def train_step(self,
image,
extractor,
loss_function,
opt,
total_variation_weight=0):
"""
Mutates image as a step to conform to training
"""
with tf.GradientTape() as tape:
outputs = extractor(image)
loss = loss_function(outputs)
loss += total_variation_weight * tf.image.total_variation(image)
grad = tape.gradient(loss, image)
opt.apply_gradients([(grad, image)])
image.assign(ImageUtils.clip_0_1(image))
# Trying face detection with OpenCV
x, y, w, h = detectAnime(path_to_pics + filename, i)
if x is not None:
print(x, y, w, h)
style_image = ImageUtils.grab_image("out.png")
# style_image = tf.image.resize_with_crop_or_pad(style_image, 1000,1000)
# Testing code ends here
style_orig = style_image
style_image = ImageUtils.image_op(
images=[content_image, style_image],
# Change this from a + b to something else
# May use CNN to map features
op=lambda a, b: ImageUtils.clip_0_1(a + b))
print(" - Generating image", i)
stylized_image = hub_model(tf.constant(content_image),
tf.constant(style_image))[0]
# Uncomment this and comment out previous stylized_image definition to
# map style image on content image without style transfer
# stylized_image = style_image
# Clear figure and update images
fig.clf()
plotset1 = ( # This shows 2x3 5-plot layout
((2, 3, 1), content_image, 'Content Image'),
((2, 3, 4), stylized_image, 'New Image'),
((1, 3, 2),
# Also, this high frequency component is basically an edge-detector.
def high_pass_x_y(image):
"""For visualizing produced artifacts"""
x_var = image[:, :, 1:, :] - image[:, :, :-1, :]
y_var = image[:, 1:, :, :] - image[:, :-1, :, :]
return x_var, y_var
x_deltas, y_deltas = high_pass_x_y(transferer.content_image)
plt.figure(figsize=(14, 10))
plt.subplot(2, 2, 1)
ImageUtils.imshow(ImageUtils.clip_0_1(2 * y_deltas + 0.5),
"Horizontal Deltas: Original")
plt.subplot(2, 2, 2)
ImageUtils.imshow(ImageUtils.clip_0_1(2 * x_deltas + 0.5),
"Vertical Deltas: Original")
x_deltas, y_deltas = high_pass_x_y(image)
plt.subplot(2, 2, 3)
ImageUtils.imshow(ImageUtils.clip_0_1(2 * y_deltas + 0.5),
"Horizontal Deltas: Styled")
plt.subplot(2, 2, 4)
ImageUtils.imshow(ImageUtils.clip_0_1(2 * x_deltas + 0.5),
"Vertical Deltas: Styled")
