ZhengGuangze/Flock4D

--- license: cc-by-nc-sa-4.0 --- # Flock4D (tar.gz format) This dataset contains the Flock4D dataset converted to the VLBM/Flock4D-compatible format. To facilitate easier downloading and storage, the 1000 sequences have been compressed into `.tar.gz` archives in chunks of 50 sequences per archive. ## Dataset Description - **Source**: Flock4D - **Format**: VLBM / Flock4D-compatible per-sequence layout compressed into tar.gz chunks - **Contents**: RGB images, dense depth maps, 2D/3D trajectories, camera intrinsics and extrinsics, visibility masks, and scene metadata ### Scale Flock4D provides 1000 sequences of birds (flocks) flying in various environments. There are 24 species of birds included: `cannada_goose`, `common_starling`, `cormorant`, `crane`, `crested_bis`, `crow`, `dove`, `duck`, `dunlin`, `eagle`, `egret`, `flamingo`, `jackdaw`, `mallard`, `parrot`, `pelican`, `pigeon`, `red_billed_starling`, `seagull`, `snow_goose`, `stork`, `swallow`, `tit`, `warbler`. ## Dataset Structure The original dataset is structured by sequence. In this Hugging Face repository, the sequences are grouped and compressed into tarballs (e.g., `flock4d_00000_00049.tar.gz`). After extracting a `.tar.gz` archive, each sequence directory follows this layout: ``` {Species}_{Background}_4k_{ID}/ ├── rgbs/ │ ├── rgb_00000.jpg │ ├── rgb_00001.jpg │ └── ... ├── depths/ │ ├── depth_00000.npz │ ├── depth_00001.npz │ └── ... ├── intrinsics.npy ├── extrinsics.npy ├── trajs_2d.npy ├── trajs_3d.npy ├── visibilities.npy └── scene_info.json ``` ### File Descriptions - `rgbs/`: RGB frames saved as JPEG (`rgb_XXXXX.jpg`). - `depths/`: Dense depth maps saved as compressed NumPy archives (`depth_XXXXX.npz`). Each archive stores a float16 array. - `intrinsics.npy`: Camera intrinsic matrices for each frame `(T, 3, 3)`. - `extrinsics.npy`: World-to-camera extrinsic matrices (W2C) for each frame `(T, 4, 4)`. - `trajs_2d.npy`: 2D trajectories `(T, N, 2)` -- pixel coordinates (x, y). - `trajs_3d.npy`: 3D trajectories `(T, N, 3)` -- world-space coordinates (x, y, z); zero-filled where invisible. - `visibilities.npy`: Visibility flags `(T, N)` (1.0 visible, 0.0 not visible). - `scene_info.json`: JSON file with per-sequence metadata, including camera properties and scene assets. ## Usage Example (Python) To use the dataset, first download the tarballs and extract them: ```bash mkdir -p data/flock4d tar -xvf flock4d_00000_00049.tar.gz -C data/flock4d/ ``` Then load the annotations in Python: ```python import numpy as np from PIL import Image from pathlib import Path import json seq_dir = Path("data/flock4d/cannada_goose_abandoned_parking_4k_313") # Load annotations trajs_2d = np.load(seq_dir / "trajs_2d.npy") # (T, N, 2) trajs_3d = np.load(seq_dir / "trajs_3d.npy") # (T, N, 3) vis = np.load(seq_dir / "visibilities.npy") # (T, N) intrinsics = np.load(seq_dir / "intrinsics.npy") # (T, 3, 3) extrinsics = np.load(seq_dir / "extrinsics.npy") # (T, 4, 4) # Load context frame_idx = 0 rgb = Image.open(seq_dir / "rgbs" / f"rgb_{frame_idx:05d}.jpg") depth_npz = np.load(seq_dir / "depths" / f"depth_{frame_idx:05d}.npz") depth = depth_npz['depth'] # float16 array (H, W) # Load scene info with open(seq_dir / "scene_info.json", 'r') as f: scene_info = json.load(f) print(scene_info) ```--- 许可协议：知识共享署名-非商业性使用-相同方式共享4.0（CC BY-NC-SA 4.0） --- # Flock4D（tar.gz格式） 本数据集为转换为VLBM/Flock4D兼容格式的Flock4D数据集。为便于下载与存储，原1000条序列被按每包50条的规格打包压缩为.tar.gz分卷归档。 ## 数据集说明 - **数据集来源**：Flock4D - **数据格式**：采用VLBM/Flock4D兼容的单序列组织结构，并打包为.tar.gz分卷压缩包 - **数据内容**：包含RGB图像、稠密深度图、二维/三维轨迹、相机内参与外参、可见性掩码以及场景元数据 ### 数据集规模 Flock4D包含1000条鸟类（鸟群）在不同环境中飞行的序列数据，涵盖24种鸟类，分别为：加拿大雁（cannada_goose）、普通椋鸟（common_starling）、鸬鹚（cormorant）、鹤（crane）、凤头朱鹮（crested_bis）、乌鸦（crow）、斑鸠（dove）、鸭（duck）、黑腹滨鹬（dunlin）、鹰（eagle）、白鹭（egret）、火烈鸟（flamingo）、寒鸦（jackdaw）、绿头鸭（mallard）、鹦鹉（parrot）、鹈鹕（pelican）、家鸽（pigeon）、红嘴椋鸟（red_billed_starling）、海鸥（seagull）、雪雁（snow_goose）、鹳（stork）、燕子（swallow）、山雀（tit）、莺（warbler）。 ## 数据集组织结构 原始数据集按序列进行组织。在本Hugging Face仓库中，所有序列被分组打包为分卷压缩包（示例命名格式为`flock4d_00000_00049.tar.gz`）。解压.tar.gz归档文件后，单个序列目录的组织结构如下： {Species}_{Background}_4k_{ID}/ ├── rgbs/ │ ├── rgb_00000.jpg │ ├── rgb_00001.jpg │ └── ... ├── depths/ │ ├── depth_00000.npz │ ├── depth_00001.npz │ └── ... ├── intrinsics.npy ├── extrinsics.npy ├── trajs_2d.npy ├── trajs_3d.npy ├── visibilities.npy └── scene_info.json ### 文件说明 - `rgbs/`：存储RGB帧，格式为JPEG，命名规则为`rgb_XXXXX.jpg`。 - `depths/`：存储稠密深度图，格式为压缩NumPy归档文件（.npz），每个归档文件存储一个float16类型的数组。 - `intrinsics.npy`：存储每一帧的相机内参矩阵，维度为`(T, 3, 3)`。 - `extrinsics.npy`：存储每一帧的世界到相机的外参矩阵（W2C），维度为`(T, 4, 4)`。 - `trajs_2d.npy`：存储二维轨迹，维度为`(T, N, 2)`，对应像素坐标(x, y)。 - `trajs_3d.npy`：存储三维轨迹，维度为`(T, N, 3)`，对应世界坐标系下的坐标(x, y, z)；不可见区域的坐标以零填充。 - `visibilities.npy`：存储可见性标记，维度为`(T, N)`，1.0表示可见，0.0表示不可见。 - `scene_info.json`：包含单序列元数据的JSON文件，涵盖相机参数与场景资源信息。 ## Python使用示例 如需使用本数据集，请先下载分卷压缩包并解压： bash mkdir -p data/flock4d tar -xvf flock4d_00000_00049.tar.gz -C data/flock4d/ 随后在Python中加载数据： python import numpy as np from PIL import Image from pathlib import Path import json seq_dir = Path("data/flock4d/cannada_goose_abandoned_parking_4k_313") # 加载标注数据 trajs_2d = np.load(seq_dir / "trajs_2d.npy") # (T, N, 2) trajs_3d = np.load(seq_dir / "trajs_3d.npy") # (T, N, 3) vis = np.load(seq_dir / "visibilities.npy") # (T, N) intrinsics = np.load(seq_dir / "intrinsics.npy") # (T, 3, 3) extrinsics = np.load(seq_dir / "extrinsics.npy") # (T, 4, 4) # 加载单帧数据 frame_idx = 0 rgb = Image.open(seq_dir / "rgbs" / f"rgb_{frame_idx:05d}.jpg") depth_npz = np.load(seq_dir / "depths" / f"depth_{frame_idx:05d}.npz") depth = depth_npz['depth'] # float16 数组，维度为(H, W) # 加载场景元数据 with open(seq_dir / "scene_info.json", 'r') as f: scene_info = json.load(f) print(scene_info)