(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool)
| 59 | print(render_path+'video.mp4') |
| 60 | |
| 61 | def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool): |
| 62 | with torch.no_grad(): |
| 63 | gaussians = GaussianModel(dataset.sh_degree, gaussian_dim=4, rot_4d=True) |
| 64 | scene = Scene(dataset, gaussians, shuffle=False) |
| 65 | |
| 66 | bg_color = [1,1,1] if dataset.white_background else [0, 0, 0] |
| 67 | bg_color = [1,1,1] |
| 68 | bg_color = [0.125,0.216,0.157] |
| 69 | background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") |
| 70 | |
| 71 | # if not skip_train: |
| 72 | # render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background) |
| 73 | |
| 74 | if not skip_test: |
| 75 | render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background) |
| 76 | |
| 77 | if __name__ == "__main__": |
| 78 | # Set up command line argument parser |
no test coverage detected