#!/usr/bin/env python3 try: import numpy as np from PIL import Image import os from copy import deepcopy import config import file from libs.pconv_hybrid_model import PConvUnet from libs.utils import * except ImportError as err: print("Error: ", err) print("Could not import modules. Make sure all dependencies are installed.") exit(1) class Decensor: def __init__(self): self.args = config.get_args() self.is_mosaic = self.args.is_mosaic self.mask_color = [float(v/255) for v in self.args.mask_color] # normalize mask color if not os.path.exists(self.args.decensor_output_path): os.makedirs(self.args.decensor_output_path) self.load_model() def get_mask(self, colored): 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.model = PConvUnet() self.model.load( r"./models/model.h5", train_bn=False, lr=0.00005 ) def decensor_all_images_in_folder(self): #load model once at beginning and reuse same model #self.load_model() color_dir = self.args.decensor_input_path file_names = os.listdir(color_dir) input_dir = self.args.decensor_input_path output_dir = self.args.decensor_output_path # Change False to True before release --> file.check_file( input_dir, output_dir, True) file_names, self.files_removed = file.check_file( input_dir, output_dir, False) #convert all images into np arrays and put them in a list for file_name in file_names: color_file_path = os.path.join(color_dir, file_name) color_bn, color_ext = os.path.splitext(file_name) if os.path.isfile(color_file_path) and color_ext.casefold() == ".png": print("--------------------------------------------------------------------------") print("Decensoring the image {}".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.args.decensor_input_original_path #since the original image might not be a png, test multiple file formats valid_formats = {".png", ".jpg", ".jpeg"} for test_file_name in os.listdir(ori_dir): test_bn, test_ext = os.path.splitext(test_file_name) if (test_bn == color_bn) 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(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.") else: self.decensor_image(colored_img, colored_img, file_name) else: print("--------------------------------------------------------------------------") print("Iregular file deteced : "+str(color_file_path)) print("--------------------------------------------------------------------------") if(self.files_removed is not None): file.error_messages(None, self.files_removed) input("\nPress anything to end...") #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(self, ori, colored, file_name=None): 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.get_mask(color_array) # mask_reshaped = mask[0,:,:,:] * 255.0 # mask_img = Image.fromarray(mask_reshaped.astype('uint8')) # mask_img.show() else: mask = self.get_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]) #unnormalize the color so it can check against pixels print("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 regions detected! Make sure you're using exactly the right color.") return output_img_array = ori_array[0].copy() for region_counter, region in enumerate(regions, 1): bounding_box = expand_bounding(ori, region) 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((512, 512)) crop_img_array = image_to_array(crop_img) crop_img_array = np.expand_dims(crop_img_array, axis = 0) #resize the mask images mask_img = mask_img.crop(bounding_box) mask_img = mask_img.resize((512, 512)) # 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 if self.is_mosaic: a, b = np.where(np.all(mask_array == 0, axis = -1)) print(a, b) coords = [coord for coord in zip(a,b) if ((coord[0] + coord[1]) % 2 == 0)] a,b = zip(*coords) mask_array[a,b] = 1 # mask_array = mask_array * 255.0 # img = Image.fromarray(mask_array.astype('uint8')) # img.show() # return mask_array = np.expand_dims(mask_array, axis = 0) # Run predictions for this batch of images pred_img_array = self.model.predict([crop_img_array, mask_array, mask_array]) pred_img_array = pred_img_array * 255.0 pred_img_array = np.squeeze(pred_img_array, axis = 0) #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_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] 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')) if file_name != None: #save the decensored image #file_name, _ = os.path.splitext(file_name) save_path = os.path.join(self.args.decensor_output_path, file_name) output_img.save(save_path) print("Decensored image saved to {save_path}!".format(save_path=save_path)) return else: print("Decensored image. Returning it.") return output_img if __name__ == '__main__': decensor = Decensor() decensor.decensor_all_images_in_folder()