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import pydiffvg_tensorflow as pydiffvg
import tensorflow as tf
import skimage
import numpy as np
canvas_width, canvas_height = 256, 256
num_control_points = tf.constant([2, 2, 2])
points = tf.constant([[120.0, 30.0], # base
[150.0, 60.0], # control point
[ 90.0, 198.0], # control point
[ 60.0, 218.0], # base
[ 90.0, 180.0], # control point
[200.0, 65.0], # control point
[210.0, 98.0], # base
[220.0, 70.0], # control point
[130.0, 55.0]]) # control point
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
is_closed = True)
shapes = [path]
path_group = pydiffvg.ShapeGroup( shape_ids = tf.constant([0], dtype=tf.int32),
fill_color = tf.constant([0.3, 0.6, 0.3, 1.0]))
shape_groups = [path_group]
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
render = pydiffvg.render
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(0), # seed
*scene_args)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img, 'results/single_curve_tf/target.png', gamma=2.2)
target = tf.identity(img)
# Move the path to produce initial guess
# normalize points for easier learning rate
points_n = tf.Variable([[100.0/256.0, 40.0/256.0], # base
[155.0/256.0, 65.0/256.0], # control point
[100.0/256.0, 180.0/256.0], # control point
[ 65.0/256.0, 238.0/256.0], # base
[100.0/256.0, 200.0/256.0], # control point
[170.0/256.0, 55.0/256.0], # control point
[220.0/256.0, 100.0/256.0], # base
[210.0/256.0, 80.0/256.0], # control point
[140.0/256.0, 60.0/256.0]]) # control point
color = tf.Variable([0.3, 0.2, 0.5, 1.0])
path.points = points_n * 256
path_group.fill_color = color
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(1), # seed
*scene_args)
pydiffvg.imwrite(img, 'results/single_curve_tf/init.png', gamma=2.2)
optimizer = tf.compat.v1.train.AdamOptimizer(1e-2)
for t in range(100):
print('iteration:', t)
with tf.GradientTape() as tape:
# Forward pass: render the image.
path.points = points_n * 256
path_group.fill_color = color
# Important to use a different seed every iteration, otherwise the result
# would be biased.
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(t+1), # seed,
*scene_args)
loss_value = tf.reduce_sum(tf.square(img - target))
print(f"loss_value: {loss_value}")
pydiffvg.imwrite(img, 'results/single_curve_tf/iter_{}.png'.format(t))
grads = tape.gradient(loss_value, [points_n, color])
print(grads)
optimizer.apply_gradients(zip(grads, [points_n, color]))
# Render the final result.
path.points = points_n * 256
path_group.fill_color = color
scene_args = pydiffvg.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(tf.constant(256), # width
tf.constant(256), # height
tf.constant(2), # num_samples_x
tf.constant(2), # num_samples_y
tf.constant(101), # seed
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img, 'results/single_curve_tf/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/single_curve_tf/iter_%d.png", "-vb", "20M",
"results/single_curve_tf/out.mp4"])
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