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from collections import defaultdict
import numpy as np
import random
def fix_seed(seed):
random.seed(seed)
np.random.seed(seed)
def collect_video_paths(base_path):
"""
Traverses the base_path directory, collecting video file paths
organized by class folder names.
Args:
base_path (str): The root directory containing class subfolders.
Returns:
dict: A dictionary mapping each class name to a list of video file paths.
"""
video_dict = defaultdict(list)
if not os.path.exists(base_path):
raise FileNotFoundError(f"Directory not found: {base_path}")
for class_name in os.listdir(base_path):
class_dir = os.path.join(base_path, class_name)
if os.path.isdir(class_dir):
for video_file in os.listdir(class_dir):
video_path = os.path.join(class_dir, video_file)
video_dict[class_name].append(video_path)
return video_dict
def separate_by_class(video_dict):
"""
Reorganizes video_dict into a nested dictionary:
{
class_name: {
video_key: [list of video paths]
}
}
where video_key is derived from the filename (e.g., 'abc_001.mp4' → 'abc')
"""
all_class_dict = defaultdict(lambda: defaultdict(list))
for video_class, videos in video_dict.items():
for v in videos:
video_name = os.path.basename(v)
split_video_name = video_name.split("_")
len_split = len(split_video_name)
video_key = "_".join(split_video_name[:min(len_split, 4) - 1])
all_class_dict[video_class][video_key].append(v)
sorted_class_dict = {}
for cls in sorted(all_class_dict.keys(), key=str):
inner_dict = all_class_dict[cls]
sorted_inner_dict = dict(sorted(inner_dict.items(), key=lambda x: str(x[0])))
sorted_class_dict[cls] = sorted_inner_dict
return sorted_class_dict
def get_train_val_test(video_list_class, split_configuration=[0.9, 0.05, 0.05]):
train_val_test_dict = defaultdict(dict)
for video_class, video_keys in video_list_class.items():
# Shuffle video keys
tmp_video_key = list(video_keys)
random.shuffle(tmp_video_key)
n_total = len(tmp_video_key)
n_train = round(split_configuration[0] * n_total)
n_val = round(split_configuration[1] * n_total)
# Make sure all samples are used, including remainder
n_test = n_total - n_train - n_val
# Split the shuffled list
train = tmp_video_key[:n_train]
val = tmp_video_key[n_train:n_train + n_val]
test = tmp_video_key[n_train + n_val:n_train + n_val + n_test]
train_val_test_dict[video_class] = {
"train": train,
"val": val,
"test": test
}
return train_val_test_dict
def collect_videos(out_data, full_video_dict, train_list, val_list, test_list):
for video_class, splits in out_data.items():
for split_name, video_keys in splits.items():
for video_key in video_keys:
video_paths = full_video_dict[video_class][video_key]
if split_name == "train":
train_list.extend(video_paths)
elif split_name == "val":
val_list.extend(video_paths)
elif split_name == "test":
test_list.extend(video_paths)
def write_txt(file_path, video_list):
with open(file_path, "w") as f:
for path in video_list:
video_name = ".".join(os.path.basename(path).split(".")[:-1])
f.write(f"{video_name}\n")
def _print_inspect(video_list_class):
for video_class, values in video_list_class.items():
print(f"### {video_class} ### with total {len(values)}")
for video_key in values:
print(video_key, len(values[video_key]))
if __name__ == "__main__":
fix_seed(11293)
nas_path = "/mnt/nas192"
train_path = os.path.join(
nas_path,
"Research_materials/PIA_clip_dataset/CLIP4Clip_format/PIA_clip_outdoor_v2/original_train_set_processed"
)
val_test_path = os.path.join(
nas_path,
"Research_materials/PIA_clip_dataset/CLIP4Clip_format/PIA_clip_outdoor_v2/original_val_test_set_processed"
)
video_list_train = collect_video_paths(train_path)
video_list_class_train = separate_by_class(video_list_train)
video_list_val_test = collect_video_paths(val_test_path)
video_list_class_val_test = separate_by_class(video_list_val_test)
split_configuration = {
"video_list_class_train": [0.94, 0.03, 0.03],
"video_list_class_val_test": [0.9, 0.05, 0.05]
}
out_train = get_train_val_test(video_list_class_train, split_configuration["video_list_class_train"])
out_val_test = get_train_val_test(video_list_class_val_test, split_configuration["video_list_class_val_test"])
train_list = []
val_list = []
test_list = []
collect_videos(out_train, video_list_class_train, train_list, val_list, test_list)
print(f"Length Train {len(train_list)} {len(val_list)} {len(test_list)}")
collect_videos(out_val_test, video_list_class_val_test, train_list, val_list, test_list)
print(f"Length Train Val Test {len(train_list)} {len(val_list)} {len(test_list)}")
# txt_out_dir = "./"
# write_txt(os.path.join(txt_out_dir, "train_list.txt"), train_list)
# write_txt(os.path.join(txt_out_dir, "val_list.txt"), val_list)
# write_txt(os.path.join(txt_out_dir, "test_list.txt"), test_list)
# print("Saved train.txt, val.txt, and test.txt in ./splits")
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