mirror of
https://github.com/Deepshift/DeepCreamPy.git
synced 2024-12-01 04:00:21 +00:00
334 lines
15 KiB
Python
Executable File
334 lines
15 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
|
|
try:
|
|
import numpy as np
|
|
from PIL import Image
|
|
import tensorflow as tf
|
|
|
|
import os, logging, sys
|
|
from copy import deepcopy
|
|
|
|
import config
|
|
import file
|
|
from model import InpaintNN
|
|
from libs.utils import *
|
|
# for QThread
|
|
from PySide2 import QtCore
|
|
|
|
except ImportError as e:
|
|
print("Error when importing libraries: ", e)
|
|
print("Some Python libraries are missing. You can install all required libraries by running in the command line 'pip install -r requirements.txt' ")
|
|
exit(1)
|
|
|
|
# #signals to the ui to print out
|
|
# class EmittingStream(QtCore.QObject):
|
|
# textWritten = QtCore.pyqtSignal(str)
|
|
|
|
# def write(self, text):
|
|
# self.textWritten.emit(str(text))
|
|
'''
|
|
print text later on other label telling status, informations ,...
|
|
custom_print -> signals."methodname".emit( ... ) later
|
|
changing GUI on other thread(not MainWindow) is not allowed
|
|
'''
|
|
class Decensor(QtCore.QThread):
|
|
def __init__(self, text_edit = None, text_cursor = None, ui_mode = None):
|
|
super().__init__()
|
|
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.signals = None # Signals class will be given by progressWindow
|
|
|
|
if ui_mode is not None:
|
|
self.ui_mode = ui_mode
|
|
else:
|
|
self.ui_mode = args.ui_mode
|
|
|
|
if not os.path.exists(self.decensor_output_path):
|
|
os.makedirs(self.decensor_output_path)
|
|
|
|
if self.ui_mode:
|
|
self.text_edit = text_edit
|
|
self.text_cursor = text_cursor
|
|
self.ui_mode = True
|
|
|
|
def run(self):
|
|
self.decensor_all_images_in_folder()
|
|
|
|
def stop(self):
|
|
# in case of stopping decensor, terminate not to run if self while MainWindow is closed
|
|
self.terminate()
|
|
|
|
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.update_progress_LABEL.emit("load_model()", "Loading neural network. This may take a while.")
|
|
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)
|
|
|
|
def decensor_all_images_in_folder(self):
|
|
#load model once at beginning and reuse same model
|
|
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...")
|
|
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))
|
|
|
|
#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)
|
|
# 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":
|
|
self.custom_print("--------------------------------------------------------------------------")
|
|
self.custom_print("Decensoring the image {}".format(color_file_path))
|
|
try :
|
|
colored_img = Image.open(color_file_path)
|
|
except:
|
|
self.custom_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
|
|
self.custom_print("Corresponding original, uncolored image not found in {}".format(color_file_path))
|
|
self.custom_print("Check if it exists and is in the PNG or JPG format.")
|
|
#if we are doing a bar decensor
|
|
else:
|
|
self.decensor_image_variations(colored_img, colored_img, file_name)
|
|
else:
|
|
self.custom_print("--------------------------------------------------------------------------")
|
|
self.custom_print("Image can't be found: "+str(color_file_path))
|
|
self.custom_print("--------------------------------------------------------------------------")
|
|
if self.files_removed is not None:
|
|
file.error_messages(None, self.files_removed)
|
|
self.custom_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.")
|
|
|
|
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)
|
|
# self.custom_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])
|
|
self.custom_print("Found {region_count} censored regions in this image!".format(region_count = len(regions)))
|
|
|
|
if len(regions) == 0 and not self.is_mosaic:
|
|
self.custom_print("No green regions detected! Make sure you're using exactly the right color.")
|
|
return
|
|
|
|
self.signals.signal_ProgressBar_update_MAX_VALUE.emit("Found {} masked regions".format(len(regions)), len(regions))
|
|
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)))
|
|
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))
|
|
# self.custom_print(a,b)
|
|
# self.custom_print(crop_img_array[a,b])
|
|
# self.custom_print(crop_img_array[a,b,0])
|
|
# self.custom_print(crop_img_array.shape)
|
|
# self.custom_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)
|
|
|
|
# self.custom_print(np.amax(crop_img_array))
|
|
# self.custom_print(np.amax(mask_array))
|
|
# self.custom_print(np.amax(masked))
|
|
|
|
# self.custom_print(np.amin(crop_img_array))
|
|
# self.custom_print(np.amin(mask_array))
|
|
# self.custom_print(np.amin(masked))
|
|
|
|
# self.custom_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
|
|
|
|
# self.custom_print(bounding_width,bounding_height)
|
|
# self.custom_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)
|
|
|
|
# self.custom_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.custom_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...")
|
|
|
|
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)
|
|
|
|
self.custom_print("Decensored image saved to {save_path}!".format(save_path=save_path))
|
|
return
|
|
else:
|
|
self.custom_print("Decensored image. Returning it.")
|
|
return output_img
|
|
|
|
def custom_print(self, text):
|
|
print(text)
|
|
# if self.ui_mode:
|
|
# from PySide2.QtGui import QTextCursor
|
|
|
|
# self.text_cursor.insertText(text)
|
|
# self.text_cursor.insertText("\n")
|
|
# self.text_edit.moveCursor(QTextCursor.End)
|
|
# else:
|
|
# print(text)
|
|
|
|
# if __name__ == '__main__':
|
|
# decensor = Decensor()
|
|
# decensor.decensor_all_images_in_folder()
|
|
# equivalent to decensor.start() (running as QtThread)
|