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deeppomf 2018-02-09 23:01:07 -05:00
parent 76ed8a8a40
commit 7355f67fdc
4 changed files with 10 additions and 8 deletions
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1
README.md
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@ -24,6 +24,7 @@ In particular, if a vagina or penis is completely censored out, THERE IS NO HOPE
- Python 2/3
- TensorFlow 1.5
- Pillow
- tqdm
# Model
The pretrained model can be downloaded from https://drive.google.com/open?id=1mWHYSj0LDSbJQQxjR4hUMykQkVve2U3Q.

4
src/decensor.py
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@ -1,6 +1,6 @@
import numpy as np
import tensorflow as tf
import cv2
from PIL import Image
import tqdm
import os
import matplotlib.pyplot as plt
@ -30,7 +30,7 @@ def test():
sess.run(init_op)
saver = tf.train.Saver()
saver.restore(sess, '/saved_models/latest')
saver.restore(sess, './saved_models/latest')
x_test = np.load(test_npy)
np.random.shuffle(x_test)

2
src/test.py
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@ -1,6 +1,6 @@
import numpy as np
import tensorflow as tf
import cv2
from PIL import Image
import tqdm
import os
import matplotlib.pyplot as plt

11
src/train.py
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@ -1,6 +1,6 @@
import numpy as np
import tensorflow as tf
import cv2
from PIL import Image
import tqdm
from model import Model
import load
@ -65,8 +65,8 @@ def train():
x_batch = x_test[:BATCH_SIZE]
completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
sample = np.array((completion[0] + 1) * 127.5, dtype=np.uint8)
cv2.imwrite('./output/{}.jpg'.format("{0:06d}".format(sess.run(epoch))), cv2.cvtColor(sample, cv2.COLOR_RGB2BGR))
result = Image.fromarray(sample)
result.save('./output/{}.jpg'.format("{0:06d}".format(sess.run(epoch))))
saver = tf.train.Saver()
saver.save(sess, '/saved_model/latest', write_meta_graph=False)
@ -106,8 +106,9 @@ def train():
x_batch = x_test[:BATCH_SIZE]
completion = sess.run(model.completion, feed_dict={x: x_batch, mask: mask_batch, is_training: False})
sample = np.array((completion[0] + 1) * 127.5, dtype=np.uint8)
cv2.imwrite('/output/{}.jpg'.format("{0:06d}".format(sess.run(epoch))), cv2.cvtColor(sample, cv2.COLOR_RGB2BGR))
result = Image.fromarray(sample)
result.save('./output/{}.jpg'.format("{0:06d}".format(sess.run(epoch))))
saver = tf.train.Saver()
saver.save(sess, '/saved_model/latest', write_meta_graph=False)