def test_image_to_sketch(self):
img = general_util.imread('12746957.jpg')
sketch = image_to_sketch(img)
cv2.imshow('Input',
cv2.cvtColor(img, cv2.COLOR_RGB2BGR).astype(np.uint8))
cv2.imshow('Sketch', sketch.astype(np.uint8))
cv2.waitKey(0)
def test_generate_hint_from_image(self):
img = general_util.imread('face_36_566_115.png')
sketch = generate_hint_from_image(img)
cv2.imshow('Input',
cv2.cvtColor(img, cv2.COLOR_RGB2BGR).astype(np.uint8))
cv2.imshow('Hint',
cv2.cvtColor(sketch.astype(np.uint8), cv2.COLOR_RGBA2BGR))
cv2.waitKey(0)
def test_get_tf_string_extension(self):
with self.test_session(config = tf.ConfigProto(device_count = {'GPU': 0})) as sess:
# img = general_util.imread('/home/jerryli27/PycharmProjects/my_pix2pix/test_collected_sketches_cropped/1383646_p0.jpg.png.png',dtype=np.uint8) # '12746957.jpg')
img = general_util.imread('2821993.jpg',dtype=np.uint8) # '12746957.jpg')
sketch = tf.image.convert_image_dtype(sketch_extractor(tf.image.convert_image_dtype(img, tf.float32) ,"rgb", max_val=1, min_val=0), tf.uint8)
actual_output, = sess.run([sketch])
cv2.imshow('Input', cv2.cvtColor(img, cv2.COLOR_RGB2BGR).astype(np.uint8))
cv2.imshow('Sketch', actual_output.astype(np.uint8))
cv2.waitKey(0)
def test_detect_complicated_img(self):
all_img_paths = general_util.get_all_image_paths_in_dir(
'/home/xor/pixiv_images/train_images/')
for img_path in all_img_paths[10:]:
print(img_path)
print(detect_complicated_img(img_path))
img = general_util.imread(img_path)
sketch = image_to_sketch(img)
cv2.imshow('Input',
cv2.cvtColor(img, cv2.COLOR_RGB2BGR).astype(np.uint8))
cv2.imshow('Sketch', sketch.astype(np.uint8))
cv2.waitKey(0)
def test_generate_training_image_pair(self):
all_img_paths = general_util.get_all_image_paths_in_dir(
'/home/xor/pixiv_images/test_images/')
for img_path in all_img_paths[10:]:
print(img_path)
img = general_util.imread(img_path)
sketch = image_to_sketch(img)
sketch, img = generate_training_image_pair(sketch, img)
cv2.imshow('Input',
cv2.cvtColor(img, cv2.COLOR_YUV2BGR).astype(np.uint8))
cv2.imshow('Sketch', sketch[..., 0].astype(np.uint8))
cv2.imshow(
'Hint',
cv2.cvtColor(sketch[..., 1:],
cv2.COLOR_YUV2BGR).astype(np.uint8))
cv2.waitKey(0)
img_shape = (128, 256)
# fig = plt.figure()
# ax = plt.axes(xlim=(0, img_shape[1]), ylim=(0, img_shape[0]))
# myimg = ax.imshow(np.zeros(img_shape, np.uint8))
# plt.show()
myimg = plt.imshow(np.zeros(img_shape, np.uint8))
fig = plt.gcf()
image_i = max(a.last_stopped, 0)
last_image_i = -1
image_i_after_cleaning = image_i
while image_i < num_images:
src_path = all_image_paths[image_i]
# src_img = mpimg.imread(src_path)
src_img = imread(src_path, shape=img_shape, dtype=np.uint8)
sketch_part = get_sketch_img(src_img)
# Only negate when the human did not perform action 1 or 2.
if last_image_i != image_i and detect_need_negate(sketch_part):
src_img = combined_image_sketch_negate(src_img)
imsave(src_path, src_img)
src_img = imread(src_path, shape=img_shape, dtype=np.uint8)
sketch_part = get_sketch_img(src_img)
assert src_img.shape[0] == img_shape[0] and src_img.shape[
1] == img_shape[1]
# if detect_wierd_distribution(sketch_img=sketch_part):
# last_image_i = image_i
# image_i += 1
myimg.set_data(src_img)
plt.pause(0.1)