PlanOpticon

Update README.md

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11
 # PlanOpticon
22
 goes through a video and captures notes and diagrams from the conversation
3
+
4
+PlanOpticon
5
+Comprehensive Video Analysis & Knowledge Extraction CLI
6
+PlanOpticon is an advanced AI-powered CLI tool that conducts thorough analysis of video content, extracting structured knowledge, diagrams, and actionable insights. Using state-of-the-art computer vision and natural language processing techniques, PlanOpticon transforms video assets into valuable, structured information.
7
+
8
+Core Features
9
+
10
+Complete Transcription: Full speech-to-text with speaker attribution and semantic segmentation
11
+Visual Element Extraction: Automated recognition and digitization of diagrams, charts, whiteboards, and visual aids
12
+Action Item Detection: Intelligent identification and prioritization of tasks, commitments, and follow-ups
13
+Knowledge Structure: Organization of extracted content into searchable, related concepts
14
+Plan Generation: Synthesis of extracted elements into cohesive action plans and summaries
15
+
16
+
17
+Technical Implementation
18
+PlanOpticon leverages multiple AI models and processing pipelines to achieve comprehensive video analysis:
19
+Architecture Overview
20
+Video Input → Frame Extraction → Parallel Processing → Knowledge Integration → Structured Output
21
+ ↓ ↓
22
+ Visual Processing Audio Processing
23
+ • Diagram Detection • Speech Recognition
24
+ • OCR Extraction • Speaker Diarization
25
+ • Content Tracking • Semantic Analysis
26
+Key Components
27
+
28
+Multi-modal neural networks for frame analysis
29
+Advanced speech recognition with contextual awareness
30
+Computer vision pipeline for visual element extraction
31
+Knowledge graph construction for relationship mapping
32
+Temporal pattern recognition across video segments
33
+
34
+
35
+Installation
36
+bash# Clone the repository
37
+git clone https://github.com/yourusername/planopticon.git
38
+cd planopticon
39
+
40
+# Create virtual environment
41
+python -m venv venv
42
+source venv/bin/activate # On Windows: venv\Scripts\activate
43
+
44
+# Install dependencies
45
+pip install -r requirements.txt
46
+
47
+# Install optional GPU dependencies (if available)
48
+pip install -r requirements-gpu.txt
49
+
50
+Usage
51
+PlanOpticon is designed as a command-line interface tool:
52
+bash# Basic usage
53
+planopticon analyze --input video.mp4 --output analysis/
54
+
55
+# Specify processing depth
56
+planopticon analyze --input video.mp4 --depth comprehensive --output analysis/
57
+
58
+# Focus on specific extraction types
59
+planopticon analyze --input video.mp4 --focus "diagrams,action-items" --output analysis/
60
+
61
+# Process with GPU acceleration
62
+planopticon analyze --input video.mp4 --use-gpu --output analysis/
63
+Output Structure
64
+analysis/
65
+├── transcript.json # Full transcription with timestamps and speakers
66
+├── key_points.md # Extracted main concepts and ideas
67
+├── diagrams/ # Extracted and digitized visual elements
68
+│ ├── diagram_001.svg
69
+│ └── whiteboard_001.svg
70
+├── action_items.json # Prioritized tasks and commitments
71
+└── knowledge_graph.json # Relationship map of concepts
72
+
73
+Development Guidelines
74
+When contributing to PlanOpticon, please adhere to these principles:
75
+Code Standards
76
+
77
+Follow PEP 8 style guidelines for all Python code
78
+Write comprehensive docstrings using NumPy/Google style
79
+Maintain test coverage above 80%
80
+Use type hints consistently throughout the codebase
81
+
82
+Architecture Considerations
83
+
84
+Optimize for cross-platform compatibility (macOS, Linux, Windows)
85
+Ensure ARM architecture support for cloud deployment and Apple Silicon
86
+Implement graceful degradation when GPU is unavailable
87
+Design modular components with clear interfaces
88
+
89
+
90
+System Requirements
91
+
92
+Python 3.9+
93
+8GB RAM minimum (16GB recommended)
94
+2GB disk space for models and dependencies
95
+CUDA-compatible GPU (optional, for accelerated processing)
96
+ARM64 or x86_64 architecture
97
+
98
+
99
+Implementation Strategy
100
+The core processing pipeline requires thoughtful implementation of several key systems:
101
+
102
+Frame extraction and analysis
103
+
104
+Implement selective sampling based on visual change detection
105
+Utilize region proposal networks for element identification
106
+
107
+
108
+Speech processing
109
+
110
+Apply time-domain speaker diarization
111
+Implement context-aware transcription with domain adaptation
112
+
113
+
114
+Visual element extraction
115
+
116
+Develop whiteboard/diagram detection with boundary recognition
117
+Implement reconstruction of visual elements into vector formats
118
+
119
+
120
+Knowledge integration
121
+
122
+Create hierarchical structure of extracted concepts
123
+Generate relationship mappings between identified elements
124
+
125
+
126
+Action item synthesis
127
+
128
+Apply intent recognition for commitment identification
129
+Implement priority scoring based on contextual importance
130
+
131
+
132
+
133
+Each component should be implemented as a separate module with clear interfaces, allowing for independent testing and optimization.
134
+
135
+Development Approach
136
+When implementing PlanOpticon, consider these architectural principles:
137
+
138
+Pipeline Architecture
139
+
140
+Design processing stages that can operate independently
141
+Implement data passing between stages using standardized formats
142
+Enable parallelization where appropriate
143
+Consider using Python's asyncio for I/O-bound operations
144
+
145
+
146
+Performance Optimization
147
+
148
+Implement batched processing for GPU acceleration
149
+Use memory mapping for large video files
150
+Consider JIT compilation for performance-critical sections
151
+Profile and optimize bottlenecks systematically
152
+
153
+
154
+Error Handling
155
+
156
+Implement comprehensive exception handling
157
+Design graceful degradation paths for each component
158
+Provide detailed logging for troubleshooting
159
+Consider retry mechanisms for transient failures
160
+
161
+
162
+Testing Strategy
163
+
164
+Create comprehensive unit tests for each module
165
+Implement integration tests for end-to-end pipeline
166
+Develop benchmark tests for performance evaluation
167
+Use property-based testing for complex components
168
+
169
+
170
+
171
+The implementation should maintain separation of concerns while ensuring efficient data flow between components. Consider using dependency injection patterns to improve testability and component isolation.
172
+
173
+License
174
+MIT License
175
+
176
+Contact
177
+For questions or contributions, please open an issue on GitHub or contact the maintainers at [email protected].
3178
--- README.md
+++ README.md
@@ -1,2 +1,177 @@
1 # PlanOpticon
2 goes through a video and captures notes and diagrams from the conversation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
--- README.md
+++ README.md
@@ -1,2 +1,177 @@
1 # PlanOpticon
2 goes through a video and captures notes and diagrams from the conversation
3
4 PlanOpticon
5 Comprehensive Video Analysis & Knowledge Extraction CLI
6 PlanOpticon is an advanced AI-powered CLI tool that conducts thorough analysis of video content, extracting structured knowledge, diagrams, and actionable insights. Using state-of-the-art computer vision and natural language processing techniques, PlanOpticon transforms video assets into valuable, structured information.
7
8 Core Features
9
10 Complete Transcription: Full speech-to-text with speaker attribution and semantic segmentation
11 Visual Element Extraction: Automated recognition and digitization of diagrams, charts, whiteboards, and visual aids
12 Action Item Detection: Intelligent identification and prioritization of tasks, commitments, and follow-ups
13 Knowledge Structure: Organization of extracted content into searchable, related concepts
14 Plan Generation: Synthesis of extracted elements into cohesive action plans and summaries
15
16
17 Technical Implementation
18 PlanOpticon leverages multiple AI models and processing pipelines to achieve comprehensive video analysis:
19 Architecture Overview
20 Video Input → Frame Extraction → Parallel Processing → Knowledge Integration → Structured Output
21 ↓ ↓
22 Visual Processing Audio Processing
23 • Diagram Detection • Speech Recognition
24 • OCR Extraction • Speaker Diarization
25 • Content Tracking • Semantic Analysis
26 Key Components
27
28 Multi-modal neural networks for frame analysis
29 Advanced speech recognition with contextual awareness
30 Computer vision pipeline for visual element extraction
31 Knowledge graph construction for relationship mapping
32 Temporal pattern recognition across video segments
33
34
35 Installation
36 bash# Clone the repository
37 git clone https://github.com/yourusername/planopticon.git
38 cd planopticon
39
40 # Create virtual environment
41 python -m venv venv
42 source venv/bin/activate # On Windows: venv\Scripts\activate
43
44 # Install dependencies
45 pip install -r requirements.txt
46
47 # Install optional GPU dependencies (if available)
48 pip install -r requirements-gpu.txt
49
50 Usage
51 PlanOpticon is designed as a command-line interface tool:
52 bash# Basic usage
53 planopticon analyze --input video.mp4 --output analysis/
54
55 # Specify processing depth
56 planopticon analyze --input video.mp4 --depth comprehensive --output analysis/
57
58 # Focus on specific extraction types
59 planopticon analyze --input video.mp4 --focus "diagrams,action-items" --output analysis/
60
61 # Process with GPU acceleration
62 planopticon analyze --input video.mp4 --use-gpu --output analysis/
63 Output Structure
64 analysis/
65 ├── transcript.json # Full transcription with timestamps and speakers
66 ├── key_points.md # Extracted main concepts and ideas
67 ├── diagrams/ # Extracted and digitized visual elements
68 │ ├── diagram_001.svg
69 │ └── whiteboard_001.svg
70 ├── action_items.json # Prioritized tasks and commitments
71 └── knowledge_graph.json # Relationship map of concepts
72
73 Development Guidelines
74 When contributing to PlanOpticon, please adhere to these principles:
75 Code Standards
76
77 Follow PEP 8 style guidelines for all Python code
78 Write comprehensive docstrings using NumPy/Google style
79 Maintain test coverage above 80%
80 Use type hints consistently throughout the codebase
81
82 Architecture Considerations
83
84 Optimize for cross-platform compatibility (macOS, Linux, Windows)
85 Ensure ARM architecture support for cloud deployment and Apple Silicon
86 Implement graceful degradation when GPU is unavailable
87 Design modular components with clear interfaces
88
89
90 System Requirements
91
92 Python 3.9+
93 8GB RAM minimum (16GB recommended)
94 2GB disk space for models and dependencies
95 CUDA-compatible GPU (optional, for accelerated processing)
96 ARM64 or x86_64 architecture
97
98
99 Implementation Strategy
100 The core processing pipeline requires thoughtful implementation of several key systems:
101
102 Frame extraction and analysis
103
104 Implement selective sampling based on visual change detection
105 Utilize region proposal networks for element identification
106
107
108 Speech processing
109
110 Apply time-domain speaker diarization
111 Implement context-aware transcription with domain adaptation
112
113
114 Visual element extraction
115
116 Develop whiteboard/diagram detection with boundary recognition
117 Implement reconstruction of visual elements into vector formats
118
119
120 Knowledge integration
121
122 Create hierarchical structure of extracted concepts
123 Generate relationship mappings between identified elements
124
125
126 Action item synthesis
127
128 Apply intent recognition for commitment identification
129 Implement priority scoring based on contextual importance
130
131
132
133 Each component should be implemented as a separate module with clear interfaces, allowing for independent testing and optimization.
134
135 Development Approach
136 When implementing PlanOpticon, consider these architectural principles:
137
138 Pipeline Architecture
139
140 Design processing stages that can operate independently
141 Implement data passing between stages using standardized formats
142 Enable parallelization where appropriate
143 Consider using Python's asyncio for I/O-bound operations
144
145
146 Performance Optimization
147
148 Implement batched processing for GPU acceleration
149 Use memory mapping for large video files
150 Consider JIT compilation for performance-critical sections
151 Profile and optimize bottlenecks systematically
152
153
154 Error Handling
155
156 Implement comprehensive exception handling
157 Design graceful degradation paths for each component
158 Provide detailed logging for troubleshooting
159 Consider retry mechanisms for transient failures
160
161
162 Testing Strategy
163
164 Create comprehensive unit tests for each module
165 Implement integration tests for end-to-end pipeline
166 Develop benchmark tests for performance evaluation
167 Use property-based testing for complex components
168
169
170
171 The implementation should maintain separation of concerns while ensuring efficient data flow between components. Consider using dependency injection patterns to improve testability and component isolation.
172
173 License
174 MIT License
175
176 Contact
177 For questions or contributions, please open an issue on GitHub or contact the maintainers at [email protected].
178

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