Clip jialin dev

.gitignore

@@ -14,6 +14,8 @@ archive
 *.ply
 eval
 out
+__pycache__
+try-runs
 
 # evaluation:
 temp/
@@ -25,3 +27,6 @@ vids/
 stylegan3/results
 eg3d/results
 eg3d_results
+
+*runs/
+*ipynb

dataset_preprocessing/abo/.gitignore

@@ -0,0 +1 @@
+*.zip

dataset_preprocessing/abo/preprocess_abo_cameras.py

@@ -0,0 +1,130 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+#############################################################
+
+# Usage: python dataset_preprocessing/shapenet/preprocess_cars_cameras.py --source ~/downloads/cars_train --dest /data/cars_preprocessed
+
+#############################################################
+
+
+# from distutils.debug import DEBUG
+import json
+import numpy as np
+import os
+from tqdm import tqdm
+import argparse
+from ipdb import set_trace as st
+
+def list_recursive(folderpath):
+    return [os.path.join(folderpath, filename) for filename in os.listdir(folderpath)]
+
+DEBUG=True
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source", type=str)
+    parser.add_argument("--max_images", type=int, default=None)
+    args = parser.parse_args()
+
+    # Parse cameras
+    dataset_path = args.source
+    cameras = {}
+
+    blender2opencv = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
+    w, h = 512, 512
+
+    #### this is for directly listing 
+    # for scene_folder_path in list_recursive(dataset_path):
+        # if not os.path.isdir(scene_folder_path): continue
+
+    #### this is using predefined list, to avoid folders that the datageneration is not complete
+    all_data = []
+    resolution = 512 
+    for split in ['train', 'val']:
+        with open(os.path.join(dataset_path, 'meta', f'abo_{resolution}_{split}.txt')) as f:
+            scans = [line.rstrip() for line in f.readlines()]
+            all_data += scans
+    print(len(all_data), all_data)
+    for scene_folder_path_rel in all_data:
+        scene_folder_path = os.path.join(dataset_path, scene_folder_path_rel)
+        # st() # the sibling folder with rgb should be mesh>: no, only intrinsics and pose
+
+        pointcloud_csv = os.path.join(scene_folder_path,'sample', f"pc.csv")
+        assert os.path.isfile(pointcloud_csv)
+        pc_relative_path = os.path.relpath(pointcloud_csv, dataset_path)
+        # print(pc_relative_path)
+
+        with open(os.path.join(scene_folder_path,'render', f"transforms.json"), 'r') as f:
+                meta = json.load(f)
+        # print(meta.keys()) ['camera_angle_x', 'frames']
+        # print(meta ['frames'][0]['file_path']) 
+
+        focal = .5 * w / np.tan(0.5 * meta['camera_angle_x'])
+        # intrinsic_for_all = np.array([[focal, 0, w / 2], [0, focal, h / 2], [0, 0, 1]])
+        ### IMPORTANT!!! EG3D use the intrinsics that agnostic of original resolution!!
+                    # intrinsics = np.array(
+                    #             [[focal / orig_img_size, 0.00000000e+00, cx / orig_img_size],
+                    #             [0.00000000e+00, focal / orig_img_size, cy / orig_img_size],
+                    #             [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]
+                    #         ).tolist()
+        intrinsic_for_all = np.array(
+                    [[focal / w, 0.00000000e+00, (w / 2)/w],
+                    [0.00000000e+00, focal / h, (h / 2)/h],
+                    [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]
+                ).tolist()
+        st()
+
+        # continue
+        # for rgb_path in list_recursive(os.path.join(scene_folder_path, 'render')):
+        for frame in meta ['frames']:
+            rgb_path = frame['file_path']
+            relative_path = os.path.relpath(rgb_path, dataset_path)
+            print(relative_path)
+
+            intrinsics = intrinsic_for_all
+            pose = (np.array(frame['transform_matrix'])@blender2opencv).tolist()
+            # print(len(pose))
+
+            cameras[relative_path] = {'pose': pose, 'intrinsics': intrinsics, 'scene-name': os.path.basename(scene_folder_path),\
+                'pc_csv':pc_relative_path}
+            # if DEBUG:
+            #     break
+
+    with open(os.path.join(dataset_path, 'cameras.json'), 'w') as outfile:
+        json.dump(cameras, outfile, indent=4)
+
+
+    camera_dataset_file = os.path.join(args.source, 'cameras.json')
+
+    with open(camera_dataset_file, "r") as f:
+        cameras = json.load(f) # same camera file as saved above
+
+    dataset = {'labels':[]}
+    # max_images = args.max_images if args.max_images is not None else len(cameras)
+    max_images = len(cameras)
+    for i, filename in tqdm(enumerate(cameras), total=max_images):
+        if (max_images is not None and i >= max_images): break
+
+        pose = np.array(cameras[filename]['pose'])
+        intrinsics = np.array(cameras[filename]['intrinsics'])
+        label = np.concatenate([pose.reshape(-1), intrinsics.reshape(-1)]).tolist()
+
+        image_path = os.path.join(args.source, filename)
+        pc_rel_path = cameras[filename]['pc_csv']
+        dataset["labels"].append([filename, label, pc_rel_path]) 
+        # also append pointcloud filename, but need to check with the dataset class too
+
+    # print(dataset)
+    # check cameras/dataset
+
+    with open(os.path.join(args.source, 'dataset.json'), "w") as f:
+        json.dump(dataset, f, indent=4)
+