Generate small subset for HDR Nerf

README-pandora.md

@@ -4,6 +4,9 @@
 
 ## 0. Setup
 
+
+Open this folder in VS Code Dev Container extension!
+
 ```
 export PYTHONPATH=$pwd:$PYTHONPATH
 pip install skylibs equilib

lantern_scripts/generate_small_subset_hdr_nerf.py

@@ -0,0 +1,135 @@
+import argparse
+import json
+import math
+import os
+import re
+import shutil
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+
+def load_from_json(filename: Path):
+    """Load a dictionary from a JSON filename.
+
+    Args:
+        filename: The filename to load from.
+    """
+    assert filename.suffix == ".json"
+    with open(filename, encoding="UTF-8") as file:
+        return json.load(file)
+
+def is_mask_all_valid(mask_path):
+    mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+    return np.all(mask > 200)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_json', type=str, required=True)
+    parser.add_argument('--output_dir', type=str, required=True)
+    parser.add_argument('--train_split_fraction', type=float, default= 0.8)
+    parser.add_argument('--eval_split_fraction', type=float, default= 0.1)
+    parser.add_argument('--min_frame_number', type=int, default=100)
+    parser.add_argument('--frames_per_direction', type=int, default=100)
+    args = parser.parse_args()
+
+    assert os.path.isfile(args.input_json) and args.input_json.endswith('.json'), 'The input JSON file is missing!'
+
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    transforms_data = load_from_json( Path(args.input_json) )
+    exposures_data = load_from_json( Path(args.input_json).parent / 'exposures.json' )
+    
+    
+    selected_left_frames = []
+    selected_right_frames = []
+    new_exposures = dict()
+    
+    os.makedirs(os.path.join(args.output_dir, 'images'), exist_ok=True)
+    os.makedirs(os.path.join(args.output_dir, 'masks'), exist_ok=True)
+    for i, frame in enumerate(transforms_data['frames']):
+        file_path = frame['file_path'] # e.g. images/left_e1_output_0010_0_linear.exr (frame num is 0010)
+        mask_path = str(Path(args.input_json).parent / frame['mask_path']) # e.g. masks/left_e1_output_0010_0.png
+        # get frame number
+        frame_num = int(re.search(r'(\d{4})', file_path).group(0))
+        direction = re.search(r'(left|right)', file_path, re.IGNORECASE).group(0).lower()
+
+        if int(frame_num) >= args.min_frame_number:
+            if direction == 'left':
+                if len(selected_left_frames) < args.frames_per_direction and is_mask_all_valid(mask_path):
+                    selected_left_frames.append(frame)
+                    
+                    shutil.copy(str(Path(args.input_json).parent / file_path), os.path.join(args.output_dir, file_path))
+                    shutil.copy(str(Path(args.input_json).parent / file_path.replace('_linear.exr', '.png')), os.path.join(args.output_dir, file_path.replace('_linear.exr', '.png')))
+                    shutil.copy(mask_path, os.path.join(args.output_dir, frame['mask_path']))
+                    shutil.copy(mask_path.replace('.png', '_saturation_mask.png'), os.path.join(args.output_dir, frame['mask_path'].replace('.png', '_saturation_mask.png')))
+
+                    new_exposures[file_path] = exposures_data[file_path]
+            else:
+                if len(selected_right_frames) < args.frames_per_direction and is_mask_all_valid(mask_path):
+                    selected_right_frames.append(frame)
+                            
+                    shutil.copy(str(Path(args.input_json).parent / file_path), os.path.join(args.output_dir, file_path))
+                    shutil.copy(str(Path(args.input_json).parent / file_path.replace('_linear.exr', '.png')), os.path.join(args.output_dir, file_path.replace('_linear.exr', '.png')))
+                    shutil.copy(mask_path, os.path.join(args.output_dir, frame['mask_path']))
+                    shutil.copy(mask_path.replace('.png', '_saturation_mask.png'), os.path.join(args.output_dir, frame['mask_path'].replace('.png', '_saturation_mask.png')))
+
+                    new_exposures[file_path] = exposures_data[file_path]
+
+        
+    new_transforms_data = {}
+    all_keys = transforms_data.keys()
+    for key in all_keys:
+        new_transforms_data[key] = transforms_data[key]
+    
+    new_transforms_data['frames'] = selected_left_frames + selected_right_frames
+
+    num_images_per_direction = len(selected_left_frames)
+
+    num_train_images = math.ceil(num_images_per_direction * args.train_split_fraction)
+    num_eval_test_images = num_images_per_direction - num_train_images
+    i_all = np.arange(num_images_per_direction)
+    i_train = np.linspace(
+        0, num_images_per_direction - 1, num_train_images, dtype=int
+    )  # equally spaced training images starting and ending at 0 and num_images-1
+    i_eval_test = np.setdiff1d(i_all, i_train)  # eval images are the remaining images
+    assert len(i_eval_test) == num_eval_test_images
+
+    num_eval_images = math.ceil(num_images_per_direction * args.eval_split_fraction)
+    num_test_images = num_eval_test_images - num_train_images
+    i_eval = np.linspace(
+        0, num_eval_test_images - 1, num_eval_images, dtype=int
+    )  # equally spaced training images starting and ending at 0 and num_images-1
+    i_eval = i_eval_test[i_eval]
+    i_test = np.setdiff1d(i_eval_test, i_eval)  # eval images are the remaining images
+    assert len(i_eval) == num_eval_images
+
+
+    # has_split_files_spec = any(f"{split}_filenames" in meta for split in ("train", "val", "test"))
+
+    train_list = []
+    for i in i_train:
+        train_list.append(selected_left_frames[i]["file_path"])
+        train_list.append(selected_right_frames[i]["file_path"])
+    new_transforms_data["train_filenames"] = train_list
+
+    test_list = []
+    for i in i_test:
+        test_list.append(selected_left_frames[i]["file_path"])
+        test_list.append(selected_right_frames[i]["file_path"])
+    new_transforms_data["test_filenames"] = test_list
+
+    eval_list = []
+    for i in i_eval:
+        eval_list.append(selected_left_frames[i]["file_path"])
+        eval_list.append(selected_right_frames[i]["file_path"])
+    new_transforms_data["val_filenames"] = eval_list
+    
+    with open(os.path.join(args.output_dir, 'transforms.json'), "w") as outfile: 
+        json.dump(new_transforms_data, outfile, indent=4)
+    
+    with open(os.path.join(args.output_dir, 'exposures.json'), "w") as outfile:
+        json.dump(new_exposures, outfile, indent=4)
+    
+    print("All done!")