now with parrallel
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lasershottt
parent
cdb18baf1f
commit
48ac1e1392
148
main.py
148
main.py
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@ -3,32 +3,10 @@ import pickle
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from typing import List
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from PIL import Image, ImageFile
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import numpy as np
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from multiprocessing import Pool, Manager
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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target_path = input('Input relative path for target image: ')
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Image.open(target_path)
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user_input = input('Input segment width count (Higher=more pictures used. Default = 40): ')
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global width_segments_count
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try:
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width_segments_count = int(user_input)
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except:
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width_segments_count = 40
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user_input = input('Input segment height count (Higher=more pictures used. Default = 40): ')
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global height_segments_count
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try:
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height_segments_count = int(user_input)
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except:
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height_segments_count = 40
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user_input = input('Input quality for filling pictures (1=default, 2=double the size): ')
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global FILLING_QUALITY
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try:
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FILLING_QUALITY = int(user_input)
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except:
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FILLING_QUALITY = 1
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class PictureInfos:
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def __init__(self, image_path, color, used):
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self.image_path = image_path
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@ -67,7 +45,7 @@ def get_picture_names(file_path):
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return os.listdir(file_path)
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def get_closest(target):
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def get_closest(target, allPictures):
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image: PictureInfos
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closest = np.inf
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closest_path = ''
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@ -83,49 +61,99 @@ def get_closest(target):
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closest_path = image.image_path
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return closest_path
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def create_picture_segment(target, allPictures, starting_layer, end_layer, width_segments_count, PIXELS_TO_FILL_WIDTH, PIXELS_TO_FILL_HEIGHT, FILLING_QUALITY):
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picture_piece = Image.new('RGB', (int(width_segments_count*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY), int((end_layer-starting_layer)*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY)))
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layer_offset = 0
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for layer in target[int(starting_layer):int(end_layer)]:
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segment_offset = 0
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for segment in layer:
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closest = get_closest(segment, allPictures)
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closest_img = Image.open(closest)
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closest_img = closest_img.resize((PIXELS_TO_FILL_WIDTH*FILLING_QUALITY, PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY))
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picture_piece.paste(closest_img, (segment_offset*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY, layer_offset*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY))
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segment_offset +=1
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layer_offset += 1
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print(layer_offset,'Layer done')
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return picture_piece
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source_categories = os.listdir('./101_ObjectCategories')
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if 'allPictures.pkl' not in os.listdir():
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allPictures = list()
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print('Getting all pictures')
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for category in source_categories:
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imgs_path = './101_ObjectCategories/'+category
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category_images = get_picture_names(imgs_path)
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for img in category_images:
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img_path = imgs_path +'/'+img
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allPictures.append(PictureInfos(img_path, get_average_color(img_path), False))
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print(category,'done')
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if __name__ == '__main__':
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### Starting sequence ###
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target_path = input('Input relative path for target image: ')
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Image.open(target_path)
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with open('allPictures.pkl', 'wb') as file:
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pickle.dump(allPictures, file)
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print('done')
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else:
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with open('allPictures.pkl', 'rb') as file:
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allPictures = pickle.load(file)
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user_input = input('Input segment width count (Higher=more pictures used. Default = 100. Musn\'t exceed pixel of original): ')
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try:
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width_segments_count = int(user_input)
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except:
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width_segments_count = 100
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print('Segmenting target')
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target = get_color_space_array(target_path)
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print('Segmenting done')
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user_input = input('Input segment height count (Higher=more pictures used. Default = 100. Musn\'t exceed pixel of original): ')
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try:
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height_segments_count = int(user_input)
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except:
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height_segments_count = 100
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user_input = input('Input quality for filling pictures (1=default, 2=double the size): ')
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try:
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FILLING_QUALITY = int(user_input)
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except:
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FILLING_QUALITY = 1
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user_input = input('Input worker count: (default 1) Recomended PC\'s cores if you dont want to kill your PC: ')
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try:
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working_size = int(user_input)
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except:
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working_size = 1
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### Getting all pictures ###
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if 'allPictures.pkl' not in os.listdir():
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source_categories = os.listdir('./101_ObjectCategories')
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allPictures = list()
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print('Getting all pictures')
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for category in source_categories:
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imgs_path = './101_ObjectCategories/'+category
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category_images = get_picture_names(imgs_path)
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for img in category_images:
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img_path = imgs_path +'/'+img
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allPictures.append(PictureInfos(img_path, get_average_color(img_path), False))
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print(category,'done')
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with open('allPictures.pkl', 'wb') as file:
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pickle.dump(allPictures, file)
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print('done')
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else:
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with open('allPictures.pkl', 'rb') as file:
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allPictures = pickle.load(file)
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print('Segmenting target')
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target = get_color_space_array(target_path)
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print('Segmenting done')
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### Start to create image ###
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# print((int(width_segments_count*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY), int(height_segments_count*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY)))
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# print((int(width_segments_count*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY), int(height_segments_count*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY)))
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new_im = Image.new('RGB', (int(width_segments_count*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY), int(height_segments_count*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY)))
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working_length = np.floor(len(target)/working_size)
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pool = Pool()
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picture_segments = pool.starmap(create_picture_segment, [(target, allPictures, working_length*i, working_length*(i+1), width_segments_count, PIXELS_TO_FILL_WIDTH, PIXELS_TO_FILL_HEIGHT, FILLING_QUALITY) for i in range(working_size)])
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pool.close()
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pool.join()
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i = 0
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print(picture_segments)
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new_im = Image.new('RGB', (int(width_segments_count*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY), int(height_segments_count*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY)))
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for segment in picture_segments:
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new_im.paste(segment, (0, int(i*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY*working_length)))
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i +=1
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new_im.show()
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new_im.save('final.jpg')
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layer_offset = 0
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for layer in target:
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segment_offset = 0
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for segment in layer:
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closest = get_closest(segment)
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closest_img = Image.open(closest)
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closest_img = closest_img.resize((PIXELS_TO_FILL_WIDTH*FILLING_QUALITY, PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY))
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new_im.paste(closest_img, (segment_offset*PIXELS_TO_FILL_WIDTH*FILLING_QUALITY, layer_offset*PIXELS_TO_FILL_HEIGHT*FILLING_QUALITY))
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segment_offset +=1
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layer_offset += 1
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print(layer_offset,'Layer done')
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new_im.show()
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new_im.save('final.jpg')
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@ -0,0 +1,20 @@
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from multiprocessing import Pool, Manager
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import random
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import time
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def some_function(some_input):
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time.sleep(random.random())
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return some_input
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def test():
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global testStr
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testStr = 'Hello'
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if __name__ == '__main__':
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test()
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pool = Pool()
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manager = Manager()
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print(pool.map(some_function, [('Hello') for i in range(4)]))
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