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test_single_triangle_camera.py
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# Tensorflow by default allocates all GPU memory, leaving very little for rendering.
# We set the environment variable TF_FORCE_GPU_ALLOW_GROWTH to true to enforce on demand
# memory allocation to reduce page faults.
import os
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
import pyredner_tensorflow as pyredner
# Optimize camera parameters of a single triangle rendering
# Use GPU if available
pyredner.set_use_gpu(tf.test.is_gpu_available(cuda_only=True, min_cuda_compute_capability=None))
# Set up the scene
with tf.device('/device:cpu:' + str(pyredner.get_cpu_device_id())):
position = tf.Variable([0.0, 0.0, -5.0], dtype=tf.float32)
look_at = tf.Variable([0.0, 0.0, 0.0], dtype=tf.float32)
up = tf.Variable([0.0, 1.0, 0.0], dtype=tf.float32)
fov = tf.Variable([45.0], dtype=tf.float32) # in degree
clip_near = 1e-2 # needs to > 0
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = (256, 256))
with tf.device(pyredner.get_device_name()):
mat_grey = pyredner.Material(
diffuse_reflectance = tf.Variable([0.5, 0.5, 0.5], dtype=tf.float32))
materials = [mat_grey]
vertices = tf.Variable([[-1.7,1.0,0.0], [1.0,1.0,0.0], [-0.5,-1.0,0.0]],
dtype=tf.float32)
indices = tf.constant([[0, 1, 2]], dtype=tf.int32)
shape_triangle = pyredner.Shape(vertices, indices, 0)
light_vertices = tf.Variable([[-1.0,-1.0,-9.0],[1.0,-1.0,-9.0],[-1.0,1.0,-9.0],[1.0,1.0,-9.0]],
dtype=tf.float32)
light_indices = tf.constant([[0,1,2],[1,3,2]], dtype=tf.int32)
shape_light = pyredner.Shape(light_vertices, light_indices, 0)
shapes = [shape_triangle, shape_light]
with tf.device('/device:cpu:' + str(pyredner.get_cpu_device_id())):
light_intensity = tf.Variable([30.0,30.0,30.0],dtype=tf.float32)
light = pyredner.AreaLight(1, light_intensity)
area_lights = [light]
scene = pyredner.Scene(cam, shapes, materials, area_lights)
scene_args = pyredner.serialize_scene(
scene = scene,
num_samples = 16,
max_bounces = 1)
# Alias of the render function
# Render our target
img = pyredner.render(0, *scene_args)
pyredner.imwrite(img, 'results/test_single_triangle_camera/target.exr')
pyredner.imwrite(img, 'results/test_single_triangle_camera/target.png')
target = pyredner.imread('results/test_single_triangle_camera/target.exr')
# Perturb the scene, this is our initial guess
with tf.device('/device:cpu:' + str(pyredner.get_cpu_device_id())):
position = tf.Variable([0.0, 0.0, -3.0], dtype=tf.float32, trainable=True)
look_at = tf.Variable([-0.5, -0.5, 0.0], dtype=tf.float32, trainable=True)
scene.camera = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
scene_args = pyredner.serialize_scene(
scene = scene,
num_samples = 16,
max_bounces = 1)
# Render the initial guess
img = pyredner.render(1, *scene_args)
pyredner.imwrite(img, 'results/test_single_triangle_camera/init.png')
diff = tf.abs(target - img)
pyredner.imwrite(diff, 'results/test_single_triangle_camera/init_diff.png')
scene.camera = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
scene_args = pyredner.serialize_scene(
scene = scene,
num_samples = 4,
max_bounces = 1)
# Optimize for camera pose
optimizer = tf.compat.v1.train.AdamOptimizer(2e-2)
for t in range(200):
print('iteration:', t)
with tf.GradientTape() as tape:
# Need to rerun the Camera constructor for autodiff to compute the derivatives
scene.camera = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
scene_args = pyredner.serialize_scene(
scene = scene,
num_samples = 4,
max_bounces = 1)
img = pyredner.render(t+1, *scene_args)
pyredner.imwrite(img, 'results/test_single_triangle_camera/iter_{}.png'.format(t))
loss = tf.reduce_sum(tf.square(img - target))
print('loss:', loss)
grads = tape.gradient(loss, [position, look_at])
optimizer.apply_gradients(zip(grads, [position, look_at]))
print('position.grad:', grads[0])
print('look_at.grad:', grads[1])
print('position:', position)
print('look_at:', look_at)
scene_args = pyredner.serialize_scene(
scene = scene,
num_samples = 16,
max_bounces = 1)
img = pyredner.render(202, *scene_args)
pyredner.imwrite(img, 'results/test_single_triangle_camera/final.exr')
pyredner.imwrite(img, 'results/test_single_triangle_camera/final.png')
pyredner.imwrite(tf.abs(target - img), 'results/test_single_triangle_camera/final_diff.png')
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/test_single_triangle_camera/iter_%d.png", "-vb", "20M",
"results/test_single_triangle_camera/out.mp4"])