From 96d3bdea136f484a30b71a690055cb28f7ab45a6 Mon Sep 17 00:00:00 2001 From: vaishnavsm Date: Tue, 1 May 2018 06:12:38 +0530 Subject: [PATCH] Project --- .gitignore | 4 + Example command.png | Bin 0 -> 56723 bytes LICENSE | 161 ++++++ README.md | 98 ++++ README1.md | 37 ++ bg/inpainter/__init__.py | 0 bg/inpainter/__main__.py | 55 ++ .../__pycache__/__main__.cpython-35.pyc | Bin 0 -> 1289 bytes .../__pycache__/__main__.cpython-36.pyc | Bin 0 -> 1209 bytes .../__pycache__/inpainter.cpython-35.pyc | Bin 0 -> 9619 bytes .../__pycache__/inpainter.cpython-36.pyc | Bin 0 -> 8755 bytes bg/inpainter/inpainter.py | 309 +++++++++++ bg/resources/image1.jpg | Bin 0 -> 40124 bytes bg/resources/image2.jpg | Bin 0 -> 14789 bytes bg/resources/image3.jpg | Bin 0 -> 7741 bytes bg/resources/image4.jpg | Bin 0 -> 28321 bytes bg/resources/image5.jpg | Bin 0 -> 17247 bytes bg/resources/image6.jpg | Bin 0 -> 111994 bytes bg/resources/image7.jpg | Bin 0 -> 21748 bytes bg/resources/mask1.jpg | Bin 0 -> 16978 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zXeqFm>u+ZP&ot;2TnU_oLuoC-!^T(|+5l67CbJJ2h^_X1 Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at [creativecommons.org/policies](http://creativecommons.org/policies), Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. +> +> Creative Commons may be contacted at creativecommons.org diff --git a/README.md b/README.md new file mode 100644 index 0000000..73962fa --- /dev/null +++ b/README.md @@ -0,0 +1,98 @@ +# Generative Image Inpainting with Contextual Attention + +[CVPR 2018 Paper](https://arxiv.org/abs/1801.07892) | [ArXiv](https://arxiv.org/abs/1801.07892) | [Project](http://jhyu.me/posts/2018/01/20/generative-inpainting.html) | [Demo](http://jhyu.me/posts/2018/01/20/generative-inpainting.html#post) | [BibTex](#citing) + + + + + +Example inpainting results of our method on images of natural scene (Places2), face (CelebA) and object (ImageNet). Missing regions are shown in white. In each pair, the left is input image and right is the direct output of our trained generative neural networks without any post-processing. + +## Run + +0. Requirements: + * Install python3. + * Install [tensorflow](https://www.tensorflow.org/install/) (tested on Release 1.3.0, 1.4.0, 1.5.0, 1.6.0, 1.7.0). + * Install tensorflow toolkit [neuralgym](https://github.com/JiahuiYu/neuralgym) (run `pip install git+https://github.com/JiahuiYu/neuralgym`). +1. Training: + * Prepare training images filelist. + * Modify [inpaint.yml](/inpaint.yml) to set DATA_FLIST, LOG_DIR, IMG_SHAPES and other parameters. + * Run `python train.py`. +2. Resume training: + * Modify MODEL_RESTORE flag in [inpaint.yml](/inpaint.yml). E.g., MODEL_RESTORE: 20180115220926508503_places2_model. + * Run `python train.py`. +3. Testing: + * Run `python test.py --image examples/input.png --mask examples/mask.png --output examples/output.png --checkpoint model_logs/your_model_dir`. + +## Pretrained models + +[CelebA-HQ](https://drive.google.com/open?id=1lpluFXyWDxTY6wcjixQGWX8jxUUMlyBW) | [Places2](https://drive.google.com/open?id=1M3AFy7x9DqXaI-fINSynW7FJSXYROfv-) | [CelebA](https://drive.google.com/open?id=1sP8ViF3mxUMN--xpKqonEeW9d8S8pJEo) | [ImageNet](https://drive.google.com/open?id=136APWSdPRAF7-XoS8sMBTLV-X3f-ogE0) | + +Download the model dirs and put it under `model_logs/` (rename `checkpoint.txt` to `checkpoint` because google drive automatically add ext after download). Run testing or resume training as described above. All models are trained with images of resolution 256x256 and largest hole size 128x128, above which the results may be deteriorated. We provide several example test cases. Please run: + +```bash +# Places2 512x680 input +python test.py --image examples/places2/wooden_input.png --mask examples/places2/wooden_mask.png --output examples/output.png --checkpoint_dir model_logs/release_places2_256 +# CelebA 256x256 input +python test.py --image examples/celeba/celebahr_patches_164036_input.png --mask examples/center_mask_256.png --output examples/output.png --checkpoint_dir model_logs/release_celeba_256/ +# CelebA-HQ 256x256 input +# Please visit CelebA-HQ demo at: jhyu.me/demo +# ImageNet 256x256 input +python test.py --image examples/imagenet/imagenet_patches_ILSVRC2012_val_00000827_input.png --mask examples/center_mask_256.png --output examples/output.png --checkpoint_dir model_logs/release_imagenet_256 +``` + +**Note:** Please make sure the mask file completely cover the masks in input file. You may check it with saving a new image to visualize `cv2.imwrite('new.png', img - mask)`. + +## TensorBoard + +Visualization on [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) for training and validation is supported. Run `tensorboard --logdir model_logs --port 6006` to view training progress. + +## License + +CC 4.0 Attribution-NonCommercial International + +The software is for educaitonal and academic research purpose only. + +## FAQ + + +* Can other types of GANs work in current setting? + +The proposed contextual attention module is independent of GAN losses. We experimented with other types of GANs in Section 5.1 and WGAN-GP is used by default. Intuitively, during training, pixel-wise reconstruction loss directly regresses holes to the current ground truth image, while WGANs implicitly learn to match potentially correct images and supervise the generator with adversarial gradients. Both WGANs and reconstruction loss measure image distance with pixel-wise L1. + +* How to determine if G is converged? + +We use a slightly modified WGAN-GP for adversarial loss. WGANs are demonstrated to have more meaningful convergent curves than others, which is also confirmed in our experiments. + +* The split of train/test data in experiments. + +We use the default training/validation data split from Places2 and CelebA. For CelebA, training/validation have no identity overlap. For DTD texture dataset which has no default split, we sample 30 images for validation. + +* How does random mask generated? + +Please check out function `random_bbox` and `bbox2mask` in file [inpaint_ops.py](/inpaint_ops.py). + +* Parameters to handle the memory overhead. + +Please check out function `contextual_attention` in file [inpaint_ops.py](/inpaint_ops.py). + +* How to implement contextual attention? + +The proposed contextual attention learns where to borrow or copy feature information from known background patches to reconstruct missing patches. It is implemented with TensorFlow conv2d, extract_image_patches and conv2d_transpose API in file [inpaint_ops.py](/inpaint_ops.py). To test the implementation, one can simply apply it on RGB feature space, using image A to reconstruct image B. This special case then turns into a naive style transfer. + + + +```bash +python inpaint_ops.py --imageA examples/style_transfer/bnw_butterfly.png --imageB examples/style_transfer/bike.jpg --imageOut examples/style_transfer/bike_style_out.png +``` + + +## Citing +``` +@article{yu2018generative, + title={Generative Image Inpainting with Contextual Attention}, + author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S}, + journal={arXiv preprint arXiv:1801.07892}, + year={2018} +} +``` diff --git a/README1.md b/README1.md new file mode 100644 index 0000000..427bb54 --- /dev/null +++ b/README1.md @@ -0,0 +1,37 @@ +# Inpaint Object Remover +This is the implementation of the algorithm described in the paper "Region Filling and Object Removal by +Exemplar-Based Image Inpainting" by A. Criminisi et al. + +## Setup +Requires python 3.5 or newer. You can download it [here](https://www.python.org/downloads/). + +### Lunix and Mac OS X +Inside the project's directory run: +``` +pip install -r requirements.txt +``` +to install the dependencies. + +### Windows +Download NumPy and SciPy from [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/), being the number after cp your version of python. (e.g. if you have python 3.5 64 bits download **numpy‑1.13.1+mkl‑cp35‑cp35m‑win_amd64.whl** and **scipy‑0.19.1‑cp35‑cp35m‑win_amd64.whl**) + +Then run `pip install ` for each file. + +After this, inside the project's directory, run: +``` +pip install -r requirements.txt +``` +to install the other dependencies. + +## How to use +Inside the project's directory run: +``` +python inpainter +``` +You can also pass the `--plot-progress` argument to watch the image being created. For example, use: +``` +python inpainter resources/image1.jpg resources/mask1.jpg --plot-progress +``` +to process image1 inside resources folder using mask1 while printing each result. I provided some images from the paper inside the resources folder. + +Use `python inpainter -h` to show the available arguments. diff --git a/bg/inpainter/__init__.py b/bg/inpainter/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/bg/inpainter/__main__.py b/bg/inpainter/__main__.py new file mode 100644 index 0000000..03097ca --- /dev/null +++ b/bg/inpainter/__main__.py @@ -0,0 +1,55 @@ +import argparse +from skimage.io import imread, imsave + +from inpainter import Inpainter + + +def main(): + args = parse_args() + + image = imread(args.input_image) + mask = imread(args.mask, as_grey=True) + + output_image = Inpainter( + image, + mask, + patch_size=args.patch_size, + plot_progress=args.plot_progress + ).inpaint() + imsave(args.output, output_image, quality=100) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '-ps', + '--patch-size', + help='the size of the patches', + type=int, + default=9 + ) + parser.add_argument( + '-o', + '--output', + help='the file path to save the output image', + default='output.jpg' + ) + parser.add_argument( + '--plot-progress', + help='plot each generated image', + action='store_true', + default=False + ) + parser.add_argument( + 'input_image', + help='the image containing objects to be removed' + ) + parser.add_argument( + 'mask', + help='the mask of the region to be removed' + ) + return parser.parse_args() + + +if __name__ == '__main__': + main() diff --git a/bg/inpainter/__pycache__/__main__.cpython-35.pyc b/bg/inpainter/__pycache__/__main__.cpython-35.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a5d2761480976cd27d5c3eb16ff38ded7705e7b2 GIT binary patch literal 1289 zcmY*Y&2G~`5T0E-v7I_iDYU@N2ZUrKS3CehAS5I%6;e4_MV1?H9jE?dcT*`=dnzx% zBXHnF;0=7`)K}od%(@B1@lIxEz8TNXe0w;ZPJaIC;~zG_Z@Bkx)ZgGzDT)kV1|>i| z=p0H06^EEZWkGBKBZLK|4b=$Z5$tH8ifYRb&U#xD}W`38wS^G zv$d%HjEwA>gSO!cVVp`P@CdM6Qm`P-myStgfp(42gDVV240#!y^dxkSV<%NfF2(v& zKWS4vUnHvNL_clIMklRo(4|!Dd|9Psq1U$<1>uCCQi&v$xsuf3ey6D0l}?H(%|&ll zsk)GKHaQ|OMiD!Rpwml>@Z)BsF(9;io07yM6DcQz^atON>^sOQ8vi^w%TMnz2zi<$ z73NBkqjpVSD{5+@+D8G*<1==^EoQMX^VmL@Gqi3y&T!-5G9CYh3U++S?wUSyd}uND zbQr!y#Z3MNline4`W|&Xn;72?^?T#lVm^yW)|IK*c~m~yqjSONQh`di0&zE1Ch=bK`v?`Q!TO~ zt{L(Q+l;OH;=S`m#wL%$r){*orz2nzN;tYej{ei9m{~xfGdULidaU zh)kPPN|9sg|DoZicacMek@4yJcG8T)CL5lxM)w}^i(wj^Zn*aS$!u0iag z!*~x-Q7=&d^N~bEB>Fjgqm%Z}Hh4&y=-%D1GxX#ECc#czm{T7Wjkzu63dG))&cjt9 pnr;)+X(f{6UU75@BvshEuc{_nmEtu~S@|e@cF6ZEf93~1`wOH@EuH`X literal 0 HcmV?d00001 diff --git a/bg/inpainter/__pycache__/__main__.cpython-36.pyc b/bg/inpainter/__pycache__/__main__.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dbeeb7fbd317b8d676e94214d6a24afab1b3dd07 GIT binary patch literal 1209 zcmZ8g%Wl&^6rCA6&csccJ}8Kt7YNBnsgT$r#6zkCiwX%gSV)%1OdY3wF*B(YD_wmGWAw^YXVvNiG+h2m_^P*)_Pf? 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+GAN_LOSS_ALPHA: 0.001 # dcgan: 0.0008, wgan: 0.0005, onegan: 0.001 +WGAN_GP_LAMBDA: 10 +COARSE_L1_ALPHA: 1.2 +L1_LOSS_ALPHA: 1.2 +AE_LOSS_ALPHA: 1.2 +GAN_WITH_MASK: False +DISCOUNTED_MASK: True +RANDOM_SEED: False +PADDING: 'SAME' + +# training +NUM_GPUS: 0 +GPU_ID: -1 # -1 indicate select any available one, otherwise select gpu ID, e.g. [0,1,3] +TRAIN_SPE: 10000 +MAX_ITERS: 1000000 +VIZ_MAX_OUT: 10 +GRADS_SUMMARY: False +GRADIENT_CLIP: False +GRADIENT_CLIP_VALUE: 0.1 +VAL_PSTEPS: 1000 + +# data +DATA_FLIST: + # https://github.com/JiahuiYu/progressive_growing_of_gans_tf + celebahq: [ + 'data/celeba_hq/train_shuffled.flist', + 'data/celeba_hq/train_shuffled.flist' + ] + # http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html, please to use RANDOM_CROP: True + celeba: [ + 'data/celeba/train_shuffled.flist', + 'data/celeba/train_shuffled.flist' + ] + # http://places2.csail.mit.edu/, please download the high-resolution dataset and use RANDOM_CROP: True + places2: [ + 'data/places2/train_shuffled.flist', + 'data/places2/static_view.flist' + ] + # http://www.image-net.org/, please use RANDOM_CROP: True + imagenet: [ + 'data/imagenet/train_shuffled.flist', + 'data/imagenet/static_view.flist', + ] + +STATIC_VIEW_SIZE: 30 +IMG_SHAPES: [256, 256, 3] +HEIGHT: 128 +WIDTH: 128 +MAX_DELTA_HEIGHT: 32 +MAX_DELTA_WIDTH: 32 +BATCH_SIZE: 16 +VERTICAL_MARGIN: 0 +HORIZONTAL_MARGIN: 0 + +# loss +AE_LOSS: True +L1_LOSS: True +GLOBAL_DCGAN_LOSS_ALPHA: 1. +GLOBAL_WGAN_LOSS_ALPHA: 1. + +# loss legacy +LOAD_VGG_MODEL: False +VGG_MODEL_FILE: data/model_zoo/vgg16.npz +FEATURE_LOSS: False +GRAMS_LOSS: False +TV_LOSS: False +TV_LOSS_ALPHA: 0. +FEATURE_LOSS_ALPHA: 0.01 +GRAMS_LOSS_ALPHA: 50 +SPATIAL_DISCOUNTING_GAMMA: 0.9 diff --git a/fg/inpaint_model.py b/fg/inpaint_model.py new file mode 100644 index 0000000..9a59ecb --- /dev/null +++ b/fg/inpaint_model.py @@ -0,0 +1,311 @@ +""" common model for DCGAN """ +import logging + +import cv2 +import neuralgym as ng +import tensorflow as tf +from tensorflow.contrib.framework.python.ops import arg_scope + +from neuralgym.models import Model +from neuralgym.ops.summary_ops import scalar_summary, images_summary +from neuralgym.ops.summary_ops import gradients_summary +from neuralgym.ops.layers import flatten, resize +from neuralgym.ops.gan_ops import gan_wgan_loss, gradients_penalty +from neuralgym.ops.gan_ops import random_interpolates + +from inpaint_ops import gen_conv, gen_deconv, dis_conv +from inpaint_ops import random_bbox, bbox2mask, local_patch +from inpaint_ops import spatial_discounting_mask +from inpaint_ops import resize_mask_like, contextual_attention + + +logger = logging.getLogger() + + +class InpaintCAModel(Model): + def __init__(self): + super().__init__('InpaintCAModel') + + def build_inpaint_net(self, x, mask, config=None, reuse=False, + training=True, padding='SAME', name='inpaint_net'): + """Inpaint network. + + Args: + x: incomplete image, [-1, 1] + mask: mask region {0, 1} + Returns: + [-1, 1] as predicted image + """ + xin = x + offset_flow = None + ones_x = tf.ones_like(x)[:, :, :, 0:1] + x = tf.concat([x, ones_x, ones_x*mask], axis=3) + + # two stage network + cnum = 32 + with tf.variable_scope(name, reuse=reuse), \ + arg_scope([gen_conv, gen_deconv], + training=training, padding=padding): + # stage1 + x = gen_conv(x, cnum, 5, 1, name='conv1') + x = gen_conv(x, 2*cnum, 3, 2, name='conv2_downsample') + x = gen_conv(x, 2*cnum, 3, 1, name='conv3') + x = gen_conv(x, 4*cnum, 3, 2, name='conv4_downsample') + x = gen_conv(x, 4*cnum, 3, 1, name='conv5') + x = gen_conv(x, 4*cnum, 3, 1, name='conv6') + mask_s = resize_mask_like(mask, x) + x = gen_conv(x, 4*cnum, 3, rate=2, name='conv7_atrous') + x = gen_conv(x, 4*cnum, 3, rate=4, name='conv8_atrous') + x = gen_conv(x, 4*cnum, 3, rate=8, name='conv9_atrous') + x = gen_conv(x, 4*cnum, 3, rate=16, name='conv10_atrous') + x = gen_conv(x, 4*cnum, 3, 1, name='conv11') + x = gen_conv(x, 4*cnum, 3, 1, name='conv12') + x = gen_deconv(x, 2*cnum, name='conv13_upsample') + x = gen_conv(x, 2*cnum, 3, 1, name='conv14') + x = gen_deconv(x, cnum, name='conv15_upsample') + x = gen_conv(x, cnum//2, 3, 1, name='conv16') + x = gen_conv(x, 3, 3, 1, activation=None, name='conv17') + x = tf.clip_by_value(x, -1., 1.) + x_stage1 = x + # return x_stage1, None, None + + # stage2, paste result as input + # x = tf.stop_gradient(x) + x = x*mask + xin*(1.-mask) + x.set_shape(xin.get_shape().as_list()) + # conv branch + xnow = tf.concat([x, ones_x, ones_x*mask], axis=3) + x = gen_conv(xnow, cnum, 5, 1, name='xconv1') + x = gen_conv(x, cnum, 3, 2, name='xconv2_downsample') + x = gen_conv(x, 2*cnum, 3, 1, name='xconv3') + x = gen_conv(x, 2*cnum, 3, 2, name='xconv4_downsample') + x = gen_conv(x, 4*cnum, 3, 1, name='xconv5') + x = gen_conv(x, 4*cnum, 3, 1, name='xconv6') + x = gen_conv(x, 4*cnum, 3, rate=2, name='xconv7_atrous') + x = gen_conv(x, 4*cnum, 3, rate=4, name='xconv8_atrous') + x = gen_conv(x, 4*cnum, 3, rate=8, name='xconv9_atrous') + x = gen_conv(x, 4*cnum, 3, rate=16, name='xconv10_atrous') + x_hallu = x + # attention branch + x = gen_conv(xnow, cnum, 5, 1, name='pmconv1') + x = gen_conv(x, cnum, 3, 2, name='pmconv2_downsample') + x = gen_conv(x, 2*cnum, 3, 1, name='pmconv3') + x = gen_conv(x, 4*cnum, 3, 2, name='pmconv4_downsample') + x = gen_conv(x, 4*cnum, 3, 1, name='pmconv5') + x = gen_conv(x, 4*cnum, 3, 1, name='pmconv6', + activation=tf.nn.relu) + x, offset_flow = contextual_attention(x, x, mask_s, 3, 1, rate=2) + x = gen_conv(x, 4*cnum, 3, 1, name='pmconv9') + x = gen_conv(x, 4*cnum, 3, 1, name='pmconv10') + pm = x + x = tf.concat([x_hallu, pm], axis=3) + + x = gen_conv(x, 4*cnum, 3, 1, name='allconv11') + x = gen_conv(x, 4*cnum, 3, 1, name='allconv12') + x = gen_deconv(x, 2*cnum, name='allconv13_upsample') + x = gen_conv(x, 2*cnum, 3, 1, name='allconv14') + x = gen_deconv(x, cnum, name='allconv15_upsample') + x = gen_conv(x, cnum//2, 3, 1, name='allconv16') + x = gen_conv(x, 3, 3, 1, activation=None, name='allconv17') + x_stage2 = tf.clip_by_value(x, -1., 1.) + return x_stage1, x_stage2, offset_flow + + def build_wgan_local_discriminator(self, x, reuse=False, training=True): + with tf.variable_scope('discriminator_local', reuse=reuse): + cnum = 64 + x = dis_conv(x, cnum, name='conv1', training=training) + x = dis_conv(x, cnum*2, name='conv2', training=training) + x = dis_conv(x, cnum*4, name='conv3', training=training) + x = dis_conv(x, cnum*8, name='conv4', training=training) + x = flatten(x, name='flatten') + return x + + def build_wgan_global_discriminator(self, x, reuse=False, training=True): + with tf.variable_scope('discriminator_global', reuse=reuse): + cnum = 64 + x = dis_conv(x, cnum, name='conv1', training=training) + x = dis_conv(x, cnum*2, name='conv2', training=training) + x = dis_conv(x, cnum*4, name='conv3', training=training) + x = dis_conv(x, cnum*4, name='conv4', training=training) + x = flatten(x, name='flatten') + return x + + def build_wgan_discriminator(self, batch_local, batch_global, + reuse=False, training=True): + with tf.variable_scope('discriminator', reuse=reuse): + dlocal = self.build_wgan_local_discriminator( + batch_local, reuse=reuse, training=training) + dglobal = self.build_wgan_global_discriminator( + batch_global, reuse=reuse, training=training) + dout_local = tf.layers.dense(dlocal, 1, name='dout_local_fc') + dout_global = tf.layers.dense(dglobal, 1, name='dout_global_fc') + return dout_local, dout_global + + def build_graph_with_losses(self, batch_data, config, training=True, + summary=False, reuse=False): + batch_pos = batch_data / 127.5 - 1. + # generate mask, 1 represents masked point + bbox = random_bbox(config) + mask = bbox2mask(bbox, config, name='mask_c') + batch_incomplete = batch_pos*(1.-mask) + x1, x2, offset_flow = self.build_inpaint_net( + batch_incomplete, mask, config, reuse=reuse, training=training, + padding=config.PADDING) + if config.PRETRAIN_COARSE_NETWORK: + batch_predicted = x1 + logger.info('Set batch_predicted to x1.') + else: + batch_predicted = x2 + logger.info('Set batch_predicted to x2.') + losses = {} + # apply mask and complete image + batch_complete = batch_predicted*mask + batch_incomplete*(1.-mask) + # local patches + local_patch_batch_pos = local_patch(batch_pos, bbox) + local_patch_batch_predicted = local_patch(batch_predicted, bbox) + local_patch_x1 = local_patch(x1, bbox) + local_patch_x2 = local_patch(x2, bbox) + local_patch_batch_complete = local_patch(batch_complete, bbox) + local_patch_mask = local_patch(mask, bbox) + l1_alpha = config.COARSE_L1_ALPHA + losses['l1_loss'] = l1_alpha * tf.reduce_mean(tf.abs(local_patch_batch_pos - local_patch_x1)*spatial_discounting_mask(config)) + if not config.PRETRAIN_COARSE_NETWORK: + losses['l1_loss'] += tf.reduce_mean(tf.abs(local_patch_batch_pos - local_patch_x2)*spatial_discounting_mask(config)) + losses['ae_loss'] = l1_alpha * tf.reduce_mean(tf.abs(batch_pos - x1) * (1.-mask)) + if not config.PRETRAIN_COARSE_NETWORK: + losses['ae_loss'] += tf.reduce_mean(tf.abs(batch_pos - x2) * (1.-mask)) + losses['ae_loss'] /= tf.reduce_mean(1.-mask) + if summary: + scalar_summary('losses/l1_loss', losses['l1_loss']) + scalar_summary('losses/ae_loss', losses['ae_loss']) + viz_img = [batch_pos, batch_incomplete, batch_complete] + if offset_flow is not None: + viz_img.append( + resize(offset_flow, scale=4, + func=tf.image.resize_nearest_neighbor)) + images_summary( + tf.concat(viz_img, axis=2), + 'raw_incomplete_predicted_complete', config.VIZ_MAX_OUT) + + # gan + batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0) + # local deterministic patch + local_patch_batch_pos_neg = tf.concat([local_patch_batch_pos, local_patch_batch_complete], 0) + if config.GAN_WITH_MASK: + batch_pos_neg = tf.concat([batch_pos_neg, tf.tile(mask, [config.BATCH_SIZE*2, 1, 1, 1])], axis=3) + # wgan with gradient penalty + if config.GAN == 'wgan_gp': + # seperate gan + pos_neg_local, pos_neg_global = self.build_wgan_discriminator(local_patch_batch_pos_neg, batch_pos_neg, training=training, reuse=reuse) + pos_local, neg_local = tf.split(pos_neg_local, 2) + pos_global, neg_global = tf.split(pos_neg_global, 2) + # wgan loss + g_loss_local, d_loss_local = gan_wgan_loss(pos_local, neg_local, name='gan/local_gan') + g_loss_global, d_loss_global = gan_wgan_loss(pos_global, neg_global, name='gan/global_gan') + losses['g_loss'] = config.GLOBAL_WGAN_LOSS_ALPHA * g_loss_global + g_loss_local + losses['d_loss'] = d_loss_global + d_loss_local + # gp + interpolates_local = random_interpolates(local_patch_batch_pos, local_patch_batch_complete) + interpolates_global = random_interpolates(batch_pos, batch_complete) + dout_local, dout_global = self.build_wgan_discriminator( + interpolates_local, interpolates_global, reuse=True) + # apply penalty + penalty_local = gradients_penalty(interpolates_local, dout_local, mask=local_patch_mask) + penalty_global = gradients_penalty(interpolates_global, dout_global, mask=mask) + losses['gp_loss'] = config.WGAN_GP_LAMBDA * (penalty_local + penalty_global) + losses['d_loss'] = losses['d_loss'] + losses['gp_loss'] + if summary and not config.PRETRAIN_COARSE_NETWORK: + gradients_summary(g_loss_local, batch_predicted, name='g_loss_local') + gradients_summary(g_loss_global, batch_predicted, name='g_loss_global') + scalar_summary('convergence/d_loss', losses['d_loss']) + scalar_summary('convergence/local_d_loss', d_loss_local) + scalar_summary('convergence/global_d_loss', d_loss_global) + scalar_summary('gan_wgan_loss/gp_loss', losses['gp_loss']) + scalar_summary('gan_wgan_loss/gp_penalty_local', penalty_local) + scalar_summary('gan_wgan_loss/gp_penalty_global', penalty_global) + + if summary and not config.PRETRAIN_COARSE_NETWORK: + # summary the magnitude of gradients from different losses w.r.t. predicted image + gradients_summary(losses['g_loss'], batch_predicted, name='g_loss') + gradients_summary(losses['g_loss'], x1, name='g_loss_to_x1') + gradients_summary(losses['g_loss'], x2, name='g_loss_to_x2') + gradients_summary(losses['l1_loss'], x1, name='l1_loss_to_x1') + gradients_summary(losses['l1_loss'], x2, name='l1_loss_to_x2') + gradients_summary(losses['ae_loss'], x1, name='ae_loss_to_x1') + gradients_summary(losses['ae_loss'], x2, name='ae_loss_to_x2') + if config.PRETRAIN_COARSE_NETWORK: + losses['g_loss'] = 0 + else: + losses['g_loss'] = config.GAN_LOSS_ALPHA * losses['g_loss'] + losses['g_loss'] += config.L1_LOSS_ALPHA * losses['l1_loss'] + logger.info('Set L1_LOSS_ALPHA to %f' % config.L1_LOSS_ALPHA) + logger.info('Set GAN_LOSS_ALPHA to %f' % config.GAN_LOSS_ALPHA) + if config.AE_LOSS: + losses['g_loss'] += config.AE_LOSS_ALPHA * losses['ae_loss'] + logger.info('Set AE_LOSS_ALPHA to %f' % config.AE_LOSS_ALPHA) + g_vars = tf.get_collection( + tf.GraphKeys.TRAINABLE_VARIABLES, 'inpaint_net') + d_vars = tf.get_collection( + tf.GraphKeys.TRAINABLE_VARIABLES, 'discriminator') + return g_vars, d_vars, losses + + def build_infer_graph(self, batch_data, config, bbox=None, name='val'): + """ + """ + config.MAX_DELTA_HEIGHT = 0 + config.MAX_DELTA_WIDTH = 0 + if bbox is None: + bbox = random_bbox(config) + mask = bbox2mask(bbox, config, name=name+'mask_c') + batch_pos = batch_data / 127.5 - 1. + edges = None + batch_incomplete = batch_pos*(1.-mask) + # inpaint + x1, x2, offset_flow = self.build_inpaint_net( + batch_incomplete, mask, config, reuse=True, + training=False, padding=config.PADDING) + if config.PRETRAIN_COARSE_NETWORK: + batch_predicted = x1 + logger.info('Set batch_predicted to x1.') + else: + batch_predicted = x2 + logger.info('Set batch_predicted to x2.') + # apply mask and reconstruct + batch_complete = batch_predicted*mask + batch_incomplete*(1.-mask) + # global image visualization + viz_img = [batch_pos, batch_incomplete, batch_complete] + if offset_flow is not None: + viz_img.append( + resize(offset_flow, scale=4, + func=tf.image.resize_nearest_neighbor)) + images_summary( + tf.concat(viz_img, axis=2), + name+'_raw_incomplete_complete', config.VIZ_MAX_OUT) + return batch_complete + + def build_static_infer_graph(self, batch_data, config, name): + """ + """ + # generate mask, 1 represents masked point + bbox = (tf.constant(config.HEIGHT//2), tf.constant(config.WIDTH//2), + tf.constant(config.HEIGHT), tf.constant(config.WIDTH)) + return self.build_infer_graph(batch_data, config, bbox, name) + + + def build_server_graph(self, batch_data, reuse=False, is_training=False): + """ + """ + # generate mask, 1 represents masked point + batch_raw, masks_raw = tf.split(batch_data, 2, axis=2) + masks = tf.cast(masks_raw[0:1, :, :, 0:1] > 127.5, tf.float32) + + batch_pos = batch_raw / 127.5 - 1. + batch_incomplete = batch_pos * (1. - masks) + # inpaint + x1, x2, flow = self.build_inpaint_net( + batch_incomplete, masks, reuse=reuse, training=is_training, + config=None) + batch_predict = x2 + # apply mask and reconstruct + batch_complete = batch_predict*masks + batch_incomplete*(1-masks) + return batch_complete diff --git a/fg/inpaint_ops.py b/fg/inpaint_ops.py new file mode 100644 index 0000000..afdec3d --- /dev/null +++ b/fg/inpaint_ops.py @@ -0,0 +1,513 @@ +import logging + +import cv2 +import numpy as np +import tensorflow as tf +from tensorflow.contrib.framework.python.ops import add_arg_scope + +from neuralgym.ops.layers import resize +from neuralgym.ops.layers import * +from neuralgym.ops.loss_ops import * +from neuralgym.ops.summary_ops import * + + +logger = logging.getLogger() +np.random.seed(2018) + + +@add_arg_scope +def gen_conv(x, cnum, ksize, stride=1, rate=1, name='conv', + padding='SAME', activation=tf.nn.elu, training=True): + """Define conv for generator. + + Args: + x: Input. + cnum: Channel number. + ksize: Kernel size. + Stride: Convolution stride. + Rate: Rate for or dilated conv. + name: Name of layers. + padding: Default to SYMMETRIC. + activation: Activation function after convolution. + training: If current graph is for training or inference, used for bn. + + Returns: + tf.Tensor: output + + """ + assert padding in ['SYMMETRIC', 'SAME', 'REFELECT'] + if padding == 'SYMMETRIC' or padding == 'REFELECT': + p = int(rate*(ksize-1)/2) + x = tf.pad(x, [[0,0], [p, p], [p, p], [0,0]], mode=padding) + padding = 'VALID' + x = tf.layers.conv2d( + x, cnum, ksize, stride, dilation_rate=rate, + activation=activation, padding=padding, name=name) + return x + + +@add_arg_scope +def gen_deconv(x, cnum, name='upsample', padding='SAME', training=True): + """Define deconv for generator. + The deconv is defined to be a x2 resize_nearest_neighbor operation with + additional gen_conv operation. + + Args: + x: Input. + cnum: Channel number. + name: Name of layers. + training: If current graph is for training or inference, used for bn. + + Returns: + tf.Tensor: output + + """ + with tf.variable_scope(name): + x = resize(x, func=tf.image.resize_nearest_neighbor) + x = gen_conv( + x, cnum, 3, 1, name=name+'_conv', padding=padding, + training=training) + return x + + +@add_arg_scope +def dis_conv(x, cnum, ksize=5, stride=2, name='conv', training=True): + """Define conv for discriminator. + Activation is set to leaky_relu. + + Args: + x: Input. + cnum: Channel number. + ksize: Kernel size. + Stride: Convolution stride. + name: Name of layers. + training: If current graph is for training or inference, used for bn. + + Returns: + tf.Tensor: output + + """ + x = tf.layers.conv2d(x, cnum, ksize, stride, 'SAME', name=name) + x = tf.nn.leaky_relu(x) + return x + + +def random_bbox(config): + """Generate a random tlhw with configuration. + + Args: + config: Config should have configuration including IMG_SHAPES, + VERTICAL_MARGIN, HEIGHT, HORIZONTAL_MARGIN, WIDTH. + + Returns: + tuple: (top, left, height, width) + + """ + img_shape = config.IMG_SHAPES + img_height = img_shape[0] + img_width = img_shape[1] + maxt = img_height - config.VERTICAL_MARGIN - config.HEIGHT + maxl = img_width - config.HORIZONTAL_MARGIN - config.WIDTH + t = tf.random_uniform( + [], minval=config.VERTICAL_MARGIN, maxval=maxt, dtype=tf.int32) + l = tf.random_uniform( + [], minval=config.HORIZONTAL_MARGIN, maxval=maxl, dtype=tf.int32) + h = tf.constant(config.HEIGHT) + w = tf.constant(config.WIDTH) + return (t, l, h, w) + + +def bbox2mask(bbox, config, name='mask'): + """Generate mask tensor from bbox. + + Args: + bbox: configuration tuple, (top, left, height, width) + config: Config should have configuration including IMG_SHAPES, + MAX_DELTA_HEIGHT, MAX_DELTA_WIDTH. + + Returns: + tf.Tensor: output with shape [1, H, W, 1] + + """ + def npmask(bbox, height, width, delta_h, delta_w): + mask = np.zeros((1, height, width, 1), np.float32) + h = np.random.randint(delta_h//2+1) + w = np.random.randint(delta_w//2+1) + mask[:, bbox[0]+h:bbox[0]+bbox[2]-h, + bbox[1]+w:bbox[1]+bbox[3]-w, :] = 1. + return mask + with tf.variable_scope(name), tf.device('/cpu:0'): + img_shape = config.IMG_SHAPES + height = img_shape[0] + width = img_shape[1] + mask = tf.py_func( + npmask, + [bbox, height, width, + config.MAX_DELTA_HEIGHT, config.MAX_DELTA_WIDTH], + tf.float32, stateful=False) + mask.set_shape([1] + [height, width] + [1]) + return mask + + +def local_patch(x, bbox): + """Crop local patch according to bbox. + + Args: + x: input + bbox: (top, left, height, width) + + Returns: + tf.Tensor: local patch + + """ + x = tf.image.crop_to_bounding_box(x, bbox[0], bbox[1], bbox[2], bbox[3]) + return x + + +def resize_mask_like(mask, x): + """Resize mask like shape of x. + + Args: + mask: Original mask. + x: To shape of x. + + Returns: + tf.Tensor: resized mask + + """ + mask_resize = resize( + mask, to_shape=x.get_shape().as_list()[1:3], + func=tf.image.resize_nearest_neighbor) + return mask_resize + + +def spatial_discounting_mask(config): + """Generate spatial discounting mask constant. + + Spatial discounting mask is first introduced in publication: + Generative Image Inpainting with Contextual Attention, Yu et al. + + Args: + config: Config should have configuration including HEIGHT, WIDTH, + DISCOUNTED_MASK. + + Returns: + tf.Tensor: spatial discounting mask + + """ + gamma = config.SPATIAL_DISCOUNTING_GAMMA + shape = [1, config.HEIGHT, config.WIDTH, 1] + if config.DISCOUNTED_MASK: + logger.info('Use spatial discounting l1 loss.') + mask_values = np.ones((config.HEIGHT, config.WIDTH)) + for i in range(config.HEIGHT): + for j in range(config.WIDTH): + mask_values[i, j] = max( + gamma**min(i, config.HEIGHT-i), + gamma**min(j, config.WIDTH-j)) + mask_values = np.expand_dims(mask_values, 0) + mask_values = np.expand_dims(mask_values, 3) + mask_values = mask_values + else: + mask_values = np.ones(shape) + return tf.constant(mask_values, dtype=tf.float32, shape=shape) + + +def contextual_attention(f, b, mask=None, ksize=3, stride=1, rate=1, + fuse_k=3, softmax_scale=10., training=True, fuse=True): + """ Contextual attention layer implementation. + + Contextual attention is first introduced in publication: + Generative Image Inpainting with Contextual Attention, Yu et al. + + Args: + x: Input feature to match (foreground). + t: Input feature for match (background). + mask: Input mask for t, indicating patches not available. + ksize: Kernel size for contextual attention. + stride: Stride for extracting patches from t. + rate: Dilation for matching. + softmax_scale: Scaled softmax for attention. + training: Indicating if current graph is training or inference. + + Returns: + tf.Tensor: output + + """ + # get shapes + raw_fs = tf.shape(f) + raw_int_fs = f.get_shape().as_list() + raw_int_bs = b.get_shape().as_list() + # extract patches from background with stride and rate + kernel = 2*rate + raw_w = tf.extract_image_patches( + b, [1,kernel,kernel,1], [1,rate*stride,rate*stride,1], [1,1,1,1], padding='SAME') + raw_w = tf.reshape(raw_w, [raw_int_bs[0], -1, kernel, kernel, raw_int_bs[3]]) + raw_w = tf.transpose(raw_w, [0, 2, 3, 4, 1]) # transpose to b*k*k*c*hw + # downscaling foreground option: downscaling both foreground and + # background for matching and use original background for reconstruction. + f = resize(f, scale=1./rate, func=tf.image.resize_nearest_neighbor) + b = resize(b, to_shape=[int(raw_int_bs[1]/rate), int(raw_int_bs[2]/rate)], func=tf.image.resize_nearest_neighbor) # https://github.com/tensorflow/tensorflow/issues/11651 + if mask is not None: + mask = resize(mask, scale=1./rate, func=tf.image.resize_nearest_neighbor) + fs = tf.shape(f) + int_fs = f.get_shape().as_list() + f_groups = tf.split(f, int_fs[0], axis=0) + # from t(H*W*C) to w(b*k*k*c*h*w) + bs = tf.shape(b) + int_bs = b.get_shape().as_list() + w = tf.extract_image_patches( + b, [1,ksize,ksize,1], [1,stride,stride,1], [1,1,1,1], padding='SAME') + w = tf.reshape(w, [int_fs[0], -1, ksize, ksize, int_fs[3]]) + w = tf.transpose(w, [0, 2, 3, 4, 1]) # transpose to b*k*k*c*hw + # process mask + if mask is None: + mask = tf.zeros([1, bs[1], bs[2], 1]) + m = tf.extract_image_patches( + mask, [1,ksize,ksize,1], [1,stride,stride,1], [1,1,1,1], padding='SAME') + m = tf.reshape(m, [1, -1, ksize, ksize, 1]) + m = tf.transpose(m, [0, 2, 3, 4, 1]) # transpose to b*k*k*c*hw + m = m[0] + mm = tf.cast(tf.equal(tf.reduce_mean(m, axis=[0,1,2], keep_dims=True), 0.), tf.float32) + w_groups = tf.split(w, int_bs[0], axis=0) + raw_w_groups = tf.split(raw_w, int_bs[0], axis=0) + y = [] + offsets = [] + k = fuse_k + scale = softmax_scale + fuse_weight = tf.reshape(tf.eye(k), [k, k, 1, 1]) + for xi, wi, raw_wi in zip(f_groups, w_groups, raw_w_groups): + # conv for compare + wi = wi[0] + wi_normed = wi / tf.maximum(tf.sqrt(tf.reduce_sum(tf.square(wi), axis=[0,1,2])), 1e-4) + yi = tf.nn.conv2d(xi, wi_normed, strides=[1,1,1,1], padding="SAME") + + # conv implementation for fuse scores to encourage large patches + if fuse: + yi = tf.reshape(yi, [1, fs[1]*fs[2], bs[1]*bs[2], 1]) + yi = tf.nn.conv2d(yi, fuse_weight, strides=[1,1,1,1], padding='SAME') + yi = tf.reshape(yi, [1, fs[1], fs[2], bs[1], bs[2]]) + yi = tf.transpose(yi, [0, 2, 1, 4, 3]) + yi = tf.reshape(yi, [1, fs[1]*fs[2], bs[1]*bs[2], 1]) + yi = tf.nn.conv2d(yi, fuse_weight, strides=[1,1,1,1], padding='SAME') + yi = tf.reshape(yi, [1, fs[2], fs[1], bs[2], bs[1]]) + yi = tf.transpose(yi, [0, 2, 1, 4, 3]) + yi = tf.reshape(yi, [1, fs[1], fs[2], bs[1]*bs[2]]) + + # softmax to match + yi *= mm # mask + yi = tf.nn.softmax(yi*scale, 3) + yi *= mm # mask + + offset = tf.argmax(yi, axis=3, output_type=tf.int32) + offset = tf.stack([offset // fs[2], offset % fs[2]], axis=-1) + # deconv for patch pasting + # 3.1 paste center + wi_center = raw_wi[0] + yi = tf.nn.conv2d_transpose(yi, wi_center, tf.concat([[1], raw_fs[1:]], axis=0), strides=[1,rate,rate,1]) / 4. + y.append(yi) + offsets.append(offset) + y = tf.concat(y, axis=0) + y.set_shape(raw_int_fs) + offsets = tf.concat(offsets, axis=0) + offsets.set_shape(int_bs[:3] + [2]) + # case1: visualize optical flow: minus current position + h_add = tf.tile(tf.reshape(tf.range(bs[1]), [1, bs[1], 1, 1]), [bs[0], 1, bs[2], 1]) + w_add = tf.tile(tf.reshape(tf.range(bs[2]), [1, 1, bs[2], 1]), [bs[0], bs[1], 1, 1]) + offsets = offsets - tf.concat([h_add, w_add], axis=3) + # to flow image + flow = flow_to_image_tf(offsets) + # # case2: visualize which pixels are attended + # flow = highlight_flow_tf(offsets * tf.cast(mask, tf.int32)) + if rate != 1: + flow = resize(flow, scale=rate, func=tf.image.resize_nearest_neighbor) + return y, flow + + +def test_contextual_attention(args): + """Test contextual attention layer with 3-channel image input + (instead of n-channel feature). + + """ + import cv2 + import os + # run on cpu + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + + rate = 2 + stride = 1 + grid = rate*stride + + b = cv2.imread(args.imageA) + b = cv2.resize(b, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC) + h, w, _ = b.shape + b = b[:h//grid*grid, :w//grid*grid, :] + b = np.expand_dims(b, 0) + logger.info('Size of imageA: {}'.format(b.shape)) + + f = cv2.imread(args.imageB) + h, w, _ = f.shape + f = f[:h//grid*grid, :w//grid*grid, :] + f = np.expand_dims(f, 0) + logger.info('Size of imageB: {}'.format(f.shape)) + + with tf.Session() as sess: + bt = tf.constant(b, dtype=tf.float32) + ft = tf.constant(f, dtype=tf.float32) + + yt, flow = contextual_attention( + ft, bt, stride=stride, rate=rate, + training=False, fuse=False) + y = sess.run(yt) + cv2.imwrite(args.imageOut, y[0]) + + +def make_color_wheel(): + RY, YG, GC, CB, BM, MR = (15, 6, 4, 11, 13, 6) + ncols = RY + YG + GC + CB + BM + MR + colorwheel = np.zeros([ncols, 3]) + col = 0 + # RY + colorwheel[0:RY, 0] = 255 + colorwheel[0:RY, 1] = np.transpose(np.floor(255*np.arange(0, RY) / RY)) + col += RY + # YG + colorwheel[col:col+YG, 0] = 255 - np.transpose(np.floor(255*np.arange(0, YG) / YG)) + colorwheel[col:col+YG, 1] = 255 + col += YG + # GC + colorwheel[col:col+GC, 1] = 255 + colorwheel[col:col+GC, 2] = np.transpose(np.floor(255*np.arange(0, GC) / GC)) + col += GC + # CB + colorwheel[col:col+CB, 1] = 255 - np.transpose(np.floor(255*np.arange(0, CB) / CB)) + colorwheel[col:col+CB, 2] = 255 + col += CB + # BM + colorwheel[col:col+BM, 2] = 255 + colorwheel[col:col+BM, 0] = np.transpose(np.floor(255*np.arange(0, BM) / BM)) + col += + BM + # MR + colorwheel[col:col+MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR)) + colorwheel[col:col+MR, 0] = 255 + return colorwheel + + +COLORWHEEL = make_color_wheel() + + +def compute_color(u,v): + h, w = u.shape + img = np.zeros([h, w, 3]) + nanIdx = np.isnan(u) | np.isnan(v) + u[nanIdx] = 0 + v[nanIdx] = 0 + # colorwheel = COLORWHEEL + colorwheel = make_color_wheel() + ncols = np.size(colorwheel, 0) + rad = np.sqrt(u**2+v**2) + a = np.arctan2(-v, -u) / np.pi + fk = (a+1) / 2 * (ncols - 1) + 1 + k0 = np.floor(fk).astype(int) + k1 = k0 + 1 + k1[k1 == ncols+1] = 1 + f = fk - k0 + for i in range(np.size(colorwheel,1)): + tmp = colorwheel[:, i] + col0 = tmp[k0-1] / 255 + col1 = tmp[k1-1] / 255 + col = (1-f) * col0 + f * col1 + idx = rad <= 1 + col[idx] = 1-rad[idx]*(1-col[idx]) + notidx = np.logical_not(idx) + col[notidx] *= 0.75 + img[:, :, i] = np.uint8(np.floor(255 * col*(1-nanIdx))) + return img + + + +def flow_to_image(flow): + """Transfer flow map to image. + Part of code forked from flownet. + """ + out = [] + maxu = -999. + maxv = -999. + minu = 999. + minv = 999. + maxrad = -1 + for i in range(flow.shape[0]): + u = flow[i, :, :, 0] + v = flow[i, :, :, 1] + idxunknow = (abs(u) > 1e7) | (abs(v) > 1e7) + u[idxunknow] = 0 + v[idxunknow] = 0 + maxu = max(maxu, np.max(u)) + minu = min(minu, np.min(u)) + maxv = max(maxv, np.max(v)) + minv = min(minv, np.min(v)) + rad = np.sqrt(u ** 2 + v ** 2) + maxrad = max(maxrad, np.max(rad)) + u = u/(maxrad + np.finfo(float).eps) + v = v/(maxrad + np.finfo(float).eps) + img = compute_color(u, v) + out.append(img) + return np.float32(np.uint8(out)) + + +def flow_to_image_tf(flow, name='flow_to_image'): + """Tensorflow ops for computing flow to image. + """ + with tf.variable_scope(name), tf.device('/cpu:0'): + img = tf.py_func(flow_to_image, [flow], tf.float32, stateful=False) + img.set_shape(flow.get_shape().as_list()[0:-1]+[3]) + img = img / 127.5 - 1. + return img + + +def highlight_flow(flow): + """Convert flow into middlebury color code image. + """ + out = [] + s = flow.shape + for i in range(flow.shape[0]): + img = np.ones((s[1], s[2], 3)) * 144. + u = flow[i, :, :, 0] + v = flow[i, :, :, 1] + for h in range(s[1]): + for w in range(s[1]): + ui = u[h,w] + vi = v[h,w] + img[ui, vi, :] = 255. + out.append(img) + return np.float32(np.uint8(out)) + + +def highlight_flow_tf(flow, name='flow_to_image'): + """Tensorflow ops for highlight flow. + """ + with tf.variable_scope(name), tf.device('/cpu:0'): + img = tf.py_func(highlight_flow, [flow], tf.float32, stateful=False) + img.set_shape(flow.get_shape().as_list()[0:-1]+[3]) + img = img / 127.5 - 1. + return img + + +def image2edge(image): + """Convert image to edges. + """ + out = [] + for i in range(image.shape[0]): + img = cv2.Laplacian(image[i, :, :, :], cv2.CV_64F, ksize=3, scale=2) + out.append(img) + return np.float32(np.uint8(out)) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--imageA', default='', type=str, help='Image A as background patches to reconstruct image B.') + parser.add_argument('--imageB', default='', type=str, help='Image B is reconstructed with image A.') + parser.add_argument('--imageOut', default='result.png', type=str, help='Image B is reconstructed with image A.') + args = parser.parse_args() + test_contextual_attention(args) diff --git a/fg/test.py b/fg/test.py new file mode 100644 index 0000000..a7898b3 --- /dev/null +++ b/fg/test.py @@ -0,0 +1,61 @@ +import argparse + +import cv2 +import numpy as np +import tensorflow as tf +import neuralgym as ng + +from inpaint_model import InpaintCAModel + + +parser = argparse.ArgumentParser() +parser.add_argument('--image', default='', type=str, + help='The filename of image to be completed.') +parser.add_argument('--mask', default='', type=str, + help='The filename of mask, value 255 indicates mask.') +parser.add_argument('--output', default='output.png', type=str, + help='Where to write output.') +parser.add_argument('--checkpoint_dir', default='', type=str, + help='The directory of tensorflow checkpoint.') + + +if __name__ == "__main__": + # ng.get_gpus(0) + args = parser.parse_args() + + model = InpaintCAModel() + image = cv2.imread(args.image) + mask = cv2.imread(args.mask) + + assert image.shape == mask.shape + + h, w, _ = image.shape + grid = 8 + image = image[:h//grid*grid, :w//grid*grid, :] + mask = mask[:h//grid*grid, :w//grid*grid, :] + print('Shape of image: {}'.format(image.shape)) + + image = np.expand_dims(image, 0) + mask = np.expand_dims(mask, 0) + input_image = np.concatenate([image, mask], axis=2) + + sess_config = tf.ConfigProto() + # sess_config.gpu_options.allow_growth = True + with tf.Session(config=sess_config) as sess: + input_image = tf.constant(input_image, dtype=tf.float32) + output = model.build_server_graph(input_image) + output = (output + 1.) * 127.5 + output = tf.reverse(output, [-1]) + output = tf.saturate_cast(output, tf.uint8) + # load pretrained model + vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) + assign_ops = [] + for var in vars_list: + vname = var.name + from_name = vname + var_value = tf.contrib.framework.load_variable(args.checkpoint_dir, from_name) + assign_ops.append(tf.assign(var, var_value)) + sess.run(assign_ops) + print('Model loaded.') + result = sess.run(output) + cv2.imwrite(args.output, result[0][:, :, ::-1]) diff --git a/fg/train.py b/fg/train.py new file mode 100644 index 0000000..3be91f6 --- /dev/null +++ b/fg/train.py @@ -0,0 +1,112 @@ +import os +import glob +import socket +import logging + +import tensorflow as tf +import neuralgym as ng + +from inpaint_model import InpaintCAModel + + +logger = logging.getLogger() + + +def multigpu_graph_def(model, data, config, gpu_id=0, loss_type='g'): + with tf.device('/cpu:0'): + images = data.data_pipeline(config.BATCH_SIZE) + if gpu_id == 0 and loss_type == 'g': + _, _, losses = model.build_graph_with_losses( + images, config, summary=True, reuse=True) + else: + _, _, losses = model.build_graph_with_losses( + images, config, reuse=True) + if loss_type == 'g': + return losses['g_loss'] + elif loss_type == 'd': + return losses['d_loss'] + else: + raise ValueError('loss type is not supported.') + + +if __name__ == "__main__": + config = ng.Config('inpaint.yml') + if config.GPU_ID != -1: + ng.set_gpus(config.GPU_ID) + else: + ng.get_gpus(config.NUM_GPUS) + # training data + with open(config.DATA_FLIST[config.DATASET][0]) as f: + fnames = f.read().splitlines() + data = ng.data.DataFromFNames( + fnames, config.IMG_SHAPES, random_crop=config.RANDOM_CROP) + images = data.data_pipeline(config.BATCH_SIZE) + # main model + model = InpaintCAModel() + g_vars, d_vars, losses = model.build_graph_with_losses( + images, config=config) + # validation images + if config.VAL: + with open(config.DATA_FLIST[config.DATASET][1]) as f: + val_fnames = f.read().splitlines() + # progress monitor by visualizing static images + for i in range(config.STATIC_VIEW_SIZE): + static_fnames = val_fnames[i:i+1] + static_images = ng.data.DataFromFNames( + static_fnames, config.IMG_SHAPES, nthreads=1, + random_crop=config.RANDOM_CROP).data_pipeline(1) + static_inpainted_images = model.build_static_infer_graph( + static_images, config, name='static_view/%d' % i) + # training settings + lr = tf.get_variable( + 'lr', shape=[], trainable=False, + initializer=tf.constant_initializer(1e-4)) + d_optimizer = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9) + g_optimizer = d_optimizer + # gradient processor + if config.GRADIENT_CLIP: + gradient_processor = lambda grad_var: ( + tf.clip_by_average_norm(grad_var[0], config.GRADIENT_CLIP_VALUE), + grad_var[1]) + else: + gradient_processor = None + # log dir + log_prefix = 'model_logs/' + '_'.join([ + ng.date_uid(), socket.gethostname(), config.DATASET, + 'MASKED' if config.GAN_WITH_MASK else 'NORMAL', + config.GAN,config.LOG_DIR]) + # train discriminator with secondary trainer, should initialize before + # primary trainer. + discriminator_training_callback = ng.callbacks.SecondaryTrainer( + pstep=1, + optimizer=d_optimizer, + var_list=d_vars, + max_iters=5, + graph_def=multigpu_graph_def, + graph_def_kwargs={ + 'model': model, 'data': data, 'config': config, 'loss_type': 'd'}, + ) + # train generator with primary trainer + trainer = ng.train.Trainer( + optimizer=g_optimizer, + var_list=g_vars, + max_iters=config.MAX_ITERS, + graph_def=multigpu_graph_def, + grads_summary=config.GRADS_SUMMARY, + gradient_processor=gradient_processor, + graph_def_kwargs={ + 'model': model, 'data': data, 'config': config, 'loss_type': 'g'}, + spe=config.TRAIN_SPE, + log_dir=log_prefix, + ) + # add all callbacks + if not config.PRETRAIN_COARSE_NETWORK: + trainer.add_callbacks(discriminator_training_callback) + trainer.add_callbacks([ + ng.callbacks.WeightsViewer(), + ng.callbacks.ModelRestorer(trainer.context['saver'], dump_prefix='model_logs/'+config.MODEL_RESTORE+'/snap', optimistic=True), + ng.callbacks.ModelSaver(config.TRAIN_SPE, trainer.context['saver'], log_prefix+'/snap'), + ng.callbacks.SummaryWriter((config.VAL_PSTEPS//1), trainer.context['summary_writer'], tf.summary.merge_all()), + ]) + # launch training + trainer.train() diff --git a/gitignore.txt b/gitignore.txt new file mode 100644 index 0000000..c69c02e --- /dev/null +++ b/gitignore.txt @@ -0,0 +1,3 @@ +fg/model_logs +fg/neuralgym_logs +fg/data diff --git a/run_algo.py b/run_algo.py new file mode 100644 index 0000000..24ce523 --- /dev/null +++ b/run_algo.py @@ -0,0 +1,44 @@ +import numpy as np +import cv2 +import sys +import os +import json +category_to_path = {"human":"celeba", "fallback":"imagenet", "place":"places2"} +if __name__ == '__main__': + img_in = sys.argv[1] + img_out = sys.argv[2] + img_mask = "" + if(len(sys.argv) > 3): + img_mask = sys.argv[3] + else: + img_mask = img_out.split('.')[0]+"-mask.png" + img_subdir = img_out.split('.')[0]+"-work" + print("Making Directory...") + os.system("mkdir %s"%img_subdir) + print("Done, proceeding to Making Saliency Map and Threshold Map...") + os.system("python split/saliency.py %s %s/saliency.png"%(img_in, img_subdir)) + print("Done. Proceeding to Making GrabCut Image and Map...") + os.system("python split/grabcut.py %s %s/saliency.png %s/grabcut.png"%(img_in, img_subdir, img_subdir)) + print("Done. Proceeding to creating Telea Infill...") + os.system("python tag/telea-infill.py %s/grabcut-image.png %s %s/telea-image.png"%(img_subdir, img_mask, img_subdir)) #Infills Completely white regions as these are probably holes + print("Done. Proceeding to Classifying Foreground...") + os.system("python tag/models/tutorials/image/imagenet/classify_image.py --image_file %s/telea-image.png --out_file %s/classifier-out.json"%(img_subdir, img_subdir)) + print("Done. Filling In Image Using Background Fill.") + #os.system("python bg/inpainter %s %s -o %s/bg-fill.png"%(img_in, img_mask, img_subdir)) + print("Done. Finding Optimal Foreground Filler.") + os.system("python tag/get-foreground-tagger.py %s/classifier-out.json %s/gan-to-use.json"%(img_subdir,img_subdir)) + print("Done. Running Optimal Foreground Filler.") + try: + file = open("%s/gan-to-use.json"%(img_subdir,), "r") + jsonf = json.loads(file.read()) + path = category_to_path[jsonf['use']] + except Exception as e: + print("Error in getting saved category, using fallback") + path = category_to_path['fallback'] + os.system("python fg/test.py --image %s --mask %s --output %s/gan-fill-0.png --checkpoint_dir fg/model_logs/%s"%(img_in, img_mask, img_subdir, path)) + print("Done. Stitching Images.") + os.system("python stitch/stitch.py %s %s %s 1 %s"%(img_in, img_mask, img_subdir, img_out)) + print("Done! Inpaint Completed!") + + + diff --git a/split/grabcut.py b/split/grabcut.py new file mode 100644 index 0000000..03acfc7 --- /dev/null +++ b/split/grabcut.py @@ -0,0 +1,25 @@ +import numpy as np +import cv2 +import sys +def largest_contour_rect(saliency): + image, contours, hierarchy = cv2.findContours(saliency * 1, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) + contours = sorted(contours, key = cv2.contourArea) + return cv2.boundingRect(contours[-1]) +def refine_saliency_with_grabcut(img, saliency): + rect = largest_contour_rect(saliency) + bgdmodel = np.zeros((1, 65),np.float64) + fgdmodel = np.zeros((1, 65),np.float64) + saliency[np.where(saliency > 0)] = cv2.GC_FGD + mask = saliency + mask, bgdModel, fgdModel = cv2.grabCut(img, mask, rect, bgdmodel, fgdmodel, 1, cv2.GC_INIT_WITH_RECT) + mask_bin = np.where((mask==2)|(mask==0),0,1).astype('uint8') + mask_img = np.where((mask==2)|(mask==0),0,255).astype('uint8') + return mask_bin, mask_img +if __name__ == '__main__': + img = cv2.imread(sys.argv[1], 1) + img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR) + mask = cv2.imread(sys.argv[2], 0) + res_mask, res = refine_saliency_with_grabcut(img, mask) + res_img = img*res_mask[:,:,np.newaxis] + cv2.imwrite(sys.argv[3], res) + cv2.imwrite(sys.argv[3].split(".")[0]+"-image.png", res_img) diff --git a/split/saliency.py b/split/saliency.py new file mode 100644 index 0000000..ac45da7 --- /dev/null +++ b/split/saliency.py @@ -0,0 +1,313 @@ +import math +import sys +import operator +import networkx as nx +#import matplotlib.pyplot as plt +import numpy as np +import scipy.spatial.distance +import scipy.signal +import skimage +import skimage.io +from skimage.segmentation import slic +from skimage.util import img_as_float +from scipy.optimize import minimize +import cv2 +#import pdb + +THRESHOLD = 0.6 + +def raster_scan(img,L,U,D): + n_rows = len(img) + n_cols = len(img[0]) + + for x in range(1,n_rows - 1): + for y in range(1,n_cols - 1): + ix = img[x][y] + d = D[x][y] + + u1 = U[x-1][y] + l1 = L[x-1][y] + + u2 = U[x][y-1] + l2 = L[x][y-1] + + b1 = max(u1,ix) - min(l1,ix) + b2 = max(u2,ix) - min(l2,ix) + + if d <= b1 and d <= b2: + continue + elif b1 < d and b1 <= b2: + D[x][y] = b1 + U[x][y] = max(u1,ix) + L[x][y] = min(l1,ix) + else: + D[x][y] = b2 + U[x][y] = max(u2,ix) + L[x][y] = min(l2,ix) + + return True + +def raster_scan_inv(img,L,U,D): + n_rows = len(img) + n_cols = len(img[0]) + + for x in range(n_rows - 2,1,-1): + for y in range(n_cols - 2,1,-1): + + ix = img[x][y] + d = D[x][y] + + u1 = U[x+1][y] + l1 = L[x+1][y] + + u2 = U[x][y+1] + l2 = L[x][y+1] + + b1 = max(u1,ix) - min(l1,ix) + b2 = max(u2,ix) - min(l2,ix) + + if d <= b1 and d <= b2: + continue + elif b1 < d and b1 <= b2: + D[x][y] = b1 + U[x][y] = max(u1,ix) + L[x][y] = min(l1,ix) + else: + D[x][y] = b2 + U[x][y] = max(u2,ix) + L[x][y] = min(l2,ix) + + return True + +def mbd(img, num_iters): + + if len(img.shape) != 2: + print('did not get 2d np array to fast mbd') + return None + if (img.shape[0] <= 3 or img.shape[1] <= 3): + print('image is too small') + return None + + L = np.copy(img) + U = np.copy(img) + D = float('Inf') * np.ones(img.shape) + D[0,:] = 0 + D[-1,:] = 0 + D[:,0] = 0 + D[:,-1] = 0 + + # unfortunately, iterating over numpy arrays is very slow + img_list = img.tolist() + L_list = L.tolist() + U_list = U.tolist() + D_list = D.tolist() + + for x in range(0,num_iters): + if x%2 == 1: + raster_scan(img_list,L_list,U_list,D_list) + else: + raster_scan_inv(img_list,L_list,U_list,D_list) + + return np.array(D_list) + + +def get_saliency_mbd(input,method='b'): + + img_path_list = [] + #we get either a filename or a list of filenames + if type(input) == type(str()): + img_path_list.append(input) + elif type(input) == type(list()): + img_path_list = input + else: + print('Input type is neither list or string') + return None + + # Saliency map calculation based on: Minimum Barrier Salient Object Detection at 80 FPS + for img_path in img_path_list: + + img = cv2.imread(img_path) + if(len(img.shape)>2 and img.shape[2]>3): + img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR) + img_mean = np.mean(img,axis=(2)) + sal = mbd(img_mean,3) + + + if method == 'b': + # get the background map + + # paper uses 30px for an image of size 300px, so we use 10% + (n_rows,n_cols,n_channels) = img.shape + img_size = math.sqrt(n_rows * n_cols) + border_thickness = int(math.floor(0.1 * img_size)) + + img_lab = img_as_float(skimage.color.rgb2lab(img)) + + px_left = img_lab[0:border_thickness,:,:] + px_right = img_lab[n_rows - border_thickness-1:-1,:,:] + + px_top = img_lab[:,0:border_thickness,:] + px_bottom = img_lab[:,n_cols - border_thickness-1:-1,:] + + px_mean_left = np.mean(px_left,axis=(0,1)) + px_mean_right = np.mean(px_right,axis=(0,1)) + px_mean_top = np.mean(px_top,axis=(0,1)) + px_mean_bottom = np.mean(px_bottom,axis=(0,1)) + + + px_left = px_left.reshape((n_cols*border_thickness,3)) + px_right = px_right.reshape((n_cols*border_thickness,3)) + + px_top = px_top.reshape((n_rows*border_thickness,3)) + px_bottom = px_bottom.reshape((n_rows*border_thickness,3)) + + cov_left = np.cov(px_left.T) + cov_right = np.cov(px_right.T) + + cov_top = np.cov(px_top.T) + cov_bottom = np.cov(px_bottom.T) + + cov_left = np.linalg.inv(cov_left) + cov_right = np.linalg.inv(cov_right) + + cov_top = np.linalg.inv(cov_top) + cov_bottom = np.linalg.inv(cov_bottom) + + + u_left = np.zeros(sal.shape) + u_right = np.zeros(sal.shape) + u_top = np.zeros(sal.shape) + u_bottom = np.zeros(sal.shape) + + u_final = np.zeros(sal.shape) + img_lab_unrolled = img_lab.reshape(img_lab.shape[0]*img_lab.shape[1],3) + + px_mean_left_2 = np.zeros((1,3)) + px_mean_left_2[0,:] = px_mean_left + + u_left = scipy.spatial.distance.cdist(img_lab_unrolled,px_mean_left_2,'mahalanobis', VI=cov_left) + u_left = u_left.reshape((img_lab.shape[0],img_lab.shape[1])) + + px_mean_right_2 = np.zeros((1,3)) + px_mean_right_2[0,:] = px_mean_right + + u_right = scipy.spatial.distance.cdist(img_lab_unrolled,px_mean_right_2,'mahalanobis', VI=cov_right) + u_right = u_right.reshape((img_lab.shape[0],img_lab.shape[1])) + + px_mean_top_2 = np.zeros((1,3)) + px_mean_top_2[0,:] = px_mean_top + + u_top = scipy.spatial.distance.cdist(img_lab_unrolled,px_mean_top_2,'mahalanobis', VI=cov_top) + u_top = u_top.reshape((img_lab.shape[0],img_lab.shape[1])) + + px_mean_bottom_2 = np.zeros((1,3)) + px_mean_bottom_2[0,:] = px_mean_bottom + + u_bottom = scipy.spatial.distance.cdist(img_lab_unrolled,px_mean_bottom_2,'mahalanobis', VI=cov_bottom) + u_bottom = u_bottom.reshape((img_lab.shape[0],img_lab.shape[1])) + + max_u_left = np.max(u_left) + max_u_right = np.max(u_right) + max_u_top = np.max(u_top) + max_u_bottom = np.max(u_bottom) + + u_left = u_left / max_u_left + u_right = u_right / max_u_right + u_top = u_top / max_u_top + u_bottom = u_bottom / max_u_bottom + + u_max = np.maximum(np.maximum(np.maximum(u_left,u_right),u_top),u_bottom) + + u_final = (u_left + u_right + u_top + u_bottom) - u_max + + u_max_final = np.max(u_final) + sal_max = np.max(sal) + sal = sal / sal_max + u_final / u_max_final + + #postprocessing + + # apply centredness map + sal = sal / np.max(sal) + + s = np.mean(sal) + alpha = 50.0 + delta = alpha * math.sqrt(s) + + xv,yv = np.meshgrid(np.arange(sal.shape[1]),np.arange(sal.shape[0])) + (w,h) = sal.shape + w2 = w/2.0 + h2 = h/2.0 + + C = 1 - np.sqrt(np.power(xv - h2,2) + np.power(yv - w2,2)) / math.sqrt(np.power(w2,2) + np.power(h2,2)) + + sal = sal * C + + #increase bg/fg contrast + + def f(x): + b = 10.0 + return 1.0 / (1.0 + math.exp(-b*(x - 0.5))) + + fv = np.vectorize(f) + + sal = sal / np.max(sal) + + sal = fv(sal) + + return sal* 255.0 + +def binarise_saliency_map(saliency_map,method='adaptive',threshold=0.5): + + # check if input is a numpy array + if type(saliency_map).__module__ != np.__name__: + print('Expected numpy array') + return None + + #check if input is 2D + if len(saliency_map.shape) != 2: + print('Saliency map must be 2D') + return None + + if method == 'fixed': + return (saliency_map > threshold) + + elif method == 'adaptive': + adaptive_threshold = 2.0 * threshold * saliency_map.mean() + return (saliency_map > adaptive_threshold) + + elif method == 'cv2': + (T, saliency) = cv2.threshold(saliency_map, 200, 255, cv2.THRESH_BINARY) + return saliency + + else: + print("Method must be one of fixed, adaptive") + return None +def backproject(source, target, levels = 2, scale = 1): + hsv = cv2.cvtColor(source, cv2.COLOR_BGR2HSV) + hsvt = cv2.cvtColor(target, cv2.COLOR_BGR2HSV) + # calculating object histogram + roihist = cv2.calcHist([hsv],[0, 1], None, \ + [levels, levels], [0, 180, 0, 256] ) + + # normalize histogram and apply backprojection + cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX) + dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256], scale) + return dst +def get_saliency_backproj(img): + img = cv2.imread(img, 1) + cv2.pyrMeanShiftFiltering(img, 2, 10, img, 4) + backproj = np.uint8(backproject(img, img, levels = 2)) + cv2.normalize(backproj,backproj,0,255,cv2.NORM_MINMAX) + saliencies = [backproj, backproj, backproj] + saliency = cv2.merge(saliencies) + cv2.pyrMeanShiftFiltering(saliency, 20, 200, saliency, 2) + saliency = cv2.cvtColor(saliency, cv2.COLOR_BGR2GRAY) + cv2.equalizeHist(saliency, saliency) + return saliency +if __name__ == '__main__': + sal = get_saliency_mbd(sys.argv[1]).astype('uint8') + binarised = 255*binarise_saliency_map(sal, method="adaptive", threshold=THRESHOLD).astype('uint8') #Thresholding + #binarised = binarise_saliency_map(sal, method="cv2", threshold=0.8).astype('uint8') + cv2.imwrite(sys.argv[2], binarised) + cv2.imwrite((sys.argv[2].split('.')[0])+"-full-saliency.png", sal) +#https://github.com/yhenon/pyimgsaliency \ No newline at end of file diff --git a/stitch/stitch.py b/stitch/stitch.py new file mode 100644 index 0000000..9e75688 --- /dev/null +++ b/stitch/stitch.py @@ -0,0 +1,33 @@ +import sys +import cv2 as cv +import numpy as np + +if __name__=="__main__": + if(len(sys.argv)<6): + print("Not Enough Arguments to stitch!") + exit(-1) + inp = sys.argv[1] + mask = sys.argv[2] + subdir = sys.argv[3] + gan_no = int(sys.argv[4]) + output = sys.argv[5] + + img_inp = cv.imread(inp) + img_mask = cv.imread(mask, 0) + img_mask[img_mask>0] = 1 #Binarise Mask so we can take products + img_gan = [cv.imread("%s/gan-fill-%s.png"%(subdir, x)) for x in range(gan_no)] + img_gan = img_gan[0] + img_gan_region = cv.imread("%s/grabcut.png"%(subdir,), 0) + img_gan_region[img_gan_region>0] = 1 + img_bg = cv.imread("%s/bg-fill.png"%(subdir,)) + img_gan_region = np.multiply(img_gan_region, img_mask) + img_out = img_inp.copy() + for i in range(img_inp.shape[0]): + for j in range(img_inp.shape[1]): + if(img_gan_region[i][j]==1): + img_out[i][j] = img_gan[i][j] + elif(img_mask[i][j]==1): + img_out[i][j] = img_bg[i][j] + else: + img_out[i][j] = img_inp[i][j] + cv.imwrite(output, img_out) diff --git a/tag/get-foreground-tagger.py b/tag/get-foreground-tagger.py new file mode 100644 index 0000000..1d1b3ee --- /dev/null +++ b/tag/get-foreground-tagger.py @@ -0,0 +1,56 @@ +import nltk +from nltk.corpus import wordnet +foreground_categories = {'human':('humans','human','faces','face','person','mask','abaya'), 'place':('building','house','city','place')} +import sys +import json +from itertools import product + +THRESHOLD = 0.8 + +if __name__ == '__main__': + if(len(sys.argv)<3): + exit(1) + classifier_out = sys.argv[1] + output = sys.argv[2] + + finp = open(classifier_out, 'r') + raw_data = json.loads(finp.read()) + data = [] + max_weight = 0.0 + for key in raw_data: + for category in key.split(','): + category = category.strip() + data.append((category, float(raw_data[key]))) + if(float(raw_data[key]) > max_weight): + max_weight = float(raw_data[key]) + best = [x[0] for x in data if x[1] == max_weight] + print("Best tags are %s"%(best,)) + try: + nltk.data.find('corpora/wordnet') + except: + nltk.download('wordnet') + tag_synsets = [] + for tag in best: + tag_synsets.extend(wordnet.synsets(tag)) + tag_synsets = set(tag_synsets) + category_max = [] + for key in foreground_categories: + category_synset = [] + for tag in foreground_categories[key]: + category_synset.extend(wordnet.synsets(tag)) + category_synset = set(category_synset) + similarity = [(wordnet.wup_similarity(s1, s2) or 0, s1, s2) for s1, s2 in product(tag_synsets, category_synset)] + max_item = max(similarity) + max_list = [x for x in (similarity) if max_item[0] == x[0]] + category_max.append((max_item[0], len(max_list), key)) + category_max.sort(reverse = True) + print("Best match is with %s with confidence %s"%(category_max[0][2], category_max[0][0])) + fout = open(output, "w") + if(category_max[0][0] < THRESHOLD): + print("But this is below threshold. Falling back to general GAN.") + fout.write(json.dumps({'use':'fallback'})) + else: + print("Using this GAN.") + fout.write(json.dumps({'use':category_max[0][2]})) + fout.close() + exit(0) diff --git a/tag/telea-infill.py b/tag/telea-infill.py new file mode 100644 index 0000000..e15be8d --- /dev/null +++ b/tag/telea-infill.py @@ -0,0 +1,21 @@ +import numpy as np +import cv2 +import sys + +if __name__ == '__main__': + img = cv2.imread(sys.argv[1], 1) + if(img is None): + raise Exception("Image Not Found") + exit() + if(len(img.shape)>2 and img.shape[2]>3): + img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR) + greyscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + greyscale[greyscale<245] = 0 + greyscale[greyscale>=245] = 1 + mask = cv2.imread(sys.argv[2], 0) + mask[mask>0] = 1 + mask = np.multiply(greyscale, mask) + mask[mask>0] = 255 + dst = cv2.inpaint(img,mask,3,cv2.INPAINT_TELEA) + cv2.imwrite(sys.argv[3].split(".")[0]+'-telea-mask.png', mask) + cv2.imwrite(sys.argv[3], dst) diff --git 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self._update_confidence() + self._update_data() + self.priority = self.confidence * self.data * self.front + + def _update_confidence(self): + new_confidence = np.copy(self.confidence) + front_positions = np.argwhere(self.front == 1) + for point in front_positions: + patch = self._get_patch(point) + new_confidence[point[0], point[1]] = sum(sum( + self._patch_data(self.confidence, patch) + ))/self._patch_area(patch) + + self.confidence = new_confidence + + def _update_data(self): + normal = self._calc_normal_matrix() + gradient = self._calc_gradient_matrix() + + normal_gradient = normal*gradient + self.data = np.sqrt( + normal_gradient[:, :, 0]**2 + normal_gradient[:, :, 1]**2 + ) + 0.001 # To be sure to have a greater than 0 data + + def _calc_normal_matrix(self): + x_kernel = np.array([[.25, 0, -.25], [.5, 0, -.5], [.25, 0, -.25]]) + y_kernel = np.array([[-.25, -.5, -.25], [0, 0, 0], [.25, .5, .25]]) + + x_normal = convolve(self.working_mask.astype(float), x_kernel) + y_normal = convolve(self.working_mask.astype(float), y_kernel) + normal = np.dstack((x_normal, y_normal)) + + height, width = normal.shape[:2] + norm = np.sqrt(y_normal**2 + x_normal**2) \ + .reshape(height, width, 1) \ + .repeat(2, axis=2) + norm[norm == 0] = 1 + + unit_normal = normal/norm + return unit_normal + + def _calc_gradient_matrix(self): + # TODO: find a better method to calc the gradient + height, width = self.working_image.shape[:2] + + grey_image = rgb2grey(self.working_image) + grey_image[self.working_mask == 1] = None + + gradient = np.nan_to_num(np.array(np.gradient(grey_image))) + gradient_val = np.sqrt(gradient[0]**2 + gradient[1]**2) + max_gradient = np.zeros([height, width, 2]) + + front_positions = np.argwhere(self.front == 1) + for point in front_positions: + patch = self._get_patch(point) + patch_y_gradient = self._patch_data(gradient[0], patch) + patch_x_gradient = self._patch_data(gradient[1], patch) + patch_gradient_val = self._patch_data(gradient_val, patch) + + patch_max_pos = np.unravel_index( + patch_gradient_val.argmax(), + patch_gradient_val.shape + ) + + max_gradient[point[0], point[1], 0] = \ + patch_y_gradient[patch_max_pos] + max_gradient[point[0], point[1], 1] = \ + patch_x_gradient[patch_max_pos] + + return max_gradient + + def _find_highest_priority_pixel(self): + point = np.unravel_index(self.priority.argmax(), self.priority.shape) + return point + + def _find_source_patch(self, target_pixel): + target_patch = self._get_patch(target_pixel) + height, width = self.working_image.shape[:2] + patch_height, patch_width = self._patch_shape(target_patch) + + best_match = None + best_match_difference = 0 + + lab_image = rgb2lab(self.working_image) + + for y in range(height - patch_height + 1): + for x in range(width - patch_width + 1): + source_patch = [ + [y, y + patch_height-1], + [x, x + patch_width-1] + ] + if self._patch_data(self.working_mask, source_patch) \ + .sum() != 0: + continue + + difference = self._calc_patch_difference( + lab_image, + target_patch, + source_patch + ) + + if best_match is None or difference < best_match_difference: + best_match = source_patch + best_match_difference = difference + return best_match + + def _update_image(self, target_pixel, source_patch): + target_patch = self._get_patch(target_pixel) + pixels_positions = np.argwhere( + self._patch_data( + self.working_mask, + target_patch + ) == 1 + ) + [target_patch[0][0], target_patch[1][0]] + patch_confidence = self.confidence[target_pixel[0], target_pixel[1]] + for point in pixels_positions: + self.confidence[point[0], point[1]] = patch_confidence + + mask = self._patch_data(self.working_mask, target_patch) + rgb_mask = self._to_rgb(mask) + source_data = self._patch_data(self.working_image, source_patch) + target_data = self._patch_data(self.working_image, target_patch) + + new_data = source_data*rgb_mask + target_data*(1-rgb_mask) + + self._copy_to_patch( + self.working_image, + target_patch, + new_data + ) + self._copy_to_patch( + self.working_mask, + target_patch, + 0 + ) + + def _get_patch(self, point): + half_patch_size = (self.patch_size-1)//2 + height, width = self.working_image.shape[:2] + patch = [ + [ + max(0, point[0] - half_patch_size), + min(point[0] + half_patch_size, height-1) + ], + [ + max(0, point[1] - half_patch_size), + min(point[1] + half_patch_size, width-1) + ] + ] + return patch + + def _calc_patch_difference(self, image, target_patch, source_patch): + mask = 1 - self._patch_data(self.working_mask, target_patch) + rgb_mask = self._to_rgb(mask) + target_data = self._patch_data( + image, + target_patch + ) * rgb_mask + source_data = self._patch_data( + image, + source_patch + ) * rgb_mask + squared_distance = ((target_data - source_data)**2).sum() + euclidean_distance = np.sqrt( + (target_patch[0][0] - source_patch[0][0])**2 + + (target_patch[1][0] - source_patch[1][0])**2 + ) # tie-breaker factor + return squared_distance + euclidean_distance + + def _finished(self): + height, width = self.working_image.shape[:2] + remaining = self.working_mask.sum() + total = height * width + print('%d of %d completed' % (total-remaining, total)) + return remaining == 0 + + @staticmethod + def _patch_area(patch): + return (1+patch[0][1]-patch[0][0]) * (1+patch[1][1]-patch[1][0]) + + @staticmethod + def _patch_shape(patch): + return (1+patch[0][1]-patch[0][0]), (1+patch[1][1]-patch[1][0]) + + @staticmethod + def _patch_data(source, patch): + return source[ + patch[0][0]:patch[0][1]+1, + patch[1][0]:patch[1][1]+1 + ] + + @staticmethod + def _copy_to_patch(dest, dest_patch, data): + dest[ + dest_patch[0][0]:dest_patch[0][1]+1, + dest_patch[1][0]:dest_patch[1][1]+1 + ] = data + + @staticmethod + def _to_rgb(image): + height, width = image.shape + return image.reshape(height, width, 1).repeat(3, axis=2) diff --git a/bg/resources/image1.jpg b/bg/resources/image1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..18b1685a6c4ca94c3b0a940b29bc93f73b22ee71 GIT binary 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