mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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5fb3d14304
* abstracted some test logic and actually using test images now * moved logging from trainer to graph * fixing dataset path * refactored instant ngp code to use inheritance * fixed my run_train * fixed data path * fixed data dir
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
"""
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Code to test ray generation.
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"""
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import imageio
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import torch
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from mattport.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one
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from mattport.nerf.dataset.image_dataset import ImageDataset, collate_batch
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from mattport.nerf.dataset.utils import get_dataset_inputs_dict
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from mattport.nerf.field_modules.ray_generator import RayGenerator
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from mattport.utils.io import get_absolute_path
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from mattport.viewer.plotly import visualize_dataset
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def visualize_batch(batch):
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"""Returns an image from a batch."""
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assert "image" in batch, "You must call collate_batch with keep_full_image=True for this to work."
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# set the color of the sampled rays
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c, y, x = [i.flatten() for i in torch.split(batch["local_indices"], 1, dim=-1)]
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batch["image"][c, y, x] = 1.0
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# batch["image"] is num_images, h, w, 3
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images = torch.split(batch["image"], 1, dim=0)
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image_list = [image[0] for image in images]
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image = torch.cat(image_list, dim=1) # cat along the width dimension
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image = (image * 255.0).to(torch.uint8)
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return image
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def test_dataloader(visualize=False):
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"""Testing for the dataloader from input dataset parameters to rays."""
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data_directory = "tests/data/lego_test"
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dataset_type = "blender"
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downscale_factor = 1
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num_images_to_sample_from = 1
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num_times_to_repeat_images = 40
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num_rays_per_batch = 1024
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num_workers = 0
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data_directory = get_absolute_path(data_directory)
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dataset_inputs = get_dataset_inputs_dict(
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data_directory=data_directory, dataset_type=dataset_type, downscale_factor=downscale_factor
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)["train"]
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image_dataset = ImageDataset(
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image_filenames=dataset_inputs.image_filenames, downscale_factor=dataset_inputs.downscale_factor
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)
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iter_dataset = CollateIterDataset(
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image_dataset,
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collate_fn=lambda batch_list: collate_batch(batch_list, num_rays_per_batch, keep_full_image=True),
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num_samples_to_collate=num_images_to_sample_from,
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num_times_to_repeat=num_times_to_repeat_images,
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)
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dataloader = DataLoader(
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iter_dataset,
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batch_size=1,
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num_workers=num_workers,
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collate_fn=collate_batch_size_one,
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pin_memory=True,
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)
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dataloader_iter = iter(dataloader)
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ray_generator = RayGenerator(dataset_inputs.intrinsics, dataset_inputs.camera_to_world)
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num_batches = 10
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for _ in tqdm(range(num_batches)):
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batch = next(dataloader_iter)
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ray_bundle = ray_generator.forward(batch["indices"])
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# visualize the batch
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image = visualize_batch(batch)
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if visualize:
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imageio.imwrite("temp0.png", image)
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# visualize the RayBundle
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small_ray_bundle = ray_bundle.sample(100)
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fig = visualize_dataset(camera_origins=dataset_inputs.camera_to_world[:, :3, 3], ray_bundle=small_ray_bundle)
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if visualize:
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fig.write_image("temp1.png")
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fig.write_html("temp1.html")
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assert True
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if __name__ == "__main__":
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test_dataloader(visualize=True)
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