Seperate stages of Pandora with flags + PanoHDRNeRF pre-procesing

lantern_scripts/combine_LDR_HDR.py

@@ -5,9 +5,9 @@ from envmap import EnvironmentMap, rotation_matrix
 from hdrio import imwrite
 
 # Outdoor Table
-LDR_dir = '/mnt/data/arch_level1/panoramas_first_100/'
-HDR_dir = '/mnt/data/arch_level1/panoramas_256x512_hdr/'
-output_dir = '/mnt/data/arch_level1/panoramas_LDR_HDR_small/'
+LDR_dir = '/mnt/data/nerfstudio_ds/real_data/lab_downstairs_data/left_e1_linearized/'
+HDR_dir = '/mnt/data/nerfstudio_ds/real_data/lab_downstairs_data/left_e1_ldr2hdr_network/'
+output_dir = '/mnt/data/nerfstudio_ds/real_data/lab_downstairs_data/left_e1_ldr2hdr_network_high_res/'
 
 imgFiles = [os.path.join(LDR_dir, f) for f in os.listdir(LDR_dir) if os.path.isfile(os.path.join(LDR_dir, f)) and f.endswith('.png')]
 imgFiles.sort()
@@ -21,7 +21,7 @@ for ldr_addr in tqdm(imgFiles):
     hdr_pano = EnvironmentMap(hdr_addr, 'latlong')
     hdr_pano.resize(1920)
 
-    out_pano = ldr_pano.data ** 2.2
+    out_pano = ldr_pano.data # ** 2.2
     list_hdr = hdr_pano.data > 1.0
     print("list_hdr.shape: ", list_hdr.shape, "list_hdr.shape: ",  ldr_pano.data.shape)
     out_pano[list_hdr] = hdr_pano.data[list_hdr]

lantern_scripts/train_test_split.py

@@ -0,0 +1,80 @@
+import argparse
+import os
+from tqdm import tqdm
+from pathlib import Path
+import json
+import numpy as np
+import math
+
+
+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)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_json', type=str)
+    parser.add_argument('--output_json', type=str)
+    parser.add_argument('--train_split_fraction', type=float, default= 0.8)
+    parser.add_argument('--eval_split_fraction', type=float, default= 0.1)
+    args = parser.parse_args()
+
+    assert os.path.isfile(args.input_json) and args.input_json.endswith('.json'), 'The input JSON file is missing!'
+
+    json_data = load_from_json( Path(args.input_json) )
+    
+    
+    new_json_data = {}
+    all_keys = json_data.keys()
+    for key in all_keys:
+        new_json_data[key] = json_data[key]
+
+
+    num_images = len(json_data['frames'])
+    num_train_images = math.ceil(num_images * args.train_split_fraction)
+    num_eval_test_images = num_images - num_train_images
+    i_all = np.arange(num_images)
+    i_train = np.linspace(
+        0, num_images - 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 * 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(json_data['frames'][i]["file_path"])
+    new_json_data["train_filenames"] = train_list
+
+    test_list = []
+    for i in i_test:
+        test_list.append(json_data['frames'][i]["file_path"])
+    new_json_data["test_filenames"] = test_list
+
+    eval_list = []
+    for i in i_eval:
+        eval_list.append(json_data['frames'][i]["file_path"])
+    new_json_data["val_filenames"] = eval_list
+    
+    with open(args.output_json, "w") as outfile: 
+        json.dump(new_json_data, outfile, indent=4)
+    
+    print("All done!")