CONTENT_LAYER
                        Content layer used for content loss.
  --init_image INIT_IMAGE
                        Initial image used to generate the final image.
                        Options are 'content', 'noise', or 'gray'
  --pool_type POOL      Pooling type. Can be "ave" for average pooling or
                        "max" for max pooling
  --preserve_color COLOR
                        Preserve original color in image
  --min_improvement MIN_IMPROVEMENT
                        Defines minimum improvement required to continue
                        script

In [0]:

    

# Image size
IMAGE_SIZE = 500

# Loss Weights
CONTENT_WEIGHT = 0.025
STYLE_WEIGHT = 1.0
STYLE_SCALE = 1.0
TOTAL_VARIATION_WEIGHT = 8.5e-5
CONTENT_LOSS_TYPE = 0

# Training arguments
NUM_ITERATIONS = 10
MODEL = 'vgg19'
RESCALE_IMAGE = 'false'
MAINTAIN_ASPECT_RATIO = 'false'  # Set to false if OOM occurs

# Transfer Arguments
CONTENT_LAYER = 'conv' + '5_2'  # only change the number 5_2 to something in a similar format
INITIALIZATION_IMAGE = 'content'
POOLING_TYPE = 'max'

# Extra arguments
PRESERVE_COLOR = 'false'
MIN_IMPROVEMENT = 0.0


    

In [25]:

    

from google.colab import files

content_img = files.upload()


    

    






     
     

      Upload widget is only available when the cell has been executed in the
      current browser session. Please rerun this cell to enable.
      
       






    




Saving night sky.jpg to night sky.jpg

In [29]:

    

import os

CONTENT_IMAGE_FN = list(content_img)[0]
CONTENT_IMAGE_FN_temp = CONTENT_IMAGE_FN.strip().replace(" ", "_")

if CONTENT_IMAGE_FN != CONTENT_IMAGE_FN_temp:
  os.rename(CONTENT_IMAGE_FN, CONTENT_IMAGE_FN_temp)
  CONTENT_IMAGE_FN = CONTENT_IMAGE_FN_temp
  
print("Content image filename :", CONTENT_IMAGE_FN)


    

    




Content image filename : night_sky.jpg

In [31]:

    

%matplotlib inline
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10, 10))
img = plt.imread(CONTENT_IMAGE_FN)
plt.axis('off')
plt.title('Content image')
plt.imshow(img)


    

    
Out[31]:





<matplotlib.image.AxesImage at 0x7f9d027a50b8>






    

In [33]:

    

style_img = files.upload()


    

    






     
     

      Upload widget is only available when the cell has been executed in the
      current browser session. Please rerun this cell to enable.
      
       






    




Saving blue_swirls.jpg to blue_swirls.jpg

In [34]:

    

STYLE_IMAGE_FN = list(style_img)[0]
STYLE_IMAGE_FN_temp = STYLE_IMAGE_FN.strip().replace(" ", "_")

if STYLE_IMAGE_FN != STYLE_IMAGE_FN_temp:
  os.rename(STYLE_IMAGE_FN, STYLE_IMAGE_FN_temp)
  STYLE_IMAGE_FN = STYLE_IMAGE_FN_temp
  
print("Style image filename :", STYLE_IMAGE_FN)


    

    




Style image filename : blue_swirls.jpg

In [35]:

    

fig = plt.figure(figsize=(10, 10))
img = plt.imread(STYLE_IMAGE_FN)
plt.axis('off')
plt.title('Style image')
plt.imshow(img)


    

    
Out[35]:





<matplotlib.image.AxesImage at 0x7f9d02775f28>






    

In [0]:

    

import os

RESULT_DIR = "generated/"
RESULT_PREFIX = RESULT_DIR + "gen"
FINAL_IMAGE_PATH = RESULT_PREFIX + "_at_iteration_%d.png" % (NUM_ITERATIONS)

if not os.path.exists(RESULT_DIR):
  os.makedirs(RESULT_DIR)


    

In [37]:

    

!python {dir_path}/{NETWORK} {CONTENT_IMAGE_FN} {STYLE_IMAGE_FN} {RESULT_PREFIX} \
  --image_size {IMAGE_SIZE} --content_weight {CONTENT_WEIGHT} --style_weight \
  {STYLE_WEIGHT} --style_scale {STYLE_SCALE} --total_variation_weight \
  {TOTAL_VARIATION_WEIGHT} --content_loss_type {CONTENT_LOSS_TYPE} --num_iter \
  {NUM_ITERATIONS} --model {MODEL} --rescale_image {RESCALE_IMAGE} \
  --maintain_aspect_ratio {MAINTAIN_ASPECT_RATIO} --content_layer {CONTENT_LAYER} \
  --init_image {INITIALIZATION_IMAGE} --pool_type {POOLING_TYPE} --preserve_color \
  {PRESERVE_COLOR} --min_improvement {MIN_IMPROVEMENT}


    

    




/usr/local/lib/python3.6/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
/usr/local/lib/python3.6/dist-packages/scipy/misc/pilutil.py:482: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
  if issubdtype(ts, int):
/usr/local/lib/python3.6/dist-packages/scipy/misc/pilutil.py:485: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  elif issubdtype(type(size), float):
2018-06-13 05:27:59.105307: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-06-13 05:27:59.105860: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2018-06-13 05:27:59.105921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-13 05:27:59.463806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-13 05:27:59.463867: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0 
2018-06-13 05:27:59.463926: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N 
2018-06-13 05:27:59.464268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10764 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
Model loaded.
Starting iteration 1 of 15
Current loss value: 2293319400.0  Improvement : 0.000 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_1.png
Iteration 1 completed in 33s
Starting iteration 2 of 15
Current loss value: 689446900.0  Improvement : 69.937 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_2.png
Iteration 2 completed in 28s
Starting iteration 3 of 15
Current loss value: 356006430.0  Improvement : 48.363 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_3.png
Iteration 3 completed in 28s
Starting iteration 4 of 15
Current loss value: 253035460.0  Improvement : 28.924 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_4.png
Iteration 4 completed in 28s
Starting iteration 5 of 15
Current loss value: 197734460.0  Improvement : 21.855 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_5.png
Iteration 5 completed in 28s
Starting iteration 6 of 15
Current loss value: 166553570.0  Improvement : 15.769 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_6.png
Iteration 6 completed in 28s
Starting iteration 7 of 15
Current loss value: 142004290.0  Improvement : 14.740 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_7.png
Iteration 7 completed in 29s
Starting iteration 8 of 15
Current loss value: 127249840.0  Improvement : 10.390 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_8.png
Iteration 8 completed in 28s
Starting iteration 9 of 15
Current loss value: 114740440.0  Improvement : 9.831 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_9.png
Iteration 9 completed in 29s
Starting iteration 10 of 15
Current loss value: 104930540.0  Improvement : 8.550 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_10.png
Iteration 10 completed in 29s
Starting iteration 11 of 15
Current loss value: 96573416.0  Improvement : 7.964 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_11.png
Iteration 11 completed in 29s
Starting iteration 12 of 15
Current loss value: 91850400.0  Improvement : 4.891 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_12.png
Iteration 12 completed in 28s
Starting iteration 13 of 15
Current loss value: 87742210.0  Improvement : 4.473 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_13.png
Iteration 13 completed in 29s
Starting iteration 14 of 15
Current loss value: 84159470.0  Improvement : 4.083 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_14.png
Iteration 14 completed in 29s
Starting iteration 15 of 15
Current loss value: 81383016.0  Improvement : 3.299 %
Rescaling Image to (500, 888)
Image saved as generatedgen_at_iteration_15.png
Iteration 15 completed in 28s

In [40]:

    

fig = plt.figure(figsize=(10, 10))
img = plt.imread(FINAL_IMAGE_PATH)
plt.axis('off')
plt.title('Generated image')
plt.imshow(img)


    

    
Out[40]:





<matplotlib.image.AxesImage at 0x7f9d026b5940>






    

In [50]:

    

COLOR_TRANSFER = 'color_transfer.py'
COLOR_FINAL_IMAGE_PATH = FINAL_IMAGE_PATH[:-4] + '_%s_color.png'

# Optional - Use Histogram matching (0 for no, 1 for yes)
HISTOGRAM_MATCH = 0

if HISTOGRAM_MATCH == 0:
  COLOR_FINAL_IMAGE_PATH = COLOR_FINAL_IMAGE_PATH % ('original')
else:
  COLOR_FINAL_IMAGE_PATH = COLOR_FINAL_IMAGE_PATH % ('histogram')
  

!python {dir_path}/{COLOR_TRANSFER} {CONTENT_IMAGE_FN} {FINAL_IMAGE_PATH} --hist_match {HISTOGRAM_MATCH}


    

    




/usr/local/lib/python3.6/dist-packages/scipy/misc/pilutil.py:482: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
  if issubdtype(ts, int):
/usr/local/lib/python3.6/dist-packages/scipy/misc/pilutil.py:485: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  elif issubdtype(type(size), float):
Image saved at path : generatedgen_at_iteration_15_original_color.png

In [51]:

    

fig = plt.figure(figsize=(10, 10))
img = plt.imread(COLOR_FINAL_IMAGE_PATH)
plt.axis('off')
plt.title('Color Transferred Generated image')
plt.imshow(img)


    

    
Out[51]:





<matplotlib.image.AxesImage at 0x7f9d025f1550>






    

In [0]:

    

# download the color transfered image
files.download(COLOR_FINAL_IMAGE_PATH)


    

In [0]:

    

files.download(FINAL_IMAGE_PATH)