mirror of
https://github.com/Deepshift/DeepCreamPy.git
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85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
import numpy as np
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import tensorflow as tf
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from PIL import Image
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import tqdm
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import os
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import matplotlib.pyplot as plt
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import sys
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sys.path.append('..')
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from model import Model
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from poisson_blend import blend
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from config import *
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#TODO: allow variable batch sizes when decensoring. changing BATCH_SIZE will likely result in crashing
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BATCH_SIZE = 1
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mask_color = [args.mask_color_red, args.mask_color_green, arg.mask_color_blue]
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poisson_blending_enabled = False
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def decensor(args):
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x = tf.placeholder(tf.float32, [BATCH_SIZE, args.input_size, args.input_size, args.input_channel_size])
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mask = tf.placeholder(tf.float32, [BATCH_SIZE, args.input_size, args.input_size, 1])
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local_x = tf.placeholder(tf.float32, [BATCH_SIZE, args.local_input_size, args.local_input_size, args.input_channel_size])
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global_completion = tf.placeholder(tf.float32, [BATCH_SIZE, args.input_size, args.input_size, args.input_channel_size])
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local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, args.local_input_size, args.local_input_size, args.input_channel_size])
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is_training = tf.placeholder(tf.bool, [])
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model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
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sess = tf.Session()
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init_op = tf.global_variables_initializer()
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sess.run(init_op)
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saver = tf.train.Saver()
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saver.restore(sess, './models/latest')
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x_decensor = []
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mask_decensor = []
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for subdir, dirs, files in sorted(os.walk(args.decensor_input_path)):
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for file in sorted(files):
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file_path = os.path.join(subdir, file)
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if os.path.isfile(file_path) and os.path.splitext(file_path)[1] == ".png":
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print(file_path)
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image = Image.open(file_path).convert('RGB')
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image = np.array(image)
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image = np.array(image / 127.5 - 1)
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x_decensor.append(image)
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x_decensor = np.array(x_decensor)
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print(x_decensor.shape)
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step_num = int(len(x_decensor) / BATCH_SIZE)
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cnt = 0
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for i in tqdm.tqdm(range(step_num)):
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x_batch = x_decensor[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]
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mask_batch = get_mask(x_batch)
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completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
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for i in range(BATCH_SIZE):
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img = completion[i]
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img = np.array((img + 1) * 127.5, dtype=np.uint8)
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original = x_batch[i]
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original = np.array((original + 1) * 127.5, dtype=np.uint8)
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if (poisson_blending_enabled):
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img = blend(original, img, mask_batch[0,:,:,0])
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output = Image.fromarray(img.astype('uint8'), 'RGB')
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dst = args.decensor_output_path + '{}.png'.format("{0:06d}".format(cnt))
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output.save(dst)
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cnt += 1
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def get_mask(x_batch):
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points = []
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mask = []
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for i in range(BATCH_SIZE):
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raw = x_batch[i]
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raw = np.array((raw + 1) * 127.5, dtype=np.uint8)
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m = np.zeros((args.input_size, args.input_size, 1), dtype=np.uint8)
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for x in range(args.input_size):
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for y in range(args.input_size):
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if np.array_equal(raw[x][y], mask_color):
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m[x, y] = 1
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mask.append(m)
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return np.array(mask)
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if __name__ == '__main__':
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if not os.path.exists(args.decensor_output_path):
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os.makedirs(args.decensor_output_path)
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decensor(args) |