Update rendering.py

rendering.py

@@ -0,0 +1,160 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+
+
+def encode_position(x):
+    """Encodes the position into its corresponding Fourier feature.
+    Args:
+        x: The input coordinate.
+    Returns:
+        Fourier features tensors of the position.
+    """
+    positions = [x]
+    for i in range(POS_ENCODE_DIMS):
+        for fn in [tf.sin, tf.cos]:
+            positions.append(fn(2.0 ** i * x))
+    return tf.concat(positions, axis=-1)
+
+
+def get_rays(height, width, focal, pose):
+    """Computes origin point and direction vector of rays.
+    Args:
+        height: Height of the image.
+        width: Width of the image.
+        focal: The focal length between the images and the camera.
+        pose: The pose matrix of the camera.
+    Returns:
+        Tuple of origin point and direction vector for rays.
+    """
+    # Build a meshgrid for the rays.
+    i, j = tf.meshgrid(
+        tf.range(width, dtype=tf.float32),
+        tf.range(height, dtype=tf.float32),
+        indexing="xy",
+    )
+
+    # Normalize the x axis coordinates.
+    transformed_i = (i - width * 0.5) / focal
+
+    # Normalize the y axis coordinates.
+    transformed_j = (j - height * 0.5) / focal
+
+    # Create the direction unit vectors.
+    directions = tf.stack([transformed_i, -transformed_j, -tf.ones_like(i)], axis=-1)
+
+    # Get the camera matrix.
+    camera_matrix = pose[:3, :3]
+    height_width_focal = pose[:3, -1]
+
+    # Get origins and directions for the rays.
+    transformed_dirs = directions[..., None, :]
+    camera_dirs = transformed_dirs * camera_matrix
+    ray_directions = tf.reduce_sum(camera_dirs, axis=-1)
+    ray_origins = tf.broadcast_to(height_width_focal, tf.shape(ray_directions))
+
+    # Return the origins and directions.
+    return (ray_origins, ray_directions)
+
+
+def render_flat_rays(ray_origins, ray_directions, near, far, num_samples, rand=False):
+    """Renders the rays and flattens it.
+    Args:
+        ray_origins: The origin points for rays.
+        ray_directions: The direction unit vectors for the rays.
+        near: The near bound of the volumetric scene.
+        far: The far bound of the volumetric scene.
+        num_samples: Number of sample points in a ray.
+        rand: Choice for randomising the sampling strategy.
+    Returns:
+       Tuple of flattened rays and sample points on each rays.
+    """
+    # Compute 3D query points.
+    # Equation: r(t) = o+td -> Building the "t" here.
+    t_vals = tf.linspace(near, far, num_samples)
+    if rand:
+        # Inject uniform noise into sample space to make the sampling
+        # continuous.
+        shape = list(ray_origins.shape[:-1]) + [num_samples]
+        noise = tf.random.uniform(shape=shape) * (far - near) / num_samples
+        t_vals = t_vals + noise
+
+    # Equation: r(t) = o + td -> Building the "r" here.
+    rays = ray_origins[..., None, :] + (
+        ray_directions[..., None, :] * t_vals[..., None]
+    )
+    rays_flat = tf.reshape(rays, [-1, 3])
+    rays_flat = encode_position(rays_flat)
+    return (rays_flat, t_vals)
+
+
+def map_fn(pose):
+    """Maps individual pose to flattened rays and sample points.
+    Args:
+        pose: The pose matrix of the camera.
+    Returns:
+        Tuple of flattened rays and sample points corresponding to the
+        camera pose.
+    """
+    (ray_origins, ray_directions) = get_rays(height=H, width=W, focal=focal, pose=pose)
+    (rays_flat, t_vals) = render_flat_rays(
+        ray_origins=ray_origins,
+        ray_directions=ray_directions,
+        near=2.0,
+        far=6.0,
+        num_samples=NUM_SAMPLES,
+        rand=True,
+    )
+    return (rays_flat, t_vals)
+
+
+def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
+    """Generates the RGB image and depth map from model prediction.
+    Args:
+        model: The MLP model that is trained to predict the rgb and
+            volume density of the volumetric scene.
+        rays_flat: The flattened rays that serve as the input to
+            the NeRF model.
+        t_vals: The sample points for the rays.
+        rand: Choice to randomise the sampling strategy.
+        train: Whether the model is in the training or testing phase.
+    Returns:
+        Tuple of rgb image and depth map.
+    """
+    # Get the predictions from the nerf model and reshape it.
+    if train:
+        predictions = model(rays_flat)
+    else:
+        predictions = model.predict(rays_flat)
+    predictions = tf.reshape(predictions, shape=(BATCH_SIZE, H, W, NUM_SAMPLES, 4))
+
+    # Slice the predictions into rgb and sigma.
+    rgb = tf.sigmoid(predictions[..., :-1])
+    sigma_a = tf.nn.relu(predictions[..., -1])
+
+    # Get the distance of adjacent intervals.
+    delta = t_vals[..., 1:] - t_vals[..., :-1]
+    # delta shape = (num_samples)
+    if rand:
+        delta = tf.concat(
+            [delta, tf.broadcast_to([1e10], shape=(BATCH_SIZE, H, W, 1))], axis=-1
+        )
+        alpha = 1.0 - tf.exp(-sigma_a * delta)
+    else:
+        delta = tf.concat(
+            [delta, tf.broadcast_to([1e10], shape=(BATCH_SIZE, 1))], axis=-1
+        )
+        alpha = 1.0 - tf.exp(-sigma_a * delta[:, None, None, :])
+
+    # Get transmittance.
+    exp_term = 1.0 - alpha
+    epsilon = 1e-10
+    transmittance = tf.math.cumprod(exp_term + epsilon, axis=-1, exclusive=True)
+    weights = alpha * transmittance
+    rgb = tf.reduce_sum(weights[..., None] * rgb, axis=-2)
+
+    if rand:
+        depth_map = tf.reduce_sum(weights * t_vals, axis=-1)
+    else:
+        depth_map = tf.reduce_sum(weights * t_vals[:, None, None], axis=-1)
+    return (rgb, depth_map)