✅ FEATURES IMPLEMENTED 📷 OpenCV (cv2) Features 1️⃣ Live Camera Capture
Uses system webcam (cv2.VideoCapture(0))
Real-time preview window
Keyboard-controlled capture:
SPACE → capture image
ESC → cancel capture
2️⃣ Local Image Storage (Temporary)
Captured images saved locally as .jpg
Timestamp-based filenames prevent collisions
3️⃣ Blur Detection (Pre-Inference Quality Gate)
Uses Laplacian variance to measure sharpness
Automatically rejects blurry images
Blur threshold configurable (threshold=100.0)
No EyePop call if image is blurry
4️⃣ Automatic Cleanup of Blurry Images
Blurry images are immediately deleted
Prevents disk clutter
Prevents accidental reuse
Improves privacy
🧠 EyePop Computer Vision Features 5️⃣ Object & Item Detection
Uses:
eyepop.image-contents:latest
Detects physical items/products in the image
6️⃣ Prompt-Driven Semantic Filtering
The prompt explicitly:
Ignores:
People
Faces
Hands
Body parts
Human features
Ignores unrelated background objects
Focuses only on:
Products
Items
Shelf goods
Receipt items
7️⃣ Automatic Context Inference (No User Input)
The system auto-handles:
Receipts / bills → item listing
Shelves → product listing
Single products → single item output
No manual “object of interest” input required
8️⃣ OCR / Text Detection Fallback
Uses:
eyepop.text-detection:latest
Triggered only when:
No objects detected, OR
Average confidence < 0.7
9️⃣ Receipt-Aware OCR Prompting
Text detection prompt:
Extracts item names only
Ignores:
Totals
Prices
Tax
Background text
People
🔟 Smart Multi-Stage Inference
Object detection first (cheaper, semantic)
OCR only when needed (fallback)
Prevents unnecessary API calls (free-trial safe)
🔁 Inference Safety & Reliability 1️⃣1️⃣ Safe Predict Wrapper
Automatic retry on EyePop failure
Single retry only (rate-limit safe)
Graceful error handling
🧹 Data Hygiene & Privacy 1️⃣2️⃣ Automatic Image Deletion
Images deleted:
If blurry
After inference completes
No long-term storage of user images
1️⃣3️⃣ Structured Output Filtering
Raw EyePop output is filtered to:
{ "category": "...", "classlabel": "...", "confidence": 0.00 }
Removes bounding boxes
Removes metadata noise
Clean, agent-ready format
1️⃣4️⃣ Deduplication Logic
Items deduplicated by classlabel
Prevents duplicate entries from:
OCR + object detection overlap
📁 File & Output Management 1️⃣5️⃣ Raw Output Preservation
Full EyePop response saved to:
./output/raw_eyepop.json
Useful for:
Debugging
Auditing
Future re-processing
🧩 System Design Features 1️⃣6️⃣ Modular Pipeline Architecture
Clear separation of:
Capture
Quality check
Vision inference
OCR fallback
Cleanup
Output formatting
This makes the code:
Extensible
Agent-friendly
Production-ready
🚀 Integration-Ready Features (Already Supported)
These are enabled by design, even if not wired yet:
1️⃣7️⃣ Agent-Compatible Output
Output format is ideal for:
agntcy agents
LLM reasoning
Orchestration pipelines
1️⃣8️⃣ Cost-Aware Design
Zero EyePop calls on blurry images
OCR only when necessary
One retry maximum
🧠 Summary (High-Level)
You’ve built a system that can:
✔ Capture images ✔ Reject bad input automatically ✔ Detect products & items ✔ Read receipts when needed ✔ Ignore humans and noise ✔ Produce clean structured output ✔ Clean up all temporary data ✔ Integrate cleanly with agent frameworks