95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
import pydiffvg_tensorflow as pydiffvg
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import tensorflow as tf
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import skimage
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import numpy as np
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canvas_width = 256
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canvas_height = 256
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circle = pydiffvg.Circle(radius = tf.constant(40.0),
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center = tf.constant([128.0, 128.0]))
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shapes = [circle]
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circle_group = pydiffvg.ShapeGroup(shape_ids = tf.constant([0], dtype = tf.int32),
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fill_color = tf.constant([0.3, 0.6, 0.3, 1.0]))
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shape_groups = [circle_group]
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scene_args = pydiffvg.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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render = pydiffvg.render
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img = render(tf.constant(256), # width
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tf.constant(256), # height
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tf.constant(2), # num_samples_x
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tf.constant(2), # num_samples_y
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tf.constant(0), # seed
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*scene_args)
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# The output image is in linear RGB space. Do Gamma correction before saving the image.
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pydiffvg.imwrite(img, 'results/single_circle_tf/target.png', gamma=2.2)
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target = tf.identity(img)
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# Move the circle to produce initial guess
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# normalize radius & center for easier learning rate
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radius_n = tf.Variable(20.0 / 256.0)
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center_n = tf.Variable([108.0 / 256.0, 138.0 / 256.0])
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color = tf.Variable([0.3, 0.2, 0.8, 1.0])
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circle.radius = radius_n * 256
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circle.center = center_n * 256
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circle_group.fill_color = color
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scene_args = pydiffvg.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(tf.constant(256), # width
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tf.constant(256), # height
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tf.constant(2), # num_samples_x
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tf.constant(2), # num_samples_y
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tf.constant(1), # seed
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*scene_args)
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pydiffvg.imwrite(img, 'results/single_circle_tf/init.png', gamma=2.2)
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optimizer = tf.compat.v1.train.AdamOptimizer(1e-2)
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for t in range(100):
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print('iteration:', t)
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with tf.GradientTape() as tape:
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# Forward pass: render the image.
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circle.radius = radius_n * 256
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circle.center = center_n * 256
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circle_group.fill_color = color
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# Important to use a different seed every iteration, otherwise the result
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# would be biased.
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scene_args = pydiffvg.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(tf.constant(256), # width
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tf.constant(256), # height
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tf.constant(2), # num_samples_x
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tf.constant(2), # num_samples_y
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tf.constant(t+1), # seed,
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*scene_args)
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loss_value = tf.reduce_sum(tf.square(img - target))
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print(f"loss_value: {loss_value}")
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pydiffvg.imwrite(img, 'results/single_circle_tf/iter_{}.png'.format(t))
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grads = tape.gradient(loss_value, [radius_n, center_n, color])
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print(grads)
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optimizer.apply_gradients(zip(grads, [radius_n, center_n, color]))
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# Render the final result.
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circle.radius = radius_n * 256
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circle.center = center_n * 256
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circle_group.fill_color = color
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scene_args = pydiffvg.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(tf.constant(256), # width
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tf.constant(256), # height
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tf.constant(2), # num_samples_x
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tf.constant(2), # num_samples_y
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tf.constant(101), # seed
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*scene_args)
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# Save the images and differences.
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pydiffvg.imwrite(img.cpu(), 'results/single_circle_tf/final.png')
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# Convert the intermediate renderings to a video.
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from subprocess import call
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call(["ffmpeg", "-framerate", "24", "-i",
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"results/single_circle_tf/iter_%d.png", "-vb", "20M",
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"results/single_circle_tf/out.mp4"])
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