Created
April 12, 2020 12:47
-
-
Save georgy7/062b6bb5a702945c4b36c91591452d37 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| #!/usr/bin/env python3 | |
| import os | |
| from PIL import Image, ImageFilter | |
| from enum import Enum | |
| import random | |
| import numpy as np | |
| import tensorflow as tf | |
| import datetime | |
| import math | |
| import time | |
| def is_png(f): | |
| return f.lower().endswith('.png') | |
| def is_jpeg(f): | |
| return f.lower().endswith('.jpg') or f.lower().endswith('.jpeg') | |
| VAL_INPUT_FOLDER = 'val_input' | |
| VAL_OUTPUT_FOLDER = 'val_output' | |
| TRAIN_INPUT_FOLDER = 'train_input' | |
| TRAIN_OUTPUT_FOLDER = 'train_output' | |
| VAL_INPUT_LIST = [ | |
| f for f in (os.listdir(VAL_INPUT_FOLDER)) if (is_png(f) or is_jpeg(f)) | |
| ] | |
| VAL_OUTPUT_LIST = [ | |
| f for f in (os.listdir(VAL_OUTPUT_FOLDER)) if (is_png(f) or is_jpeg(f)) | |
| ] | |
| TRAIN_INPUT_LIST = [ | |
| f for f in (os.listdir(TRAIN_INPUT_FOLDER)) if (is_png(f) or is_jpeg(f)) | |
| ] | |
| TRAIN_OUTPUT_LIST = [ | |
| f for f in (os.listdir(TRAIN_OUTPUT_FOLDER)) if (is_png(f) or is_jpeg(f)) | |
| ] | |
| VAL_INPUT_LIST.sort() | |
| VAL_OUTPUT_LIST.sort() | |
| TRAIN_INPUT_LIST.sort() | |
| TRAIN_OUTPUT_LIST.sort() | |
| assert VAL_INPUT_LIST == VAL_OUTPUT_LIST | |
| assert TRAIN_INPUT_LIST == TRAIN_OUTPUT_LIST | |
| ##################################################################### | |
| MAX_BLOCK_WIDTH = 160 | |
| MAX_BLOCK_HEIGHT = 160 | |
| class DataPurpose(Enum): | |
| TRAINING = 1 | |
| VALIDATION = 2 | |
| def get_random_image_pair(purpose: DataPurpose): | |
| if purpose == DataPurpose.VALIDATION: | |
| settings = (VAL_INPUT_FOLDER, VAL_OUTPUT_FOLDER, VAL_INPUT_LIST) | |
| else: | |
| settings = (TRAIN_INPUT_FOLDER, TRAIN_OUTPUT_FOLDER, TRAIN_INPUT_LIST) | |
| name = random.choice(settings[2]) | |
| input = Image.open(os.path.join(settings[0], name)) | |
| output = Image.open(os.path.join(settings[1], name)) | |
| assert input.size[0] * 2 == output.size[0] | |
| assert input.size[1] * 2 == output.size[1] | |
| return (input, output) | |
| def random_image_block_pair(image_pair): | |
| input, output = image_pair | |
| assert input.size[0] * 2 == output.size[0] | |
| assert input.size[1] * 2 == output.size[1] | |
| bw = min(MAX_BLOCK_WIDTH, input.size[0]) | |
| bh = min(MAX_BLOCK_HEIGHT, input.size[1]) | |
| x = random.randint(0, input.size[0] - bw) | |
| y = random.randint(0, input.size[1] - bh) | |
| x2 = x + bw | |
| y2 = y + bh | |
| big_x = 2 * x | |
| big_y = 2 * y | |
| big_x2 = big_x + 2 * bw | |
| big_y2 = big_y + 2 * bh | |
| input = input.crop((x, y, x2, y2)) | |
| output = output.crop((big_x, big_y, big_x2, big_y2)) | |
| assert input.size[0] == bw, "{} != {}".format(input.size[0], bw) | |
| assert input.size[1] == bh, "{} != {}".format(input.size[1], bh) | |
| assert input.size[0] * 2 == output.size[0] | |
| assert input.size[1] * 2 == output.size[1] | |
| return input, output | |
| def training_generator(): | |
| while (True): | |
| images_per_step = 10 | |
| blocks_per_image = 5 | |
| batch_size = images_per_step * blocks_per_image | |
| bw = 2 * MAX_BLOCK_WIDTH | |
| bh = 2 * MAX_BLOCK_HEIGHT | |
| inputs, outputs = np.zeros((batch_size, MAX_BLOCK_HEIGHT, MAX_BLOCK_WIDTH, 4), dtype=np.float32), \ | |
| np.zeros((batch_size, bh, bw, 2), dtype=np.float32) | |
| image_index = 0 | |
| while image_index < images_per_step: | |
| image_pair = get_random_image_pair(DataPurpose.TRAINING) | |
| for block_index in range(blocks_per_image): | |
| pair = random_image_block_pair(image_pair) | |
| # Some color space or other converting here... | |
| input_image_array = TODO | |
| output_image_array = TODO | |
| inputs[blocks_per_image * image_index + block_index] = input_image_array | |
| outputs[blocks_per_image * image_index + block_index] = output_image_array | |
| image_index += 1 | |
| yield inputs, outputs | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment