Add advanced computer vision module for robotics AGI

[Copilot]

Adds a comprehensive vision/ Python package providing modular, lazily-loaded computer vision capabilities (detection, segmentation, depth, tracking, pose, VLM, motion, scene understanding) for a robotics AGI system, along with ROS2 integration, example demo scripts, and benchmark tooling.

Module structure

• detection/ — YOLODetector (ultralytics), DETRDetector (HF transformers), PersonDetector, HandDetector (MediaPipe), GraspDetector; shared BaseDetector with NMS
• segmentation/ — SemanticSegmentor (DeepLabV3+), InstanceSegmentor (Mask R-CNN), SAMSegmentor, PanopticSegmentor
• depth/ — MonocularDepthEstimator (MiDaS), DepthAnythingEstimator, StereoDepthEstimator, PointCloudGenerator
• tracking/ — MultiObjectTracker (Kalman filter), ByteTracker, DeepSORTTracker, ReIDModel
• pose/ — HumanPoseEstimator (17 keypoints, fall detection), ObjectPoseEstimator (6-DoF PnP), HandPoseEstimator (gestures), Pose3DEstimator
• features/ — FeatureExtractor (ORB/SIFT/AKAZE/deep), FeatureMatcher, VisualOdometry
• scene/ — SceneAnalyzer, AffordanceDetector, SceneGraph, SpatialRelations
• vlm/ — CLIPInterface, VisualQA (BLIP-2), ImageCaptioner (BLIP), VisualGrounding (OWL-ViT)
• motion/ — OpticalFlow (Farneback/LK), MotionSegmentor, MotionPredictor
• preprocessing/ — ImageEnhancer, Denoiser, SuperResolution
• utils/ — visualization helpers, transforms, metrics (mAP/MOTA/PCK), camera projection utilities
• models/ — ModelLoader with registry + disk cache, SimpleCNN, UNet, DetectionHead

Unified pipeline

All modules are accessible via a single lazy-loading entry point:

from vision import VisionPipeline

vision = VisionPipeline(enable_detection=True, enable_depth=True, device="cuda")

results = vision.process_frame(image)
# results.objects, results.depth_map, results.scene_description, ...

cup = vision.find_object("red cup")   # CLIP-guided retrieval
vision.answer("Is the door open?")    # VQA

ROS2 Integration (ros2_interface/)

• VisionNode — ROS2 node subscribing to /camera/image_raw; publishes to /vision/detections, /vision/segmentation, /vision/depth, /vision/poses, /vision/scene_description, /vision/pointcloud
• Services — /vision/find_object (text query → bbox + confidence) and /vision/analyze_scene (full scene JSON)
• ImageConverter — ROS Image ↔ numpy conversion with manual byte-level fallback when cv_bridge is absent
• PointCloudPublisher — Nx3 numpy array → sensor_msgs/PointCloud2 with explicit little-endian encoding
• Launch file, parameter YAML (vision_node_params.yaml), package.xml, and README included
• All ROS2 imports are guarded with try/except so the package is importable without a ROS2 installation

Example notebooks (notebooks/)

Seven standalone Python demo scripts covering every major module — detection, segmentation, depth, tracking, pose, vision-language, and the full unified pipeline. Scripts run without GPU using numpy-only synthetic images; missing ML dependencies are skipped gracefully.

Benchmarks (benchmarks/)

• benchmark_fps.py — measures FPS for each vision module with --device, --iterations, and --modules CLI args; outputs a results table
• benchmark_accuracy.py — computes mAP, MOTA, and PCK metrics on synthetic data

Supporting files

• vision/config/ — YAML configs for detection, segmentation, and depth defaults
• requirements.txt / setup.py — full dependency manifest
• tests/test_vision/ — 74 unit tests; heavy model paths mocked so the suite runs without GPU
• tests/test_ros2/ — 26 tests for VisionNode and ImageConverter with mocked rclpy/cv_bridge; 103 tests total
• .gitignore — excludes __pycache__, model weights, build artifacts

Original prompt

Add Advanced Computer Vision Functions for Robotics AGI

Objective

Enhance the Agentic AGI robotics system with comprehensive computer vision capabilities using PyTorch, OpenCV, and state-of-the-art vision models. This should provide robust visual perception for robots to understand and interact with their environment.

Technology Stack

• PyTorch - Deep learning framework
• OpenCV - Computer vision library
• torchvision - Pre-trained models and utilities
• Ultralytics - YOLOv8/YOLOv9 for object detection
• Segment Anything (SAM) - Advanced segmentation
• CLIP - Vision-language understanding
• DepthAnything - Monocular depth estimation
• Transformers - Vision transformers and models

Core Computer Vision Functions to Implement

1. Object Detection (vision/detection/)

Multi-Model Detection System

# detection/detector.py
- YOLOv8/v9 detector (real-time performance)
- Faster R-CNN (high accuracy)
- DETR (transformer-based detection)
- Custom object detector training pipeline
- Multi-scale detection
- Tracking integration (DeepSORT, ByteTrack)

Features:

• Real-time object detection (30+ FPS)
• Custom class training
• Bounding box regression
• Confidence scoring
• Non-maximum suppression (NMS)
• Multi-object tracking with unique IDs
• Object persistence across frames

Specific Detectors

• Person Detection: Face detection, pose estimation, person re-identification
• Hand Detection: Hand tracking and gesture recognition
• Grasp Detection: Robotic grasp point estimation
• Tool Detection: Recognize tools and manipulation objects

2. Image Segmentation (vision/segmentation/)

Semantic Segmentation

# segmentation/semantic.py
- DeepLabv3+ implementation
- U-Net for detailed segmentation
- Segment Anything Model (SAM) integration
- Real-time semantic segmentation
- Scene parsing (floor, walls, furniture, etc.)

Instance Segmentation

# segmentation/instance.py
- Mask R-CNN for instance masks
- YOLACT for real-time instance segmentation
- Panoptic segmentation
- Object boundary refinement

Interactive Segmentation

• Point-based segmentation (click to segment)
• Box-prompted segmentation
• Text-prompted segmentation with CLIP

3. Depth Estimation (vision/depth/)

# depth/depth_estimator.py
- Monocular depth estimation (DepthAnything, MiDaS)
- Stereo depth estimation
- Depth map refinement
- Point cloud generation from RGB-D
- 3D bounding box estimation
- Distance measurement to objects

Features:

• Real-time depth prediction
• Metric depth estimation
• Depth completion for sparse data
• Normal map generation
• 3D reconstruction

4. Object Tracking (vision/tracking/)

# tracking/tracker.py
- Multi-object tracking (MOT)
- DeepSORT integration
- ByteTrack implementation
- Re-identification models
- Kalman filtering for prediction
- Track lifecycle management
- Occlusion handling

Capabilities:

• Track multiple objects simultaneously
• Maintain object identity across occlusions
• Predict future positions
• Handle object entry/exit
• Cross-camera tracking

5. Pose Estimation (vision/pose/)

Human Pose Estimation

# pose/human_pose.py
- 2D pose estimation (OpenPose, MediaPipe, HRNet)
- 3D pose estimation
- Multi-person pose tracking
- Skeleton joint detection (17+ keypoints)
- Action recognition from poses
- Fall detection
- Gesture recognition

Object Pose Estimation

# pose/object_pose.py
- 6DoF object pose estimation
- PVN3D for 3D pose
- Template matching
- Point cloud registration
- AR marker detection

6. Visual Features (vision/features/)

# features/feature_extractor.py
- SIFT, SURF, ORB features
- Deep features (ResNet, ViT)
- Feature matching
- Homography estimation
- Image alignment
- Visual odometry
- Feature-based SLAM

7. Scene Understanding (vision/scene/)

# scene/scene_analyzer.py
- Scene classification
- Room layout estimation
- Affordance detection (where objects can be placed/grasped)
- Spatial relationships (on, in, next to, etc.)
- Scene graph generation
- Free space detection for navigation

Advanced Features:

• 3D scene reconstruction
• Semantic scene completion
• Object permanence tracking
• Scene change detection
• Anomaly detection

8. Vision-Language Models (vision/vlm/)

# vlm/clip_interface.py
- CLIP for vision-language understanding
- Zero-shot object classification
- Text-guided object detection
- Visual question answering (VQA)
- Image captioning
- Visual grounding (text to region mapping)

Capabilities:

• "Find the red cup" → locate object
• "Is the door open?" → answer questions
• "What's on the table?" → describe scene
• Natural language-based object retrieval

9. Optical Flow & Motion (vision/motion/)

# motion/optical_flow.py
- Dense optical flow (Farneback, RAFT)
- Sparse opti...

</details>



<!-- START COPILOT CODING AGENT SUFFIX -->

*This pull request was created from Copilot chat.*
>

<!-- START COPILOT CODING AGENT TIPS -->
---

✨ Let Copilot coding agent [set things up for you](https://github.com/Stacey77/rag7/issues/new?title=✨+Set+up+Copilot+instructions&body=Configure%20instructions%20for%20this%20repository%20as%20documented%20in%20%5BBest%20practices%20for%20Copilot%20coding%20agent%20in%20your%20repository%5D%28https://gh.io/copilot-coding-agent-tips%29%2E%0A%0A%3COnboard%20this%20repo%3E&assignees=copilot) — coding agent works faster and does higher quality work when set up for your repo.

[Stacey77]

@copilot choose next step

[Copilot]

@copilot choose next step

The vision module core is done. The remaining deliverables from the problem statement are ROS2 integration, example notebooks, and benchmarks. Implementing all three now — starting with the ROS2 vision nodes (the most critical for robotics), then notebooks and benchmark scripts.

Warning

Firewall rules blocked me from connecting to one or more addresses (expand for details)

I tried to connect to the following addresses, but was blocked by firewall rules:

• huggingface.co
  • Triggering command: /usr/bin/python3 python3 -m pytest tests/ -v --tb=short (dns block)

If you need me to access, download, or install something from one of these locations, you can either:

• Configure Actions setup steps to set up my environment, which run before the firewall is enabled
• Add the appropriate URLs or hosts to the custom allowlist in this repository's Copilot coding agent settings (admins only)

[Stacey77]

DEPLOY