Datasets:
HenryAI
/
KerasCodeExamples.txt

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ax[1].imshow(keras.preprocessing.image.array_to_img(recons_img))
ax[1].set_title(\"Reconstructed\")
ax[2].imshow(
keras.preprocessing.image.array_to_img(depth_map[..., None]), cmap=\"inferno\"
)
ax[2].set_title(\"Depth Map\")
png
Render 3D Scene
Here we will synthesize novel 3D views and stitch all of them together to render a video encompassing the 360-degree view.
def get_translation_t(t):
\"\"\"Get the translation matrix for movement in t.\"\"\"
matrix = [
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, t],
[0, 0, 0, 1],
]
return tf.convert_to_tensor(matrix, dtype=tf.float32)
def get_rotation_phi(phi):
\"\"\"Get the rotation matrix for movement in phi.\"\"\"
matrix = [
[1, 0, 0, 0],
[0, tf.cos(phi), -tf.sin(phi), 0],
[0, tf.sin(phi), tf.cos(phi), 0],
[0, 0, 0, 1],
]
return tf.convert_to_tensor(matrix, dtype=tf.float32)
def get_rotation_theta(theta):
\"\"\"Get the rotation matrix for movement in theta.\"\"\"
matrix = [
[tf.cos(theta), 0, -tf.sin(theta), 0],
[0, 1, 0, 0],
[tf.sin(theta), 0, tf.cos(theta), 0],
[0, 0, 0, 1],
]
return tf.convert_to_tensor(matrix, dtype=tf.float32)
def pose_spherical(theta, phi, t):
\"\"\"
Get the camera to world matrix for the corresponding theta, phi
and t.
\"\"\"
c2w = get_translation_t(t)
c2w = get_rotation_phi(phi / 180.0 * np.pi) @ c2w
c2w = get_rotation_theta(theta / 180.0 * np.pi) @ c2w
c2w = np.array([[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) @ c2w
return c2w
rgb_frames = []
batch_flat = []
batch_t = []
# Iterate over different theta value and generate scenes.
for index, theta in tqdm(enumerate(np.linspace(0.0, 360.0, 120, endpoint=False))):
# Get the camera to world matrix.
c2w = pose_spherical(theta, -30.0, 4.0)
#
ray_oris, ray_dirs = get_rays(H, W, focal, c2w)
rays_flat, t_vals = render_flat_rays(
ray_oris, ray_dirs, near=2.0, far=6.0, num_samples=NUM_SAMPLES, rand=False
)
if index % BATCH_SIZE == 0 and index > 0:
batched_flat = tf.stack(batch_flat, axis=0)
batch_flat = [rays_flat]
batched_t = tf.stack(batch_t, axis=0)
batch_t = [t_vals]
rgb, _ = render_rgb_depth(
nerf_model, batched_flat, batched_t, rand=False, train=False
)
temp_rgb = [np.clip(255 * img, 0.0, 255.0).astype(np.uint8) for img in rgb]
rgb_frames = rgb_frames + temp_rgb
else:
batch_flat.append(rays_flat)
batch_t.append(t_vals)
rgb_video = \"rgb_video.mp4\"
imageio.mimwrite(rgb_video, rgb_frames, fps=30, quality=7, macro_block_size=None)
120it [00:12, 9.24it/s]
Visualize the video
Here we can see the rendered 360 degree view of the scene. The model has successfully learned the entire volumetric space through the sparse set of images in only 20 epochs. You can view the rendered video saved locally, named rgb_video.mp4.
rendered-video
Conclusion