mirror of
https://github.com/Deepshift/DeepCreamPy.git
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252 lines
11 KiB
Python
Executable File
252 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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try:
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import numpy as np
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from PIL import Image
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import os
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from copy import deepcopy
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import config
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import file
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from model import InpaintNN
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from libs.utils import *
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except ImportError as e:
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print("Error when importing libraries: ", e)
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print("Some Python libraries are missing. You can install all requirements by running in the command line 'pip install -r requirements.txt' ")
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exit(1)
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class Decensor:
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def __init__(self):
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self.args = config.get_args()
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self.is_mosaic = self.args.is_mosaic
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self.mask_color = [self.args.mask_color_red/255.0, self.args.mask_color_green/255.0, self.args.mask_color_blue/255.0]
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if not os.path.exists(self.args.decensor_output_path):
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os.makedirs(self.args.decensor_output_path)
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self.load_model()
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def get_mask(self, colored):
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mask = np.ones(colored.shape, np.uint8)
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i, j = np.where(np.all(colored[0] == self.mask_color, axis=-1))
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mask[0, i, j] = 0
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return mask
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def load_model(self):
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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)
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def decensor_all_images_in_folder(self):
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#load model once at beginning and reuse same model
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#self.load_model()
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color_dir = self.args.decensor_input_path
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file_names = os.listdir(color_dir)
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input_dir = self.args.decensor_input_path
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output_dir = self.args.decensor_output_path
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# Change False to True before release --> file.check_file( input_dir, output_dir, True)
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file_names, self.files_removed = file.check_file( input_dir, output_dir, False)
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#convert all images into np arrays and put them in a list
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for file_name in file_names:
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color_file_path = os.path.join(color_dir, file_name)
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color_bn, color_ext = os.path.splitext(file_name)
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if os.path.isfile(color_file_path) and color_ext.casefold() == ".png":
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print("--------------------------------------------------------------------------")
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print("Decensoring the image {}".format(color_file_path))
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try :
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colored_img = Image.open(color_file_path)
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except:
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print("Cannot identify image file (" +str(color_file_path)+")")
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self.files_removed.append((color_file_path,3))
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# incase of abnormal file format change (ex : text.txt -> text.png)
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continue
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#if we are doing a mosaic decensor
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if self.is_mosaic:
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#get the original file that hasn't been colored
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ori_dir = self.args.decensor_input_original_path
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#since the original image might not be a png, test multiple file formats
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valid_formats = {".png", ".jpg", ".jpeg"}
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for test_file_name in os.listdir(ori_dir):
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test_bn, test_ext = os.path.splitext(test_file_name)
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if (test_bn == color_bn) and (test_ext.casefold() in valid_formats):
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ori_file_path = os.path.join(ori_dir, test_file_name)
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ori_img = Image.open(ori_file_path)
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# colored_img.show()
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self.decensor_image(ori_img, colored_img, file_name)
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break
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else: #for...else, i.e if the loop finished without encountering break
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print("Corresponding original, uncolored image not found in {}".format(color_file_path))
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print("Check if it exists and is in the PNG or JPG format.")
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else:
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self.decensor_image(colored_img, colored_img, file_name)
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else:
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print("--------------------------------------------------------------------------")
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print("Irregular file detected : "+str(color_file_path))
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print("--------------------------------------------------------------------------")
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if(self.files_removed is not None):
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file.error_messages(None, self.files_removed)
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print("\nDecensoring complete!")
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#decensors one image at a time
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#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)
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def decensor_image(self, ori, colored, file_name=None):
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width, height = ori.size
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#save the alpha channel if the image has an alpha channel
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has_alpha = False
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if (ori.mode == "RGBA"):
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has_alpha = True
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alpha_channel = np.asarray(ori)[:,:,3]
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alpha_channel = np.expand_dims(alpha_channel, axis =-1)
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ori = ori.convert('RGB')
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ori_array = image_to_array(ori)
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ori_array = np.expand_dims(ori_array, axis = 0)
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if self.is_mosaic:
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#if mosaic decensor, mask is empty
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# mask = np.ones(ori_array.shape, np.uint8)
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# print(mask.shape)
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colored = colored.convert('RGB')
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color_array = image_to_array(colored)
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color_array = np.expand_dims(color_array, axis = 0)
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mask = self.get_mask(color_array)
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mask_reshaped = mask[0,:,:,:] * 255.0
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mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
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# mask_img.show()
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else:
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mask = self.get_mask(ori_array)
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#colored image is only used for finding the regions
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regions = find_regions(colored.convert('RGB'), [v*255 for v in self.mask_color])
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print("Found {region_count} censored regions in this image!".format(region_count = len(regions)))
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if len(regions) == 0 and not self.is_mosaic:
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print("No green regions detected! Make sure you're using exactly the right color.")
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return
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output_img_array = ori_array[0].copy()
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for region_counter, region in enumerate(regions, 1):
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bounding_box = expand_bounding(ori, region, expand_factor=1.5)
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crop_img = ori.crop(bounding_box)
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# crop_img.show()
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#convert mask back to image
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mask_reshaped = mask[0,:,:,:] * 255.0
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mask_img = Image.fromarray(mask_reshaped.astype('uint8'))
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#resize the cropped images
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crop_img = crop_img.resize((256, 256))
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crop_img_array = image_to_array(crop_img)
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#resize the mask images
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mask_img = mask_img.crop(bounding_box)
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mask_img = mask_img.resize((256, 256))
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# mask_img.show()
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#convert mask_img back to array
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mask_array = image_to_array(mask_img)
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#the mask has been upscaled so there will be values not equal to 0 or 1
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# mask_array[mask_array > 0] = 1
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# crop_img_array[..., :-1][mask_array==0] = (0,0,0)
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if not self.is_mosaic:
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a, b = np.where(np.all(mask_array == 0, axis = -1))
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# print(a,b)
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# print(crop_img_array[a,b])
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# print(crop_img_array[a,b,0])
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# print(crop_img_array.shape)
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# print(type(crop_img_array[0,0]))
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crop_img_array[a,b,:] = 0.
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temp = Image.fromarray((crop_img_array * 255.0).astype('uint8'))
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# temp.show()
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# if self.is_mosaic:
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# a, b = np.where(np.all(mask_array == 0, axis = -1))
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# print(a, b)
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# coords = [coord for coord in zip(a,b) if ((coord[0] + coord[1]) % 2 == 0)]
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# a,b = zip(*coords)
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# mask_array[a,b] = 1
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# mask_array = mask_array * 255.0
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# img = Image.fromarray(mask_array.astype('uint8'))
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# img.show()
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# return
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crop_img_array = np.expand_dims(crop_img_array, axis = 0)
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mask_array = np.expand_dims(mask_array, axis = 0)
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# print(np.amax(crop_img_array))
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# print(np.amax(mask_array))
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# # print(np.amax(masked))
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# print(np.amin(crop_img_array))
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# print(np.amin(mask_array))
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# # print(np.amin(masked))
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# print(mask_array)
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crop_img_array = crop_img_array * 2.0 - 1
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# mask_array = mask_array / 255.0
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# Run predictions for this batch of images
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pred_img_array = self.model.predict(crop_img_array, crop_img_array, mask_array)
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pred_img_array = np.squeeze(pred_img_array, axis = 0)
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pred_img_array = (255.0 * ((pred_img_array + 1.0) / 2.0)).astype(np.uint8)
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#scale prediction image back to original size
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bounding_width = bounding_box[2]-bounding_box[0]
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bounding_height = bounding_box[3]-bounding_box[1]
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#convert np array to image
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# print(bounding_width,bounding_height)
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# print(pred_img_array.shape)
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pred_img = Image.fromarray(pred_img_array.astype('uint8'))
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# pred_img.show()
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pred_img = pred_img.resize((bounding_width, bounding_height), resample = Image.BICUBIC)
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# pred_img.show()
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pred_img_array = image_to_array(pred_img)
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# print(pred_img_array.shape)
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pred_img_array = np.expand_dims(pred_img_array, axis = 0)
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# copy the decensored regions into the output image
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for i in range(len(ori_array)):
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for col in range(bounding_width):
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for row in range(bounding_height):
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bounding_width_index = col + bounding_box[0]
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bounding_height_index = row + bounding_box[1]
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if (bounding_width_index, bounding_height_index) in region:
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output_img_array[bounding_height_index][bounding_width_index] = pred_img_array[i,:,:,:][row][col]
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print("{region_counter} out of {region_count} regions decensored.".format(region_counter=region_counter, region_count=len(regions)))
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output_img_array = output_img_array * 255.0
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#restore the alpha channel if the image had one
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if has_alpha:
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output_img_array = np.concatenate((output_img_array, alpha_channel), axis = 2)
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output_img = Image.fromarray(output_img_array.astype('uint8'))
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if file_name != None:
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#save the decensored image
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#file_name, _ = os.path.splitext(file_name)
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save_path = os.path.join(self.args.decensor_output_path, file_name)
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output_img.save(save_path)
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print("Decensored image saved to {save_path}!".format(save_path=save_path))
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return
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else:
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print("Decensored image. Returning it.")
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return output_img
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if __name__ == '__main__':
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decensor = Decensor()
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decensor.decensor_all_images_in_folder()
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