deep-photo-styletransfer

Based on "Deep Photo Style Transfer". Amended (Now heavily amended) from here. PLEASE NOTE RESTRICTIONS ON USAGE OF ORIGINAL CODE.

Features

Dockerised for ease of installing.
Matting Laplacian calculations are faster than original MATLAB code.
No dependency on MATLAB.
Adpoted newer neural sytle codebase, adding new features - including multi-GPU.
Consistent image scaling is managed automatically, rather than having to manually rescale images.
No longer a requirement to use particular filenames and directories.

Setup

Build this image using something like:

docker build -t deep_photo .

To run the container you'll need recent nvidia drivers installed and nvidia-docker (from here: https://github.com/NVIDIA/nvidia-docker). Then run something like:

nvidia-docker run -it --name deep_photo deep_photo

Usage

Run:

python3 deep_photo.py <options>

Arguments

usage: deep_photo.py [-h] [-content_image CONTENT_IMAGE]
                     [-content_seg CONTENT_SEG] [-style_image STYLE_IMAGE]
                     [-style_blend_weights STYLE_BLEND_WEIGHTS]
                     [-style_seg STYLE_SEG] [-laplacian LAPLACIAN]
                     [-output_image OUTPUT_IMAGE] [-image_size IMAGE_SIZE]
                     [-gpu GPU] [-multigpu_strategy MULTIGPU_STRATEGY]
                     [-content_weight CONTENT_WEIGHT]
                     [-style_weight STYLE_WEIGHT] [-tv_weight TV_WEIGHT]
                     [-num_iterations NUM_ITERATIONS] [-init {random,image}]
                     [-init_image INIT_IMAGE] [-optimizer {lbfgs,adam}]
                     [-learning_rate LEARNING_RATE]
                     [-lbfgs_num_correction LBFGS_NUM_CORRECTION]
                     [-print_iter PRINT_ITER] [-save_iter SAVE_ITER]
                     [-style_scale STYLE_SCALE] [-original_colors {0,1}]
                     [-pooling {max,avg}] [-proto_file PROTO_FILE]
                     [-model_file MODEL_FILE] [-backend {nn,cudnn,clnn}]
                     [-cudnn_autotune] [-seed SEED]
                     [-content_layers CONTENT_LAYERS]
                     [-style_layers STYLE_LAYERS] [-lambda PHOTO_LAMBDA]
                     [-patch PATCH] [-eps EPS] [-f_radius F_RADIUS]
                     [-f_edge F_EDGE]

optional arguments:
  -h, --help            show this help message and exit
  -content_image CONTENT_IMAGE
                        content image location
  -content_seg CONTENT_SEG
                        content segmentation location
  -style_image STYLE_IMAGE
                        style image locations
  -style_blend_weights STYLE_BLEND_WEIGHTS
                        style image blending weights
  -style_seg STYLE_SEG  style segmentation locations
  -laplacian LAPLACIAN  laplacian file location
  -output_image OUTPUT_IMAGE
                        output image name
  -image_size IMAGE_SIZE
                        Maximum height / width of generated image
  -gpu GPU              GPU indices
  -multigpu_strategy MULTIGPU_STRATEGY
                        multi-GPU layer splits
  -content_weight CONTENT_WEIGHT
                        content weight
  -style_weight STYLE_WEIGHT
                        style weight
  -tv_weight TV_WEIGHT  tv weight
  -num_iterations NUM_ITERATIONS
                        iterations
  -init {random,image}  initialisation type
  -init_image INIT_IMAGE
                        initial image
  -optimizer {lbfgs,adam}
                        optimiser
  -learning_rate LEARNING_RATE
                        learning rate (adam only)
  -lbfgs_num_correction LBFGS_NUM_CORRECTION
                        lbfgs num correction
  -print_iter PRINT_ITER
                        print interval
  -save_iter SAVE_ITER  save interval
  -style_scale STYLE_SCALE
                        style scale
  -original_colors {0,1}
                        use original colours
  -pooling {max,avg}    pooling type
  -proto_file PROTO_FILE
                        VGG 19 proto file location
  -model_file MODEL_FILE
                        VGG 19 model file location
  -backend {nn,cudnn,clnn}
                        backend
  -cudnn_autotune       cudnn autotune flag
  -seed SEED            random number seed
  -content_layers CONTENT_LAYERS
                        VGG 19 content layers
  -style_layers STYLE_LAYERS
                        VGG 19 style layers
  -lambda PHOTO_LAMBDA  photorealism weight
  -patch PATCH          matting patch size
  -eps EPS              matting epsilon
  -f_radius F_RADIUS    f radius
  -f_edge F_EDGE        f edge

Examples

Using images and masks in the examples directory. Assumes 2 GPUs, but can be changed to one (or more for that matter) easily.

Masked style transfer

Example #7 from @luanfujun repo. Results are not identical, but then neither is the process...

python3 deep_photo.py -content_image examples/waterfront.png -content_seg examples/waterfront_seg.png -style_image examples/city_night.png -style_seg examples/city_night_seg.png -laplacian examples/waterfront700.csv -output_image examples/waterfront_city_night.png -image_size 700 -gpu 0,1 -multigpu_strategy 8

Multi-style image example

python3 deep_photo.py -content_image examples/vase.png -content_seg examples/vase_seg.png -style_image examples/fire.png,examples/glass.png -style_seg examples/fire_seg.png,examples/glass_seg.png -laplacian examples/vase700.csv -output_image examples/test.png -image_size 700 -gpu 0,1 -multigpu_strategy 8

shayan-taheri / deep-photo-styletransfer Goto Github PK