initial commit

apps/single_circle_tf.py

@@ -0,0 +1,94 @@
+import pydiffvg_tensorflow as pydiffvg
+import tensorflow as tf
+import skimage
+import numpy as np
+
+canvas_width = 256
+canvas_height = 256
+circle = pydiffvg.Circle(radius = tf.constant(40.0),
+                         center = tf.constant([128.0, 128.0]))
+shapes = [circle]
+circle_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 = [circle_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_circle_tf/target.png', gamma=2.2)
+target = tf.identity(img)
+
+# Move the circle to produce initial guess
+# normalize radius & center for easier learning rate
+radius_n = tf.Variable(20.0 / 256.0)
+center_n = tf.Variable([108.0 / 256.0, 138.0 / 256.0])
+color = tf.Variable([0.3, 0.2, 0.8, 1.0])
+circle.radius = radius_n * 256
+circle.center = center_n * 256
+circle_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_circle_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.
+        circle.radius = radius_n * 256
+        circle.center = center_n * 256
+        circle_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_circle_tf/iter_{}.png'.format(t))
+
+    grads = tape.gradient(loss_value, [radius_n, center_n, color])
+    print(grads)
+    optimizer.apply_gradients(zip(grads, [radius_n, center_n, color]))
+
+# Render the final result.
+circle.radius = radius_n * 256
+circle.center = center_n * 256
+circle_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.cpu(), 'results/single_circle_tf/final.png')
+
+# Convert the intermediate renderings to a video.
+from subprocess import call
+call(["ffmpeg", "-framerate", "24", "-i",
+    "results/single_circle_tf/iter_%d.png", "-vb", "20M",
+    "results/single_circle_tf/out.mp4"])