110 lines
4.4 KiB
Python
110 lines
4.4 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, canvas_height = 256, 256
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num_control_points = tf.constant([2, 2, 2])
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points = tf.constant([[120.0, 30.0], # base
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[150.0, 60.0], # control point
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[ 90.0, 198.0], # control point
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[ 60.0, 218.0], # base
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[ 90.0, 180.0], # control point
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[200.0, 65.0], # control point
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[210.0, 98.0], # base
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[220.0, 70.0], # control point
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[130.0, 55.0]]) # control point
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path = pydiffvg.Path(num_control_points = num_control_points,
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points = points,
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is_closed = True)
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shapes = [path]
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path_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 = [path_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_curve_tf/target.png', gamma=2.2)
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target = tf.identity(img)
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# Move the path to produce initial guess
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# normalize points for easier learning rate
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points_n = tf.Variable([[100.0/256.0, 40.0/256.0], # base
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[155.0/256.0, 65.0/256.0], # control point
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[100.0/256.0, 180.0/256.0], # control point
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[ 65.0/256.0, 238.0/256.0], # base
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[100.0/256.0, 200.0/256.0], # control point
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[170.0/256.0, 55.0/256.0], # control point
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[220.0/256.0, 100.0/256.0], # base
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[210.0/256.0, 80.0/256.0], # control point
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[140.0/256.0, 60.0/256.0]]) # control point
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color = tf.Variable([0.3, 0.2, 0.5, 1.0])
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path.points = points_n * 256
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path_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_curve_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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path.points = points_n * 256
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path_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_curve_tf/iter_{}.png'.format(t))
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grads = tape.gradient(loss_value, [points_n, color])
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print(grads)
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optimizer.apply_gradients(zip(grads, [points_n, color]))
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# Render the final result.
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path.points = points_n * 256
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path_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, 'results/single_curve_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_curve_tf/iter_%d.png", "-vb", "20M",
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"results/single_curve_tf/out.mp4"])
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