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# Project: Scale QR Code Reader # Scale QR Code Reader
**Created:** 2026-02-24 ## Project Overview
**Source:** #work-assistant Research project for implementing QR code reading with IP cameras for vehicle identification at weigh scales.
**Status:** Testing Phase
--- ---
## Overview ## Research Findings
Use an IP camera with a vision model to read QR codes and other identifying markings on vehicles stopping to weigh on a scale.
## Purpose ### Summary
- Identify vehicles via QR code/markings This research covers QR code detection libraries, IP camera integration methods, reading QR codes at distance/in motion, camera hardware specifications, and lighting/mounting considerations for weigh scale applications.
- Store vehicle data with timestamp and weight
- Populate database for further processing
## Components
- IP camera
- Vision model for QR/markings recognition
- Database for storage
- Scale integration
## Current Status
✅ **Testing Phase**
- Software has been written
- Currently in testing
## Tasks
- [ ] Complete testing
- [ ] Deploy to production
## Notes
*(Add technical details, camera specs, vision model used, etc.)*
## Related
- [Scale Weight Readout](Scale%20Weight%20Readout.md) — companion scale project
--- ---
*Extracted from Projects to be organized* ### 1. QR Code Detection Libraries
#### Recommended Libraries (Ranked)
**1. pyzbar (ZBar)** - Best Overall for Python
- **Pros:** Fastest runtime (~157ms), good reading rate, supports Python 2 & 3, easy integration
- **Cons:** Struggles with blurred, curved, and damaged codes
- **Install:** `pip install pyzbar`
- **Best for:** General purpose QR reading with static/slow-moving vehicles
**2. OpenCV WeChat QR Code Detector**
- **Pros:** Excellent with damaged and pathological QR codes, handles non-compliant codes (colored, logos), robust in challenging conditions
- **Cons:** Slower runtime (~758ms), struggles with multiple codes in one image
- **Best for:** Dirty/damaged codes on industrial vehicles
**3. BoofCV**
- **Pros:** Fastest runtime (~105ms), excellent with multiple codes per image, open-source
- **Cons:** Poor performance on non-compliant codes
- **Best for:** Multiple QR codes or high-speed processing needs
**4. YOLO-QR (Deep Learning)**
- **Pros:** Can detect QR codes in complex scenes, handles motion blur better than traditional methods
- **Cons:** Requires training data, more complex setup, computationally intensive
- **Approach:** Train custom YOLO model to detect QR codes, then extract region and decode with traditional library
- **Best for:** Advanced scenarios with challenging angles and motion
#### Library Comparison from Benchmarks
| Library | Reading Rate | Runtime | Blurred | Damaged | Multiple Codes |
|---------|-------------|---------|---------|---------|----------------|
| Dynamsoft (Commercial) | 83.29% | 195ms | 66% | 51% | 100% |
| BoofCV | 60.69% | 105ms | 38% | 16% | 99.76% |
| ZBar (pyzbar) | 38.95% | 157ms | 35% | 26% | 18% |
| OpenCV WeChat | 48.89% | 758ms | 46% | 30% | 0% |
---
### 2. IP Camera Integration Methods
#### Primary Methods
**1. RTSP Streaming (Recommended)**
```python
import cv2
# Hikvision RTSP URL
rtsp_url = "rtsp://username:password@camera_ip:554/Streaming/channels/101"
# Dahua RTSP URL
rtsp_url = "rtsp://username:password@camera_ip:554/cam/realmonitor?channel=1&subtype=0"
cap = cv2.VideoCapture(rtsp_url)
while True:
ret, frame = cap.read()
if ret:
# Process frame for QR codes
process_frame(frame)
```
**2. HTTP Snapshot (Lower Latency)**
- Hikvision: `http://user:pass@camera_ip/ISAPI/Streaming/channels/101/picture`
- Useful for single-frame capture on trigger
**3. ONVIF Control**
```python
from onvif import ONVIFCamera
# Camera control for PTZ, presets
mycam = ONVIFCamera('192.168.1.10', 80, 'admin', 'password')
ptz = mycam.create_ptz_service()
# Can move to preset positions, control zoom
```
**Library Requirements:**
```bash
pip install opencv-python pyzbar onvif-zeep
```
---
### 3. Reading QR Codes at Distance and in Motion
#### Key Challenges & Solutions
**Motion Blur:**
- Vehicles moving >35 km/h cause significant motion blur
- **Solutions:**
- Fast shutter speed: 1/1000s minimum for moving vehicles
- Higher frame rate cameras (30fps minimum, 60fps preferred)
- Use multiple frame capture and select sharpest image
**Distance Factors:**
- QR code size vs. distance calculation:
- Minimum: QR code should be at least 80-130 pixels wide in image
- Formula: Distance (feet) ≈ (MP × 10) / QR width in inches
- For 4MP camera reading 4" QR code: ~40 feet maximum
**Reading Angle:**
- Vertical angle: Maximum 30° from perpendicular
- Horizontal angle: Maximum 30° from perpendicular
- Tilt tolerance: +/- 5°
#### Recommended Approach for Moving Vehicles
1. **Trigger-based capture:** Use vehicle detection/loop sensors to trigger capture
2. **Burst mode:** Take 5-10 frames when vehicle is in sweet spot
3. **Select best frame:** Use image quality metrics (blur detection, contrast)
4. **Multi-library attempt:** Try pyzbar first, fallback to OpenCV WeChat
---
### 4. Camera Hardware Specifications
#### Recommended Camera Specs for Weigh Scale
| Feature | Minimum | Recommended | Optimal |
|---------|---------|-------------|---------|
| Resolution | 2MP (1920x1080) | 4MP (2560x1440) | 4K (8MP) |
| Frame Rate | 25fps | 30fps | 60fps |
| Shutter Speed | 1/500s | 1/1000s | 1/2000s |
| WDR | 120dB | 120dB+ | 140dB+ |
| IR Range | 20m | 30m | 50m |
| Lens | 4mm | 6-12mm varifocal | 6-12mm motor zoom |
| Protection | IP66 | IP67 | IP67+IK10 |
#### Sensor Size Considerations
- **1/2.8" or larger:** Better low-light performance for outdoor/industrial use
- **1/1.8":** Excellent for challenging lighting conditions
#### Lens Selection Formula
```
Focal Length (mm) = (Sensor Width × Distance) / Field of View Width
Example: For 4m wide lane at 6m distance with 1/3" sensor:
= (4.8mm × 6000mm) / 4000mm = 7.2mm focal length
```
#### Recommended Camera Models
- **Hikvision:** DS-2CD2643G2-IZS (4MP, WDR, motor zoom)
- **Dahua:** IPC-HFW5442T-ASE (4MP, WDR, IR 50m)
- **Axis:** P1375 (Lightfinder, WDR, pole mount)
---
### 5. Lighting and Mounting Considerations
#### Mounting Position
**Height:**
- 2.5-3.5 meters above ground (8-12 feet)
- Higher mounting = larger detection zone but requires zoom lens
**Horizontal Distance:**
- 4-8 meters from capture point
- Rule: Distance ≈ 2 × mounting height
**Angles:**
- Vertical angle: Max 30° looking down
- Horizontal angle: Max 30° from vehicle path
- Avoid direct headlight glare into lens
**Position Options:**
1. **Side-mounted:** Camera perpendicular to vehicle side
2. **Overhead:** Camera looking down at windshield (less optimal for QR)
3. **Gantry-mounted:** Over the scale, perpendicular to direction of travel
#### Lighting Requirements
**Daytime:**
- Avoid direct sunlight into lens (causes glare)
- Position camera with sun behind it when possible
- Use lens hood/cover
**Nighttime/Infrared:**
- IR illuminators essential for 24/7 operation
- Built-in IR range must cover distance to vehicle
- External IR illuminators for longer distances (20-50m)
- IR wavelength: 850nm (visible red glow) or 940nm (covert)
**Shutter Speed Settings:**
- Stationary vehicles: 1/100 - 1/300
- Moving vehicles (30mph): 1/1000 - 1/1500
- High speed (70mph): 1/2500 - 1/3500
#### Environmental Protection
- **IP67:** Protected against dust and temporary water immersion
- **IK10:** Vandal-resistant housing
- **Operating temperature:** -30°C to +60°C for outdoor use
- **Heater/blower:** For cold climates to prevent condensation
---
### Implementation Steps
#### Phase 1: Proof of Concept
1. Install camera with RTSP access
2. Implement basic QR detection with pyzbar
3. Test with stationary vehicles at various distances
4. Determine optimal capture zone
#### Phase 2: Optimization
1. Add motion trigger/sensor integration
2. Implement burst capture (5-10 frames)
3. Add frame selection algorithm (blur detection)
4. Test with slow-moving vehicles
#### Phase 3: Production
1. Weatherproof housing installation
2. IR lighting setup for night operation
3. Integration with weigh scale software
4. Database logging and reporting
#### Sample Python Implementation
```python
import cv2
import pyzbar.pyzbar as pyzbar
import numpy as np
class QRCodeReader:
def __init__(self, rtsp_url):
self.cap = cv2.VideoCapture(rtsp_url)
self.frame_buffer = []
def capture_burst(self, num_frames=5):
"""Capture multiple frames for selection"""
frames = []
for _ in range(num_frames):
ret, frame = self.cap.read()
if ret:
frames.append(frame)
return frames
def detect_blur(self, frame, threshold=100):
"""Detect image blur using Laplacian variance"""
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
return cv2.Laplacian(gray, cv2.CV_64F).var()
def decode_qr(self, frame):
"""Decode QR codes from frame"""
decoded = pyzbar.decode(frame)
results = []
for obj in decoded:
results.append({
'data': obj.data.decode('utf-8'),
'type': obj.type,
'rect': obj.rect
})
return results
def process_vehicle(self):
"""Main processing when vehicle detected"""
frames = self.capture_burst(10)
# Select sharpest frame
best_frame = max(frames, key=lambda f: self.detect_blur(f))
# Try primary decoder
results = self.decode_qr(best_frame)
# Fallback to OpenCV if needed
if not results:
qr_detector = cv2.QRCodeDetector()
retval, decoded_info, _ = qr_detector.detectAndDecode(best_frame)
if retval:
results = [{'data': decoded_info, 'type': 'QR-Code'}]
return results
```
---
### Source Links
1. **QR Code Library Benchmarks:**
- <https://www.dynamsoft.com/codepool/qr-code-reading-benchmark-and-comparison.html>
- <https://boofcv.org/index.php?title=Performance:QrCode>
2. **Library Implementation Guides:**
- <https://learnopencv.com/barcode-and-qr-code-scanner-using-zbar-and-opencv/>
- <https://pyimagesearch.com/2018/05/21/an-opencv-barcode-and-qr-code-scanner-with-zbar/>
3. **YOLO QR Detection:**
- <https://www.dynamsoft.com/codepool/qr-code-detect-decode-yolo-opencv.html>
- <https://www.sciencedirect.com/science/article/pii/S0045790624003045>
4. **Weighbridge Systems:**
- <https://punitinstrument.com/a-complete-guide-to-unmanned-erp-integrated-automated-weighbridge-systems/>
- <https://endel.digital/weighbridge-software/>
5. **Camera Positioning (ANPR - applies to QR):**
- <https://platerecognizer.com/camera-setup-for-best-anpr/>
- <https://www.dipolnet.com/license_plate_recognition_lpr_systems_-_part_1_camera_positioning_bib318.htm>
6. **ONVIF Camera Control:**
- <https://github.com/RichardoMrMu/python-onvif>
- <https://www.onvif.org/specs/srv/ptz/ONVIF-PTZ-Service-Spec-v1712.pdf>
7. **Motion Blur Research:**
- <https://arxiv.org/html/2410.05497v1>
- <https://dl.acm.org/doi/10.1016/j.neucom.2022.04.041>
---
*Research compiled: 2026-02-24*