split scripts into 2 eval/render (#637)

* done splitting script

* udpate docs

* woops forgot to add function

* update camera panel command

scripts/render.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+"""
+render.py
+"""
+from __future__ import annotations
+
+import dataclasses
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import Literal
+
+import dcargs
+import mediapy as media
+import torch
+from rich.console import Console
+from rich.progress import (
+    BarColumn,
+    Progress,
+    TaskProgressColumn,
+    TextColumn,
+    TimeRemainingColumn,
+)
+from typing_extensions import assert_never
+
+from nerfstudio.cameras.camera_paths import get_path_from_json, get_spiral_path
+from nerfstudio.cameras.cameras import Cameras
+from nerfstudio.configs.base_config import Config  # pylint: disable=unused-import
+from nerfstudio.pipelines.base_pipeline import Pipeline
+from nerfstudio.utils.eval_utils import eval_setup
+from nerfstudio.utils.rich_utils import ItersPerSecColumn
+
+console = Console(width=120)
+
+logging.basicConfig(format="[%(filename)s:%(lineno)d] %(message)s", level=logging.INFO)
+
+
+def _render_trajectory_video(
+    pipeline: Pipeline,
+    cameras: Cameras,
+    output_filename: Path,
+    rendered_output_name: str,
+    rendered_resolution_scaling_factor: float = 1.0,
+    seconds: float = 5.0,
+) -> None:
+    """Helper function to create a video of the spiral trajectory.
+
+    Args:
+        pipeline: Pipeline to evaluate with.
+        cameras: Cameras to render.
+        output_filename: Name of the output file.
+        rendered_output_name: Name of the renderer output to use.
+        rendered_resolution_scaling_factor: Scaling factor to apply to the camera image resolution.
+        seconds: Number for the output video.
+    """
+    console.print("[bold green]Creating trajectory video")
+    images = []
+    cameras.rescale_output_resolution(rendered_resolution_scaling_factor)
+
+    progress = Progress(
+        TextColumn(":movie_camera: Rendering :movie_camera:"),
+        BarColumn(),
+        TaskProgressColumn(show_speed=True),
+        ItersPerSecColumn(suffix="fps"),
+        TimeRemainingColumn(elapsed_when_finished=True, compact=True),
+    )
+    with progress:
+        for camera_idx in progress.track(range(cameras.size), description=""):
+            camera_ray_bundle = cameras.generate_rays(camera_indices=camera_idx).to(pipeline.device)
+            with torch.no_grad():
+                outputs = pipeline.model.get_outputs_for_camera_ray_bundle(camera_ray_bundle)
+            if rendered_output_name not in outputs:
+                console.rule("Error", style="red")
+                console.print(f"Could not find {rendered_output_name} in the model outputs", justify="center")
+                console.print(f"Please set --rendered_output_name to one of: {outputs.keys()}", justify="center")
+                sys.exit(1)
+            image = outputs[rendered_output_name].cpu().numpy()
+            images.append(image)
+
+    fps = len(images) / seconds
+    with console.status("[yellow]Saving video", spinner="bouncingBall"):
+        media.write_video(output_filename, images, fps=fps)
+    console.rule("[green] :tada: :tada: :tada: Success :tada: :tada: :tada:")
+    console.print(f"[green]Saved video to {output_filename}", justify="center")
+
+
+@dataclasses.dataclass
+class RenderTrajectory:
+    """Load a checkpoint, render a trajectory, and save to a video file."""
+
+    # Path to config YAML file.
+    load_config: Path
+    # Name of the renderer output to use. rgb, depth, etc.
+    rendered_output_name: str = "rgb"
+    #  Trajectory to render.
+    traj: Literal["spiral", "interp", "filename"] = "spiral"
+    # Scaling factor to apply to the camera image resolution.
+    downscale_factor: int = 1
+    # Filename of the camera path to render.
+    camera_path_filename: Path = Path("camera_path.json")
+    # Name of the output file.
+    output_path: Path = Path("output.mp4")
+    # How long the video should be.
+    seconds: float = 5.0
+
+    def main(self) -> None:
+        """Main function."""
+        _, pipeline, _ = eval_setup(self.load_config)
+
+        seconds = self.seconds
+
+        # TODO(ethan): use camera information from parsing args
+        if self.traj == "spiral":
+            camera_start = pipeline.datamanager.eval_dataloader.get_camera(image_idx=0)
+            # TODO(ethan): pass in the up direction of the camera
+            camera_path = get_spiral_path(camera_start, steps=30, radius=0.1)
+        elif self.traj == "interp":
+            # cameras_a = pipeline.datamanager.eval_dataloader.get_camera(image_idx=0)
+            # cameras_b = pipeline.datamanager.eval_dataloader.get_camera(image_idx=10)
+            # camera_path = get_interpolated_camera_path(cameras, steps=30)
+            raise NotImplementedError("Interpolated camera path not implemented.")
+        elif self.traj == "filename":
+            with open(self.camera_path_filename, "r", encoding="utf-8") as f:
+                camera_path = json.load(f)
+            seconds = camera_path["seconds"]
+            camera_path = get_path_from_json(camera_path)
+        else:
+            assert_never(self.traj)
+
+        _render_trajectory_video(
+            pipeline,
+            camera_path,
+            output_filename=self.output_path,
+            rendered_output_name=self.rendered_output_name,
+            rendered_resolution_scaling_factor=1.0 / self.downscale_factor,
+            seconds=seconds,
+        )
+
+
+def entrypoint():
+    """Entrypoint for use with pyproject scripts."""
+    dcargs.extras.set_accent_color("bright_yellow")
+    dcargs.cli(RenderTrajectory).main()
+
+
+if __name__ == "__main__":
+    entrypoint()