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