Update app.py

app.py

@@ -70,7 +70,9 @@ def get_rays(height, width, focal, pose):
 
 
 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.
@@ -78,13 +80,18 @@ def render_flat_rays(ray_origins, ray_directions, near, far, num_samples, rand=F
         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]
@@ -92,6 +99,7 @@ def render_flat_rays(ray_origins, ray_directions, near, far, num_samples, rand=F
         t_vals = t_vals + noise
 
     # Equation: r(t) = o + td -> Building the "r" here.
+
     rays = ray_origins[..., None, :] + (
         ray_directions[..., None, :] * t_vals[..., None]
     )
@@ -101,13 +109,17 @@ def render_flat_rays(ray_origins, ray_directions, near, far, num_samples, rand=F
 
 
 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,
@@ -117,11 +129,14 @@ def map_fn(pose):
         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.
@@ -130,9 +145,11 @@ def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
         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)
@@ -147,6 +164,7 @@ def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
     # 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
@@ -171,7 +189,6 @@ def render_rgb_depth(model, rays_flat, t_vals, rand=True, train=True):
         depth_map = tf.reduce_sum(weights * t_vals[:, None, None], axis=-1)
     return (rgb, depth_map)
 
-
 def get_translation_t(t):
     """Get the translation matrix for movement in t."""
     matrix = [