DeepCreamPy/decensor.py

#!/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)
        if(self.args.autoclose == False):
            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(mask)  # use mask to find all the colored regions
        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()