diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..896399b6cd082d3cf04572fd57ec9a503892c22d --- /dev/null +++ b/.gitattributes @@ -0,0 +1,61 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.avro filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text +train/metadata.jsonl filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d4acb129404bed3873b24577d20f7aee8b2e35fb --- /dev/null +++ b/README.md @@ -0,0 +1,149 @@ +--- +license: cc-by-4.0 +configs: +- config_name: default + data_files: + - split: train + path: data/train-* +dataset_info: + features: + - name: image_id + dtype: string + - name: image + dtype: image + - name: resolution + list: int32 + - name: fx + dtype: float64 + - name: fy + dtype: float64 + - name: cx + dtype: float64 + - name: cy + dtype: float64 + - name: camera_view_matrix + list: + list: float64 + - name: camera_projection_matrix + list: + list: float64 + - name: labels + list: string + - name: bbox_2d_loose + list: + list: float64 + - name: size_local + list: + list: float64 + - name: center_local + list: + list: float64 + - name: local_to_world_transform + list: + list: + list: float64 + splits: + - name: train + num_bytes: 5647010348 + num_examples: 12040 + download_size: 5485176011 + dataset_size: 5647010348 +task_categories: +- object-detection +tags: +- robotics +- 6d-pose-estimation +- agriculture +- strawberry +--- + +# Straw6D: In-Field 6D Pose Dataset for Robotic Strawberry Harvesting + +This dataset accompanies the paper: + +> **From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting** +> Woojung Son, Won Suk Lee, Zijing Huang, Daeun Choi, Catia Silva, Yu She, Yan Gu +> University of Florida & Purdue University +> [[arXiv]](https://arxiv.org/abs/2606.11381) + +## Overview + +To the best of our knowledge, Straw6D is the **first real-world 6D pose ground truth dataset of strawberries collected in actual agricultural fields**. Collecting 6D pose ground truth in real agricultural environments is inherently challenging; prior work has largely relied on synthetic data with limited realism. This dataset bridges that gap by providing per-frame 6D pose annotations paired with RGB images captured directly in the field. + +- **12,040** RGB images with **16,037** annotated strawberry instances +- **Single category**: `strawberry` (red-stage / ripe only) +- Recorded using an Intel RealSense D435i camera + +## Annotation Pipeline + +Ground truth 6D poses were obtained through a multi-step process: +1. Checkerboard-based camera calibration (PnP algorithm) +2. Metric-scale 3D reconstruction via COLMAP +3. Manual 3D bounding box annotation on reconstructed point clouds +4. 6D pose derived from camera-to-world and local-to-world transformations + +## Dataset Structure + +Each sample provides image, per-frame camera parameters, and per-object pose annotations. + +**Camera fields** (per frame): + +| Field | Description | +|---|---| +| `image_id` | Zero-padded frame index (e.g. `000042`) | +| `image` | RGB image | +| `resolution` | [width, height] in pixels | +| `fx`, `fy`, `cx`, `cy` | Camera intrinsics in pixels | +| `camera_view_matrix` | 4×4 world-to-camera transform | +| `camera_projection_matrix` | 4×4 OpenGL projection matrix | + +**Object fields** (list, one entry per object in the frame): + +| Field | Description | +|---|---| +| `labels` | Category label (`"strawberry"`) | +| `bbox_2d_loose` | 2D bounding box [x_min, y_min, x_max, y_max] in pixels | +| `size_local` | Object size [x, y, z] in meters | +| `center_local` | Object center in local space | +| `local_to_world_transform` | 4×4 object-local-to-world transform | + +All 4×4 matrices are **row-major**: `p_out = p_in @ M`. + +## Usage + +```python +from datasets import load_dataset + +ds = load_dataset("WoojungSon/Straw6D") +sample = ds["train"][0] + +print(sample["image_id"]) # "000000" +print(sample["labels"]) # ["strawberry"] +sample["image"] # PIL Image +``` + +## Visualization + +A visualization script is provided in `scripts/visualization.py`. It projects the 6D pose axes onto the RGB image: + +```bash +python scripts/visualization.py \ + --image path/to/rgb/000000.png \ + --json path/to/json/000000.json \ + --output output.png +``` + +## Citation + +```bibtex +@article{son2025straw6d, + title={From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting}, + author={Son, Woojung and Lee, Won Suk and Huang, Zijing and Choi, Daeun and Silva, Catia and She, Yu and Gu, Yan}, + journal={arXiv preprint arXiv:2606.11381}, + year={2025} +} +``` + +## License + +This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. diff --git a/scripts/visualization.py b/scripts/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..741a9d7fc11238395e483fff5f76dbbefe58e210 --- /dev/null +++ b/scripts/visualization.py @@ -0,0 +1,146 @@ +""" +Visualize 6D pose annotations by projecting local frame axes onto RGB images. + +Coordinate conventions +---------------------- +- All matrices are **row-major** (Isaac Sim / USD convention). + Transforms are applied as: p_out = p_in @ M (row vector on the left) + +- World / object space uses a **right-handed, Y-up** frame (OpenGL convention). + +X : right + +Y : up + -Z : into the scene (camera looks toward -Z in camera space) + +- Camera space also follows **OpenGL**: + +X : right, +Y : up, -Z : into the scene + +- Screen space follows **OpenCV / image** convention: + +u : right, +v : down, origin at top-left + + Conversion from OpenGL camera space → OpenCV screen space: + x_cv = x_gl + y_cv = -y_gl (flip Y) + z_cv = -z_gl (flip Z; positive depth is in front) + +Matrix fields in the JSON +-------------------------- +- camera_view_matrix (4x4, row-major) : world → camera +- local_to_world_transform (4x4, row-major) : object-local → world +- intrinsics : {fx, fy, cx, cy} in pixels +""" + +import json +import numpy as np +from PIL import Image, ImageDraw +import argparse + + +def project_world_point_to_screen(world_point, view_matrix, intrinsics): + """Project a homogeneous world-space point to pixel coordinates. + + Uses row-major convention: cam = world_point @ view_matrix. + Converts OpenGL camera space (y-up, -z forward) to OpenCV screen space (y-down, +z forward). + Returns None if the point is behind the camera (z_cv <= 0). + """ + p = np.array([*world_point[:3], 1.0]) if len(world_point) == 3 else np.array(world_point) + cam = p @ view_matrix # row-major: point on the left + x, y, z = cam[0], -cam[1], -cam[2] # OpenGL → OpenCV axis flip + if z <= 0: + return None + u = intrinsics['fx'] * x / z + intrinsics['cx'] + v = intrinsics['fy'] * y / z + intrinsics['cy'] + return round(u), round(v) + + +def draw_local_frame_axes(draw, local_to_world_transform, camera_view_matrix, intrinsics, size_local, origin_local, axes_length_perc=1.5): + """Draw X/Y/Z axes of the object's local frame onto the image. + + Axis length is scaled by the mean object size * axes_length_perc. + Pipeline: local → world (@ L), world → screen (project_world_point_to_screen). + """ + L = np.array(local_to_world_transform) # row-major local→world (4x4) + V = np.array(camera_view_matrix) # row-major world→camera (4x4) + ax_len = np.mean(size_local) * axes_length_perc + ox, oy, oz = origin_local + + points_local = { + 'origin': [ox, oy, oz, 1], + 'x': [ox + ax_len, oy, oz, 1], + 'y': [ox, oy + ax_len, oz, 1], + 'z': [ox, oy, oz + ax_len, 1], + } + pts2d = {k: project_world_point_to_screen(np.array(v) @ L, V, intrinsics) + for k, v in points_local.items()} + + o = pts2d['origin'] + for key, color in [('x', 'red'), ('y', 'green'), ('z', 'blue')]: + if o is not None and pts2d[key] is not None: + draw.line([o, pts2d[key]], fill=color, width=5) + + +def draw_world_frame_axes_bottom_left(draw, camera_view_matrix, intrinsics, screen_size, axes_scale=0.1, margin_percentage=0.05): + """Draw world-frame X/Y/Z axes in the bottom-left corner as a reference gizmo. + + Places a virtual origin 1 unit in front of the camera (OpenGL: z=-1 in camera space), + converts it to world space via the inverse view matrix, then re-projects to screen. + The axes are offset so they appear anchored to the bottom-left corner. + """ + V = np.array(camera_view_matrix) + V_inv = np.linalg.inv(V) + # z=-1 in OpenGL camera space = 1 unit in front of the camera + origin_world = np.array([0, 0, -1.0, 1]) @ V_inv # camera → world + + pts2d = {} + pts2d['origin'] = project_world_point_to_screen(origin_world, V, intrinsics) + for key, delta in [('x', [axes_scale, 0, 0, 0]), ('y', [0, axes_scale, 0, 0]), ('z', [0, 0, axes_scale, 0])]: + pts2d[key] = project_world_point_to_screen(origin_world + np.array(delta), V, intrinsics) + + if any(v is None for v in pts2d.values()): + return + + # Shift projected axes to bottom-left corner + margin = int(margin_percentage * min(screen_size)) + all_x = [pts2d[k][0] for k in pts2d] + all_y = [pts2d[k][1] for k in pts2d] + ox = margin - min(all_x) + oy = screen_size[1] - margin - max(all_y) + + o = (pts2d['origin'][0] + ox, pts2d['origin'][1] + oy) + for key, color in [('x', 'red'), ('y', 'green'), ('z', 'blue')]: + end = (pts2d[key][0] + ox, pts2d[key][1] + oy) + draw.line([o, end], fill=color, width=3) + + +def main(image_path, json_path, output_path): + rgb_img = Image.open(image_path) + draw = ImageDraw.Draw(rgb_img) + + with open(json_path, 'r') as f: + data = json.load(f) + + camera_data = data["camera_data"] + V = camera_data["camera_view_matrix"] # row-major world→camera (4x4) + intrinsics = camera_data["intrinsics"] # {fx, fy, cx, cy} in pixels + screen_size = tuple(camera_data["resolution"]) # (width, height) + + for obj in data["objects"]: + b = obj["bbox_3d_local"] + size_local = [b["x_max"] - b["x_min"], b["y_max"] - b["y_min"], b["z_max"] - b["z_min"]] + center_local = [(b["x_min"] + b["x_max"]) / 2, (b["y_min"] + b["y_max"]) / 2, (b["z_min"] + b["z_max"]) / 2] + + draw_local_frame_axes(draw, obj["local_to_world_transform"], V, intrinsics, size_local, center_local) + + draw_world_frame_axes_bottom_left(draw, V, intrinsics, screen_size) + + rgb_img.save(output_path) + print(f"Overlay image saved to: {output_path}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--image", type=str, required=True) + parser.add_argument("--json", type=str, required=True) + parser.add_argument("--output", type=str, required=True) + args = parser.parse_args() + + main(args.image, args.json, args.output) diff --git a/test/metadata.jsonl b/test/metadata.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b78163e319b5c0124dd985efb6c9fb0c9c5034ed --- /dev/null +++ b/test/metadata.jsonl @@ -0,0 +1,1043 @@ +{"file_name": "plant_000/000.png", "image_id": "000", "resolution": [638, 478], "fx": 623.6826339840109, "fy": 623.6826339840109, "cx": 319.0, "cy": 239.0, "camera_view_matrix": [[-0.8579194322550956, -0.07316190044422927, 0.5085485071086961, 0.0], [-0.043931772870208455, 0.9966303881031564, 0.06926665028592568, 0.0], [-0.5119025757813447, 0.03708376777973078, -0.8582427087227037, 0.0], [0.17945640774315508, -0.10705942549511012, -0.5194469479373642, 1.0]], "camera_projection_matrix": [[1.9551179748715075, 0.0, 0.0, 0.0], [0.0, 2.609550769807577, 0.0, 0.0], [0.0, 0.0, 0.0, -1.0], [0.0, 0.0, 0.05, 0.0]], "labels": ["strawberry"], "bbox_2d_loose": [[0.00031900000000106843, 0.0, 55.52609699999999, 50.169923999999995]], "size_local": [[0.0405, 0.0405, 0.056]], "center_local": [[0.0, 0.0, 0.0]], "local_to_world_transform": [[[-0.49513403390905397, -0.4304134462345795, 0.7547095823993627, 0.0], [-0.6560590264866261, 0.7547095823993627, 0.0, 0.0], [0.5695865537654204, 0.49513403390905397, 0.6560590264866261, 0.0], [0.37383599, 0.2424453, 0.00361553, 1.0]]]} +{"file_name": "plant_000/001.png", "image_id": "001", "resolution": [642, 480], "fx": 632.7028712251207, "fy": 632.7028712251207, "cx": 321.0, "cy": 240.0, "camera_view_matrix": [[-0.9809593254116276, -0.19421326598115043, -9.594217250279667e-05, 0.0], [-0.19245885378511687, 0.97203140859439, 0.13459023109321874, 0.0], [-0.026045949544690285, 0.1320460072207484, -0.9909013374142622, 0.0], [0.28726994499457853, -0.04126034341353107, -0.397461544150389, 1.0]], "camera_projection_matrix": [[1.9710369820097218, 0.0, 0.0, 0.0], [0.0, 2.636261963438003, 0.0, 0.0], [0.0, 0.0, 0.0, -1.0], [0.0, 0.0, 0.05, 0.0]], "labels": ["strawberry"], "bbox_2d_loose": [[70.820625, 6.580079999999995, 141.029103, 58.05]], "size_local": [[0.0405, 0.0405, 0.056]], "center_local": [[0.0, 0.0, 0.0]], "local_to_world_transform": [[[-0.49513403390905397, -0.4304134462345795, 0.7547095823993627, 0.0], [-0.6560590264866261, 0.7547095823993627, 0.0, 0.0], [0.5695865537654204, 0.49513403390905397, 0.6560590264866261, 0.0], [0.37383599, 0.2424453, 0.00361553, 1.0]]]} +{"file_name": "plant_000/002.png", "image_id": "002", "resolution": [631, 473], "fx": 543.6286117516593, "fy": 543.6286117516593, "cx": 315.5, "cy": 236.5, "camera_view_matrix": [[-0.8247589295953677, 0.46972230675873866, -0.3148549865985183, 0.0], [0.39666564641268603, 0.8773910537647898, 0.26989128131438, 0.0], [0.40302490370783495, 0.0977030874986114, -0.9099588087845125, 0.0], [0.10695962800025523, -0.17390304417360242, -0.452163822655799, 1.0]], "camera_projection_matrix": [[1.723070084791313, 0.0, 0.0, 0.0], [0.0, 2.2986410644890456, 0.0, 0.0], [0.0, 0.0, 0.0, -1.0], [0.0, 0.0, 0.05, 0.0]], "labels": ["strawberry"], "bbox_2d_loose": [[193.81543599999998, -0.00023650000000063898, 228.096404, 13.1553125]], "size_local": [[0.0405, 0.0405, 0.056]], "center_local": [[0.0, 0.0, 0.0]], "local_to_world_transform": [[[-0.49513403390905397, -0.4304134462345795, 0.7547095823993627, 0.0], [-0.6560590264866261, 0.7547095823993627, 0.0, 0.0], [0.5695865537654204, 0.49513403390905397, 0.6560590264866261, 0.0], [0.37383599, 0.2424453, 0.00361553, 1.0]]]} +{"file_name": "plant_000/003.png", "image_id": "003", "resolution": [634, 475], "fx": 691.9149127721394, "fy": 691.9149127721394, "cx": 317.0, "cy": 237.5, "camera_view_matrix": [[-0.7524041504539607, 0.5730633986874737, -0.32478659988431713, 0.0], [0.5462825433508595, 0.8183733523532088, 0.17843889426996412, 0.0], [0.3680534977551092, -0.04316708517994623, -0.9288020378677564, 0.0], [-0.03542580286419639, -0.2304913630095267, -0.5476155901213504, 1.0]], "camera_projection_matrix": [[2.182696885716528, 0.0, 0.0, 0.0], [0.0, 2.913325948514271, 0.0, 0.0], [0.0, 0.0, 0.0, -1.0], [0.0, 0.0, 0.05, 0.0]], "labels": ["strawberry"], "bbox_2d_loose": [[95.436654, 18.129087500000004, 141.510702, 59.68161250000001]], "size_local": [[0.0405, 0.0405, 0.056]], "center_local": [[0.0, 0.0, 0.0]], "local_to_world_transform": [[[-0.49513403390905397, -0.4304134462345795, 0.7547095823993627, 0.0], [-0.6560590264866261, 0.7547095823993627, 0.0, 0.0], [0.5695865537654204, 0.49513403390905397, 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