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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]) | |
| 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 | |
| path = pydiffvg.Path(num_control_points = num_control_points, | |
| points = points, | |
| is_closed = False, | |
| stroke_width = tf.constant(15.0)) | |
| shapes = [path] | |
| path_group = pydiffvg.ShapeGroup( shape_ids = tf.constant([0], dtype=tf.int32), | |
| fill_color = tf.constant([0.0, 0.0, 0.0, 0.0]), | |
| stroke_color = tf.constant([0.6, 0.3, 0.6, 0.8])) | |
| 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_stroke_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 | |
| ) | |
| stroke_color = tf.Variable([0.4, 0.7, 0.5, 0.5]) | |
| stroke_width_n = tf.Variable(5.0 / 100.0) | |
| path.points = points_n * 256 | |
| path.stroke_width = stroke_width_n * 100 | |
| path_group.stroke_color = stroke_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_stroke_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.stroke_width = stroke_width_n * 100 | |
| path_group.stroke_color = stroke_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_stroke_tf/iter_{}.png'.format(t)) | |
| grads = tape.gradient(loss_value, [points_n, stroke_width_n, stroke_color]) | |
| print(grads) | |
| optimizer.apply_gradients(zip(grads, [points_n, stroke_width_n, stroke_color])) | |
| # Render the final result. | |
| path.points = points_n * 256 | |
| path_group.stroke_color = stroke_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_stroke_tf/final.png') | |
| # Convert the intermediate renderings to a video. | |
| from subprocess import call | |
| call(["ffmpeg", "-framerate", "24", "-i", | |
| "results/single_stroke_tf/iter_%d.png", "-vb", "20M", | |
| "results/single_curve_tf/out.mp4"]) | |