diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..079d9d5 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,63 @@ +name: Release CLI Binaries + +on: + release: + types: [ published ] + +jobs: + build: + strategy: + matrix: + os: [ windows-latest, ubuntu-latest, macos-latest ] # macos-13 for x86_64 support? + runs-on: ${{ matrix.os }} + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Build executable with Versatile PyInstaller + uses: sayyid5416/pyinstaller@v1 + with: + python_ver: '3.12' + pyinstaller_ver: '==6.11.1' + requirements: 'server/requirements.txt' + spec: 'server/main.spec' + upload_exe_with_name: 'DeepCreamPy-${{ runner.os }}' + + release: + needs: build + runs-on: ubuntu-latest + steps: + - name: Download artifacts + uses: actions/download-artifact@v4 + with: + path: artifacts + + - name: Upload Windows binary + uses: actions/upload-release-asset@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + upload_url: ${{ github.event.release.upload_url }} + asset_path: artifacts/DeepCreamPy-Windows/DeepCreamPy.exe + asset_name: DeepCreamPy-x86_64-pc-windows.exe + asset_content_type: application/octet-stream + + - name: Upload Linux binary + uses: actions/upload-release-asset@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + upload_url: ${{ github.event.release.upload_url }} + asset_path: artifacts/DeepCreamPy-Linux/DeepCreamPy + asset_name: DeepCreamPy-x86_64-unknown-linux + asset_content_type: application/octet-stream + + - name: Upload macOS binary + uses: actions/upload-release-asset@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + upload_url: ${{ github.event.release.upload_url }} + asset_path: artifacts/DeepCreamPy-macOS/DeepCreamPy + asset_name: DeepCreamPy-aarch64-apple-darwin + asset_content_type: application/octet-stream \ No newline at end of file diff --git a/.gitignore b/.gitignore index 844f948..70dfa4f 100644 --- a/.gitignore +++ b/.gitignore @@ -27,6 +27,7 @@ .idea/**/usage.statistics.xml .idea/**/dictionaries .idea/**/shelf +.idea # Generated files .idea/**/contentModel.xml @@ -247,6 +248,8 @@ $RECYCLE.BIN/ *.msm *.msp +.DS_Store + # Windows shortcuts *.lnk diff --git a/README.md b/README.md index 607fb64..642bf43 100644 --- a/README.md +++ b/README.md @@ -9,8 +9,9 @@ A deep learning-based tool to automatically replace parts of artworks with plausible reconstructions. Before using DeepCreamPy, the user must mark regions in the artwork using green color with an image editing program (e.g., GIMP, Photoshop). DeepCreamPy takes the images with green colored regions as input, and a neural network automatically fills in the highlighted regions. +You could also create a mask file that is white where the image needs to be decensored -You can download the latest release for Windows 64-bit [here](https://github.com/Deepshift/DeepCreamPy/releases/latest). +You can download the latest release [here](https://github.com/Deepshift/DeepCreamPy/releases/latest). For users interested in compiling DeepCreamPy themselves, DeepCreamPy can run on Windows, Mac, and Linux. @@ -37,7 +38,7 @@ It does NOT work with: ## Table of Contents Setup: -* [Running latest Window 64-bit release](docs/INSTALLATION_BINARY.md) +* [Running latest release](docs/INSTALLATION_BINARY.md) * [Running code yourself](docs/INSTALLATION.md) Usage: @@ -49,8 +50,6 @@ Miscellaneous: * [FAQ](docs/FAQ.md) ## To do -- Moving to PyTorch or newer versions of TensorFlow -- Improving UI - Error logging ## Contributions diff --git a/cleardcp.py b/cleardcp.py deleted file mode 100644 index b52ede4..0000000 --- a/cleardcp.py +++ /dev/null @@ -1,30 +0,0 @@ -# cleardcp.py -# This script removes all files from `decensor_input', `decensor_input_original' and `decensor_output' directories. -# Originally written by github.com/DioKyrie -# - -import os -from typing import Union - -def clear_folder(folder_path: Union[str, bytes]) -> None: - try: - for filename in os.listdir(folder_path): - file_path = os.path.join(folder_path, filename) - if os.path.isfile(file_path): - os.remove(file_path) - elif os.path.isdir(file_path): - clear_folder(file_path) - print(f"Content of '{folder_path}' cleared successfully.") - except Exception as e: - print(f"Error clearing content of '{folder_path}': {e}") - - -def main(): - folders_to_clear = ["decensor_input", "decensor_input_original", "decensor_output"] - for folder in folders_to_clear: - folder_path = os.path.join(os.path.dirname(__file__), folder) - clear_folder(folder_path) - - -if __name__ == "__main__": - main() diff --git a/config.py b/config.py deleted file mode 100755 index 2bfe16b..0000000 --- a/config.py +++ /dev/null @@ -1,43 +0,0 @@ -import argparse - -def str2floatarr(v): - if type(v) == str: - try: - return [float(v) for v in v.split(',')] - except: - raise argparse.ArgumentTypeError('Integers separated by commas expected.') - else: - raise argparse.ArgumentTypeError('Integers separated by commas expected.') - -def str2bool(v): - if v.lower() in ('yes', 'true', 't', 'y', '1', True): - return True - elif v.lower() in ('no', 'false', 'f', 'n', '0', False): - return False - else: - raise argparse.ArgumentTypeError('Boolean value expected.') - -def get_args(): - parser = argparse.ArgumentParser(description='') - - #Input output folders settings - parser.add_argument('--decensor_input_path', default='./decensor_input/', help='input images with censored regions colored green to be decensored by decensor.py path') - parser.add_argument('--decensor_input_original_path', default='./decensor_input_original/', help='input images with no modifications to be decensored by decensor.py path') - parser.add_argument('--decensor_output_path', default='./decensor_output/', help='output images generated from running decensor.py path') - parser.add_argument('--clean-up-input-dirs', dest='clean_up_input_dirs', action='store_true', default=False, help='whether to delete all image files in the input directories when decensoring is done') - - #Decensor settings - parser.add_argument('--mask_color_red', default=0, help='red channel of mask color in decensoring') - parser.add_argument('--mask_color_green', default=255, help='green channel of mask color in decensoring') - parser.add_argument('--mask_color_blue', default=0, help='blue channel of mask color in decensoring') - parser.add_argument('--is_mosaic', type=str2bool, default='False', help='true if image has mosaic censoring, false otherwise') - parser.add_argument('--variations', type=int, choices=[1, 2, 4], default=1, help='number of decensor variations to be generated') - - #Other settings - parser.add_argument('--ui_mode', default=False, help='true if you want ui mode, false if you want command line interface') - - args = parser.parse_args() - return args - -if __name__ == '__main__': - get_args() diff --git a/decensor.py b/decensor.py deleted file mode 100755 index 7699f76..0000000 --- a/decensor.py +++ /dev/null @@ -1,414 +0,0 @@ - -try: - # default library - import os, logging, sys, config -except ImportError as e: - print("Error when importing DEFAULT library : ", e) - print("\nIf you made script named [\"os.py\", \"logging.py\", \"sys.py\", \"config.py\"] rename it") - print("If not, re-install python or check your Python environment variables") -try: - # local library - import file - from model import InpaintNN - from libs.utils import * - # external library - import numpy as np - from PIL import Image - import tensorflow as tf - from PySide2 import QtCore # for QThread -except ImportError as e: - print("\n"+ '='*20 + " ImportError " + "=" * 20 + "\n") - if e.__class__.__name__ == "ModuleNotFoundError": - print(e) - print("Python libraries are missing. You can install all required libraries by running in the command line (terminal)") - print("cpu version : pip install -r requirements-cpu.txt") - print("gpu version : pip install -r requirements-gpu.txt") - else: - print("Error when importing libraries: ", e) - print("\nIf pip doesn't work, try update through Anaconda") - print("install Anaconda : https://www.anaconda.com/distribution/ \n") - -""" -to allow decensoring in CLI, ignore all methods targeting Qt signals, -when they aren't initialized (i.e. when the class isn't created by the GUI) -""" -class IgnoreAll(object): - def __getattr__(self,attr): - return IgnoreAll() - - def __call__(self, *args, **kwargs): - return IgnoreAll() - -class Decensor(QtCore.QThread): - def __init__(self, parentThread = None, text_edit = None, text_cursor = None, ui_mode = None): - super().__init__(parentThread) - args = config.get_args() - self.is_mosaic = args.is_mosaic - self.variations = args.variations - self.mask_color = [args.mask_color_red/255.0, args.mask_color_green/255.0, args.mask_color_blue/255.0] - self.decensor_input_path = args.decensor_input_path - self.decensor_input_original_path = args.decensor_input_original_path - self.decensor_output_path = args.decensor_output_path - self.clean_up_input_dirs = args.clean_up_input_dirs - - self.signals = IgnoreAll() # Signals class will be given by progressWindow - - self.model = None - self.warm_up = False - - # if ui_mode is not None: - # self.ui_mode = ui_mode - # else: - # self.ui_mode = args.ui_mode - # - # if self.ui_mode: - # self.text_edit = text_edit - # self.text_cursor = text_cursor - # self.ui_mode = True - - if not os.path.exists(self.decensor_output_path): - os.makedirs(self.decensor_output_path) - - def run(self): - if not self.warm_up : - print("if self.warm_up :") - self.load_model() - return - elif self.warm_up: - print("elif not self.warm_up:") - self.decensor_all_images_in_folder() - self.do_post_jobs() - - def stop(self): - # in case of stopping decensor, terminate not to run if self while MainWindow is closed - self.terminate() - - def set_clean_up_input_dirs(self, value): - self.clean_up_input_dirs = value - - def find_mask(self, colored): - # self.signals.update_progress_LABEL.emit("find_mask()", "finding mask...") - mask = np.ones(colored.shape, np.uint8) - i, j = np.where(np.all(colored[0] == self.mask_color, axis=-1)) - mask[0, i, j] = 0 - return mask - - def load_model(self): - self.signals.insertText_progressCursor.emit("Loading model ... please wait ...\n") - if self.model is None : - self.model = InpaintNN(bar_model_name = "./models/bar/Train_775000.meta", - bar_checkpoint_name = "./models/bar/", - mosaic_model_name = "./models/mosaic/Train_290000.meta", - mosaic_checkpoint_name = "./models/mosaic/", - is_mosaic=self.is_mosaic) - self.warm_up = True - print("load model finished") - self.signals.insertText_progressCursor.emit("Loading model finished!\n") - self.signals.update_decensorButton_Text.emit("Decensor Your Images") - self.signals.update_decensorButton_Enabled.emit(True) - - def decensor_all_images_in_folder(self): - #load model once at beginning and reuse same model - if not self.warm_up : - # incase of running by source code - self.load_model() - - input_color_dir = self.decensor_input_path - file_names = os.listdir(input_color_dir) - - input_dir = self.decensor_input_path - output_dir = self.decensor_output_path - - # Change False to True before release --> file.check_file(input_dir, output_dir, True) - # self.signals.update_progress_LABEL.emit("file.check_file()", "Checking image files and directory...") - self.signals.insertText_progressCursor.emit("Checking image files and directory...\n") - - file_names, self.files_removed = file.check_file(input_dir, output_dir, False) - - # self.signals.total_ProgressBar_update_MAX_VALUE.emit("set total progress bar MaxValue : "+str(len(file_names)),len(file_names)) - ''' - print("set total progress bar MaxValue : "+str(len(file_names))) - self.signals.update_ProgressBar_MAX_VALUE.emit(len(file_names)) - ''' - self.signals.insertText_progressCursor.emit("Decensoring {} image files\n".format(len(file_names))) - - #convert all images into np arrays and put them in a list - for n, file_name in enumerate(file_names, start = 1): - # self.signals.total_ProgressBar_update_VALUE.emit("Decensoring {} / {}".format(n, len(file_names)), n) - ''' - self.update_ProgressBar_SET_VALUE.emit(n) - print("Decensoring {} / {}".format(n, len(file_names))) - ''' - self.signals.insertText_progressCursor.emit("Decensoring image file : {}\n".format(file_name)) - - # signal progress bar value == masks decensored on image , - # e.g) sample image : 17 - # self.signals.signal_ProgressBar_update_VALUE.emit("reset value", 0) # set to 0 for every image at start - # self.signals.update_progress_LABEL.emit("for-loop, \"for file_name in file_names:\"","Decensoring : "+str(file_name)) - - color_file_path = os.path.join(input_color_dir, file_name) - color_basename, color_ext = os.path.splitext(file_name) - if os.path.isfile(color_file_path) and color_ext.casefold() == ".png": - print("--------------------------------------------------------------------------") - print("Decensoring the image {}\n".format(color_file_path)) - try : - colored_img = Image.open(color_file_path) - except: - print("Cannot identify image file (" +str(color_file_path)+")") - self.files_removed.append((color_file_path,3)) - # incase of abnormal file format change (ex : text.txt -> text.png) - continue - - #if we are doing a mosaic decensor - if self.is_mosaic: - #get the original file that hasn't been colored - ori_dir = self.decensor_input_original_path - test_file_names = os.listdir(ori_dir) - #since the original image might not be a png, test multiple file formats - valid_formats = {".png", ".jpg", ".jpeg"} - for test_file_name in test_file_names: - test_basename, test_ext = os.path.splitext(test_file_name) - if (test_basename == color_basename) and (test_ext.casefold() in valid_formats): - ori_file_path = os.path.join(ori_dir, test_file_name) - ori_img = Image.open(ori_file_path) - # colored_img.show() - self.decensor_image_variations(ori_img, colored_img, file_name) - break - else: #for...else, i.e if the loop finished without encountering break - print("Corresponding original, uncolored image not found in {}".format(color_file_path)) - print("Check if it exists and is in the PNG or JPG format.") - self.signals.insertText_progressCursor.emit("Corresponding original, uncolored image not found in {}\n".format(color_file_path)) - self.signals.insertText_progressCursor.emit("Check if it exists and is in the PNG or JPG format.\n") - #if we are doing a bar decensor - else: - self.decensor_image_variations(colored_img, colored_img, file_name) - else: - print("--------------------------------------------------------------------------") - print("Image can't be found: "+str(color_file_path)) - self.signals.insertText_progressCursor.emit("Image can't be found: "+str(color_file_path) + "\n") - - print("--------------------------------------------------------------------------") - if self.files_removed is not None: - file.error_messages(None, self.files_removed) - print("\nDecensoring complete!") - - #unload model to prevent memory issues - # self.signals.update_progress_LABEL.emit("finished", "Decensoring complete! Close this window and reopen DCP to start a new session.") - self.signals.insertText_progressCursor.emit("\nDecensoring complete! remove decensored file before decensoring again not to overwrite") - self.signals.update_decensorButton_Enabled.emit(True) - tf.reset_default_graph() - - def decensor_image_variations(self, ori, colored, file_name=None): - for i in range(self.variations): - self.decensor_image_variation(ori, colored, i, file_name) - - #create different decensors of the same image by flipping the input image - def apply_variant(self, image, variant_number): - if variant_number == 0: - return image - elif variant_number == 1: - return image.transpose(Image.FLIP_LEFT_RIGHT) - elif variant_number == 2: - return image.transpose(Image.FLIP_TOP_BOTTOM) - else: - return image.transpose(Image.FLIP_LEFT_RIGHT).transpose(Image.FLIP_TOP_BOTTOM) - - #decensors one image at a time - #TODO: decensor all cropped parts of the same image in a batch (then i need input for colored an array of those images and make additional changes) - def decensor_image_variation(self, ori, colored, variant_number, file_name): - ori = self.apply_variant(ori, variant_number) - colored = self.apply_variant(colored, variant_number) - width, height = ori.size - #save the alpha channel if the image has an alpha channel - has_alpha = False - if (ori.mode == "RGBA"): - has_alpha = True - alpha_channel = np.asarray(ori)[:,:,3] - alpha_channel = np.expand_dims(alpha_channel, axis =-1) - ori = ori.convert('RGB') - - ori_array = image_to_array(ori) - ori_array = np.expand_dims(ori_array, axis = 0) - - if self.is_mosaic: - #if mosaic decensor, mask is empty - # mask = np.ones(ori_array.shape, np.uint8) - # print(mask.shape) - colored = colored.convert('RGB') - color_array = image_to_array(colored) - color_array = np.expand_dims(color_array, axis = 0) - mask = self.find_mask(color_array) - mask_reshaped = mask[0,:,:,:] * 255.0 - mask_img = Image.fromarray(mask_reshaped.astype('uint8')) - # mask_img.show() - - else: - mask = self.find_mask(ori_array) - - #colored image is only used for finding the regions - regions = find_regions(colored.convert('RGB'), [v*255 for v in self.mask_color]) - print("Found {region_count} censored regions in this image!".format(region_count = len(regions))) - self.signals.insertText_progressCursor.emit("Found {region_count} censored regions in this image!".format(region_count = len(regions))) - - if len(regions) == 0 and not self.is_mosaic: - print("No green (0,255,0) regions detected! Make sure you're using exactly the right color.") - self.signals.insertText_progressCursor.emit("No green (0,255,0) regions detected! Make sure you're using exactly the right color.\n") - return - - # self.signals.signal_ProgressBar_update_MAX_VALUE.emit("Found {} masked regions".format(len(regions)), len(regions)) - print("Found {} masked regions".format(len(regions))) - - # self.signals.insertText_progressCursor.emit("Found {} masked regions\n".format(len(regions))) - self.signals.update_ProgressBar_MAX_VALUE.emit(len(regions)) - self.signals.update_ProgressBar_SET_VALUE.emit(0) - - output_img_array = ori_array[0].copy() - - for region_counter, region in enumerate(regions, 1): - # self.signals.update_progress_LABEL.emit("\"Decensoring regions in image\"","Decensoring censor {}/{}".format(region_counter,len(regions))) - self.signals.insertText_progressCursor.emit("Decensoring regions in image, Decensoring censor {}/{}".format(region_counter,len(regions))) - bounding_box = expand_bounding(ori, region, expand_factor=1.5) - crop_img = ori.crop(bounding_box) - # crop_img.show() - #convert mask back to image - mask_reshaped = mask[0,:,:,:] * 255.0 - mask_img = Image.fromarray(mask_reshaped.astype('uint8')) - #resize the cropped images - crop_img = crop_img.resize((256, 256)) - crop_img_array = image_to_array(crop_img) - #resize the mask images - mask_img = mask_img.crop(bounding_box) - mask_img = mask_img.resize((256, 256)) - # mask_img.show() - #convert mask_img back to array - mask_array = image_to_array(mask_img) - #the mask has been upscaled so there will be values not equal to 0 or 1 - - # mask_array[mask_array > 0] = 1 - # crop_img_array[..., :-1][mask_array==0] = (0,0,0) - - if not self.is_mosaic: - a, b = np.where(np.all(mask_array == 0, axis = -1)) - # print(a,b) - # print(crop_img_array[a,b]) - # print(crop_img_array[a,b,0]) - # print(crop_img_array.shape) - # print(type(crop_img_array[0,0])) - crop_img_array[a,b,:] = 0. - # temp = Image.fromarray((crop_img_array * 255.0).astype('uint8')) - # temp.show() - - crop_img_array = np.expand_dims(crop_img_array, axis = 0) - mask_array = np.expand_dims(mask_array, axis = 0) - - # print(np.amax(crop_img_array)) - # print(np.amax(mask_array)) - # print(np.amax(masked)) - - # print(np.amin(crop_img_array)) - # print(np.amin(mask_array)) - # print(np.amin(masked)) - - # print(mask_array) - - crop_img_array = crop_img_array * 2.0 - 1 - # mask_array = mask_array / 255.0 - - # Run predictions for this batch of images - pred_img_array = self.model.predict(crop_img_array, crop_img_array, mask_array) - - pred_img_array = np.squeeze(pred_img_array, axis = 0) - pred_img_array = (255.0 * ((pred_img_array + 1.0) / 2.0)).astype(np.uint8) - - #scale prediction image back to original size - bounding_width = bounding_box[2]-bounding_box[0] - bounding_height = bounding_box[3]-bounding_box[1] - #convert np array to image - - # print(bounding_width,bounding_height) - # print(pred_img_array.shape) - - pred_img = Image.fromarray(pred_img_array.astype('uint8')) - # pred_img.show() - pred_img = pred_img.resize((bounding_width, bounding_height), resample = Image.BICUBIC) - # pred_img.show() - - pred_img_array = image_to_array(pred_img) - - # print(pred_img_array.shape) - pred_img_array = np.expand_dims(pred_img_array, axis = 0) - - # copy the decensored regions into the output image - for i in range(len(ori_array)): - for col in range(bounding_width): - for row in range(bounding_height): - bounding_width_index = col + bounding_box[0] - bounding_height_index = row + bounding_box[1] - if (bounding_width_index, bounding_height_index) in region: - output_img_array[bounding_height_index][bounding_width_index] = pred_img_array[i,:,:,:][row][col] - # self.signals.signal_ProgressBar_update_VALUE.emit("{} out of {} regions decensored.".format(region_counter, len(regions)), region_counter) - self.signals.update_ProgressBar_SET_VALUE.emit(region_counter) - self.signals.insertText_progressCursor.emit("{} out of {} regions decensored.\n".format(region_counter, len(regions))) - print("{region_counter} out of {region_count} regions decensored.".format(region_counter=region_counter, region_count=len(regions))) - - output_img_array = output_img_array * 255.0 - - #restore the alpha channel if the image had one - if has_alpha: - output_img_array = np.concatenate((output_img_array, alpha_channel), axis = 2) - - output_img = Image.fromarray(output_img_array.astype('uint8')) - output_img = self.apply_variant(output_img, variant_number) - - # self.signals.update_progress_LABEL.emit("current image finished", "Decensoring of current image finished. Saving image...") - self.signals.insertText_progressCursor.emit("Decensoring of current image finished. Saving image...") - print("current image finished") - - if file_name != None: - #save the decensored image - base_name, ext = os.path.splitext(file_name) - file_name = base_name + " " + str(variant_number) + ext - save_path = os.path.join(self.decensor_output_path, file_name) - output_img.save(save_path) - print("Decensored image saved to {save_path}!".format(save_path=save_path)) - self.signals.insertText_progressCursor.emit("Decensored image saved to {save_path}!".format(save_path=save_path)) - self.signals.insertText_progressCursor.emit("="*30) - else: - # Legacy Code piece ↓, used when DCPv1 had ui with Painting - print("Decensored image. Returning it.") - return output_img - - def clean_input_directories(self): - """Removes .png, .jpg, and .jpeg files from input directories.""" - - allowed_extensions = {".png", ".jpg", ".jpeg"} - - self.signals.insertText_progressCursor.emit("Cleaning {}...".format(self.decensor_input_path)) - - for file_name in os.listdir(self.decensor_input_path): - file_path = os.path.join(self.decensor_input_path, file_name) - if os.path.isfile(file_path) and os.path.splitext(file_name)[1].lower() in allowed_extensions: - os.remove(file_path) - - if self.is_mosaic: - self.signals.insertText_progressCursor.emit( - "Cleaning {}...".format(self.decensor_input_original_path) - ) - for file_name in os.listdir(self.decensor_input_original_path): - file_path = os.path.join(self.decensor_input_original_path, - file_name) - if os.path.isfile(file_path) and os.path.splitext(file_name)[1].lower() in allowed_extensions: - os.remove(file_path) - - self.signals.insertText_progressCursor.emit("Done!") - - def do_post_jobs(self): - if self.clean_up_input_dirs: - self.clean_input_directories() - - - -if __name__ == '__main__': - decensor = Decensor() - decensor.decensor_all_images_in_folder() - # equivalent to decensor.start() (running as QtThread) diff --git a/docs/INSTALLATION.md b/docs/INSTALLATION.md index c20c5b1..2bf9cb6 100644 --- a/docs/INSTALLATION.md +++ b/docs/INSTALLATION.md @@ -2,13 +2,21 @@ ## Download Prebuilt Binaries You can download the latest release [here](https://github.com/Deepshift/DeepCreamPy/releases/latest) or find all previous releases [here](https://github.com/Deepshift/DeepCreamPy/releases). -Binary only available for Windows 64-bit. +Binary are available for Windows 64-bit, Linux-64-bit & MacOS aarch64 ## Run Code Yourself +TODO: update + If you want to run the code yourself, you can clone this repo and download the model from https://drive.google.com/open?id=1IMwzqZUuRnTv5jcuKdvZx-RZweknww5x. Unzip the file into the /models/ folder. If you want access to older models, see https://drive.google.com/open?id=1_A0xFeJhrqpmulA6cC-a7RxJoQOD2RKm. +``` +$ python3 -m venv venv +$ source venv/bin/activate +$ pip install -r requirements.txt +``` + ## running the code using Docker Once the input images and model have been placed in `decensor_input` and `models` respectively, @@ -31,50 +39,16 @@ docker run --rm -v $(pwd)/models:/opt/DeepCreamPy/models -v $(pwd)/decensor_inpu the contents of `decensor_input` and `decensor_input_original` are explained in the [decensoring tutorial](USAGE.md). -### Dependencies (for running the code yourself) -- Python 3.6.7 -- TensorFlow 1.14 -- Keras 2.2.4 +### Dependencies (for lib) +- Python 3.12 +- TensorFlow 2 +- Keras - Pillow -- Scipy -- OpenCV - -No GPU required! Tested on Ubuntu 16.04 and Windows. Tensorflow on Windows is compatible with Python 3 and not Python 2. Tensorflow is not compatible with Python 3.7. - -Tensorflow, Keras, Pillow, and h5py can all be installed by running in the command line -``` -$ pip install -r requirements.txt -``` - -## Run Code Yourself on CPUs that don't support AVX instructions - -CPUs that don't support AVX instructions may experience this error when using the above install instructions: - -``` -ModuleNotFoundError: No module named '_pywrap_tensorflow_internal' -``` - -Follow these alternate install instructions if that happens: - -1. Start from a clean Python 3.6.7 install. -2. Download a version of tensorflow that does not support AVX instructions from (https://github.com/fo40225/tensorflow-windows-wheel/tree/master/1.10.0/py36/CPU/sse2). I assume you picked tensorflow-1.10.0-cp36-cp36m-win_amd64.whl for 64-bit and the other for 32-bit computers. -3. Open the command line in the same directory as the file downloaded in step 2. Run - -``` -pip install tensorflow-1.10.0-cp36-cp36m-win_amd64.whl -``` - -or - -``` -pip install tensorflow-1.10.0-cp36-cp36m-win32.whl -``` -depending on what you installed in step 2. - -4. Open the command line in the directory of "DeepCreamPy-master" and run -``` -pip install -r requirements.txt -``` +### Dependencies (for app) +- fastapi +- uvicorn +- pydantic +- lib-cream-py -Instructions are from https://github.com/deeppomf/DeepCreamPy/issues/26#issuecomment-434043166. +No GPU required! diff --git a/docs/TROUBLESHOOTING.md b/docs/TROUBLESHOOTING.md index 62c0e01..d08ff7d 100644 --- a/docs/TROUBLESHOOTING.md +++ b/docs/TROUBLESHOOTING.md @@ -1,13 +1,11 @@ # Troubleshooting Install -## Running Binary - -Remember the binary is only for 64-bit Windows systems. - ## Running Code ### I'm running a Linux OS, and I get the error message "Using TensorFlow backend. Illegal instruction" when running decensor.py +TODO: find out if this is still an issue in tf2 + Unfortunately, you can't run DCP on your machine because your CPU doesn't support AVX instructions. Newer versions of tensorflow can't run on these CPUs. This problem doesn't exist for Windows with non-AVX CPUs because there are custom Windows Tensorflow wheels, you can find it [here](https://github.com/fo40225/tensorflow-windows-wheel/tree/master/1.10.0/py36/CPU/sse2). Upgrading your CPU to a newer CPU will probably fix this issue. diff --git a/docs/USAGE.md b/docs/USAGE.md index 6e57f6b..a95c0f5 100644 --- a/docs/USAGE.md +++ b/docs/USAGE.md @@ -17,17 +17,45 @@ Save these images in the PNG format to the "decensor_input" folder. **They MUST ### A. Using the binary -Decensor the images by double-clicking on the decensor file. - -### B. Running from scratch - -Decensor the images by running +Start a web server and open http://127.0.0.1:8000 in the browser +``` +$ binary-name --ui-mode +``` +``` +$ binary-name --ui-mode --host 127.0.0.1 --port 8000 +``` +Or use the cli +``` +$ binary-name --decensor_input_path='path/to/input' +``` ``` -$ python main.py +usage: main.py [-h] [--decensor_input_path DECENSOR_INPUT_PATH] [--decensor_input_original_path DECENSOR_INPUT_ORIGINAL_PATH] + [--decensor_output_path DECENSOR_OUTPUT_PATH] [--clean-up-input-dirs] [--mask MASK] [--is_mosaic IS_MOSAIC] [--variations {1,2,4}] [--ui-mode] + [--port PORT] [--host HOST] + +options: + -h, --help show this help message and exit + --decensor_input_path DECENSOR_INPUT_PATH + input images with censored regions colored green to be decensored by decensor.py path + --decensor_input_original_path DECENSOR_INPUT_ORIGINAL_PATH + input images with no modifications to be decensored by decensor.py path + --decensor_output_path DECENSOR_OUTPUT_PATH + output images generated from running decensor.py path + --clean-up-input-dirs + whether to delete all image files in the input directories when decensoring is done + --mask MASK red channel of mask color in decensoring + --is_mosaic IS_MOSAIC + true if image has mosaic censoring, false otherwise + --variations {1,2,4} number of decensor variations to be generated + --port [DEFAULT: 8000] + --host [DEFAULT: 127.0.0.1] ``` -Decensored images will be saved to the "decensor_output" folder. Decensoring takes a few minutes per image. +### B. Running from scratch +``` +$ python app/main.py -h +``` ## II. Decensoring mosaic censors @@ -43,11 +71,7 @@ Decensor the images by double-clicking on the decensor_mosaic file. ### B. Running from scratch -Decensor the images by running - -``` -$ python main.py -``` +TODO: write documentation Decensored images will be saved to the "decensor_output" folder. Decensoring takes a few minutes per image. diff --git a/file.py b/file.py deleted file mode 100755 index d027738..0000000 --- a/file.py +++ /dev/null @@ -1,54 +0,0 @@ -import os - -def check_file(input_dir, output_dir, release_version = True): - file_list = [] - output_file_list = [] - files_removed = [] - input_dir = os.listdir(input_dir) - output_dir = os.listdir(output_dir) - - for file_in in input_dir: - if not file_in.startswith('.'): - file_list.append(file_in) - - if release_version is True: - print("\nChecking valid files...") - for file_out in output_dir: - if file_out.lower().endswith('.png'): - output_file_list.append(file_out) - - # solving https://github.com/deeppomf/DeepCreamPy/issues/25 - # appending in list with reason as tuple (file name, reason) - for lhs in file_list: - lhs.lower() - if not lhs.lower().endswith('.png') : - files_removed.append((lhs, 0)) - for rhs in output_file_list: - if(lhs == rhs): - files_removed.append((lhs, 1)) - - # seperated detecting same file names and deleting file name list - # just in case of index_error and show list of files which will not go though - # decensor process - print("\n＃＃＃ These files will not be decensored for following reason ＃＃＃\n") - - error_messages(file_list, files_removed) - input("\nPress anything to continue...") - - print("\n＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃\n") - - return file_list, files_removed - -def error_messages(file_list, files_removed): - if files_removed is None: - return - - for remove_this, reason in files_removed: - if file_list is not None: - file_list.remove(remove_this) - if reason == 0: - print(" REMOVED : (" + str(remove_this) +") is not PNG file format") - elif reason == 1: - print(" REMOVED : (" + str(remove_this) +") already exists") - elif reason == 2: - print(" REMOVED : (" + str(remove_this) +") file unreadable") diff --git a/frontend/.editorconfig b/frontend/.editorconfig new file mode 100644 index 0000000..7f5b23f --- /dev/null +++ b/frontend/.editorconfig @@ -0,0 +1,9 @@ +[*.{js,jsx,mjs,cjs,ts,tsx,mts,cts,vue}] +charset = utf-8 +indent_size = 2 +indent_style = space +insert_final_newline = true +trim_trailing_whitespace = true + +end_of_line = lf +max_line_length = 100 diff --git a/frontend/.gitattributes b/frontend/.gitattributes new file mode 100644 index 0000000..6313b56 --- /dev/null +++ b/frontend/.gitattributes @@ -0,0 +1 @@ +* text=auto eol=lf diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..8ee54e8 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,30 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +.DS_Store +dist +dist-ssr +coverage +*.local + +/cypress/videos/ +/cypress/screenshots/ + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +*.tsbuildinfo diff --git a/frontend/.prettierrc.json b/frontend/.prettierrc.json new file mode 100644 index 0000000..17a23d0 --- /dev/null +++ b/frontend/.prettierrc.json @@ -0,0 +1,7 @@ + +{ + "$schema": "https://json.schemastore.org/prettierrc", + "semi": false, + "singleQuote": true, + "printWidth": 100 +} diff --git a/frontend/.vscode/extensions.json b/frontend/.vscode/extensions.json new file mode 100644 index 0000000..c92168f --- /dev/null +++ b/frontend/.vscode/extensions.json @@ -0,0 +1,8 @@ +{ + "recommendations": [ + "Vue.volar", + "dbaeumer.vscode-eslint", + "EditorConfig.EditorConfig", + "esbenp.prettier-vscode" + ] +} diff --git a/frontend/env.d.ts b/frontend/env.d.ts new file mode 100644 index 0000000..11f02fe --- /dev/null +++ b/frontend/env.d.ts @@ -0,0 +1 @@ +/// diff --git a/frontend/eslint.config.ts b/frontend/eslint.config.ts new file mode 100644 index 0000000..d3d8f17 --- /dev/null +++ b/frontend/eslint.config.ts @@ -0,0 +1,24 @@ +import pluginVue from 'eslint-plugin-vue' +import { defineConfigWithVueTs, vueTsConfigs } from '@vue/eslint-config-typescript' +import skipFormatting from '@vue/eslint-config-prettier/skip-formatting' + +// To allow more languages other than `ts` in `.vue` files, uncomment the following lines: +// import { configureVueProject } from '@vue/eslint-config-typescript' +// configureVueProject({ scriptLangs: ['ts', 'tsx'] }) +// More info at https://github.com/vuejs/eslint-config-typescript/#advanced-setup + +export default defineConfigWithVueTs( + { + name: 'app/files-to-lint', + files: ['**/*.{ts,mts,tsx,vue}'], + }, + + { + name: 'app/files-to-ignore', + ignores: ['**/dist/**', '**/dist-ssr/**', '**/coverage/**'], + }, + + pluginVue.configs['flat/essential'], + vueTsConfigs.recommended, + skipFormatting, +) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..9e5fc8f --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + Vite App + + +
+ + + diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000..c923206 --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,43 @@ +{ + "name": "deepcreampy", + "version": "0.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vite", + "build": "run-p type-check \"build-only {@}\" --", + "preview": "vite preview", + "build-only": "vite build", + "type-check": "vue-tsc --build", + "lint": "eslint . --fix", + "format": "prettier --write src/" + }, + "dependencies": { + "@arco-design/web-vue": "^2.56.3", + "vue": "^3.5.13" + }, + "devDependencies": { + "@tailwindcss/forms": "^0.5.10", + "@tailwindcss/typography": "^0.5.16", + "@tailwindcss/vite": "^4.0.4", + "@tsconfig/node22": "^22.0.0", + "@types/node": "^22.10.7", + "@vitejs/plugin-vue": "^5.2.1", + "@vue/eslint-config-prettier": "^10.1.0", + "@vue/eslint-config-typescript": "^14.3.0", + "@vue/tsconfig": "^0.7.0", + "autoprefixer": "^10.4.20", + "eslint": "^9.18.0", + 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vue-tsc@2.2.0(typescript@5.7.3): + dependencies: + '@volar/typescript': 2.4.11 + '@vue/language-core': 2.2.0(typescript@5.7.3) + typescript: 5.7.3 + + vue@3.5.13(typescript@5.7.3): + dependencies: + '@vue/compiler-dom': 3.5.13 + '@vue/compiler-sfc': 3.5.13 + '@vue/runtime-dom': 3.5.13 + '@vue/server-renderer': 3.5.13(vue@3.5.13(typescript@5.7.3)) + '@vue/shared': 3.5.13 + optionalDependencies: + typescript: 5.7.3 + + which@2.0.2: + dependencies: + isexe: 2.0.0 + + which@5.0.0: + dependencies: + isexe: 3.1.1 + + word-wrap@1.2.5: {} + + xml-name-validator@4.0.0: {} + + yallist@3.1.1: {} + + yocto-queue@0.1.0: {} + + yoctocolors@2.1.1: {} diff --git a/frontend/public/favicon.ico b/frontend/public/favicon.ico new file mode 100644 index 0000000..df36fcf Binary files /dev/null and b/frontend/public/favicon.ico differ diff --git a/frontend/src/App.vue b/frontend/src/App.vue new file mode 100644 index 0000000..7e8b60a --- /dev/null +++ b/frontend/src/App.vue @@ -0,0 +1,196 @@ + + + diff --git a/frontend/src/components/Button.vue b/frontend/src/components/Button.vue new file mode 100644 index 0000000..c60c0e9 --- /dev/null +++ b/frontend/src/components/Button.vue @@ -0,0 +1,58 @@ + + + diff --git a/frontend/src/components/DropOver.vue b/frontend/src/components/DropOver.vue new file mode 100644 index 0000000..223127d --- /dev/null +++ b/frontend/src/components/DropOver.vue @@ -0,0 +1,48 @@ + + + diff --git a/frontend/src/components/SubmitButton.vue b/frontend/src/components/SubmitButton.vue new file mode 100644 index 0000000..b2e8a7f --- /dev/null +++ b/frontend/src/components/SubmitButton.vue @@ -0,0 +1,33 @@ + + + diff --git a/frontend/src/components/UploadPage.vue b/frontend/src/components/UploadPage.vue new file mode 100644 index 0000000..f70b905 --- /dev/null +++ b/frontend/src/components/UploadPage.vue @@ -0,0 +1,201 @@ + + + diff --git a/frontend/src/components/ViewFiles.vue b/frontend/src/components/ViewFiles.vue new file mode 100644 index 0000000..52d43db --- /dev/null +++ b/frontend/src/components/ViewFiles.vue @@ -0,0 +1,32 @@ + + + diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000..444daa0 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,6 @@ +@import "tailwindcss"; + +@plugin '@tailwindcss/forms'; +@plugin '@tailwindcss/typography'; + +@variant dark (&:where(.dark, .dark *)); \ No newline at end of file diff --git a/frontend/src/main.ts b/frontend/src/main.ts new file mode 100644 index 0000000..3c7c661 --- /dev/null +++ b/frontend/src/main.ts @@ -0,0 +1,10 @@ +import {createApp} from 'vue' +import App from './App.vue' +import ArcoVue from '@arco-design/web-vue'; +import '@arco-design/web-vue/dist/arco.css'; +import './index.css' + +const app = createApp(App); +app.use(ArcoVue); + +app.mount('#app') diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json new file mode 100644 index 0000000..913b8f2 --- /dev/null +++ b/frontend/tsconfig.app.json @@ -0,0 +1,12 @@ +{ + "extends": "@vue/tsconfig/tsconfig.dom.json", + "include": ["env.d.ts", "src/**/*", "src/**/*.vue"], + "exclude": ["src/**/__tests__/*"], + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + + "paths": { + "@/*": ["./src/*"] + } + } +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 0000000..66b5e57 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,11 @@ +{ + "files": [], + "references": [ + { + "path": "./tsconfig.node.json" + }, + { + "path": "./tsconfig.app.json" + } + ] +} diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json new file mode 100644 index 0000000..a83dfc9 --- /dev/null +++ b/frontend/tsconfig.node.json @@ -0,0 +1,19 @@ +{ + "extends": "@tsconfig/node22/tsconfig.json", + "include": [ + "vite.config.*", + "vitest.config.*", + "cypress.config.*", + "nightwatch.conf.*", + "playwright.config.*", + "eslint.config.*" + ], + "compilerOptions": { + "noEmit": true, + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + + "module": "ESNext", + "moduleResolution": "Bundler", + "types": ["node"] + } +} diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts new file mode 100644 index 0000000..9205b27 --- /dev/null +++ b/frontend/vite.config.ts @@ -0,0 +1,20 @@ +import { fileURLToPath, URL } from 'node:url' + +import { defineConfig } from 'vite' +import vue from '@vitejs/plugin-vue' +import vueDevTools from 'vite-plugin-vue-devtools' +import tailwindcss from '@tailwindcss/vite'; + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [ + vue(), + vueDevTools(), + tailwindcss(), + ], + resolve: { + alias: { + '@': fileURLToPath(new URL('./src', import.meta.url)) + }, + }, +}) diff --git a/lib-cream-py/.DS_Store b/lib-cream-py/.DS_Store new file mode 100644 index 0000000..81cda4f Binary files /dev/null and b/lib-cream-py/.DS_Store differ diff --git a/lib-cream-py/pyproject.toml b/lib-cream-py/pyproject.toml new file mode 100644 index 0000000..6379273 --- /dev/null +++ b/lib-cream-py/pyproject.toml @@ -0,0 +1,27 @@ +[project] +name = "lib-cream-py" +version = "1.0.0" + +description = "DeepCreamPy library" + +keywords = [] +authors = [] + +requires-python = ">= 3.9" +dependencies = [ + "keras", + "tensorflow", + "numpy", + "Pillow" +] + +[tool.setuptools.packages.find] +where = [ + "src" +] + +[project.urls] +Documentation = "https://github.com/Deepshift/DeepCreamPy/blob/master/docs" +Repository = "https://github.com/Deepshift/DeepCreamPy" +"Bug Tracker" = "https://github.com/Deepshift/DeepCreamPy/issues" +Changelog = "https://github.com/Deepshift/DeepCreamPy/blob/master/CHANGELOG.md" diff --git a/lib-cream-py/requirements.txt b/lib-cream-py/requirements.txt new file mode 100644 index 0000000..d720b13 --- /dev/null +++ b/lib-cream-py/requirements.txt @@ -0,0 +1,4 @@ +keras +tensorflow +numpy +Pillow \ No newline at end of file diff --git a/lib-cream-py/src/__init__.py b/lib-cream-py/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lib-cream-py/src/lib_cream_py.py b/lib-cream-py/src/lib_cream_py.py new file mode 100644 index 0000000..ecc2c61 --- /dev/null +++ b/lib-cream-py/src/lib_cream_py.py @@ -0,0 +1,151 @@ +from typing import Callable + +import numpy as np +from PIL import Image + +from logger import Logger +from mask import Mask, ColorMask, RawMask, RawMask2 +from model.model import InpaintNN +from util import apply_variant, image_to_array, expand_bounding, find_regions + + +def test(): + color_file_path = "../decensor_input/mermaid_censored.png" + colored_img = Image.open(color_file_path) + model = InpaintNN("../models/bar.keras") + is_mosaic = False + save_image = lambda i, img: img.save("output.png") + mask_gen = lambda i, ori, colored: ColorMask(colored if is_mosaic else ori, rgb=(0, 1, 0)) + decensor_image_variations(model, colored_img, colored_img, mask_gen, 1, False, save_image) + + +def decensor_image_variations(model: InpaintNN, ori: Image, colored: Image, + mask_gen: Callable[[int, Image, Image], Mask], + variations: int, is_mosaic: bool, callback: Callable[[int, Image], None], + logger=Logger()): + for i in range(variations): + logger.info("apply-variant", i) + ori = apply_variant(ori, i) + colored = apply_variant(colored, i) + logger.info("generate-mask") + mask = mask_gen(i, ori, colored) + out = decensor_image(model, mask, ori, colored, is_mosaic, logger) + out = apply_variant(out, i) + callback(i, out) + logger.info("finished") + + +def save_alpha(img): + if img.mode == "RGBA": + alpha_channel = np.asarray(img)[:, :, 3] + alpha_channel = np.expand_dims(alpha_channel, axis=-1) + return img.convert('RGB'), True, alpha_channel + else: + return img, False, None + + +# TODO: decensor all cropped parts of the same image in a batch (then i need input for colored an array of those images and make additional changes) +def decensor_image(model: InpaintNN, mask: Mask, ori: Image, colored: Image, is_mosaic: bool, + logger=Logger()) -> Image.Image: + ori, has_alpha, alpha_channel = save_alpha(ori) + logger.info("remove-alpha") + + ori_array = image_to_array(ori) + ori_array = np.expand_dims(ori_array, axis=0) + mask_arr = mask.find_mask_simple() + mask_img_big = mask.display() + + # colored image is only used for finding the regions + logger.info("find-regions") + regions = find_regions(colored.convert('RGB'), mask_arr) + + if len(regions) == 0 and not is_mosaic: + logger.error("no-regions") + raise Exception("No regions found") + logger.debug("found-regions", len(regions)) + + output_img_array = ori_array[0].copy() + + for region_counter, region in enumerate(regions, 1): + bounding_box = expand_bounding(ori, region, expand_factor=1.5, logger=logger) + crop_img = ori.crop(bounding_box) + + # resize the cropped images + crop_img = crop_img.resize((256, 256), resample=Image.Resampling.NEAREST) + crop_img_array = image_to_array(crop_img) + + # resize the mask images + mask_img = mask_img_big.crop(bounding_box) + mask_img = mask_img.resize((256, 256), resample=Image.Resampling.NEAREST) + # convert mask_img back to array + mask_array = image_to_array(mask_img) + # the mask has been upscaled so there will be values not equal to 0 or 1 + + # mask_array[mask_array > 0] = 1 + # crop_img_array[..., :-1][mask_array==0] = (0,0,0) + + if not is_mosaic: + a, b = np.where(np.all(mask_array == 0, axis=-1)) + crop_img_array[a, b, :] = 0. + + crop_img_array = np.expand_dims(crop_img_array, axis=0) + mask_array = np.expand_dims(mask_array, axis=0) + + crop_img_array = crop_img_array * 2.0 - 1 + + # Run predictions for this batch of images + logger.info("decensor-segment", (region_counter, len(regions))) + + pred_img_array = model.predict_image(crop_img_array, crop_img_array, mask_array) + + pred_img_array = np.squeeze(pred_img_array, axis=0) + pred_img_array = (255.0 * ((pred_img_array + 1.0) / 2.0)).astype(np.uint8) + + # scale prediction image back to original size + bounding_width = bounding_box[2] - bounding_box[0] + bounding_height = bounding_box[3] - bounding_box[1] + # convert np array to image + + pred_img = Image.fromarray(pred_img_array.astype('uint8')) + pred_img = pred_img.resize((bounding_width, bounding_height), resample=Image.Resampling.BICUBIC) + + pred_img_array = image_to_array(pred_img) + + pred_img_array = np.expand_dims(pred_img_array, axis=0) + + # copy the decensored regions into the output image + for i in range(len(ori_array)): + for col in range(bounding_width): + for row in range(bounding_height): + bounding_width_index = col + bounding_box[0] + bounding_height_index = row + bounding_box[1] + if (bounding_width_index, bounding_height_index) in region: + output_img_array[bounding_height_index][bounding_width_index] = pred_img_array[i, :, :, :][row][ + col] + + output_img_array = output_img_array * 255.0 + + # restore the alpha channel if the image had one + if has_alpha: + logger.info("restore-alpha") + output_img_array = np.concatenate((output_img_array, alpha_channel), axis=2) + + return Image.fromarray(output_img_array.astype('uint8')) + + +def train(): + y = np.load("y.npy").astype(np.float32) + x = np.load("x.npy").astype(np.float32) + mask = np.load("mask.npy").astype(np.float32) + model = InpaintNN("./temp/mosaic.keras", create_model=True) + model.train(0, [(y, y, mask)], "./temp/checkpoints") + model.migrate_weights() + # img = model.predict_image(x, x, mask) + # img = np.squeeze(img, axis=0) + # img = (255.0 * ((img + 1.0) / 2.0)).astype(np.uint8) + # img = Image.fromarray(img.astype('uint8')) + # img.save("example1.png") + + +if __name__ == "__main__": + train() diff --git a/lib-cream-py/src/logger.py b/lib-cream-py/src/logger.py new file mode 100644 index 0000000..4e8dfe6 --- /dev/null +++ b/lib-cream-py/src/logger.py @@ -0,0 +1,12 @@ +class Logger: + def warn(self, id: str, info=None): + pass + + def info(self, id: str, info=None): + pass + + def debug(self, id: str, info=None): + pass + + def error(self, id: str, info=None): + pass diff --git a/lib-cream-py/src/mask.py b/lib-cream-py/src/mask.py new file mode 100644 index 0000000..b67fba9 --- /dev/null +++ b/lib-cream-py/src/mask.py @@ -0,0 +1,126 @@ +from abc import ABC, abstractmethod +from typing import Union + +import numpy as np +from PIL import Image +from numpy import ndarray + +from util import image_to_array + + +class Mask(ABC): + @abstractmethod + def find_mask(self) -> ndarray: + """ + This method returns the mask + shape: (A, B, 3) + values from [0.0, 255.0] + """ + pass + + @abstractmethod + def find_mask_simple(self): + """ + This method returns the mask + shape: (A, B) + values from [0, 1] uint8 + 1 = True + 0 = False + """ + pass + + def display(self): + return Image.fromarray(self.find_mask().astype('uint8')) + + +def find_mask(colored: ndarray, color): + return mask + + +class ColorMask(Mask): + def __init__(self, image: Union[Image, ndarray], rgb: Union[tuple[float, float, float], tuple[int, int, int]]): + """ + :param image: ndarray = AxBx3 or AxBx1 or AxB. values should be in the range [0, 1]. [0, 255] + """ + self.color = [v / 255.0 if v > 1 else v for v in rgb] + if isinstance(image, Image.Image): + if image.mode != "RGB": + image = image.convert("RGB") + arr = image_to_array(image) + elif isinstance(image, np.ndarray): + arr = image + if arr.ndim == 2: + arr = np.stack([arr] * 3, axis=-1) + elif arr.ndim == 3 and arr.shape[-1] == 1: + arr = np.repeat(arr, 3, axis=-1) + + if arr.max() > 1: + arr = np.array(arr / 255.0) + else: + raise TypeError("Expected image to be a PIL Image or a NumPy ndarray") + self.mask = self.find_mask_logic(arr) + + def find_mask_logic(self, image): + """ + return AxB with values from [0, 1] 1 is found + """ + m = np.zeros(image.shape[:2], np.uint8) + i, j = np.where(np.all(image == self.color, axis=-1)) + + if len(i) > 0: + m[i, j] = 1 + + return m + + def find_mask_simple(self): + return self.mask + + def find_mask(self) -> ndarray: + """ + shape: (A, B, 3) + values from [0.0, 255.0] 0 is found + """ + arr = np.stack([self.find_mask_simple()] * 3, axis=-1) + arr = np.logical_not(arr) * 255.0 + return arr + + +def convert_matrix(matrix): + if not np.all(matrix[:, :, 0] == matrix[:, :, 1]) or not np.all(matrix[:, :, 1] == matrix[:, :, 2]): + raise ValueError("Values in all three dimensions must match") + + return matrix.mean(axis=2) + + +class RawMask2(ColorMask): + def __init__(self, image: Union[Image, ndarray], upper_is_mask: bool, + threshold: Union[float, int]): + super().__init__(image, (threshold, threshold, threshold)) + self.upper_is_mask = upper_is_mask + + def find_mask_logic(self, image): + """ + return AxB with values from [0, 1] 1 is found + """ + m = np.zeros(image.shape[:2], np.uint8) + image = convert_matrix(image) + i, j = np.where(np.all(image > self.color[0] if self.upper_is_mask else image < self.color[0], axis=-1)) + + if len(i) > 0: + m[i, j] = 1 + + return m + + +class RawMask(ColorMask): + def __init__(self, image: Union[Image, ndarray], mask_black=True): + """ + :param image: ndarray = AxBx3 or AxBx1 or AxB. values should be in the range [0, 1] + """ + super().__init__(image, (0, 0, 0) if mask_black else (1, 1, 1)) + + +if __name__ == '__main__': + img = Image.open('../../decensor_input/mermaid_censored.png') + mask = ColorMask(img, (0, 1, 0)).display() + mask2 = RawMask(mask, mask_black=True).find_mask() diff --git a/lib-cream-py/src/model/__init__.py b/lib-cream-py/src/model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lib-cream-py/src/model/contextual_block.py b/lib-cream-py/src/model/contextual_block.py new file mode 100644 index 0000000..a953864 --- /dev/null +++ b/lib-cream-py/src/model/contextual_block.py @@ -0,0 +1,176 @@ +import tensorflow as tf +from keras import Layer +from keras.src.layers import Conv2D + + +def ceil(a, b): + return -(-a // b) + + +def calc_c(h, kn, w, stride): + # c = 0 + # for _ in range(kn, h - kn, stride): + # for q in range(kn, w - kn, stride): + # c += 1 + return ceil((h - 2 * kn), stride) * ceil((w - 2 * kn), stride) + + +def softmax(x): + # todo: why - 3 + exp_x = tf.exp(x - 3) + return exp_x / tf.reduce_sum(exp_x, axis=-1, keepdims=True) + + +class ContextualBlock(Layer): + def __init__(self, k_size, lamda, stride=1, name=str, **kwargs): + super(ContextualBlock, self).__init__(name=name, **kwargs) + self.conv_layer = None + self.k_size = k_size + self.lamda = lamda + self.stride = stride + + def get_config(self): + base_config = super().get_config() + config = { + "k_size": self.k_size, + "lamda": self.lamda, + } + return {**base_config, **config} + + @classmethod + def from_config(cls, config): + k_size = config.pop("k_size") + lamda = config.pop("lamda") + return cls(k_size, lamda, **config) + + def build(self, input_shape): + bg_in, fg_in, mask = input_shape + + b, h, w, dims = bg_in + self.conv_layer = Conv2D(dims, (1, 1), padding="valid", name="ML", activation="elu") + + def call(self, inputs): + bg_in, fg_in, mask = inputs + b, h, w, dims = bg_in.shape + + # Resize mask + temp = tf.image.resize(mask, (h, w), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) + temp = tf.expand_dims(temp[:, :, :, 0], axis=-1) + mask_r = tf.tile(temp, [1, 1, 1, dims]) + + bg = bg_in * mask_r + + kn = (self.k_size - 1) // 2 + c = calc_c(h, kn, w, self.stride) + # Extract patches + patch1 = tf.image.extract_patches(bg, + sizes=[1, self.k_size, self.k_size, 1], + strides=[1, self.stride, self.stride, 1], + rates=[1, 1, 1, 1], + padding='VALID') + + patch1 = tf.reshape(patch1, (b, 1, 1, c, self.k_size * self.k_size * dims)) + patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3]) + + patch2 = tf.image.extract_patches(fg_in, + sizes=[1, self.k_size, self.k_size, 1], + strides=[1, 1, 1, 1], + rates=[1, 1, 1, 1], + padding='SAME') + + ACL = [] + for ib in range(b): + k1 = patch1[ib] + k1d = tf.reduce_sum(tf.square(k1), axis=2) + k2 = tf.reshape(k1, (self.k_size, self.k_size, dims, c)) + + ww = patch2[ib] + wwd = tf.reduce_sum(tf.square(ww), axis=2, keepdims=True) + ft = tf.expand_dims(ww, 0) + + CS = tf.nn.conv2d(ft, k1, strides=[1, 1, 1, 1], padding='SAME') + + tt = k1d + wwd + DS1 = tf.expand_dims(tt, 0) - 2 * CS + DS2 = (DS1 - tf.reduce_mean(DS1, axis=-1, keepdims=True)) / tf.math.reduce_std(DS1, axis=-1, keepdims=True) + DS2 = -1 * tf.nn.tanh(DS2) + + CA = softmax(self.lamda * DS2) + + ACLt = tf.nn.conv2d_transpose(CA, k2, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME') + ACLt = ACLt / (self.k_size ** 2) + + if ib == 0: + ACL = ACLt + else: + ACL = tf.concat([ACL, ACLt], axis=0) + + ACL = bg + ACL * (1.0 - mask_r) + + con1 = tf.concat([bg_in, ACL], axis=-1) + ACL2 = self.conv_layer(con1) + return ACL2 + + +if __name__ == '__main__': + import math + + + # The original function with the loop + def calc_c(h, kn, w, stride): + c = 0 + for _ in range(kn, h - kn, stride): + for q in range(kn, w - kn, stride): + c += 1 + return c + + + # The formula function + def calc_c_formula(h, kn, w, stride): + return math.ceil((h - 2 * kn) / stride) * math.ceil((w - 2 * kn) / stride) + + + # Test to compare both functions + def test_calc_c_equal(): + test_cases = [ + (5, 1, 5, 1), # Expected output: 9 + (6, 1, 6, 1), # Expected output: 16 + + # Test case with larger stride + (10, 1, 10, 2), # Expected output: 9 + (10, 1, 10, 3), # Expected output: 4 + + # Test case with larger kernel size + (10, 3, 10, 1), # Expected output: 4 + (10, 3, 10, 2), # Expected output: 4 + + # Test case with large height and width + (100, 5, 100, 10), # Expected output: 90 + (100, 5, 100, 20), # Expected output: 45 + + # Test case with small stride and kernel + (15, 1, 15, 1), # Expected output: 169 + (15, 1, 15, 3), # Expected output: 36 + + # Test case with kernel and stride being equal + (20, 2, 20, 2), # Expected output: 36 + (20, 4, 20, 4), # Expected output: 16 + + # Test case with larger height and width with minimal stride + (50, 3, 50, 1), # Expected output: 44 * 44 = 1936 + (50, 4, 50, 1), # Expected output: 43 * 43 = 1849 + + # Test case with minimal kernel and larger stride + (30, 1, 30, 5), # Expected output: 6 * 6 = 36 + (30, 2, 30, 6), # Expected output: 5 * 5 = 25 + ] + + for h, kn, w, stride in test_cases: + loop_result = calc_c(h, kn, w, stride) + formula_result = calc_c_formula(h, kn, w, stride) + + assert loop_result == formula_result, f"Failed for {h=}, {kn=}, {w=}, {stride=}: loop result ({loop_result}) != formula result ({formula_result})" + + + # Run the test + test_calc_c_equal() diff --git a/lib-cream-py/src/model/decoder.py b/lib-cream-py/src/model/decoder.py new file mode 100644 index 0000000..f7e841e --- /dev/null +++ b/lib-cream-py/src/model/decoder.py @@ -0,0 +1,83 @@ +import tensorflow as tf + +from keras import Layer +from keras.src.layers import Conv2D + + +class ConvNN(Layer): + def __init__(self, dims1, dims2, size1, size2, k_size=3, **kwargs): + super(ConvNN, self).__init__(**kwargs) + self.dims1 = dims1 + self.dims2 = dims2 + self.size1 = size1 + self.size2 = size2 + self.k_size = k_size + + # Define layers + self.conv1 = Conv2D(dims1, (k_size, k_size), strides=(1, 1), padding='valid', activation="elu") + self.conv2 = Conv2D(dims2, (k_size, k_size), strides=(1, 1), padding='valid', activation="elu") + + def get_config(self): + base_config = super().get_config() + config = { + "dims1": self.dims1, + "dims2": self.dims2, + "size1": self.size1, + "size2": self.size2, + } + return {**base_config, **config} + + @classmethod + def from_config(cls, config): + size1 = config.pop("size1") + size2 = config.pop("size2") + dims1 = config.pop("dims1") + dims2 = config.pop("dims2") + return cls(dims1, dims2, size1, size2, **config) + + def call(self, inputs, training=False): + x = tf.pad(inputs, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") + x = self.conv1(x) + + x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") + x = self.conv2(x) + + # Resize output + x = tf.image.resize(x, (self.size1, self.size2), method='nearest') + return x + + +class Decoder(Layer): + def __init__(self, size1, size2, name: str, **kwargs): + super(Decoder, self).__init__(name=name, **kwargs) + self.size1 = size1 + self.size2 = size2 + self.name = name + self.dl1 = ConvNN(128, 128, int(size1 // 4), int(size2 // 4)) + self.dl2 = ConvNN(64, 64, int(size1 // 2), int(size2 // 2)) + self.dl3 = ConvNN(32, 32, int(size1), int(size2)) + self.dl4 = ConvNN(16, 16, int(size1), int(size2)) + self.final_conv = Conv2D(3, (3, 3), strides=(1, 1), padding='same', activation=None) + + def get_config(self): + base_config = super().get_config() + config = { + "size1": self.size1, + "size2": self.size2, + } + return {**base_config, **config} + + @classmethod + def from_config(cls, config): + size1 = config.pop("size1") + size2 = config.pop("size2") + return cls(size1, size2, **config) + + def call(self, inputs, training=False): + x = self.dl1(inputs) + x = self.dl2(x) + x = self.dl3(x) + x = self.dl4(x) + x = self.final_conv(x) + x = tf.clip_by_value(x, -1.0, 1.0) + return x diff --git a/lib-cream-py/src/model/disciminator_red.py b/lib-cream-py/src/model/disciminator_red.py new file mode 100644 index 0000000..eb1c74f --- /dev/null +++ b/lib-cream-py/src/model/disciminator_red.py @@ -0,0 +1,181 @@ +import tensorflow as tf +from keras import Layer, Model +from keras.src.initializers import TruncatedNormal + + +class SNConv2D(Layer): + def __init__(self, output_dim, kernel_size, stride, name: str, **kwargs): + super(SNConv2D, self).__init__(name=name, **kwargs) + self.u = None + self.stride = stride + self.b = None + self.w = None + self.output_dim = output_dim + self.kernel_size = kernel_size + self.name = name + + def get_config(self): + base_config = super().get_config() + config = { + "output_dim": self.output_dim, + "kernel_size": self.kernel_size, + "stride": self.stride, + } + return {**base_config, **config} + + @classmethod + def from_config(cls, config): + output_dim = config.pop("output_dim") + kernel_size = config.pop("kernel_size") + stride = config.pop("stride") + return cls(output_dim, kernel_size, stride, **config) + + def build(self, input_shape): + c = input_shape[3] + + self.w = self.add_weight( + name='w', + shape=(self.kernel_size, self.kernel_size, c, self.output_dim), + initializer="glorot_uniform", + trainable=True, + ) + + self.b = self.add_weight( + name='b', + shape=(self.output_dim,), + initializer=tf.zeros_initializer(), + trainable=True, + ) + + self.u = self.add_weight( + name='wu', + shape=[1, self.w.shape[-1]], initializer=TruncatedNormal, + trainable=False + ) + + def call(self, inputs, training=False): + return tf.nn.conv2d(inputs, filters=spectral_norm(self.w, self.u), strides=[1, self.stride, self.stride, 1], + padding='SAME') + self.b + + +class DiscriminatorRed(Model): + def __init__(self, name=str, **kwargs): + super(DiscriminatorRed, self).__init__(name=name, **kwargs) + + self.conv1 = SNConv2D(64, 5, 2, 'l1') + self.conv2 = SNConv2D(128, 5, 2, 'l2') + self.conv3 = SNConv2D(256, 5, 2, 'l3') + self.conv4 = SNConv2D(256, 5, 2, 'l4') + self.conv5 = SNConv2D(256, 5, 2, 'l5') + self.conv6 = SNConv2D(512, 5, 2, 'l6') + self.dense = DenseRedSN('l7') + + def build(self, input_shape): + # todo: dont hardcode dims + self.conv1.build(input_shape) + self.conv2.build((1, 128, 128, 64)) + self.conv3.build((1, 64, 64, 128)) + self.conv4.build((1, 32, 32, 256)) + self.conv5.build((1, 16, 16, 256)) + self.conv6.build((1, 8, 8, 256)) + self.dense.build((1, 4, 4, 512)) + + def call(self, inputs): + x = self.conv1(inputs) + # L1 = instance_norm(L1, 'di1') + x = tf.nn.leaky_relu(x) + + x = self.conv2(x) + # L2 = instance_norm(L2, 'di2') + x = tf.nn.leaky_relu(x) + + x = self.conv3(x) + # L3 = instance_norm(L3, 'di3') + x = tf.nn.leaky_relu(x) + + x = self.conv4(x) + # L4 = instance_norm(L4, 'di4') + x = tf.nn.leaky_relu(x) + + x = self.conv5(x) + # L5 = instance_norm(L5, 'di5') + x = tf.nn.leaky_relu(x) + + x = self.conv6(x) + # L6 = instance_norm(L6, 'di6') + x = tf.nn.leaky_relu(x) + + x = self.dense(x) + + return x + + +class DenseRedSN(Layer): + def __init__(self, name: str, **kwargs): + super(DenseRedSN, self).__init__(name=name, **kwargs) + self.u = None + self.bias = None + self.weight = None + self.name = name + + def build(self, input_shape): + h, w, c = input_shape[1], input_shape[2], input_shape[3] + + self.weight = self.add_weight( + name="_w", + shape=[h * w, 1, c, 1], + initializer="glorot_uniform", + trainable=True + ) + + self.bias = self.add_weight( + name="_b", + shape=[1, h, w, 1], + initializer="zeros", + trainable=True + ) + self.u = [ + self.add_weight(name=f"w_{i}u", shape=[1, self.weight.shape[-1]], initializer=TruncatedNormal, + trainable=False) + for i in range(h * w) + ] + + def call(self, inputs, training=False): + h, w, c = inputs.shape[1], inputs.shape[2], inputs.shape[3] + sn_w_list = [spectral_norm(self.weight[i: i + 1, :, :, :], self.u[i]) + for i in range(h * w) + ] + sn_w = tf.concat(sn_w_list, axis=0) + w_rs = tf.reshape(sn_w, [h, w, c, 1]) + w_rs_t = tf.transpose(w_rs, [3, 0, 1, 2]) + + output_red = tf.reduce_sum(inputs * w_rs_t + self.bias, axis=3, keepdims=True) + return output_red + + +def spectral_norm(w, u, iteration=1): + """Applies spectral normalization to weight tensor w.""" + w_reshaped = tf.reshape(w, [-1, w.shape[-1]]) + u_hat = u + v_hat = None + for _ in range(iteration): + """ + power iteration + Usually iteration = 1 will be enough + """ + + v_ = tf.matmul(u_hat, w_reshaped, transpose_b=True) + v_hat = l2_norm(v_) + + u_ = tf.matmul(v_hat, w_reshaped) + u_hat = l2_norm(u_) + + sigma = tf.matmul(tf.matmul(v_hat, w_reshaped), u_hat, transpose_b=True) + w_norm = w_reshaped / sigma + + u.assign(u_hat) + return tf.reshape(w_norm, w.shape) + + +def l2_norm(v, eps=1e-12): + return v / (tf.reduce_sum(v ** 2) ** 0.5 + eps) diff --git a/lib-cream-py/src/model/encoder.py b/lib-cream-py/src/model/encoder.py new file mode 100644 index 0000000..98c7b6e --- /dev/null +++ b/lib-cream-py/src/model/encoder.py @@ -0,0 +1,71 @@ +import tensorflow as tf +from keras import Model +from keras.src.layers import Activation, Conv2D + + +def reflect_pad(x, pad_size): + return tf.pad(x, [[0, 0], [pad_size, pad_size], [pad_size, pad_size], [0, 0]], mode="REFLECT") + + +class Encoder(Model): + def __init__(self, name: str, **kwargs): + super(Encoder, self).__init__(**kwargs) + self.name = name + + # Define convolutional layers + self.conv1 = Conv2D(32, (5, 5), strides=(1, 1), padding='valid') + self.conv2 = Conv2D(64, (3, 3), strides=(2, 2), padding='valid') + self.conv3 = Conv2D(64, (3, 3), strides=(1, 1), padding='valid') + self.conv4 = Conv2D(128, (3, 3), strides=(2, 2), padding='valid') + self.conv5 = Conv2D(128, (3, 3), strides=(1, 1), padding='valid') + self.conv6 = Conv2D(256, (3, 3), strides=(2, 2), padding='valid') + + # Dilated Convolutions + self.dilated1 = Conv2D(256, (3, 3), dilation_rate=2, strides=(1, 1), padding='valid') + self.dilated2 = Conv2D(256, (3, 3), dilation_rate=4, strides=(1, 1), padding='valid') + self.dilated3 = Conv2D(256, (3, 3), dilation_rate=8, strides=(1, 1), padding='valid') + self.dilated4 = Conv2D(256, (3, 3), dilation_rate=16, strides=(1, 1), padding='valid') + + def call(self, inputs, training=False): + x = reflect_pad(inputs, 2) + x = self.conv1(x) + x = Activation('elu')(x) # 256 256 32 + + x = reflect_pad(x, 1) + x = self.conv2(x) + x = Activation('elu')(x) # 128 128 64 + + x = reflect_pad(x, 1) + x = self.conv3(x) + x = Activation('elu')(x) # 128 128 64 + + x = reflect_pad(x, 1) + x = self.conv4(x) + x = Activation('elu')(x) # 64 64 128 + + x = reflect_pad(x, 1) + x = self.conv5(x) + x = Activation('elu')(x) # 64 64 128 + + x = reflect_pad(x, 1) + x = self.conv6(x) + x = Activation('elu')(x) # 32 32 128 + + # Dilated Convolutions + x = reflect_pad(x, 2) + x = self.dilated1(x) + x = Activation('elu')(x) + + x = reflect_pad(x, 4) + x = self.dilated2(x) + x = Activation('elu')(x) + + x = reflect_pad(x, 8) + x = self.dilated3(x) + x = Activation('elu')(x) + + x = reflect_pad(x, 16) + x = self.dilated4(x) + x = Activation('elu')(x) # 32 32 128 + + return x diff --git a/lib-cream-py/src/model/model.py b/lib-cream-py/src/model/model.py new file mode 100644 index 0000000..a4bde7f --- /dev/null +++ b/lib-cream-py/src/model/model.py @@ -0,0 +1,264 @@ +import os + +import tensorflow as tf +from keras import Model, Input +from keras.src import optimizers, layers +from keras.src.saving import load_model + +from logger import Logger +from .contextual_block import ContextualBlock +from .decoder import Decoder +from .disciminator_red import DiscriminatorRed +from .encoder import Encoder + + +class InpaintModel(Model): + def __init__(self, input_height=256, input_width=256, **kwargs): + super(InpaintModel, self).__init__(name="", **kwargs) + self.input_height = input_height + self.input_width = input_width + self.encoder = Encoder(name='G_en') + self.cb1 = ContextualBlock(name='CB1', k_size=3, lamda=50.0, stride=1) + self.decoder = Decoder(name='G_de', size1=self.input_height, size2=self.input_height) + + def call(self, inputs, training=False): + X, Y, MASK = inputs + input_tensor = layers.Concatenate(axis=-1)([X, MASK]) + vec_en = self.encoder(input_tensor) + vec_con = self.cb1((vec_en, vec_en, MASK)) + if training: + I_co = self.decoder(vec_en) + I_ge = self.decoder(vec_con) + + image_result = I_ge * (1 - MASK) + Y * MASK + if training: + return image_result, I_ge, I_co + else: + return image_result + + +class InpaintNN: + def __init__(self, model_path: str, input_height=256, input_width=256, batch_size=1, create_model=False, + logger=Logger()): + super(InpaintNN, self).__init__() + + self.new_name_map = None + self.input_height = input_height + self.input_width = input_width + self.batch_size = batch_size + self.model_path = model_path + self.create_model = create_model + self.logger = logger + if not create_model: + self.check_model_file() + self.model = load_model(self.model_path) + + def check_model_file(self): + if not os.path.exists(self.model_path): + self.logger.error("missing-model") + raise FileNotFoundError + + def migrate_weights(self, model_path='./models/bar/Train_775000'): + self.disc_red = DiscriminatorRed(name='disc_red') + self.disc_red.build((None, 256, 256, 3)) + + # model_path = '../models/mosaic/Train_290000' + # model_path = '../models/bar/Train_775000' + reader = tf.compat.v1.train.NewCheckpointReader(model_path) + variable_map = reader.get_variable_to_shape_map() + lookup_model = { + 'G_en/conv2d/kernel': 'G_en/Conv/weights', + 'G_en/conv2d/bias': 'G_en/Conv/biases', + 'G_en/conv2d_1/kernel': 'G_en/Conv_1/weights', + 'G_en/conv2d_1/bias': 'G_en/Conv_1/biases', + 'G_en/conv2d_2/kernel': 'G_en/Conv_2/weights', + 'G_en/conv2d_2/bias': 'G_en/Conv_2/biases', + 'G_en/conv2d_3/kernel': 'G_en/Conv_3/weights', + 'G_en/conv2d_3/bias': 'G_en/Conv_3/biases', + 'G_en/conv2d_4/kernel': 'G_en/Conv_4/weights', + 'G_en/conv2d_4/bias': 'G_en/Conv_4/biases', + 'G_en/conv2d_5/kernel': 'G_en/Conv_5/weights', + 'G_en/conv2d_5/bias': 'G_en/Conv_5/biases', + 'G_en/conv2d_6/kernel': 'G_en/Conv_6/weights', + 'G_en/conv2d_6/bias': 'G_en/Conv_6/biases', + 'G_en/conv2d_7/kernel': 'G_en/Conv_7/weights', + 'G_en/conv2d_7/bias': 'G_en/Conv_7/biases', + 'G_en/conv2d_8/kernel': 'G_en/Conv_8/weights', + 'G_en/conv2d_8/bias': 'G_en/Conv_8/biases', + 'G_en/conv2d_9/kernel': 'G_en/Conv_9/weights', + 'G_en/conv2d_9/bias': 'G_en/Conv_9/biases', + 'G_de/conv_nn/conv2d_10/kernel': 'G_de/Conv/weights', + 'G_de/conv_nn/conv2d_10/bias': 'G_de/Conv/biases', + 'G_de/conv_nn/conv2d_11/kernel': 'G_de/Conv_1/weights', + 'G_de/conv_nn/conv2d_11/bias': 'G_de/Conv_1/biases', + 'G_de/conv_nn_1/conv2d_12/kernel': 'G_de/Conv_2/weights', + 'G_de/conv_nn_1/conv2d_12/bias': 'G_de/Conv_2/biases', + 'G_de/conv_nn_1/conv2d_13/kernel': 'G_de/Conv_3/weights', + 'G_de/conv_nn_1/conv2d_13/bias': 'G_de/Conv_3/biases', + 'G_de/conv_nn_2/conv2d_14/kernel': 'G_de/Conv_4/weights', + 'G_de/conv_nn_2/conv2d_14/bias': 'G_de/Conv_4/biases', + 'G_de/conv_nn_2/conv2d_15/kernel': 'G_de/Conv_5/weights', + 'G_de/conv_nn_2/conv2d_15/bias': 'G_de/Conv_5/biases', + 'G_de/conv_nn_3/conv2d_16/kernel': 'G_de/Conv_6/weights', + 'G_de/conv_nn_3/conv2d_16/bias': 'G_de/Conv_6/biases', + 'G_de/conv_nn_3/conv2d_17/kernel': 'G_de/Conv_7/weights', + 'G_de/conv_nn_3/conv2d_17/bias': 'G_de/Conv_7/biases', + 'G_de/conv2d_18/kernel': 'G_de/Conv_8/weights', + 'G_de/conv2d_18/bias': 'G_de/Conv_8/biases', + 'CB1/ML/kernel': 'CB1/ML/weights', + 'CB1/ML/bias': 'CB1/ML/biases' + } + lookup_disc = { + 'disc_red/l1/w': 'disc_red/l1w', + 'disc_red/l1/b': 'disc_red/l1b', + 'disc_red/l1/wu': 'disc_red/l1wu', + 'disc_red/l2/w': 'disc_red/l2w', + 'disc_red/l2/b': 'disc_red/l2b', + 'disc_red/l2/wu': 'disc_red/l2wu', + 'disc_red/l3/w': 'disc_red/l3w', + 'disc_red/l3/b': 'disc_red/l3b', + 'disc_red/l3/wu': 'disc_red/l3wu', + 'disc_red/l4/w': 'disc_red/l4w', + 'disc_red/l4/b': 'disc_red/l4b', + 'disc_red/l4/wu': 'disc_red/l4wu', + 'disc_red/l5/w': 'disc_red/l5w', + 'disc_red/l5/b': 'disc_red/l5b', + 'disc_red/l5/wu': 'disc_red/l5wu', + 'disc_red/l6/w': 'disc_red/l6w', + 'disc_red/l6/b': 'disc_red/l6b', + 'disc_red/l6/wu': 'disc_red/l6wu', + 'disc_red/l7/_w': 'disc_red/l7_w', + 'disc_red/l7/_b': 'disc_red/l7_b', + 'disc_red/l7/w_0u': 'disc_red/l7w_0u', + 'disc_red/l7/w_1u': 'disc_red/l7w_1u', + 'disc_red/l7/w_2u': 'disc_red/l7w_2u', + 'disc_red/l7/w_3u': 'disc_red/l7w_3u', + 'disc_red/l7/w_4u': 'disc_red/l7w_4u', + 'disc_red/l7/w_5u': 'disc_red/l7w_5u', + 'disc_red/l7/w_6u': 'disc_red/l7w_6u', + 'disc_red/l7/w_7u': 'disc_red/l7w_7u', + 'disc_red/l7/w_8u': 'disc_red/l7w_8u', + 'disc_red/l7/w_9u': 'disc_red/l7w_9u', + 'disc_red/l7/w_10u': 'disc_red/l7w_10u', + 'disc_red/l7/w_11u': 'disc_red/l7w_11u', + 'disc_red/l7/w_12u': 'disc_red/l7w_12u', + 'disc_red/l7/w_13u': 'disc_red/l7w_13u', + 'disc_red/l7/w_14u': 'disc_red/l7w_14u', + 'disc_red/l7/w_15u': 'disc_red/l7w_15u' + } + variable_map = dict([(name, variable_map[name]) for name in variable_map if not name.__contains__("Adam")]) + + for var in self.model.variables: + old_name = lookup_model[var.path] + old_shape = variable_map.pop(old_name) + assert old_shape == var.shape + v = reader.get_tensor(old_name) + var.assign(v) + for var in self.disc_red.variables: + old_name = lookup_disc[var.path] + old_shape = variable_map.pop(old_name) + assert old_shape == var.shape + v = reader.get_tensor(old_name) + var.assign(v) + variable_map.pop('beta1_power') + variable_map.pop('beta2_power') + variable_map.pop('beta1_power_1') + variable_map.pop('beta2_power_1') + if len(variable_map) != 0: + import pprint + pprint.pprint(variable_map) + raise Exception("Variable map is not empty") + self.model.save(self.model_path) + + def train(self, epochs: int, dataset, checkpoint_path: str): + X = Input(shape=(self.input_height, self.input_width, 3), batch_size=self.batch_size, dtype=tf.float32) + Y = Input(shape=(self.input_height, self.input_width, 3), batch_size=self.batch_size, dtype=tf.float32) + MASK = Input(shape=(self.input_height, self.input_width, 3), batch_size=self.batch_size, dtype=tf.float32) + # todo: unclear + IT = 0 + + output = InpaintModel(self.input_height, self.input_width)((X, Y, MASK)) + + # Discriminator + disc_red = DiscriminatorRed(name='disc_red') + + # todo never used? + # SSIM (Structural Similarity Index) + # A = tf.image.rgb_to_yuv((image_result + 1) / 2.0) + # A_Y = tf.cast(A[:, :, :, 0:1] * 255.0, tf.int32) + # B = tf.image.rgb_to_yuv((X + 1) / 2.0) + # B_Y = tf.cast(B[:, :, :, 0:1] * 255.0, tf.int32) + # ssim = tf.reduce_mean(tf.image.ssim(tf.cast(A_Y, tf.float32), tf.cast(B_Y, tf.float32), max_val=255.0)) + + alpha = IT / 1000000 + + # Optimizers + optimizer_D = optimizers.Adam(learning_rate=0.0004, beta_1=0.5, + beta_2=0.9) # .minimize(Loss_D, var_list=var_D) + optimizer_G = optimizers.Adam(learning_rate=0.0001, beta_1=0.5, + beta_2=0.9) # .minimize(Loss_G, var_list=var_G) + + model = Model(inputs=[X, Y, MASK], outputs=[output]) + + checkpoint = tf.train.Checkpoint(generator=model, discriminator=disc_red, optimizer_G=optimizer_G, + optimizer_D=optimizer_D) + checkpoint_manager = tf.train.CheckpointManager(checkpoint, checkpoint_path, max_to_keep=3) + + if checkpoint_manager.latest_checkpoint: + checkpoint.restore(checkpoint_manager.latest_checkpoint) + self.logger.info(f"checkpoint restored", checkpoint_manager.latest_checkpoint) + + @tf.function + def train_step(real_images, y, masks): + with tf.GradientTape() as tape_D, tf.GradientTape() as tape_G: + fake_images, I_ge, I_co = model([real_images, y, masks], training=True) + + D_real = disc_red(real_images) + D_fake = disc_red(fake_images) + + loss_D = tf.reduce_mean(tf.nn.relu(1 + D_fake)) + tf.reduce_mean(tf.nn.relu(1 - D_real)) + loss_GAN = -tf.reduce_mean(D_fake) + loss_s_re = tf.reduce_mean(tf.abs(I_ge - real_images)) + loss_hat = tf.reduce_mean(tf.abs(I_co - real_images)) + + loss_G = 0.1 * loss_GAN + 10 * loss_s_re + 5 * (1 - alpha) * loss_hat + + # Compute gradients + grads_D = tape_D.gradient(loss_D, disc_red.trainable_variables) + grads_G = tape_G.gradient(loss_G, model.trainable_variables) + + # Apply gradients + optimizer_D.apply_gradients(zip(grads_D, disc_red.trainable_variables)) + optimizer_G.apply_gradients(zip(grads_G, model.trainable_variables)) + + return loss_D, loss_G + + # Training loop + for epoch in range(epochs): + self.logger.info("start-epoch", epoch) + + for step, (real_images, y, masks) in enumerate(dataset): + d_loss, g_loss = train_step(real_images, y, masks) + + if step % 100 == 0: + self.logger.info("train-step", (d_loss.numpy(), g_loss.numpy())) + + checkpoint_manager.save() + self.logger.info("checkpoint-saved", checkpoint_manager.latest_checkpoint) + + if epochs == 0: + optimizer_G.build(model.trainable_variables) + disc_red.build((None, 256, 256, 3)) + optimizer_D.build(disc_red.trainable_variables) + else: + model.save(self.model_path) + self.model = model + + def predict_image(self, censored, unused, mask): + image_result = self.model((censored, unused, mask), training=False) + return image_result + + +if __name__ == "__main__": + m = InpaintNN("", create_model=True) + m.model.summary(expand_nested=True) diff --git a/libs/utils.py b/lib-cream-py/src/util.py old mode 100755 new mode 100644 similarity index 64% rename from libs/utils.py rename to lib-cream-py/src/util.py index 2e4d1f9..4c243fd --- a/libs/utils.py +++ b/lib-cream-py/src/util.py @@ -1,20 +1,24 @@ +# convert PIL image to numpy array import numpy as np -from PIL import Image, ImageDraw +from PIL import Image +from numpy import ndarray -#convert PIL image to numpy array -def image_to_array(image): - array = np.asarray(image) - return np.array(array / 255.0) +from logger import Logger + + +def is_on_mask(cords, mask: ndarray): + x, y = cords + return mask[y, x] == 1 -#find strongly connected components with the mask color -def find_regions(image, mask_color): + +def find_regions(image, mask: ndarray): pixel = image.load() neighbors = dict() width, height = image.size for x in range(width): for y in range(height): - if is_right_color(pixel[x,y], *mask_color): - neighbors[x, y] = {(x,y)} + if is_on_mask([x, y], mask): + neighbors[x, y] = {(x, y)} for x, y in neighbors: candidates = (x + 1, y), (x, y + 1) for candidate in candidates: @@ -37,39 +41,45 @@ def connected_component(pixel): for pixel in neighbors: if pixel not in closed_list: regions.append(connected_component(pixel)) - regions.sort(key = len, reverse = True) + regions.sort(key=len, reverse=True) return regions + +def image_to_array(image: Image): + array = np.asarray(image) + return np.array(array / 255.0) + + # risk of box being bigger than the image -def expand_bounding(img, region, expand_factor=1.5, min_size = 256): - #expand bounding box to capture more context +def expand_bounding(img: Image, region, expand_factor=1.5, min_size=256, logger=Logger()): + # expand bounding box to capture more context x, y = zip(*region) min_x, min_y, max_x, max_y = min(x), min(y), max(x), max(y) width, height = img.size - width_center = width//2 - height_center = height//2 + width_center = width // 2 + height_center = height // 2 bb_width = max_x - min_x bb_height = max_y - min_y - x_center = (min_x + max_x)//2 - y_center = (min_y + max_y)//2 + x_center = (min_x + max_x) // 2 + y_center = (min_y + max_y) // 2 current_size = max(bb_width, bb_height) - current_size = int(current_size * expand_factor) + current_size = int(current_size * expand_factor) max_size = min(width, height) if current_size > max_size: current_size = max_size elif current_size < min_size: current_size = min_size - x1 = x_center - current_size//2 - x2 = x_center + current_size//2 - y1 = y_center - current_size//2 - y2 = y_center + current_size//2 + x1 = x_center - current_size // 2 + x2 = x_center + current_size // 2 + y1 = y_center - current_size // 2 + y2 = y_center + current_size // 2 x1_square = x1 y1_square = y1 x2_square = x2 y2_square = y2 - #move bounding boxes that are partially outside of the image inside the image + # move bounding boxes that are partially outside of the image inside the image if (y1_square < 0 or y2_square > (height - 1)) and (x1_square < 0 or x2_square > (width - 1)): - #conservative square region + # conservative square region if x1_square < 0 and y1_square < 0: x1_square = 0 y1_square = 0 @@ -114,22 +124,34 @@ def expand_bounding(img, region, expand_factor=1.5, min_size = 256): y2_square -= difference # if y1_square < 0 or y2_square > (height - 1): - #if bounding box goes outside of the image for some reason, set bounds to original, unexpanded values - #print(width, height) + # if bounding box goes outside of the image for some reason, set bounds to original, unexpanded values + # print(width, height) if x2_square > width or y2_square > height: - print("bounding box out of bounds!") - print(x1_square, y1_square, x2_square, y2_square) + logger.error("bounding-box-out-of-bounds", (x1_square, y1_square, x2_square, y2_square)) x1_square, y1_square, x2_square, y2_square = min_x, min_y, max_x, max_y return x1_square, y1_square, x2_square, y2_square -def is_right_color(pixel, r2, g2, b2): - r1, g1, b1 = pixel - return r1 == r2 and g1 == g2 and b1 == b2 + +# create different decensors of the same image by flipping the input image +def apply_variant(image: Image, variant_number: int): + if variant_number == 0: + return image + elif variant_number == 1: + return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + elif variant_number == 2: + return image.transpose(Image.Transpose.FLIP_TOP_BOTTOM) + else: + return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT).transpose(Image.Transpose.FLIP_TOP_BOTTOM) + if __name__ == '__main__': + from PIL import ImageDraw + from mask import ColorMask + image = Image.open('') no_alpha_image = image.convert('RGB') draw = ImageDraw.Draw(no_alpha_image) - for region in find_regions(no_alpha_image, [0,255,0]): + mask = ColorMask(image, rgb=(0, 1, 0)) + for region in find_regions(no_alpha_image, mask.find_mask_simple()): draw.rectangle(expand_bounding(no_alpha_image, region), outline=(0, 255, 0)) - no_alpha_image.show() \ No newline at end of file + no_alpha_image.show() diff --git a/main.py b/main.py deleted file mode 100644 index 11697d3..0000000 --- a/main.py +++ /dev/null @@ -1,193 +0,0 @@ -#!/usr/bin/python3 - -#tooltips -# Please read this tutorial on how to prepare your images for use with DeepCreamPy. -# The greater the number of variations, the longer decensoring process will be. - -import sys, time -from PySide2.QtWidgets import QWidget, QHBoxLayout, QVBoxLayout, QGridLayout, QGroupBox, QDesktopWidget, QApplication, QCheckBox -from PySide2.QtWidgets import QAction, qApp, QApplication, QMessageBox, QRadioButton, QPushButton, QTextEdit, QLabel -from PySide2.QtWidgets import QSizePolicy,QMainWindow, QStatusBar, QProgressBar -from PySide2.QtCore import Qt, QObject -from PySide2.QtGui import QFont, QTextCursor -from decensor import Decensor -from signals import Signals - -# from decensor import Decensor - -# from progressWindow import ProgressWindow - -class MainWindow(QWidget): - - def __init__(self): - super().__init__() - self.signals = Signals() - self.initUI() - self.decensor = Decensor(self) - self.setSignals() - self.load_model() - - def initUI(self): - - grid_layout = QGridLayout() - grid_layout.setSpacing(10) - self.setLayout(grid_layout) - - #Tutorial - self.tutorialLabel = QLabel() - self.tutorialLabel.setText("Welcome to DeepCreamPy!\n\nIf you're new to DCP, please read the README.\nThis program does nothing without the proper setup of your images.\n\nReport any bugs you encounter to me on Github or Twitter @deeppomf.") - self.tutorialLabel.setAlignment(Qt.AlignCenter) - self.tutorialLabel.setFont(QFont('Sans Serif', 13)) - - #Censor type group - self.censorTypeGroupBox = QGroupBox('Censor Type') - - barButton = QRadioButton('Bar censor') - mosaicButton = QRadioButton('Mosaic censor') - barButton.setChecked(True) - - censorLayout = QVBoxLayout() - censorLayout.addWidget(barButton) - censorLayout.addWidget(mosaicButton) - # censorLayout.addStretch(1) - self.censorTypeGroupBox.setLayout(censorLayout) - - #Variation count group - self.variationsGroupBox = QGroupBox('Number of Decensor Variations') - - var1Button = QRadioButton('1') - var2Button = QRadioButton('2') - var3Button = QRadioButton('4') - var1Button.setChecked(True) - - varLayout = QVBoxLayout() - varLayout.addWidget(var1Button) - varLayout.addWidget(var2Button) - varLayout.addWidget(var3Button) - # varLayout.addStretch(1) - self.variationsGroupBox.setLayout(varLayout) - - #Decensor button - self.decensorButton = QPushButton('Decensor Your Images') - self.decensorButton.clicked.connect(self.decensorClicked) - self.decensorButton.setSizePolicy( - QSizePolicy.Preferred, - QSizePolicy.Preferred) - - #Progress message - # self.progressGroupBox = QGroupBox('Progress') - - self.progressMessage = QTextEdit() - self.progressCursor = QTextCursor(self.progressMessage.document()) - self.progressMessage.setTextCursor(self.progressCursor) - self.progressMessage.setReadOnly(True) - self.progressCursor.insertText("After you prepared your images, click on the decensor button once to begin decensoring.\nPlease be patient.\nDecensoring will take time.\n") - - # Progress Bar - self.statusBar = QStatusBar(self) - self.progressBar = QProgressBar() - self.progressBar.setMinimum(0) - self.progressBar.setMaximum(100) - self.progressBar.setValue(0) - self.statusLabel = QLabel("Showing Progress") - - self.statusBar.addWidget(self.statusLabel, 1) - self.statusBar.addWidget(self.progressBar, 2) - - self.cleanInputDirectoryCheckbox = QCheckBox( - 'Clean up input directories after decensoring' - ) - - #put all groups into grid - # addWidget(row, column, rowSpan, columnSpan) - grid_layout.addWidget(self.tutorialLabel, 0, 0, 1, 2) - grid_layout.addWidget(self.censorTypeGroupBox, 1, 0, 1, 1) - grid_layout.addWidget(self.variationsGroupBox, 1, 1, 1, 1) - grid_layout.addWidget(self.cleanInputDirectoryCheckbox, 2, 0, 1, 2) - grid_layout.addWidget(self.decensorButton, 3, 0, 1, 2) - grid_layout.addWidget(self.progressMessage, 4, 0, 1, 2) - grid_layout.addWidget(self.statusBar, 5, 0, 1, 2) - - #window size settings - self.resize(900, 600) - self.center() - self.setWindowTitle('DeepCreamPy v2.2.0-beta') - self.show() - - def load_model(self): - # load model to make able to decensor several times - self.decensorButton.setEnabled(False) - self.decensorButton.setText("Loading Machine Learning Model (Please Wait...)") - self.decensor.start() - self.decensor.signals = self.signals - self.progressCursor.insertText("Loading Decensor app consumes 6 GB memory at maximum") - - def setSignals(self): - self.signals.update_decensorButton_Text.connect(self.decensorButton.setText) - self.signals.update_decensorButton_Enabled.connect(self.decensorButton.setEnabled) - self.signals.update_statusLabel_Text.connect(self.statusLabel.setText) - self.signals.update_ProgressBar_SET_VALUE.connect(self.progressBar.setValue) - self.signals.update_ProgressBar_MAX_VALUE.connect(self.progressBar.setMaximum) - self.signals.update_ProgressBar_MIN_VALUE.connect(self.progressBar.setMinimum) - # self.signals.insertText_progressCursor.connect(self.progressCursor.insertText) - self.signals.insertText_progressCursor.connect(self.progressMessage.append) - self.signals.clear_progressMessage.connect(self.progressMessage.clear) - self.signals.appendText_progressMessage.connect(self.progressMessage.append) - self.signals.update_clean_up_input_dirs_flag.connect( - self.decensor.set_clean_up_input_dirs - ) - - def decensorClicked(self): - self.decensorButton.setEnabled(False) - self.progressMessage.clear() - self.progressCursor.insertText("Decensoring has begun!\n") - - # for now, decensor is initiated when this app is started - # self.decensor = Decensor(text_edit = self.progressMessage, text_cursor = self.progressCursor, ui_mode = True) - - #https://stackoverflow.com/questions/42349470/pyqt-find-checked-radiobutton-in-a-group - #set decensor to right settings - #censor type - censorTypeElements = self.censorTypeGroupBox.children() - censorButtons = [elem for elem in censorTypeElements if isinstance(elem, QRadioButton)] - for cb in censorButtons: - if cb.isChecked(): - censorType = cb.text() - if censorType == 'Bar censor': - self.decensor.is_mosaic = False - else: - self.decensor.is_mosaic = True - - #variations count - variationsElements = self.variationsGroupBox.children() - variationsButtons = [elem for elem in variationsElements if isinstance(elem, QRadioButton)] - for vb in variationsButtons: - if vb.isChecked(): - variations = int(vb.text()) - self.decensor.variations = variations - - self.signals.update_clean_up_input_dirs_flag.emit( - self.cleanInputDirectoryCheckbox.isChecked() - ) - - self.decensorButton.setEnabled(False) - self.decensor.start() - # decensor.decensor_all_images_in_folder() - - # #centers the main window - def center(self): - qr = self.frameGeometry() - cp = QDesktopWidget().availableGeometry().center() - qr.moveCenter(cp) - self.move(qr.topLeft()) - -if __name__ == '__main__': - import os - # you could remove this if statement if there's no error without this - if os.name == 'nt': - import PySide2 - pyqt = os.path.dirname(PySide2.__file__) - QApplication.addLibraryPath(os.path.join(pyqt, "plugins")) - app = QApplication(sys.argv) - ex = MainWindow() - sys.exit(app.exec_()) diff --git a/model.py b/model.py deleted file mode 100755 index 0491591..0000000 --- a/model.py +++ /dev/null @@ -1,112 +0,0 @@ -import tensorflow as tf -import os -import numpy as np -import module as mm - -#suppress tensorflow deprecation warnings -tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) - -class InpaintNN: - - def __init__(self, input_height=256, input_width=256, batch_size = 1, bar_model_name=None, bar_checkpoint_name=None, mosaic_model_name=None, mosaic_checkpoint_name = None, is_mosaic=False): - self.bar_model_name = bar_model_name - self.bar_checkpoint_name = bar_checkpoint_name - self.mosaic_model_name = mosaic_model_name - self.mosaic_checkpoint_name = mosaic_checkpoint_name - self.is_mosaic = is_mosaic - - self.input_height = input_height - self.input_width = input_width - self.batch_size = batch_size - - self.check_model_file() - self.build_model() - - def check_model_file(self): - if not os.path.exists(self.bar_model_name) or not os.path.exists(self.mosaic_model_name) : - print("\nMissing Train Model, download train model") - print("Read : https://github.com/deeppomf/DeepCreamPy/blob/master/docs/INSTALLATION.md#run-code-yourself \n") - exit(-1) - - def build_model(self): - # ------- variables - - self.X = tf.placeholder(tf.float32, [self.batch_size, self.input_height, self.input_width, 3]) - self.Y = tf.placeholder(tf.float32, [self.batch_size, self.input_height, self.input_width, 3]) - - self.MASK = tf.placeholder(tf.float32, [self.batch_size, self.input_height, self.input_width, 3]) - IT = tf.placeholder(tf.float32) - - # ------- structure - - input = tf.concat([self.X, self.MASK], 3) - - vec_en = mm.encoder(input, reuse=False, name='G_en') - - vec_con = mm.contextual_block(vec_en, vec_en, self.MASK, 3, 50.0, 'CB1', stride=1) - - I_co = mm.decoder(vec_en, self.input_height, self.input_height, reuse=False, name='G_de') - I_ge = mm.decoder(vec_con, self.input_height, self.input_height, reuse=True, name='G_de') - - self.image_result = I_ge * (1-self.MASK) + self.Y*self.MASK - - D_real_red = mm.discriminator_red(self.Y, reuse=False, name='disc_red') - D_fake_red = mm.discriminator_red(self.image_result, reuse=True, name='disc_red') - - # ------- Loss - - Loss_D_red = tf.reduce_mean(tf.nn.relu(1+D_fake_red)) + tf.reduce_mean(tf.nn.relu(1-D_real_red)) - - Loss_D = Loss_D_red - - Loss_gan_red = -tf.reduce_mean(D_fake_red) - - Loss_gan = Loss_gan_red - - Loss_s_re = tf.reduce_mean(tf.abs(I_ge - self.Y)) - Loss_hat = tf.reduce_mean(tf.abs(I_co - self.Y)) - - A = tf.image.rgb_to_yuv((self.image_result+1)/2.0) - A_Y = tf.to_int32(A[:, :, :, 0:1]*255.0) - - B = tf.image.rgb_to_yuv((self.Y+1)/2.0) - B_Y = tf.to_int32(B[:, :, :, 0:1]*255.0) - - ssim = tf.reduce_mean(tf.image.ssim(A_Y, B_Y, 255.0)) - - alpha = IT/1000000 - - Loss_G = 0.1*Loss_gan + 10*Loss_s_re + 5*(1-alpha) * Loss_hat - - # --------------------- variable & optimizer - - var_D = [v for v in tf.global_variables() if v.name.startswith('disc_red')] - var_G = [v for v in tf.global_variables() if v.name.startswith('G_en') or v.name.startswith('G_de') or v.name.startswith('CB1')] - - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - - with tf.control_dependencies(update_ops): - optimize_D = tf.train.AdamOptimizer(learning_rate=0.0004, beta1=0.5, beta2=0.9).minimize(Loss_D, var_list=var_D) - optimize_G = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.5, beta2=0.9).minimize(Loss_G, var_list=var_G) - - config = tf.ConfigProto() - # config.gpu_options.per_process_gpu_memory_fraction = 0.4 - # config.gpu_options.allow_growth = False - - self.sess = tf.Session(config=config) - - init = tf.global_variables_initializer() - self.sess.run(init) - saver = tf.train.Saver() - - if self.is_mosaic: - Restore = tf.train.import_meta_graph(self.mosaic_model_name) - Restore.restore(self.sess, tf.train.latest_checkpoint(self.mosaic_checkpoint_name)) - else: - Restore = tf.train.import_meta_graph(self.bar_model_name) - Restore.restore(self.sess, tf.train.latest_checkpoint(self.bar_checkpoint_name)) - - def predict(self, censored, unused, mask): - img_sample = self.sess.run(self.image_result, feed_dict={self.X: censored, self.Y: unused, self.MASK: mask}) - - return img_sample diff --git a/models/.gitignore b/models/.gitignore index 0704f82..cce79a0 100644 --- a/models/.gitignore +++ b/models/.gitignore @@ -1,3 +1,3 @@ -*.h5 +*.keras /bar/* /mosaic/* diff --git a/module.py b/module.py deleted file mode 100755 index 5545191..0000000 --- a/module.py +++ /dev/null @@ -1,305 +0,0 @@ -from __future__ import division -from ops import * -import tensorflow.contrib.layers as layers -import math - -def conv_nn(input, dims1, dims2, size1, size2, k_size = 3): - - pp = tf.pad(input, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - L1 = layers.conv2d(pp, dims1, [k_size, k_size], stride=[1, 1], padding='VALID', activation_fn=None) - L1 = tf.nn.elu(L1) - - pp = tf.pad(L1, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - L2 = layers.conv2d(pp, dims2, [k_size, k_size], stride=[1, 1], padding='VALID', activation_fn=None) - L2 = tf.nn.elu(L2) - L2 = tf.image.resize_nearest_neighbor(L2, (size1, size2)) - - return L2 - -def encoder(input, reuse, name): - with tf.variable_scope(name): - if reuse: - tf.get_variable_scope().reuse_variables() - else: - assert tf.get_variable_scope().reuse is False - - p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - CL1 = layers.conv2d(p, 32, [5, 5], stride=[1, 1], padding='VALID', activation_fn=None) - CL1 = tf.nn.elu(CL1) # 256 256 32 - - p = tf.pad(CL1, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - CL2 = layers.conv2d(p, 64, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None) - CL2 = tf.nn.elu(CL2) # 128 128 64 - - p = tf.pad(CL2, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - CL3 = layers.conv2d(p, 64, [3, 3], stride=[1, 1], padding='VALID', activation_fn=None) - CL3 = tf.nn.elu(CL3) # 128 128 64 - - p = tf.pad(CL3, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - CL4 = layers.conv2d(p, 128, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None) - CL4 = tf.nn.elu(CL4) # 64 64 128 - - p = tf.pad(CL4, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - CL5 = layers.conv2d(p, 128, [3, 3], stride=[1, 1], padding='VALID', activation_fn=None) - CL5 = tf.nn.elu(CL5) # 64 64 128 - - p = tf.pad(CL5, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT") - CL6 = layers.conv2d(p, 256, [3, 3], stride=[2, 2], padding='VALID', activation_fn=None) - CL6 = tf.nn.elu(CL6) # 32 32 128 - - p = tf.pad(CL6, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - DCL1 = layers.conv2d(p, 256, [3, 3], rate=2, stride=[1, 1], padding='VALID', activation_fn=None) - DCL1 = tf.nn.elu(DCL1) - p = tf.pad(DCL1, [[0, 0], [4, 4], [4, 4], [0, 0]], "REFLECT") - DCL2 = layers.conv2d(p, 256, [3, 3], rate=4, stride=[1, 1], padding='VALID', activation_fn=None) - DCL2 = tf.nn.elu(DCL2) - p = tf.pad(DCL2, [[0, 0], [8, 8], [8, 8], [0, 0]], "REFLECT") - DCL3 = layers.conv2d(p, 256, [3, 3], rate=8, stride=[1, 1], padding='VALID', activation_fn=None) - DCL3 = tf.nn.elu(DCL3) - p = tf.pad(DCL3, [[0, 0], [16, 16], [16, 16], [0, 0]], "REFLECT") - DCL4 = layers.conv2d(p, 256, [3, 3], rate=16, stride=[1, 1], padding='VALID', activation_fn=None) - DCL4 = tf.nn.elu(DCL4) # 32 32 128 - - return DCL4 - -def decoder(input, size1, size2, reuse, name): - with tf.variable_scope(name): - if reuse: - tf.get_variable_scope().reuse_variables() - else: - assert tf.get_variable_scope().reuse is False - - DL1 = conv_nn(input, 128, 128, int(size1/4), int(size2/4)) # 64 64 128 - - DL2 = conv_nn(DL1, 64, 64, int(size1/2), int(size2/2)) # 128 128 64 - - DL3 = conv_nn(DL2, 32, 32, int(size1), int(size2)) - - DL4 = conv_nn(DL3, 16, 16, int(size1), int(size2)) - - LL2 = layers.conv2d(DL4, 3, [3, 3], stride=[1, 1], padding='SAME', activation_fn=None) # 256 256 3 - LL2 = tf.clip_by_value(LL2, -1.0, 1.0) - - return LL2 - -def discriminator_G(input, reuse, name): - with tf.variable_scope(name): - # image is 256 x 256 x input_c_dim - if reuse: - tf.get_variable_scope().reuse_variables() - else: - assert tf.get_variable_scope().reuse is False - - p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L1 = layers.conv2d(p, 64, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L1 = instance_norm(L1, 'di1') - L1 = tf.nn.leaky_relu(L1) - - p = tf.pad(L1, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L2 = layers.conv2d(p, 128, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L2 = instance_norm(L2, 'di2') - L2 = tf.nn.leaky_relu(L2) - - p = tf.pad(L2, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L3 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L3 = instance_norm(L3, 'di3') - L3 = tf.nn.leaky_relu(L3) - - p = tf.pad(L3, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L4 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L4 = instance_norm(L4, 'di4') - L4 = tf.nn.leaky_relu(L4) - L4 = layers.flatten(L4) - - L5 = tf.layers.dense(L4, 1) - - return L5 - -def discriminator_L(input, reuse, name): - with tf.variable_scope(name): - # image is 256 x 256 x input_c_dim - if reuse: - tf.get_variable_scope().reuse_variables() - else: - assert tf.get_variable_scope().reuse is False - - p = tf.pad(input, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L1 = layers.conv2d(p, 64, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L1 = instance_norm(L1, 'di1l') - L1 = tf.nn.leaky_relu(L1) # 32 32 64 - - p = tf.pad(L1, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L2 = layers.conv2d(p, 128, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L2 = instance_norm(L2, 'di2l') - L2 = tf.nn.leaky_relu(L2) # 16 16 128 - - p = tf.pad(L2, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L3 = layers.conv2d(p, 256, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L3 = instance_norm(L3, 'di3l') - L3 = tf.nn.leaky_relu(L3) # 8 8 256 - - p = tf.pad(L3, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT") - L4 = layers.conv2d(p, 512, [5, 5], stride=2, padding='VALID', activation_fn=None) - #L4 = instance_norm(L4, 'di4l') - L4 = tf.nn.leaky_relu(L4) # 4 4 512 - L4 = layers.flatten(L4) - - L5 = tf.layers.dense(L4, 1) - - return L5 - -def discriminator_red(input, reuse, name): - with tf.variable_scope(name): - # image is 256 x 256 x input_c_dim - if reuse: - tf.get_variable_scope().reuse_variables() - else: - assert tf.get_variable_scope().reuse is False - - L1 = convolution_SN(input, 64, 5, 2, 'l1') - # L1 = instance_norm(L1, 'di1') - L1 = tf.nn.leaky_relu(L1) - - L2 = convolution_SN(L1, 128, 5, 2, 'l2') - # L2 = instance_norm(L2, 'di2') - L2 = tf.nn.leaky_relu(L2) - - L3 = convolution_SN(L2, 256, 5, 2, 'l3') - # L3 = instance_norm(L3, 'di3') - L3 = tf.nn.leaky_relu(L3) - - L4 = convolution_SN(L3, 256, 5, 2, 'l4') - # L4 = instance_norm(L4, 'di4') - L4 = tf.nn.leaky_relu(L4) - - L5 = convolution_SN(L4, 256, 5, 2, 'l5') - # L5 = instance_norm(L5, 'di5') - L5 = tf.nn.leaky_relu(L5) - - L6 = convolution_SN(L5, 512, 5, 2, 'l6') - # L6 = instance_norm(L6, 'di6') - L6 = tf.nn.leaky_relu(L6) - - L7 = dense_RED_SN(L6, 'l7') - - return L7 - -def contextual_block(bg_in, fg_in, mask, k_size, lamda, name, stride=1): - with tf.variable_scope(name): - b, h, w, dims = [i.value for i in bg_in.get_shape()] - temp = tf.image.resize_nearest_neighbor(mask, (h, w)) - temp = tf.expand_dims(temp[:, :, :, 0], 3) # b 128 128 1 - mask_r = tf.tile(temp, [1, 1, 1, dims]) # b 128 128 128 - bg = bg_in * mask_r - - kn = int((k_size - 1) / 2) - c = 0 - for p in range(kn, h - kn, stride): - for q in range(kn, w - kn, stride): - c += 1 - - patch1 = tf.extract_image_patches(bg, [1, k_size, k_size, 1], [1, stride, stride, 1], [1, 1, 1, 1], 'VALID') - - patch1 = tf.reshape(patch1, (b, 1, c, k_size*k_size*dims)) - patch1 = tf.reshape(patch1, (b, 1, 1, c, k_size * k_size * dims)) - patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3]) - - patch2 = tf.extract_image_patches(fg_in, [1,k_size,k_size,1], [1,1,1,1], [1,1,1,1], 'SAME') - ACL = [] - - for ib in range(b): - - k1 = patch1[ib, :, :, :, :] - k1d = tf.reduce_sum(tf.square(k1), axis=2) - k2 = tf.reshape(k1, (k_size, k_size, dims, c)) - ww = patch2[ib, :, :, :] - wwd = tf.reduce_sum(tf.square(ww), axis=2, keepdims=True) - ft = tf.expand_dims(ww, 0) - - CS = tf.nn.conv2d(ft, k1, strides=[1, 1, 1, 1], padding='SAME') - - tt = k1d + wwd - - DS1 = tf.expand_dims(tt, 0) - 2 * CS - - DS2 = (DS1 - tf.reduce_mean(DS1, 3, True)) / reduce_std(DS1, 3, True) - DS2 = -1 * tf.nn.tanh(DS2) - - CA = softmax(lamda * DS2) - - ACLt = tf.nn.conv2d_transpose(CA, k2, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME') - ACLt = ACLt / (k_size ** 2) - - if ib == 0: - ACL = ACLt - else: - ACL = tf.concat((ACL, ACLt), 0) - - ACL = bg + ACL * (1.0 - mask_r) - - con1 = tf.concat([bg_in, ACL], 3) - ACL2 = layers.conv2d(con1, dims, [1, 1], stride=[1, 1], padding='VALID', activation_fn=None, scope='ML') - ACL2 = tf.nn.elu(ACL2) - - return ACL2 - -def contextual_block_cs(bg_in, fg_in, mask, k_size, lamda, name, stride=1): - with tf.variable_scope(name): - b, h, w, dims = [i.value for i in bg_in.get_shape()] - temp = tf.image.resize_nearest_neighbor(mask, (h, w)) - temp = tf.expand_dims(temp[:, :, :, 0], 3) # b 128 128 1 - mask_r = tf.tile(temp, [1, 1, 1, dims]) # b 128 128 128 - bg = bg_in * mask_r - - kn = int((k_size - 1) / 2) - c = 0 - for p in range(kn, h - kn, stride): - for q in range(kn, w - kn, stride): - c += 1 - - patch1 = tf.extract_image_patches(bg, [1, k_size, k_size, 1], [1, stride, stride, 1], [1, 1, 1, 1], 'VALID') - - patch1 = tf.reshape(patch1, (b, 1, c, k_size*k_size*dims)) - patch1 = tf.reshape(patch1, (b, 1, 1, c, k_size * k_size * dims)) - patch1 = tf.transpose(patch1, [0, 1, 2, 4, 3]) - - patch2 = tf.extract_image_patches(fg_in, [1,k_size,k_size,1], [1,1,1,1], [1,1,1,1], 'SAME') - ACL = [] - - fuse_weight = tf.reshape(tf.eye(3), [3, 3, 1, 1]) - - for ib in range(b): - - k1 = patch1[ib, :, :, :, :] - k2 = k1 / tf.sqrt(tf.reduce_sum(tf.square(k1), axis=2, keepdims=True) + 1e-16) - k1 = tf.reshape(k1, (k_size, k_size, dims, c)) - ww = patch2[ib, :, :, :] - ft = ww / tf.sqrt(tf.reduce_sum(tf.square(ww), axis=2, keepdims=True) + 1e-16) - ft = tf.expand_dims(ft, 0) - - CA = tf.nn.conv2d(ft, k2, strides=[1, 1, 1, 1], padding='SAME') - - CA = tf.reshape(CA, [1, h * w, c, 1]) - CA = tf.nn.conv2d(CA, fuse_weight, strides=[1, 1, 1, 1], padding='SAME') - CA = tf.reshape(CA, [1, h, w, int(math.sqrt(c)), int(math.sqrt(c))]) - CA = tf.transpose(CA, [0, 2, 1, 4, 3]) - CA = tf.reshape(CA, [1, h * w, c, 1]) - CA = tf.nn.conv2d(CA, fuse_weight, strides=[1, 1, 1, 1], padding='SAME') - CA = tf.reshape(CA, [1, h, w, int(math.sqrt(c)), int(math.sqrt(c))]) - CA = tf.transpose(CA, [0, 2, 1, 4, 3]) - CA = tf.reshape(CA, [1, h, w, c]) - - CA2 = softmax(lamda * CA) - - ACLt = tf.nn.conv2d_transpose(CA2, k1, output_shape=[1, h, w, dims], strides=[1, 1, 1, 1], padding='SAME') - ACLt = ACLt / (k_size ** 2) - - if ib == 0: - ACL = ACLt - else: - ACL = tf.concat((ACL, ACLt), 0) - - ACL2 = bg + ACL * (1.0 - mask_r) - - return ACL2 - diff --git a/ops.py b/ops.py deleted file mode 100755 index cd4e36b..0000000 --- a/ops.py +++ /dev/null @@ -1,233 +0,0 @@ -import tensorflow as tf -import tensorflow.contrib.layers as layers -import numpy as np -import random as rr -import math as mt -import cv2 -from scipy import misc - -def instance_norm(input, name="instance_norm"): - with tf.variable_scope(name): - depth = input.get_shape()[3] - scale = tf.get_variable("scale", [depth], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32)) - offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0)) - mean, variance = tf.nn.moments(input, axes=[1,2], keep_dims=True) - epsilon = 1e-5 - inv = tf.rsqrt(variance + epsilon) - normalized = (input-mean)*inv - return scale*normalized + offset - -def make_sq_mask(size, m_size, batch_size): - - start_x = rr.randint(0, size - m_size-1) - start_y = rr.randint(0, size - m_size-1) - - temp = np.ones([batch_size, size, size, 3]) - - temp[:, start_x:start_x + m_size, start_y:start_y + m_size, 0:3] *= 0 - - return temp, start_x, start_y - -def softmax(input): - - k = tf.exp(input - 3) - k = tf.reduce_sum(k, 3, True) - # k = k - num * tf.ones_like(k) - - ouput = tf.exp(input - 3) / k - - return ouput - -def reduce_var(x, axis=None, keepdims=False): - """Variance of a tensor, alongside the specified axis. - - # Arguments - x: A tensor or variable. - axis: An integer, the axis to compute the variance. - keepdims: A boolean, whether to keep the dimensions or not. - If `keepdims` is `False`, the rank of the tensor is reduced - by 1. If `keepdims` is `True`, - the reduced dimension is retained with length 1. - - # Returns - A tensor with the variance of elements of `x`. - """ - m = tf.reduce_mean(x, axis=axis, keepdims=True) - devs_squared = tf.square(x - m) - return tf.reduce_mean(devs_squared, axis=axis, keepdims=keepdims) - -def reduce_std(x, axis=None, keepdims=False): - """Standard deviation of a tensor, alongside the specified axis. - - # Arguments - x: A tensor or variable. - axis: An integer, the axis to compute the standard deviation. - keepdims: A boolean, whether to keep the dimensions or not. - If `keepdims` is `False`, the rank of the tensor is reduced - by 1. If `keepdims` is `True`, - the reduced dimension is retained with length 1. - - # Returns - A tensor with the standard deviation of elements of `x`. - """ - return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims)) - -def l2_norm(v, eps=1e-12): - return v / (tf.reduce_sum(v ** 2) ** 0.5 + eps) - -def ff_mask(size, b_zise, maxLen, maxWid, maxAng, maxNum, maxVer, minLen = 20, minWid = 15, minVer = 5): - - mask = np.ones((b_zise, size, size, 3)) - - num = rr.randint(3, maxNum) - - for i in range(num): - startX = rr.randint(0, size) - startY = rr.randint(0, size) - numVer = rr.randint(minVer, maxVer) - width = rr.randint(minWid, maxWid) - for j in range(numVer): - angle = rr.uniform(-maxAng, maxAng) - length = rr.randint(minLen, maxLen) - - endX = min(size-1, max(0, int(startX + length * mt.sin(angle)))) - endY = min(size-1, max(0, int(startY + length * mt.cos(angle)))) - - if endX >= startX: - lowx = startX - highx = endX - else: - lowx = endX - highx = startX - if endY >= startY: - lowy = startY - highy = endY - else: - lowy = endY - highy = startY - - if abs(startY-endY) + abs(startX - endX) != 0: - - wlx = max(0, lowx-int(abs(width * mt.cos(angle)))) - whx = min(size - 1, highx+1 + int(abs(width * mt.cos(angle)))) - wly = max(0, lowy - int(abs(width * mt.sin(angle)))) - why = min(size - 1, highy+1 + int(abs(width * mt.sin(angle)))) - - for x in range(wlx, whx): - for y in range(wly, why): - - d = abs((endY-startY)*x - (endX -startX)*y - endY*startX + startY*endX) / mt.sqrt((startY-endY)**2 + (startX -endX)**2) - - if d <= width: - mask[:, x, y, :] = 0 - - wlx = max(0, lowx-width) - whx = min(size - 1, highx+width+1) - wly = max(0, lowy - width) - why = min(size - 1, highy + width + 1) - - for x2 in range(wlx, whx): - for y2 in range(wly, why): - - d1 = (startX - x2) ** 2 + (startY - y2) ** 2 - d2 = (endX - x2) ** 2 + (endY - y2) ** 2 - - if np.sqrt(d1) <= width: - mask[:, x2, y2, :] = 0 - if np.sqrt(d2) <= width: - mask[:, x2, y2, :] = 0 - startX = endX - startY = endY - - return mask - -def ff_mask_batch(size, b_size, maxLen, maxWid, maxAng, maxNum, maxVer, minLen = 20, minWid = 15, minVer = 5): - - mask = None - temp = ff_mask(size, 1, maxLen, maxWid, maxAng, maxNum, maxVer, minLen=minLen, minWid=minWid, minVer=minVer) - temp = temp[0] - for ib in range(b_size): - if ib == 0: - mask = np.expand_dims(temp, 0) - else: - mask = np.concatenate((mask, np.expand_dims(temp, 0)), 0) - - temp = cv2.rotate(temp, cv2.ROTATE_90_CLOCKWISE) - if ib == 3: - temp = cv2.flip(temp, 0) - - return mask - -def spectral_norm(w, name, iteration=1): - w_shape = w.shape.as_list() - w = tf.reshape(w, [-1, w_shape[-1]]) - - u = tf.get_variable(name+"u", [1, w_shape[-1]], initializer=tf.truncated_normal_initializer(), trainable=False) - - u_hat = u - v_hat = None - for i in range(iteration): - """ - power iteration - Usually iteration = 1 will be enough - """ - v_ = tf.matmul(u_hat, tf.transpose(w)) - v_hat = l2_norm(v_) - - u_ = tf.matmul(v_hat, w) - u_hat = l2_norm(u_) - - sigma = tf.matmul(tf.matmul(v_hat, w), tf.transpose(u_hat)) - w_norm = w / sigma - - with tf.control_dependencies([u.assign(u_hat)]): - w_norm = tf.reshape(w_norm, w_shape) - - return w_norm - -def convolution_SN(tensor, output_dim, kernel_size, stride, name): - _, h, w, c = [i.value for i in tensor.get_shape()] - - w = tf.get_variable(name=name + 'w', shape=[kernel_size, kernel_size, c, output_dim], initializer=layers.xavier_initializer()) - b = tf.get_variable(name=name + 'b', shape=[output_dim], initializer=tf.constant_initializer(0.0)) - - output = tf.nn.conv2d(tensor, filter=spectral_norm(w, name=name + 'w'), strides=[1, stride, stride, 1], padding='SAME') + b - - return output - -def dense_SN(tensor, output_dim, name): - _, h, w, c = [i.value for i in tensor.get_shape()] - - w = tf.get_variable(name=name + 'w', shape=[h, w, c, output_dim], initializer=layers.xavier_initializer()) - b = tf.get_variable(name=name + 'b', shape=[output_dim], initializer=tf.constant_initializer(0.0)) - - output = tf.nn.conv2d(tensor, filter=spectral_norm(w, name=name + 'w'), strides=[1, 1, 1, 1], padding='VALID') + b - - return output - -def dense_RED_SN(tensor, name): - sn_w = None - - _, h, w, c = [i.value for i in tensor.get_shape()] - h = int(h) - w = int(w) - c = int(c) - - weight = tf.get_variable(name=name + '_w', shape=[h*w, 1, c, 1], initializer=layers.xavier_initializer()) - b = tf.get_variable(name=name + '_b', shape=[1, h, w, 1], initializer=tf.constant_initializer(0.0)) - - for it in range(h*w): - w_pixel = weight[it:it+1, :, :, :] - sn_w_pixel = spectral_norm(w_pixel, name=name + 'w_%d' %it) - - if it == 0: - sn_w = sn_w_pixel - else: - sn_w = tf.concat([sn_w, sn_w_pixel], axis=0) - - w_rs = tf.reshape(sn_w, [h, w, c, 1]) - w_rs_t = tf.transpose(w_rs, [3, 0, 1, 2]) - - output_RED = tf.reduce_sum(tensor*w_rs_t + b, axis=3, keepdims=True) - - return output_RED \ No newline at end of file diff --git a/requirements-cpu.txt b/requirements-cpu.txt deleted file mode 100644 index 1f60e98..0000000 --- a/requirements-cpu.txt +++ /dev/null @@ -1,23 +0,0 @@ -absl-py==0.7.1 -astor==0.8.0 -gast==0.2.2 -google-pasta==0.1.7 -grpcio==1.22.0 -h5py==2.9.0 -Keras-Applications==1.0.8 -Keras-Preprocessing==1.1.0 -Markdown==3.1.1 -numpy==1.17.0 -opencv-python==4.1.0.25 -Pillow==6.2.2 -protobuf==3.9.1 -PySide2==5.13.0 -scipy==1.3.0 -shiboken2==5.13.0 -six==1.12.0 -tensorboard==1.14.0 -tensorflow-estimator==1.14.0 -tensorflow==1.14.0 -termcolor==1.1.0 -Werkzeug==0.15.5 -wrapt==1.11.2 diff --git a/requirements-gpu.txt b/requirements-gpu.txt deleted file mode 100644 index a1fd815..0000000 --- a/requirements-gpu.txt +++ /dev/null @@ -1,23 +0,0 @@ -absl-py==0.7.1 -astor==0.8.0 -gast==0.2.2 -google-pasta==0.1.7 -grpcio==1.22.0 -h5py==2.9.0 -Keras-Applications==1.0.8 -Keras-Preprocessing==1.1.0 -Markdown==3.1.1 -numpy==1.17.0 -opencv-python==4.3.0.38 -Pillow==6.2.2 -protobuf==3.9.1 -PySide2==5.13.0 -scipy==1.3.0 -shiboken2==5.13.0 -six==1.12.0 -tensorboard==1.15.0 -tensorflow-estimator==1.15.1 -tensorflow-gpu==1.15.2 -termcolor==1.1.0 -Werkzeug==0.15.5 -wrapt==1.11.2 diff --git a/server/config.py b/server/config.py new file mode 100644 index 0000000..fb1c485 --- /dev/null +++ b/server/config.py @@ -0,0 +1,34 @@ +import argparse + + +def get_args(): + parser = argparse.ArgumentParser(description='') + + # Input output folders settings + parser.add_argument('-i', '--input', default='./decensor_input/', + help='input images with censored regions will be decensored') + parser.add_argument('--input-original-path', default='./decensor_input_original/', + help='input images with no modifications to be decensored by decensor.py path') + parser.add_argument('-o', '--output', default='./decensor_output/', help='output images folder') + parser.add_argument('-c', '--clean-up-input-dirs', dest='clean_up_input_dirs', action='store_true', default=False, + help='whether to delete all image files in the input directories when decensoring is done') + + # Decensor settings + parser.add_argument('-m', '--mask', default="rgb-0,255,0", + help='mask string file-{suffix} or rgb-{r,g,b} example: rgb-0,255,0 or file-_mask') # todo: or detect + parser.add_argument('--is-mosaic', action='store_true', help='true if image has mosaic censoring, false otherwise') + parser.add_argument('--variations', type=int, choices=[1, 2, 4], default=1, + help='number of decensor variations to be generated') + + # Other settings + parser.add_argument('--ui-mode', action='store_true', + help='if you want ui mode, if missing: command line interface') + parser.add_argument('-p', '--port', type=int, default=8000, help='Server port') + parser.add_argument('--host', type=str, default="127.0.0.1", help='Server host') + parser.add_argument('--require-keep-connection', action='store_true', help='If this flag is set it requires the user to keep the connection. Decensor requests will be removed if the client disconnects.') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + get_args() diff --git a/server/index.css b/server/index.css new file mode 100644 index 0000000..25b179f --- /dev/null +++ b/server/index.css @@ -0,0 +1 @@ +/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */html{line-height:1.15;-webkit-text-size-adjust:100%;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}body{margin:0}main{display:block}h1{margin:.67em 0;font-size:2em}hr{box-sizing:content-box;height:0;overflow:visible}pre{font-size:1em;font-family:monospace,monospace}a{background-color:transparent}abbr[title]{text-decoration:underline;text-decoration:underline 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e.size&&(a.fontSize=Se(e.size)?`${e.size}px`:e.size),e.rotate&&(a.transform=`rotate(${e.rotate}deg)`),a});return{cls:l,innerStyle:r,onClick:a=>{t("click",a)}}}}),U_=["stroke-width","stroke-linecap","stroke-linejoin"],Z_=G("path",{d:"M24 14v10h9.5m8.5 0c0 9.941-8.059 18-18 18S6 33.941 6 24 14.059 6 24 6s18 8.059 18 18Z"},null,-1),G_=[Z_];function X_(e,t,n,l,r,o){return C(),L("svg",{viewBox:"0 0 48 48",fill:"none",xmlns:"http://www.w3.org/2000/svg",stroke:"currentColor",class:O(e.cls),style:Oe(e.innerStyle),"stroke-width":e.strokeWidth,"stroke-linecap":e.strokeLinecap,"stroke-linejoin":e.strokeLinejoin,onClick:t[0]||(t[0]=(...a)=>e.onClick&&e.onClick(...a))},G_,14,U_)}var jc=ue(Y_,[["render",X_]]);const C2=Object.assign(jc,{install:(e,t)=>{var n;const l=(n=t==null?void 0:t.iconPrefix)!=null?n:"";e.component(l+jc.name,jc)}});var Q_=Object.defineProperty,J_=Object.defineProperties,e7=Object.getOwnPropertyDescriptors,sg=Object.getOwnPropertySymbols,t7=Object.prototype.hasOwnProperty,n7=Object.prototype.propertyIsEnumerable,ug=(e,t,n)=>t in e?Q_(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,wi=(e,t)=>{for(var n in t||(t={}))t7.call(t,n)&&ug(e,n,t[n]);if(sg)for(var n of sg(t))n7.call(t,n)&&ug(e,n,t[n]);return e},$2=(e,t)=>J_(e,e7(t));const k2=6,Hf=7,l7=k2*Hf;function r7(e){return{label:e.date(),value:e}}const a7=Y({name:"DatePanel",components:{PanelHeader:nu,PanelBody:lu,PanelWeekList:M_,TimePanel:gs,IconCalendar:Jp,IconClockCircle:C2},props:{isRange:{type:Boolean},value:{type:Object},rangeValues:{type:Array},headerValue:{type:Object,required:!0},footerValue:{type:Object},timePickerValue:{type:Object},headerOperations:{type:Object,default:()=>({})},headerIcons:{type:Object,default:()=>({})},dayStartOfWeek:{type:Number,default:0},disabledDate:{type:Function},disabledTime:{type:Function},isSameTime:{type:Function},mode:{type:String,default:"date"},showTime:{type:Boolean},timePickerProps:{type:Object},currentView:{type:String},dateRender:{type:Function},disabled:{type:Boolean},onHeaderLabelClick:{type:Function}},emits:["select","time-picker-select","cell-mouse-enter","current-view-change","update:currentView"],setup(e,{emit:t}){const{isRange:n,headerValue:l,footerValue:r,dayStartOfWeek:o,isSameTime:a,mode:i,showTime:s,currentView:c,disabledTime:u}=Ce(e),f=bo(),v=p(()=>(i==null?void 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C(),L("div",{class:O(e.classNames)},[e.showDateView?(C(),L("div",{key:0,class:O(`${e.prefixCls}-inner`)},[d(a,pe($2(wi({},e.headerOperations),{icons:e.headerIcons}),{"prefix-cls":e.pickerPrefixCls,title:e.headerTitle,mode:e.mode,value:e.headerValue,"on-label-click":e.onHeaderLabelClick}),null,16,["prefix-cls","title","mode","value","on-label-click"]),d(i,{"prefix-cls":e.pickerPrefixCls,"week-list":e.weekList},null,8,["prefix-cls","week-list"]),d(s,{mode:e.mode,"prefix-cls":e.pickerPrefixCls,rows:e.rows,value:e.isRange?void 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i7=Y({name:"WeekPanel",components:{DatePanel:ev},props:{dayStartOfWeek:{type:Number,default:0}},emits:["select","cell-mouse-enter"],setup(e,{emit:t}){return Lt(),{isSameTime:(l,r)=>Dt.isSameWeek(l,r,e.dayStartOfWeek),onSelect:l=>{const r=Dt.startOfWeek(l,e.dayStartOfWeek);t("select",r)},onCellMouseEnter:l=>{const r=Dt.startOfWeek(l,e.dayStartOfWeek);t("cell-mouse-enter",r)}}}});function s7(e,t,n,l,r,o){const a=ee("DatePanel");return C(),oe(a,pe(e.$attrs,{mode:"week","is-week":"","day-start-of-week":e.dayStartOfWeek,"is-same-time":e.isSameTime,onSelect:e.onSelect,onCellMouseEnter:e.onCellMouseEnter}),null,16,["day-start-of-week","is-same-time","onSelect","onCellMouseEnter"])}var w2=ue(i7,[["render",s7]]),u7=Object.defineProperty,c7=Object.defineProperties,d7=Object.getOwnPropertyDescriptors,cg=Object.getOwnPropertySymbols,f7=Object.prototype.hasOwnProperty,p7=Object.prototype.propertyIsEnumerable,dg=(e,t,n)=>t in e?u7(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,v7=(e,t)=>{for(var n in t||(t={}))f7.call(t,n)&&dg(e,n,t[n]);if(cg)for(var n of cg(t))p7.call(t,n)&&dg(e,n,t[n]);return e},m7=(e,t)=>c7(e,d7(t));const h7=["January","February","March","April","May","June","July","August","September","October","November","December"],g7=12,y7=4,fg=3,b7=Y({name:"MonthPanel",components:{PanelHeader:nu,PanelBody:lu},props:{headerValue:{type:Object,required:!0},headerOperations:{type:Object,default:()=>({})},headerIcons:{type:Object,default:()=>({})},value:{type:Object},disabledDate:{type:Function},rangeValues:{type:Array},dateRender:{type:Function},onHeaderLabelClick:{type:Function},abbreviation:{type:Boolean,default:!0}},emits:["select","cell-mouse-enter"],setup(e,{emit:t}){const n=bo(),{headerValue:l}=Ce(e),r=p(()=>J("panel-month")),o=J("picker"),a=p(()=>l.value.format("YYYY")),i=p(()=>{const f=l.value.year(),v=e.abbreviation?"short":"long",m=oa(g7).map((y,g)=>({label:n(`datePicker.month.${v}.${h7[g]}`),value:Nt(`${f}-${g+1}`,"YYYY-M")}));return oa(y7).map((y,g)=>m.slice(g*fg,(g+1)*fg))}),s=(f,v)=>f.isSame(v,"month");function c(f){t("select",f.value)}function u(f){t("cell-mouse-enter",f.value)}return{prefixCls:r,pickerPrefixCls:o,headerTitle:a,rows:i,isSameTime:s,onCellClick:c,onCellMouseEnter:u}}});function C7(e,t,n,l,r,o){const a=ee("PanelHeader"),i=ee("PanelBody");return C(),L("div",{class:O(e.prefixCls)},[G("div",{class:O(`${e.prefixCls}-inner`)},[d(a,pe(m7(v7({},e.headerOperations),{icons:e.headerIcons}),{"prefix-cls":e.pickerPrefixCls,title:e.headerTitle,mode:"month",value:e.headerValue,"on-label-click":e.onHeaderLabelClick}),null,16,["prefix-cls","title","value","on-label-click"]),d(i,{mode:"month","prefix-cls":e.pickerPrefixCls,rows:e.rows,value:e.value,"range-values":e.rangeValues,"disabled-date":e.disabledDate,"is-same-time":e.isSameTime,"date-render":e.dateRender,onCellClick:e.onCellClick,onCellMouseEnter:e.onCellMouseEnter},null,8,["prefix-cls","rows","value","range-values","disabled-date","is-same-time","date-render","onCellClick","onCellMouseEnter"])],2)],2)}var S2=ue(b7,[["render",C7]]),$7=Object.defineProperty,k7=Object.defineProperties,w7=Object.getOwnPropertyDescriptors,pg=Object.getOwnPropertySymbols,S7=Object.prototype.hasOwnProperty,P7=Object.prototype.propertyIsEnumerable,vg=(e,t,n)=>t in e?$7(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,O7=(e,t)=>{for(var n in t||(t={}))S7.call(t,n)&&vg(e,n,t[n]);if(pg)for(var n of pg(t))P7.call(t,n)&&vg(e,n,t[n]);return e},_7=(e,t)=>k7(e,w7(t));const Wf=4,Si=3,I7=Wf*Si,zc=10,V7=Y({name:"YearPanel",components:{PanelHeader:nu,PanelBody:lu},props:{headerValue:{type:Object,required:!0},headerOperations:{type:Object,default:()=>({})},headerIcons:{type:Object,default:()=>({})},value:{type:Object},disabledDate:{type:Function},rangeValues:{type:Array},dateRender:{type:Function}},emits:["select","cell-mouse-enter"],setup(e,{emit:t}){const{headerValue:n}=Ce(e),l=p(()=>J("panel-year")),r=J("picker"),o=p(()=>{const u=Math.floor(n.value.year()/zc)*zc-1,f=oa(I7).map((m,h)=>({label:u+h,value:Nt(`${u+h}`,"YYYY"),isPrev:h<1,isNext:h>zc}));return oa(Wf).map((m,h)=>f.slice(h*Si,(h+1)*Si))}),a=p(()=>`${o.value[0][1].label}-${o.value[Wf-1][Si-1].label}`),i=(u,f)=>u.isSame(f,"year");function s(u){t("select",u.value)}function c(u){t("cell-mouse-enter",u.value)}return{prefixCls:l,pickerPrefixCls:r,headerTitle:a,rows:o,isSameTime:i,onCellClick:s,onCellMouseEnter:c}}});function L7(e,t,n,l,r,o){const a=ee("PanelHeader"),i=ee("PanelBody");return C(),L("div",{class:O(e.prefixCls)},[G("div",{class:O(`${e.prefixCls}-inner`)},[d(a,pe(_7(O7({},e.headerOperations),{icons:e.headerIcons}),{"prefix-cls":e.pickerPrefixCls,title:e.headerTitle}),null,16,["prefix-cls","title"]),d(i,{mode:"year","prefix-cls":e.pickerPrefixCls,rows:e.rows,value:e.value,"range-values":e.rangeValues,"disabled-date":e.disabledDate,"is-same-time":e.isSameTime,"date-render":e.dateRender,onCellClick:e.onCellClick,onCellMouseEnter:e.onCellMouseEnter},null,8,["prefix-cls","rows","value","range-values","disabled-date","is-same-time","date-render","onCellClick","onCellMouseEnter"])],2)],2)}var P2=ue(V7,[["render",L7]]),B7=Object.defineProperty,T7=Object.defineProperties,E7=Object.getOwnPropertyDescriptors,mg=Object.getOwnPropertySymbols,M7=Object.prototype.hasOwnProperty,j7=Object.prototype.propertyIsEnumerable,hg=(e,t,n)=>t in e?B7(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,z7=(e,t)=>{for(var n in t||(t={}))M7.call(t,n)&&hg(e,n,t[n]);if(mg)for(var n of mg(t))j7.call(t,n)&&hg(e,n,t[n]);return e},A7=(e,t)=>T7(e,E7(t));const N7=Y({name:"QuarterPanel",components:{PanelHeader:nu,PanelBody:lu},props:{headerValue:{type:Object,required:!0},headerOperations:{type:Object,default:()=>({})},headerIcons:{type:Object,default:()=>({})},value:{type:Object},disabledDate:{type:Function},rangeValues:{type:Array},dateRender:{type:Function},onHeaderLabelClick:{type:Function}},emits:["select","cell-mouse-enter"],setup(e,{emit:t}){const{headerValue:n}=Ce(e),l=p(()=>J("panel-quarter")),r=J("picker"),o=p(()=>n.value.format("YYYY")),a=p(()=>{const u=n.value.year();return[[1,2,3,4].map(f=>({label:`Q${f}`,value:Nt(`${u}-${yr((f-1)*3+1,2,"0")}-01`)}))]}),i=(u,f)=>u.isSame(f,"month")||u.isSame(f,"year")&&Math.floor(u.month()/3)===Math.floor(f.month()/3);function s(u){t("select",u.value)}function c(u){t("cell-mouse-enter",u.value)}return{prefixCls:l,pickerPrefixCls:r,headerTitle:o,rows:a,isSameTime:i,onCellClick:s,onCellMouseEnter:c}}});function x7(e,t,n,l,r,o){const a=ee("PanelHeader"),i=ee("PanelBody");return C(),L("div",{class:O(e.prefixCls)},[G("div",{class:O(`${e.prefixCls}-inner`)},[d(a,pe(A7(z7({},e.headerOperations),{icons:e.headerIcons}),{"prefix-cls":e.pickerPrefixCls,title:e.headerTitle,mode:"quarter",value:e.headerValue,"on-label-click":e.onHeaderLabelClick}),null,16,["prefix-cls","title","value","on-label-click"]),d(i,{mode:"quarter","prefix-cls":e.pickerPrefixCls,rows:e.rows,value:e.value,"range-values":e.rangeValues,"disabled-date":e.disabledDate,"is-same-time":e.isSameTime,"date-render":e.dateRender,onCellClick:e.onCellClick,onCellMouseEnter:e.onCellMouseEnter},null,8,["prefix-cls","rows","value","range-values","disabled-date","is-same-time","date-render","onCellClick","onCellMouseEnter"])],2)],2)}var O2=ue(N7,[["render",x7]]);const D7=Y({name:"IconLink",props:{size:{type:[Number,String]},strokeWidth:{type:Number,default:4},strokeLinecap:{type:String,default:"butt",validator:e=>["butt","round","square"].includes(e)},strokeLinejoin:{type:String,default:"miter",validator:e=>["arcs","bevel","miter","miter-clip","round"].includes(e)},rotate:Number,spin:Boolean},emits:{click:e=>!0},setup(e,{emit:t}){const n=J("icon"),l=p(()=>[n,`${n}-link`,{[`${n}-spin`]:e.spin}]),r=p(()=>{const a={};return e.size&&(a.fontSize=Se(e.size)?`${e.size}px`:e.size),e.rotate&&(a.transform=`rotate(${e.rotate}deg)`),a});return{cls:l,innerStyle:r,onClick:a=>{t("click",a)}}}}),F7=["stroke-width","stroke-linecap","stroke-linejoin"],R7=G("path",{d:"m14.1 25.414-4.95 4.95a6 6 0 0 0 8.486 8.485l8.485-8.485a6 6 0 0 0 0-8.485m7.779.707 4.95-4.95a6 6 0 1 0-8.486-8.485l-8.485 8.485a6 6 0 0 0 0 8.485"},null,-1),H7=[R7];function W7(e,t,n,l,r,o){return C(),L("svg",{viewBox:"0 0 48 48",fill:"none",xmlns:"http://www.w3.org/2000/svg",stroke:"currentColor",class:O(e.cls),style:Oe(e.innerStyle),"stroke-width":e.strokeWidth,"stroke-linecap":e.strokeLinecap,"stroke-linejoin":e.strokeLinejoin,onClick:t[0]||(t[0]=(...a)=>e.onClick&&e.onClick(...a))},H7,14,F7)}var Ac=ue(D7,[["render",W7]]);const K7=Object.assign(Ac,{install:(e,t)=>{var n;const l=(n=t==null?void 0:t.iconPrefix)!=null?n:"";e.component(l+Ac.name,Ac)}}),q7=Y({name:"Link",components:{IconLink:K7,IconLoading:gn},props:{href:String,status:{type:String,default:"normal"},hoverable:{type:Boolean,default:!0},icon:Boolean,loading:Boolean,disabled:Boolean},emits:{click:e=>!0},setup(e,{slots:t,emit:n}){const l=J("link"),r=v2(e,t,"icon"),o=i=>{if(e.disabled||e.loading){i.preventDefault();return}n("click",i)};return{cls:p(()=>[l,`${l}-status-${e.status}`,{[`${l}-disabled`]:e.disabled,[`${l}-loading`]:e.loading,[`${l}-hoverless`]:!e.hoverable,[`${l}-with-icon`]:e.loading||r.value}]),prefixCls:l,showIcon:r,handleClick:o}}}),Y7=["href"];function U7(e,t,n,l,r,o){const a=ee("icon-loading"),i=ee("icon-link");return C(),L("a",{href:e.disabled?void 0:e.href,class:O(e.cls),onClick:t[0]||(t[0]=(...s)=>e.handleClick&&e.handleClick(...s))},[e.loading||e.showIcon?(C(),L("span",{key:0,class:O(`${e.prefixCls}-icon`)},[e.loading?(C(),oe(a,{key:0})):Q(e.$slots,"icon",{key:1},()=>[d(i)])],2)):ne("v-if",!0),Q(e.$slots,"default")],10,Y7)}var Nc=ue(q7,[["render",U7]]);const _2=Object.assign(Nc,{install:(e,t)=>{Ye(e,t);const n=qe(t);e.component(n+Nc.name,Nc)}}),Z7=Y({name:"PanelFooter",components:{Link:_2,Button:yn},props:{prefixCls:{type:String,required:!0},showTodayBtn:{type:Boolean},showConfirmBtn:{type:Boolean},confirmBtnDisabled:{type:Boolean}},emits:["today-btn-click","confirm-btn-click"],setup(e,{emit:t}){return{datePickerT:bo(),onTodayClick:()=>{t("today-btn-click")},onConfirmBtnClick:()=>{t("confirm-btn-click")}}}});function G7(e,t,n,l,r,o){const a=ee("Link"),i=ee("Button");return C(),L("div",{class:O(`${e.prefixCls}-footer`)},[e.$slots.extra?(C(),L("div",{key:0,class:O(`${e.prefixCls}-footer-extra-wrapper`)},[Q(e.$slots,"extra")],2)):ne("v-if",!0),e.showTodayBtn?(C(),L("div",{key:1,class:O(`${e.prefixCls}-footer-now-wrapper`)},[d(a,{onClick:e.onTodayClick},{default:me(()=>[Ke(Ee(e.datePickerT("datePicker.today")),1)]),_:1},8,["onClick"])],2)):ne("v-if",!0),e.$slots.btn||e.showConfirmBtn?(C(),L("div",{key:2,class:O(`${e.prefixCls}-footer-btn-wrapper`)},[Q(e.$slots,"btn"),e.showConfirmBtn?(C(),oe(i,{key:0,class:O(`${e.prefixCls}-btn-confirm`),type:"primary",size:"mini",disabled:e.confirmBtnDisabled,onClick:e.onConfirmBtnClick},{default:me(()=>[Ke(Ee(e.datePickerT("datePicker.ok")),1)]),_:1},8,["class","disabled","onClick"])):ne("v-if",!0)],2)):ne("v-if",!0)],2)}var I2=ue(Z7,[["render",G7]]);function V2(e){const{mode:t}=Ce(e),n=p(()=>({date:1,week:1,year:10*12,quarter:12,month:12})[t.value]),l=p(()=>["year"].includes(t.value)?10*12:12);return{span:n,superSpan:l}}function ys(e){const{mode:t,value:n,defaultValue:l,selectedValue:r,format:o,onChange:a}=Ce(e),i=p(()=>(t==null?void 0:t.value)||"date"),{span:s,superSpan:c}=V2(be({mode:i})),u=(P,S)=>{const _=i.value==="date"||i.value==="week"?"M":"y";return P.isSame(S,_)},f=p(()=>zn(n==null?void 0:n.value,o.value)),v=p(()=>zn(l==null?void 0:l.value,o.value)),m=j(v.value||on()),h=p(()=>f.value||m.value),y=P=>{P&&(m.value=P)},g=(P,S=!0)=>{var _;P&&(S&&!u(h.value,P)&&((_=a==null?void 0:a.value)==null||_.call(a,P)),y(P))};r!=null&&r.value&&y(r.value),Le(()=>r==null?void 0:r.value,P=>{g(P)});function b(){return(r==null?void 0:r.value)||v.value||on()}function w(P=!0){const S=b();P?g(S):y(S)}const k=p(()=>s.value!==c.value),$=p(()=>({onSuperPrev:()=>{g(Dt.subtract(h.value,c.value,"M"))},onPrev:k.value?()=>{g(Dt.subtract(h.value,s.value,"M"))}:void 0,onNext:k.value?()=>{g(Dt.add(h.value,s.value,"M"))}:void 0,onSuperNext:()=>{g(Dt.add(h.value,c.value,"M"))}}));return{headerValue:h,setHeaderValue:g,headerOperations:$,resetHeaderValue:w,getDefaultLocalValue:b}}const X7=Y({name:"DatePikerPanel",components:{DatePanel:ev,PanelShortcuts:g2,PanelFooter:I2,WeekPanel:w2,MonthPanel:S2,YearPanel:P2,QuarterPanel:O2,RenderFunction:Ml},props:{mode:{type:String},headerMode:{type:String},prefixCls:{type:String,required:!0},value:{type:Object},headerValue:{type:Object,required:!0},timePickerValue:{type:Object},showTime:{type:Boolean},showConfirmBtn:{type:Boolean},shortcuts:{type:Array,default:()=>[]},shortcutsPosition:{type:String,default:"bottom"},format:{type:String,required:!0},dayStartOfWeek:{type:Number,default:0},disabledDate:{type:Function},disabledTime:{type:Function},timePickerProps:{type:Object},extra:{type:Function},dateRender:{type:Function},hideTrigger:{type:Boolean},confirmBtnDisabled:{type:Boolean},showNowBtn:{type:Boolean},headerIcons:{type:Object,default:()=>({})},headerOperations:{type:Object},abbreviation:{type:Boolean}},emits:["cell-click","time-picker-select","shortcut-click","shortcut-mouse-enter","shortcut-mouse-leave","confirm","today-btn-click","header-label-click","header-select","month-header-click"],setup(e,{emit:t}){const{prefixCls:n,shortcuts:l,shortcutsPosition:r,format:o,value:a,disabledDate:i,hideTrigger:s,showNowBtn:c,dateRender:u,showConfirmBtn:f,headerValue:v,headerIcons:m,headerOperations:h,headerMode:y}=Ce(e),g=p(()=>!!(l.value&&l.value.length)),b=p(()=>c.value&&f.value&&!g.value),w=p(()=>b.value||g.value),k=p(()=>w.value&&r.value==="left"),$=p(()=>w.value&&r.value==="right"),P=p(()=>w.value&&r.value==="bottom"),S=p(()=>[`${n.value}-container`,{[`${n.value}-container-panel-only`]:s.value,[`${n.value}-container-shortcuts-placement-left`]:k.value,[`${n.value}-container-shortcuts-placement-right`]:$.value}]),_=p(()=>(a==null?void 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LhVe48ilq1O+cqwqBHqqHmrIrjzJdleNy6crl6+acPZy4B+S6xnl2DzEAOH7gyywKWrzyoaiwI7h8OyY6/qBWM2rtnJvRerLjGGOs7Qm0H0T2Z9RiIytRiqzHrU7bRWiXbfnXT+dpH17A8KOd6g32io1PmoVvAFjX1VxbXAgYAUpvq2hQ6WhfLXc5M9DxcSOXFjGabfW6CjUqcGTz8XU3IY3u1wERkKc4vn72rWN6IXxWvRfSQngWrFWjfTG+aPZ2lFtFWsrJ6zjqLbQUxTFG1DimGmq97hK1LRALVj2HMz1atyuok3o8XU+0qZ5tFKdCJOm7EzqZyGqUGdGiIgaxjbpMzS3VYltfnEd3tRq7FOFqnOHzylivZWZzzcKB3su8hc5TQ4saQOQu3WCOdvpfFSLQD0VlKCsThPrvvtjxJRqPmd6nMydihraL4mAkkGynnVbrrjD6q3OLGD8x+6AvnyObv+W1uTcJkcSd/sjslZFR3Iqq+srQ6Liiotr2fY4vc/yMT75pm+hsaCk9pXDhZ3E5U+bIUx3FPi+8v2U3X+m3hEji9pW6uKOJRxpHxmg88A1gXfDR+ZouiDnuaLw8iu0TY4LrPjb78L5UOTxsRHxQPr3nDOq7hEjyHwaKDjpFsTJzJZGL3Cdaeo7Fw1fRM1FKQWUBxkIC73Hu0Y8zeExrfcRbVUcuNVN4Heu9h4ivEiLQUawDhkAOALRgsEzxTZ9xrScz94kbkFGA47sb6g5Lab+q0Sw9Pi7ZxxjzP68kqTVyHvxOPmTHULgPClz/YurVQiTpu/fDAAkQcMa6Fkcbq15XVQFGxwd53wGIx0udBxAXFzxdorJkz+kcVMBt7e3vR73r/2FCJPm7GRkgIJGpwJhAq9D8tJ3qicdHLbpHqNaF6CAt3hQe88kIWPn61KXrXwAf/uLphwuR5O/mAzEIUe1u17XwO8Y1t1rdQPb+dQwlzw0jhJmVHCNAlYGkTMCf8U7/pwkRGH+XDRhztAhgROhTBaUC8SqNWjzHygBTlgY4ZejV+1aX6aiT1x7uTWGn6PW3iMmkjBEeWzLk6y7TyWObu9YobrZ2CEt3fN7jfa23+TWNmwiRpL9dE0HwaOdbGeIC6e44P2LpVSG3IHePfiSQlBUAVNDX5nvvpZsKkURhZpYF5AXpKN3gcx6TFDkeKU0Elvg3eukGiBWOz9/6t2zuIkRgfFknQoJRhUaL4wo6nDKAEVWAvBzom9aRq492VIDrXhS9hu4mRJL+LKaDE1IE+9vz84azu9hIgBHAigCOWqgKj6+j8dr/9rfdnHzLi+Qul9eAectHD3rpfbf43maOi442gX5kg5vcFOZZfmUx/mXYO5AyRC3MXWP0mbLUE3BKUWrCds1ix+OPHLe3WXC5XNL53ptOMxElLaz7ybUsufadBY9/fu4mugZgf7vZx6SQz/hvF043ISX/Mdsoz9QYmCX8fMYL0hEqzXLWM1me02knpuRbXsC8tdM+x7vmWmrrQh/P7mgMVGU4A3B5jU49OaXX/i8Gy3N+iizKEbPKkVvsma1P6SEmqeRVH2U8CwGa/wHjXqG++KPWrHniGePeET3UZJX05VhgLouR/H526uxWdc7PoIectBJ/a8B3872yoxvHwHxu51EFCPwHhAjMv/gBzNUYIP5duEeJe0f0L+D749HrhKeLAAAAAElFTkSuQmCC"},null),d("image",{id:"_Image10",width:"107px",height:"62px",href:"data:image/png;base64,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a/server/local.py b/server/local.py new file mode 100644 index 0000000..5aa6292 --- /dev/null +++ b/server/local.py @@ -0,0 +1,154 @@ +import os +from typing import List, Tuple + +from PIL import Image + +from lib_cream_py import RawMask, ColorMask, decensor_image_variations, apply_variant, Logger + + +class CliLogger(Logger): + def warn(self, id: str, info=None): + match id: + case _: + print("[WARN]", "UNKNOWN", id) + + def info(self, id: str, info=None): + match id: + case "apply-variant": + print("[INFO]", "Apply variant", info) + case "generate-mask": + print("[INFO]", "Generating mask") + case "finished": + print("[INFO]", "Finished processing") + case "remove-alpha": + print("[INFO]", "Removing alpha channel") + case "find-regions": + print("[INFO]", "Trying to find regions") + case "decensor-segment": + region_counter, region_count = info + print("[INFO]", "Processing", region_counter, "segment of", region_count) + case "restore-alpha": + print("[INFO]", "Restoring alpha channel") + case _: + print("[INFO]", "UNKNOWN", id) + + def debug(self, id: str, info=None): + match id: + case "found-regions": + region_count = info + print("[DEBUG]", "Found", region_count, "regions") + case _: + print("[DEBUG]", "UNKNOWN", id) + + def error(self, id: str, info=None): + match id: + case "no-regions": + print("[ERROR]", "Couldn't find any regions") + case "missing-model": + print( + "[ERROR] Missing Model, download model\nRead: https://github.com/deeppomf/DeepCreamPy/blob/master/docs/INSTALLATION.md#run-code-yourself") + case "bounding-box-out-of-bounds": + x1_square, y1_square, x2_square, y2_square = info + print("[ERROR]", "Bounding box out of bounds", x1_square, y1_square, x2_square, y2_square) + case _: + print("[ERROR]", "UNKNOWN", id) + + +class MaskInfo: + def __init__(self, v: str): + if v.startswith("rgb-"): + self.file = False + try: + rgb_values = v[4:].split(",") + self.rgb = tuple(int(value) for value in rgb_values) + if not all(0 <= value <= 255 for value in self.rgb): + raise ValueError("RGB values must be between 0 and 255") + if len(self.rgb) != 3: + raise ValueError("RGB values must be 3 numbers") + self.rgb = (self.rgb[0], self.rgb[1], self.rgb[2]) + except ValueError: + raise Exception("Invalid RGB format. Expected format: rgb-R,G,B") + elif v.startswith("file-"): + self.file = True + self.mask_name = v[5:] + if len(self.mask_name) == 0: + raise ValueError("Mask name must not be empty") + else: + raise ValueError(f"Unknown mask type: {v}") + + +def generate_out_path(out_folder: str, file_path: str, index: int) -> str: + file_name, file_extension = os.path.splitext(os.path.basename(file_path)) + + new_file_name = f"{file_name}_{index}{file_extension}" + + return os.path.join(out_folder, new_file_name) + + +def process(path: str, out_path: str, mask: MaskInfo, model, variations: int, is_mosaic: bool, cleanup: bool, logger: Logger): + imgs = get_imgs(path) + if mask.file: + imgs = join_mask(imgs, mask.mask_name) + for (img, mask) in imgs: + save_image = lambda i, out_img: out_img.save(generate_out_path(out_path, img.path, i)) + mask_gen = lambda i, x, y: RawMask(apply_variant(mask.img, i)) + decensor_image_variations(model, img.img, img.img, mask_gen, variations, is_mosaic, save_image, logger=logger) + if cleanup: + try: + img.delete() + mask.delete() + except Exception as e: + print("[ERROR] Failed to delete img: ", img.path) + else: + for img in imgs: + try: + save_image = lambda i, out_img: out_img.save(generate_out_path(out_path, img.path, i)) + mask_gen = lambda i, ori, colored: ColorMask(colored if is_mosaic else ori, rgb=mask.rgb) + decensor_image_variations(model, img.img, img.img, mask_gen, variations, is_mosaic, save_image, logger=logger) + except Exception as e: + print("[ERROR]", e) + if cleanup: + try: + img.delete() + except Exception as e: + print("[ERROR] Failed to delete img: ", img.path) + + +class MyImage: + def __init__(self, path: str): + self.img = Image.open(path) + self.path = path + + def delete(self): + os.remove(self.path) + + +def get_imgs(path: str) -> list[MyImage]: + img_list = [] + for filename in os.listdir(path): + file_path = os.path.join(path, filename) + try: + img_list.append(MyImage(file_path)) + except Exception as e: + print("[WARN] Failed to load image:", e) + return img_list + + +def join_mask(imgs: List[MyImage], suffix: str) -> List[Tuple[MyImage, MyImage]]: + no_suffix_imgs = {} + mask_imgs = {} + result = [] + + for img in imgs: + base_name = os.path.splitext(os.path.basename(img.path))[0] + if base_name.endswith(suffix): + mask_imgs[base_name[:-len(suffix)]] = img + else: + no_suffix_imgs[base_name] = img + + for base_name, img in no_suffix_imgs.items(): + if base_name in mask_imgs: + mask_img = mask_imgs[base_name] + result.append((img, mask_img)) + + return result diff --git a/server/main.py b/server/main.py new file mode 100644 index 0000000..b494722 --- /dev/null +++ b/server/main.py @@ -0,0 +1,23 @@ +from config import get_args +from lib_cream_py import InpaintNN +from local import MaskInfo + +if __name__ == '__main__': + args = get_args() + if args.ui_mode: + import uvicorn + import server as ui + + ui.check_disconnect = args.require_keep_connection + uvicorn.run(ui.app, host=args.host, port=args.port) + else: + from local import process, CliLogger + + # todo: use input-original-path + try: + logger = CliLogger() + model = InpaintNN('./models/mosaic.keras' if args.is_mosaic else './models/bar.keras', logger=logger) + process(args.input, args.output, MaskInfo(args.mask), model, args.variations, args.is_mosaic, + args.clean_up_input_dirs, logger) + except Exception as e: + print("[ERROR] {}".format(e)) diff --git a/server/main.spec b/server/main.spec new file mode 100644 index 0000000..fa7d4d8 --- /dev/null +++ b/server/main.spec @@ -0,0 +1,41 @@ +# -*- mode: python ; coding: utf-8 -*- + + +a = Analysis( + ['main.py'], + pathex=[], + binaries=[], + datas=[('index.html', '.'), + ('index.css', '.'), + ('index.js', '.')], + hiddenimports=[], + hookspath=[], + hooksconfig={}, + runtime_hooks=[], + excludes=[], + noarchive=False, + optimize=0, +) +pyz = PYZ(a.pure) + +exe = EXE( + pyz, + a.scripts, + a.binaries, + a.zipfiles, + a.datas, + [], + exclude_binaries=False, + name='DeepCreamPy', + debug=False, + bootloader_ignore_signals=False, + strip=False, + upx=True, + console=True, + disable_windowed_traceback=False, + argv_emulation=False, + target_arch=None, + codesign_identity=None, + entitlements_file=None, + onefile=True, +) diff --git a/server/requirements.txt b/server/requirements.txt new file mode 100644 index 0000000..538faef --- /dev/null +++ b/server/requirements.txt @@ -0,0 +1,7 @@ +fastapi +uvicorn +pydantic +Pillow +numpy +python-multipart +git+https://github.com/Deepshift/DeepCreamPy.git@master#egg=lib-cream-py&subdirectory=lib-cream-py \ No newline at end of file diff --git a/server/server.py b/server/server.py new file mode 100644 index 0000000..a48550b --- /dev/null +++ b/server/server.py @@ -0,0 +1,118 @@ +import asyncio +import os +import sys +import uuid +from pathlib import Path + +from fastapi import FastAPI, UploadFile, HTTPException, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse, StreamingResponse +from pydantic import BaseModel + +from serverr.instance import executor_instances, get_file_path +from serverr.myqueue import task_queue, QueueElement, wait_in_queue +from serverr.task import DecensorItem + +app = FastAPI() +check_disconnect = False + +app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"]) + +DATA_DIR = Path("temp") +if DATA_DIR.exists(): + import shutil + + shutil.rmtree(DATA_DIR) +DATA_DIR.mkdir(exist_ok=True) + +if getattr(sys, 'frozen', False): # Running in a PyInstaller bundle + BASE_DIR = sys._MEIPASS +else: + BASE_DIR = os.path.dirname(__file__) + + +@app.get("/") +async def read_index(): + return FileResponse(os.path.join(os.path.dirname(__file__), "index.html")) + + +@app.get("/assets/index.css") +async def read_index(): + return FileResponse(os.path.join(os.path.dirname(__file__), "index.css")) + + +@app.get("/assets/index.js") +async def read_index(): + return FileResponse(os.path.join(os.path.dirname(__file__), "index.js")) + + +@app.post("/images", response_model=list[str]) +async def upload_files(files: list[UploadFile]): + ids = [] + for file in files: + img_id = str(uuid.uuid4()) + ids.append(img_id) + ext = Path(file.filename).suffix + file_path = DATA_DIR / f"{img_id}{ext}" + with file_path.open("wb") as buffer: + buffer.write(await file.read()) + return ids + + +@app.delete("/image/{image_id}") +async def delete_file(image_id: str): + os.remove(get_file_path(image_id)) + + +class DecensorRequest(BaseModel): + imgs: list[DecensorItem] + + +async def stream(messages): + while True: + message = await messages.get() + yield message + # 200 == success, 201 == cancel, 202 == error + if message[0] >= 200: + break + + +def notify(code: int, data: bytes, messages: asyncio.Queue): + encoded_result = code.to_bytes(1, 'big') + len(data).to_bytes(4, 'big') + data + messages.put_nowait(encoded_result) + + +async def while_streaming(req: Request, items: list[DecensorItem]): + task = QueueElement(req, items) + task_queue.add_task(task) + + messages = asyncio.Queue() + + def notify_internal(code: int, data: bytes) -> None: + notify(code, data, messages) + + streaming_response = StreamingResponse(stream(messages), media_type="application/octet-stream") + asyncio.create_task(wait_in_queue(task, notify_internal, check_disconnect=check_disconnect)) + return streaming_response + + +@app.post("/decensor", response_class=StreamingResponse) +async def decensor(req: Request, files: DecensorRequest): + return await while_streaming(req, files.imgs) + + +@app.get("/tasks", response_model=list[(str, int)]) +async def tasks() -> list[(str, int)]: + return [(x.item_id, len(x.items)) for x in task_queue.queue] + [(x.busy, -1) for x in + executor_instances.list if x.busy is not None] + + +@app.delete("/task/{task_id}", response_class=StreamingResponse) +async def cancel_task(task_id: str): + task = next((x for x in task_queue.queue if x.item_id == task_id)) + if task is None: + executor = next((x for x in executor_instances.list if x.busy == task_id)) + if executor is None: + raise HTTPException(status_code=422, detail="Task not found") + executor.stop = True + task_queue.remove(task) diff --git a/server/serverr/__init__.py b/server/serverr/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/server/serverr/instance.py b/server/serverr/instance.py new file mode 100644 index 0000000..3e6a55c --- /dev/null +++ b/server/serverr/instance.py @@ -0,0 +1,204 @@ +import asyncio +import os +from asyncio import Event, Lock +from typing import List, Optional, Callable + +from PIL import Image +from fastapi import HTTPException + +from lib_cream_py import InpaintNN, decensor_image_variations, Logger, apply_variant, ColorMask, RawMask +from .task import DecensorItem + + +class MaskInfo: + def __init__(self, v: str): + if v.startswith("rgb-"): + self.file = False + try: + rgb_values = v[4:].split(",") + self.rgb = tuple(int(value) for value in rgb_values) + if not all(0 <= value <= 255 for value in self.rgb): + raise ValueError("RGB values must be between 0 and 255") + if len(self.rgb) != 3: + raise ValueError("RGB values must be 3 numbers") + self.rgb = (self.rgb[0], self.rgb[1], self.rgb[2]) + except ValueError: + raise Exception("Invalid RGB format. Expected format: rgb-R,G,B") + elif v.startswith("file-"): + self.file = True + self.mask_name = v[5:] + if len(self.mask_name) == 0: + raise ValueError("Mask name must not be empty") + else: + raise ValueError(f"Unknown mask type: {v}") + + +def generate_out_path(out_folder: str, file_path: str, index: int) -> str: + file_name, file_extension = os.path.splitext(os.path.basename(file_path)) + + new_file_name = f"{file_name}_{index}{file_extension}" + + return os.path.join(out_folder, new_file_name) + + +NotifyType = Optional[Callable[[int, bytes], None]] + + +# todo: is this thread save +class WebLogger(Logger): + def __init__(self, sender): + self.sender = sender + + def warn(self, id: str, info=None): + match id: + case _: + self.sender(17, id.encode('utf-8')) + + def info(self, id: str, info=None): + match id: + case "apply-variant": + self.sender(3, bytes(info)) + case "generate-mask": + self.sender(4, b"") + case "finished": + self.sender(5, b"") + case "remove-alpha": + self.sender(6, b"") + case "find-regions": + self.sender(7, b"") + case "decensor-segment": + region_counter, region_count = info + self.sender(8, (bytes(region_counter) + bytes(region_count))) + case "restore-alpha": + self.sender(9, b"") + case _: + self.sender(10, id.encode('utf-8')) + + def debug(self, id: str, info=None): + match id: + case "found-regions": + region_count = info + self.sender(11, bytes(region_count)) + case _: + self.sender(12, id.encode('utf-8')) + + def error(self, id: str, info=None): + match id: + case "no-regions": + self.sender(13, b"") + case "missing-model": + # "Missing Model, download model\nRead: https://github.com/deeppomf/DeepCreamPy/blob/master/docs/INSTALLATION.md#run-code-yourself" + self.sender(14, b"") + case "bounding-box-out-of-bounds": + x1_square, y1_square, x2_square, y2_square = info + self.sender(15, b"") + case _: + self.sender(16, id.encode('utf-8')) + + +def get_img(id: str) -> Image.Image: + return Image.open(get_file_path(id)) + + +def get_file_path(id: str) -> str: + for file in os.listdir("./temp"): + filename_without_ext, _ = os.path.splitext(file) + + if filename_without_ext == id: + return os.path.join("./temp", file) + + raise HTTPException(status_code=422, detail="File " + id + " not found") + + +class ExecutorInstance: + _model_mosaic: Optional[InpaintNN] = None + _model_bar: Optional[InpaintNN] = None + + busy: Optional[str] = None + stop = False + + @property + def model_mosaic(self) -> InpaintNN: + if self._model_mosaic is None: + self._model_mosaic = InpaintNN("./models/mosaic.keras") + return self._model_mosaic + + @property + def model_bar(self) -> InpaintNN: + if self._model_bar is None: + self._model_bar = InpaintNN("./models/bar.keras") + return self._model_bar + + def free_executor(self): + self.busy = None + + async def sent(self, items: list[DecensorItem]): + await self.sent_stream(items, None) + + async def sent_stream(self, items: list[DecensorItem], sender: NotifyType): + logger = WebLogger(sender) if sender else Logger() + for index, item in enumerate(items): + (self.model_mosaic if item.is_mosaic else self.model_bar).logger = logger + if self.stop: + sender(201, b"") + self.stop = False + break + sender(1, bytes(index) + bytes(len(items))) + img = get_img(item.img_id) + os.makedirs(item.output, exist_ok=True) + save_image = lambda i, out_img: out_img.save(generate_out_path(item.output, item.output_name, i)) + mask = MaskInfo(item.mask) + if mask.file: + mask_img = get_img(mask.mask_name) + mask_gen = lambda i, ori, colored: RawMask(apply_variant(mask_img, i)) + else: + mask_gen = lambda i, ori, colored: ColorMask(colored if item.is_mosaic else ori, rgb=mask.rgb) + try: + await asyncio.to_thread( + lambda: decensor_image_variations( + self.model_mosaic if item.is_mosaic else self.model_bar, + img, img, mask_gen, item.variations, item.is_mosaic, save_image, logger=logger + ) + ) + except Exception as e: + sender(202, b"") + return + sender(2, bytes(index) + bytes(len(items))) + sender(200, b"") + + +class Executors: + def __init__(self): + self.list: List[ExecutorInstance] = [ExecutorInstance()] + self.lock: Lock = Lock() + self.event = Event() + + def register(self, instance: ExecutorInstance): + self.list.append(instance) + + def free_executors(self) -> int: + return len([item for item in self.list if item.busy is None]) + + async def _find_instance(self): + while True: + instance = next((x for x in self.list if x.busy is None), None) + if instance is not None: + return instance + # todo: cricial error: warn should never happen + await self.event.wait() + + async def find_executor(self, task_id: str) -> ExecutorInstance: + async with self.lock: # Using async with for lock management + instance = await self._find_instance() + instance.busy = task_id + return instance + + async def free_executor(self, instance: ExecutorInstance): + from .myqueue import task_queue + instance.free_executor() + self.event.set() + self.event.clear() + await task_queue.update_event() + + +executor_instances: Executors = Executors() diff --git a/server/serverr/myqueue.py b/server/serverr/myqueue.py new file mode 100644 index 0000000..fff96f0 --- /dev/null +++ b/server/serverr/myqueue.py @@ -0,0 +1,91 @@ +import asyncio +import uuid +from typing import List, Optional + +from fastapi import HTTPException +from fastapi.requests import Request + +from .instance import executor_instances, NotifyType +from .task import DecensorItem + + +class QueueElement: + item_id: str + items: list[DecensorItem] + + def __init__(self, req: Request, items: list[DecensorItem]): + self.item_id = str(uuid.uuid4()) + self.items = items + self.req = req + + async def is_client_disconnected(self) -> bool: + if await self.req.is_disconnected(): + return True + return False + + +class TaskQueue: + def __init__(self): + self.queue: List[QueueElement] = [] + self.queue_event: asyncio.Event = asyncio.Event() + + def add_task(self, task: QueueElement): + self.queue.append(task) + + def get_pos(self, task: QueueElement) -> Optional[int]: + try: + return self.queue.index(task) + except ValueError: + return None + + async def update_event(self): + self.queue = [task for task in self.queue if not await task.is_client_disconnected()] + self.queue_event.set() + self.queue_event.clear() + + async def remove(self, task: QueueElement): + self.queue.remove(task) + await self.update_event() + + async def wait_for_event(self): + await self.queue_event.wait() + + +task_queue = TaskQueue() + + +async def wait_in_queue(task: QueueElement, notify: NotifyType, check_disconnect: bool): + """Will get task position report it. If its in the range of translators then it will try to aquire an instance(blockig) and sent a task to it. when done the item will be removed from the queue and result will be returned""" + while True: + queue_pos = task_queue.get_pos(task) + if queue_pos is None: + if notify: + return + else: + raise HTTPException(500, detail="User is no longer connected") # just for the logs + if notify: + notify(198, str(queue_pos).encode('utf-8')) + print(executor_instances.free_executors()) + if queue_pos < executor_instances.free_executors(): + if check_disconnect and await task.is_client_disconnected(): + await task_queue.update_event() + if notify: + return + else: + raise HTTPException(500, detail="User is no longer connected") # just for the logs + instance = await executor_instances.find_executor(task.item_id) + await task_queue.remove(task) + if notify: + notify(199, b"") + if notify: + await instance.sent_stream(task.items, notify) + else: + result = await instance.sent(task.items) + await executor_instances.free_executor(instance) + + if notify: + return + else: + return result + else: + await task_queue.wait_for_event() diff --git a/server/serverr/task.py b/server/serverr/task.py new file mode 100644 index 0000000..25b5f5a --- /dev/null +++ b/server/serverr/task.py @@ -0,0 +1,18 @@ +from typing import Optional + +from pydantic import BaseModel + + +class DecensorItem(BaseModel): + output_name: str + """e.g. filename.png""" + img_id: str + """Images were uploaded earlier.""" + variations: int + """How many variations to generate""" + is_mosaic: bool + """Which model to use""" + mask: str + """RGB mask or file""" + output: Optional[str] + """None will return to web""" diff --git a/signals.py b/signals.py deleted file mode 100644 index ca9e5d4..0000000 --- a/signals.py +++ /dev/null @@ -1,44 +0,0 @@ -from PySide2 import QtCore - -# Signals used for sharing status between threads(IPC, InterProcess Connection) -class Signals(QtCore.QObject): - - # usage example in other class(thread) : - # → self.signals..emit() - - # str : String to update label - # direct connect to decensorButton.setText(str) - update_decensorButton_Text = QtCore.Signal(str) - - # bool : set QPushButton Enabled (True or False) - # direct connect to decensorButton.setEnabled(bool) - update_decensorButton_Enabled = QtCore.Signal(bool) - - # direct connect to progressMessage.clear(None) - clear_progressMessage = QtCore.Signal() - - # str : text to change - # direct connect to statusLabel.setText - update_statusLabel_Text = QtCore.Signal(str) - - # int : value to change - # direct connect to progressBar.setValue(int) - update_ProgressBar_SET_VALUE = QtCore.Signal(int) - - # int : value to change - # direct connect to progressBar.setMaximum(int) - update_ProgressBar_MAX_VALUE = QtCore.Signal(int) - - # int : value to change - # direct connect to self.progressBar.setMinimum(int) - update_ProgressBar_MIN_VALUE = QtCore.Signal(int) - - # str : value to change - # direct connect to self.progressCursor.insertText(str) - insertText_progressCursor = QtCore.Signal(str) - - # str : value to change - # direct connect to self.progressMessage.append(str) - appendText_progressMessage = QtCore.Signal(str) - - update_clean_up_input_dirs_flag = QtCore.Signal(bool)