INGP data converter

README.md

@@ -78,6 +78,12 @@ https://drive.google.com/file/d/1PG-KllCv4vSRPO7n5lpBjyTjlUyT8Nag/view?usp=shari
 **Note: we currently do not support the instant-ngp format data (since the project was released before NGP). Using it will trigger the nerf-synthetic (Blender) data loader
 due to similarity, but will not train properly. For real data we use the NSVF format.**
 
+To help convert instant-ngp data, please try
+```
+cd opt/scripts
+python ingp2nsvf.py <ingp_data_dir> <output_data_dir>
+```
+
 ## Voxel Optimization (aka Training)
 
 For training a single scene, see `opt/opt.py`. The launch script makes this easier.

opt/scripts/ingp2nsvf.py

@@ -0,0 +1,141 @@
+"""
+Convert NeRF-iNGP data to NSVF
+python ingp2nsvf.py <ngp_data_dir> <our_data_dir>
+"""
+import os
+import shutil
+from glob import glob
+import json
+
+import numpy as np
+from PIL import Image
+import argparse
+
+def convert(data_dir : str, out_data_dir : str):
+    """
+    Convert Instant-NGP (modified NeRF) data to NSVF
+
+    :param data_dir: the dataset dir (NeRF-NGP format) to convert
+    :param out_data_dir: output dataset directory NSVF
+    """
+
+    images_dir_name = os.path.join(out_data_dir, "images")
+    pose_dir_name = os.path.join(out_data_dir, "pose")
+
+    os.makedirs(images_dir_name, exist_ok=True)
+    os.makedirs(pose_dir_name, exist_ok=True)
+
+    def get_subdir(name):
+        if name.endswith("_train.json"):
+            return "train"
+        elif name.endswith("_val.json"):
+            return "val"
+        elif name.endswith("_test.json"):
+            return "test"
+        return ""
+
+    def get_out_prefix(name):
+        if name.endswith("_train.json"):
+            return "0_"
+        elif name.endswith("_val.json"):
+            return "1_"
+        elif name.endswith("_test.json"):
+            return "2_"
+        return ""
+
+    jsons = {
+        x: (get_subdir(x), get_out_prefix(x))
+        for x in glob(os.path.join(data_dir, "*.json"))
+    }
+
+    # OpenGL -> OpenCV
+    cam_trans = np.diag(np.array([1.0, -1.0, -1.0, 1.0]))
+
+    # fmt: off
+    world_trans = np.array(
+        [
+            [0.0, -1.0, 0.0, 0.0],
+            [0.0, 0.0, -1.0, 0.0],
+            [1.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    # fmt: on
+
+    assert len(jsons) > 0, f"No jsons found in {data_dir}, can't convert"
+    cnt = 0
+
+    example_fpath = None
+    tj = {}
+    for tj_path, (tj_subdir, tj_out_prefix) in jsons.items():
+        with open(tj_path, "r") as f:
+            tj = json.load(f)
+        if "frames" not in tj:
+            print(f"No frames in json {tj_path}, skipping")
+            continue
+
+        for frame in tj["frames"]:
+            # Try direct relative path (used in newer NGP datasets)
+            fpath = os.path.join(data_dir, frame["file_path"])
+            if not os.path.isfile(fpath):
+                # Legacy path (NeRF)
+                fpath = os.path.join(
+                    data_dir, tj_subdir, os.path.basename(frame["file_path"]) + ".png"
+                )
+            example_fpath = fpath
+            if not os.path.isfile(fpath):
+                print("Could not find image:", frame["file_path"], "(this may be ok)")
+                continue
+
+            ext = os.path.splitext(fpath)[1]
+
+            c2w = np.array(frame["transform_matrix"])
+            c2w = world_trans @ c2w @ cam_trans  # To OpenCV
+
+            image_fname = tj_out_prefix + f"{cnt:05d}"
+
+            pose_path = os.path.join(pose_dir_name, image_fname + ".txt")
+
+            # Save 4x4 OpenCV C2W pose
+            np.savetxt(pose_path, c2w)
+
+            # Copy images
+            new_fpath = os.path.join(images_dir_name, image_fname + ext)
+            shutil.copyfile(fpath, new_fpath)
+            cnt += 1
+
+    assert len(tj) > 0, f"No valid jsons found in {data_dir}, can't convert"
+
+    w = tj.get("w")
+    h = tj.get("h")
+
+    if w is None or h is None:
+        assert example_fpath is not None
+        # Pose not available so load a image and get the size
+        w, h = Image.open(example_fpath).size
+
+    fx = float(0.5 * w / np.tan(0.5 * tj["camera_angle_x"]))
+    if "camera_angle_y" in tj:
+        fy = float(0.5 * h / np.tan(0.5 * tj["camera_angle_y"]))
+    else:
+        fy = fx
+
+    cx = tj.get("cx", w * 0.5)
+    cy = tj.get("cy", h * 0.5)
+
+    intrin_mtx = np.array([
+        [fx, 0.0, cx, 0.0],
+        [0.0, fy, cy, 0.0],
+        [0.0, 0.0, 1.0, 0.0],
+        [0.0, 0.0, 0.0, 1.0],
+    ])
+    # Write intrinsics
+    np.savetxt(os.path.join(out_data_dir, "intrinsics.txt"), intrin_mtx)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("data_dir", type=str, help="NeRF-NGP data directory")
+    parser.add_argument("out_data_dir", type=str, help="Output NSVF data directory")
+    args = parser.parse_args()
+    convert(args.data_dir, args.out_data_dir)

opt/util/nerf_dataset.py

@@ -15,6 +15,8 @@
 class NeRFDataset(DatasetBase):
     """
     NeRF dataset loader
+
+    WARNING: this is only intended for use with NeRF Blender data!!!!
     """
 
     focal: float
@@ -58,6 +60,8 @@ def __init__(
         data_json = path.join(root, "transforms_" + split_name + ".json")
 
         print("LOAD DATA", data_path)
+        print("WARNING: This data loader is ONLY intended for use with NeRF-synthetic Blender data!!!!")
+        print("If you want to try running this code on Instant-NGP data please use scripts/ingp2nsvf.py")
 
         j = json.load(open(data_json, "r"))