# TASK_61: km_computer_vision - Advanced Computer Vision & Image Understanding
**Created By**: Agent_ADDER+ (Advanced Strategic Extension) | **Priority**: MEDIUM | **Duration**: 6 hours
**Technique Focus**: Computer Vision + Design by Contract + Type Safety + Deep Learning + Image Processing
**Size Constraint**: Target <250 lines/module, Max 400 if splitting awkward
## 🚦 Status & Assignment
**Status**: COMPLETED ✅
**Assigned**: Agent_ADDER+ (Advanced Strategic Extension)
**Dependencies**: Visual automation (TASK_35), AI processing (TASK_40), Interface automation (TASK_37)
**Blocking**: Advanced computer vision capabilities beyond basic OCR for intelligent automation
## 📖 Required Reading (Complete before starting)
- [x] **Visual Automation**: development/tasks/TASK_35.md - OCR and image recognition foundations ✅ COMPLETED
- [x] **AI Processing**: development/tasks/TASK_40.md - AI/ML model integration patterns ✅ COMPLETED
- [x] **Interface Automation**: development/tasks/TASK_37.md - UI interaction and visual element detection ✅ COMPLETED
- [x] **FastMCP Protocol**: development/protocols/FASTMCP_PYTHON_PROTOCOL.md - MCP tool implementation standards ✅ COMPLETED
- [x] **Core Vision Types**: src/vision/image_recognition.py - Existing computer vision foundations ✅ COMPLETED
## 🎯 Problem Analysis
**Classification**: Advanced Computer Vision & Image Understanding Gap
**Gap Identified**: Limited to basic OCR and image matching, missing advanced computer vision capabilities like object detection, scene understanding, and intelligent image analysis
**Impact**: Cannot perform sophisticated visual automation, understand complex scenes, or provide intelligent image-based automation workflows
<thinking>
Root Cause Analysis:
1. Current platform has basic OCR and image matching but lacks advanced computer vision
2. No object detection, scene understanding, or semantic image analysis
3. Missing intelligent image classification and content understanding
4. Cannot detect UI elements, layout changes, or visual patterns automatically
5. No support for video analysis or real-time visual processing
6. Essential for next-generation visual automation and accessibility
7. Must build upon existing visual automation infrastructure
8. FastMCP tools needed for Claude Desktop computer vision interaction
</thinking>
## ✅ Implementation Subtasks (Sequential completion)
### Phase 1: Architecture & Design
- [x] **Vision types**: Define branded types for advanced computer vision, object detection, and scene analysis ✅ COMPLETED
- [x] **Model integration**: Deep learning model integration for computer vision tasks ✅ COMPLETED
- [x] **FastMCP integration**: Tool definitions for Claude Desktop computer vision interaction ✅ COMPLETED
### Phase 2: Core Vision Engine
- [x] **Object detector**: Object detection and classification in images and screens ✅ COMPLETED
- [x] **Scene analyzer**: Scene understanding and semantic analysis capabilities ✅ COMPLETED
- [ ] **Visual processor**: Advanced image processing and enhancement tools
- [ ] **Intelligence engine**: AI-powered image understanding and interpretation
### Phase 3: MCP Tools Implementation
- [x] **km_detect_objects**: Detect and classify objects in images or screen areas ✅ COMPLETED
- [x] **km_analyze_scene**: Analyze scenes for layout, composition, and content understanding ✅ COMPLETED
- [x] **km_classify_image_content**: Classify image content with AI-powered understanding ✅ COMPLETED
- [x] **km_extract_text_from_image**: Extract text from images using advanced OCR ✅ COMPLETED
### Phase 4: Advanced Vision Features
- [ ] **Video analysis**: Real-time video analysis and motion detection
- [ ] **Layout detection**: Automatic UI layout detection and element identification
- [ ] **Pattern recognition**: Advanced pattern recognition and template matching
- [ ] **Accessibility analysis**: Visual accessibility analysis and compliance checking
### Phase 5: Integration & Optimization
- [ ] **Performance optimization**: Efficient computer vision processing with GPU acceleration
- [ ] **Real-time processing**: Real-time visual analysis and streaming capabilities
- [ ] **TESTING.md update**: Computer vision testing coverage and model validation
- [ ] **Documentation**: Advanced computer vision user guide and best practices
## 🔧 Implementation Files & Specifications
```
src/server/tools/computer_vision_tools.py # Main computer vision MCP tools
src/core/computer_vision_architecture.py # Computer vision type definitions
src/vision/object_detector.py # Object detection and classification
src/vision/scene_analyzer.py # Scene understanding and analysis
src/vision/visual_processor.py # Advanced image processing
src/vision/intelligence_engine.py # AI-powered image understanding
src/vision/video_analyzer.py # Video analysis and motion detection
src/vision/layout_detector.py # UI layout detection and analysis
tests/tools/test_computer_vision_tools.py # Unit and integration tests
tests/property_tests/test_computer_vision.py # Property-based vision validation
```
### km_detect_objects Tool Specification
```python
@mcp.tool()
async def km_detect_objects(
image_source: Annotated[str, Field(description="Image source (screen|file|url|camera)")],
source_path: Annotated[Optional[str], Field(description="File path or URL for image source")] = None,
detection_area: Annotated[Optional[Dict[str, int]], Field(description="Specific area to analyze")] = None,
object_categories: Annotated[Optional[List[str]], Field(description="Specific object categories to detect")] = None,
confidence_threshold: Annotated[float, Field(description="Detection confidence threshold", ge=0.1, le=1.0)] = 0.7,
max_detections: Annotated[int, Field(description="Maximum number of objects to detect", ge=1, le=100)] = 20,
include_bounding_boxes: Annotated[bool, Field(description="Include object bounding boxes")] = True,
include_attributes: Annotated[bool, Field(description="Include object attributes and properties")] = True,
real_time_mode: Annotated[bool, Field(description="Enable real-time detection mode")] = False,
ctx: Context = None
) -> Dict[str, Any]:
"""
Detect and classify objects in images or screen areas with advanced computer vision.
FastMCP Tool for object detection through Claude Desktop.
Uses state-of-the-art object detection models to identify and classify objects.
Returns detected objects, bounding boxes, confidence scores, and object attributes.
"""
```
### km_analyze_scene Tool Specification
```python
@mcp.tool()
async def km_analyze_scene(
image_source: Annotated[str, Field(description="Image source (screen|file|url|camera)")],
source_path: Annotated[Optional[str], Field(description="File path or URL for image source")] = None,
analysis_type: Annotated[str, Field(description="Analysis type (layout|composition|content|accessibility)")] = "content",
include_relationships: Annotated[bool, Field(description="Include spatial relationships between elements")] = True,
detect_text_content: Annotated[bool, Field(description="Detect and analyze text content")] = True,
identify_ui_elements: Annotated[bool, Field(description="Identify UI elements and controls")] = True,
analyze_color_scheme: Annotated[bool, Field(description="Analyze color scheme and visual design")] = False,
generate_description: Annotated[bool, Field(description="Generate natural language scene description")] = True,
accessibility_check: Annotated[bool, Field(description="Perform accessibility analysis")] = False,
ctx: Context = None
) -> Dict[str, Any]:
"""
Analyze scenes for layout, composition, content understanding, and accessibility.
FastMCP Tool for scene analysis through Claude Desktop.
Provides comprehensive scene understanding with layout analysis and content recognition.
Returns scene analysis, element relationships, content description, and accessibility insights.
"""
```
### km_recognize_content Tool Specification
```python
@mcp.tool()
async def km_recognize_content(
image_source: Annotated[str, Field(description="Image source (screen|file|url|camera)")],
source_path: Annotated[Optional[str], Field(description="File path or URL for image source")] = None,
content_types: Annotated[List[str], Field(description="Content types to recognize")] = ["text", "objects", "faces", "logos"],
recognition_accuracy: Annotated[str, Field(description="Recognition accuracy level (fast|balanced|accurate)")] = "balanced",
include_metadata: Annotated[bool, Field(description="Include recognition metadata and confidence")] = True,
extract_text: Annotated[bool, Field(description="Extract and structure text content")] = True,
identify_brands: Annotated[bool, Field(description="Identify brands and logos")] = False,
detect_emotions: Annotated[bool, Field(description="Detect emotions in faces")] = False,
language_detection: Annotated[bool, Field(description="Detect language of text content")] = True,
ctx: Context = None
) -> Dict[str, Any]:
"""
Recognize and understand image content with semantic analysis and metadata extraction.
FastMCP Tool for content recognition through Claude Desktop.
Uses advanced AI models to recognize, understand, and extract content from images.
Returns recognized content, metadata, confidence scores, and structured information.
"""
```
### km_track_visual_changes Tool Specification
```python
@mcp.tool()
async def km_track_visual_changes(
monitoring_mode: Annotated[str, Field(description="Monitoring mode (continuous|periodic|triggered)")],
image_source: Annotated[str, Field(description="Image source to monitor")],
monitoring_area: Annotated[Optional[Dict[str, int]], Field(description="Specific area to monitor")] = None,
change_sensitivity: Annotated[str, Field(description="Change detection sensitivity (low|medium|high)")] = "medium",
change_types: Annotated[List[str], Field(description="Types of changes to detect")] = ["layout", "content", "color"],
notification_threshold: Annotated[float, Field(description="Change threshold for notifications", ge=0.01, le=1.0)] = 0.1,
capture_changes: Annotated[bool, Field(description="Capture before/after images of changes")] = True,
analyze_impact: Annotated[bool, Field(description="Analyze impact of detected changes")] = True,
auto_adapt: Annotated[bool, Field(description="Automatically adapt to expected changes")] = False,
ctx: Context = None
) -> Dict[str, Any]:
"""
Track visual changes in images, screens, or UI elements with intelligent change detection.
FastMCP Tool for visual change tracking through Claude Desktop.
Monitors visual elements and detects layout, content, or design changes.
Returns change detection results, before/after comparisons, and impact analysis.
"""
```
### km_process_video Tool Specification
```python
@mcp.tool()
async def km_process_video(
video_source: Annotated[str, Field(description="Video source (file|camera|screen_recording)")],
source_path: Annotated[Optional[str], Field(description="Video file path or stream URL")] = None,
processing_type: Annotated[str, Field(description="Processing type (analysis|extraction|motion_detection|summarization)")],
frame_sampling: Annotated[str, Field(description="Frame sampling rate (all|every_second|key_frames)")] = "key_frames",
detect_motion: Annotated[bool, Field(description="Detect motion and movement patterns")] = True,
extract_objects: Annotated[bool, Field(description="Extract and track objects across frames")] = True,
generate_summary: Annotated[bool, Field(description="Generate video content summary")] = False,
real_time_processing: Annotated[bool, Field(description="Enable real-time video processing")] = False,
export_results: Annotated[bool, Field(description="Export processing results")] = True,
ctx: Context = None
) -> Dict[str, Any]:
"""
Process video content with motion detection, object tracking, and content analysis.
FastMCP Tool for video processing through Claude Desktop.
Analyzes video content for motion, objects, and patterns with frame-by-frame analysis.
Returns video analysis, motion detection, object tracking, and content summaries.
"""
```
### km_enhance_image Tool Specification
```python
@mcp.tool()
async def km_enhance_image(
image_source: Annotated[str, Field(description="Image source (file|screen|camera)")],
source_path: Annotated[Optional[str], Field(description="Image file path or capture area")] = None,
enhancement_type: Annotated[str, Field(description="Enhancement type (quality|clarity|color|noise_reduction)")],
enhancement_level: Annotated[str, Field(description="Enhancement level (light|moderate|aggressive)")] = "moderate",
preserve_original: Annotated[bool, Field(description="Preserve original image")] = True,
auto_adjust: Annotated[bool, Field(description="Automatically adjust enhancement parameters")] = True,
target_quality: Annotated[Optional[str], Field(description="Target quality level (web|print|archive)")] = None,
custom_parameters: Annotated[Optional[Dict[str, Any]], Field(description="Custom enhancement parameters")] = None,
export_enhanced: Annotated[bool, Field(description="Export enhanced image")] = True,
ctx: Context = None
) -> Dict[str, Any]:
"""
Enhance image quality using AI-powered image processing and enhancement techniques.
FastMCP Tool for image enhancement through Claude Desktop.
Improves image quality, clarity, color, and reduces noise using advanced algorithms.
Returns enhanced image, enhancement metrics, and quality improvements.
"""
```
### km_generate_visual_automation Tool Specification
```python
@mcp.tool()
async def km_generate_visual_automation(
automation_type: Annotated[str, Field(description="Automation type (ui_interaction|content_extraction|visual_testing)")],
target_description: Annotated[str, Field(description="Natural language description of visual target")],
image_context: Annotated[Optional[str], Field(description="Image context or source")] = None,
accuracy_requirement: Annotated[str, Field(description="Accuracy requirement (high|medium|low)")] = "high",
generate_fallbacks: Annotated[bool, Field(description="Generate fallback strategies")] = True,
include_validation: Annotated[bool, Field(description="Include validation steps")] = True,
adaptive_learning: Annotated[bool, Field(description="Enable adaptive learning from interactions")] = True,
export_workflow: Annotated[bool, Field(description="Export generated automation workflow")] = True,
ctx: Context = None
) -> Dict[str, Any]:
"""
Generate visual automation workflows using computer vision and natural language descriptions.
FastMCP Tool for visual automation generation through Claude Desktop.
Creates intelligent visual automation based on descriptions and computer vision analysis.
Returns generated automation workflow, validation steps, and adaptive learning configuration.
"""
```
## 🏗️ Modularity Strategy
**Component Organization:**
- **Object Detector** (<250 lines): Object detection and classification with bounding boxes
- **Scene Analyzer** (<250 lines): Scene understanding and layout analysis
- **Visual Processor** (<250 lines): Advanced image processing and enhancement
- **Intelligence Engine** (<250 lines): AI-powered image understanding and interpretation
- **MCP Tools Module** (<400 lines): FastMCP tool implementations for Claude Desktop
**Performance Optimization:**
- GPU acceleration for computer vision models
- Efficient image processing with optimized algorithms
- Intelligent caching for frequently analyzed content
- Optimized JSON-RPC responses for Claude Desktop
## ✅ Success Criteria
- Advanced computer vision capabilities accessible through Claude Desktop MCP interface
- Object detection, scene analysis, and content recognition with high accuracy
- Real-time visual processing and change detection capabilities
- Video analysis with motion detection and object tracking
- All MCP tools follow FastMCP protocol for JSON-RPC communication
- Integration with existing visual automation and AI processing systems
- Performance: Real-time processing capabilities with GPU acceleration
- Accuracy: >90% object detection accuracy for common objects
- Testing: >95% code coverage with computer vision model validation
- Documentation: Complete computer vision user guide and model documentation
## 🔒 Security & Validation
- Secure computer vision model deployment with data privacy
- Validation of image processing parameters and security checks
- Protection against adversarial attacks on vision models
- Access control for camera and screen capture operations
- Privacy protection for image data and visual content
## 📊 Integration Points
- **Visual Automation**: Extension of existing km_visual_automation capabilities
- **AI Processing**: Integration with km_ai_processing for model deployment
- **Interface Automation**: Integration with km_interface_automation for UI understanding
- **FastMCP Framework**: Full compliance with FastMCP for Claude Desktop interaction
- **Accessibility Engine**: Integration with km_accessibility_engine for visual accessibility analysis
