The list that remembers: mutable default arguments in Python

Python · Language

def append_to(item, target=[]) looks like it starts from an empty list every time. It does not. One list is created when the def statement runs, and every call for the rest of the program's life shares it.

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The loop variable that changed: late binding and default arguments in Python

Python · Language

You define a function inside a loop, collect the functions, call them later — and every single one returns the last value. The usual fix is a strange-looking line where the same name appears twice. Here is what is actually going on, and why the fix works.

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pytest, from your first test to your own plugin

Python · Testing

Most pytest tutorials stop right after assert 1 + 1 == 2, which is roughly where the interesting part begins. This one keeps going — through fixtures, parametrization and the configuration that makes a suite pleasant to live with, and out the other side into hooks and plugins.

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FREE ANPR/ALPR/LPR API - Try Before You Buy (1000 Requests/Day)


🎁 FREE License Plate Recognition API - No Credit Card Required!

Want to try ANPR (Automatic Number Plate Recognition) / ALPR (Automatic License Plate Recognition) / LPR (License Plate Recognition) without buying a license? We offer a completely FREE test API with 1000 requests per day!

✨ What You Get (FREE!)

  • ✅ 1000 requests/day - Perfect for testing and evaluation
  • ✅ No credit card required
  • ✅ No registration needed
  • ✅ 5 regions supported: Korea, Europe, USA/Canada, China, Universal
  • ✅ Multiple models to test (pico to large)
  • ✅ Works instantly - just install and run!

🚀 Quick Start (30 Seconds)

# Install
pip install marearts-anpr

# Test immediately (NO CONFIG NEEDED!)
ma-anpr test-api your-plate.jpg --region eup

# That's it! 🎉

🌍 Supported Regions

Region Code Coverage Example
kr South Korea 123가4567
eup Europe (EU standards) AB-123-CD
na USA, Canada, Mexico ABC-1234
cn China 京A·12345
univ Universal (all) Any format

💻 Usage Examples

Command Line (Easiest!)

# European plates
ma-anpr test-api eu-plate.jpg --region eup

# Korean plates
ma-anpr test-api kr-plate.jpg --region kr

# US plates
ma-anpr test-api us-plate.jpg --region na

# Chinese plates
ma-anpr test-api cn-plate.jpg --region cn

# Unknown region? Use universal
ma-anpr test-api unknown-plate.jpg --region univ

Python Script

#!/usr/bin/env python3
import subprocess

def test_free_anpr(image_path, region='eup'):
    """Test free ANPR API - no credentials needed!"""
    
    cmd = f'ma-anpr test-api "{image_path}" --region {region}'
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
    
    if result.returncode == 0:
        print(result.stdout)
        return True
    else:
        print(f"Error: {result.stderr}")
        return False

# Test European plate
test_free_anpr("plate.jpg", "eup")

# Test Korean plate
test_free_anpr("plate2.jpg", "kr")

Test Multiple Regions

# Test same image with different regions
for region in eup kr na cn univ; do
    echo "Testing $region..."
    ma-anpr test-api plate.jpg --region $region
done

🎯 Advanced Options

Try Different Models

# List all available models
ma-anpr test-api --list-models

# Try different detector models
ma-anpr test-api plate.jpg --region eup --detector small_640p_fp32
ma-anpr test-api plate.jpg --region eup --detector medium_640p_fp32
ma-anpr test-api plate.jpg --region eup --detector large_640p_fp32

# Try different OCR models
ma-anpr test-api plate.jpg --region eup --ocr small_fp32
ma-anpr test-api plate.jpg --region eup --ocr medium_fp32
ma-anpr test-api plate.jpg --region eup --ocr large_fp32

Batch Testing

# Test all images in a folder
for img in ./plates/*.jpg; do
    echo "Processing $img..."
    ma-anpr test-api "$img" --region eup
done

📊 Sample Output

{
  "results": [
    {
      "ocr": "AB-123-CD",
      "ocr_conf": 98.5,
      "ltrb": [120, 230, 380, 290],
      "ltrb_conf": 95
    }
  ],
  "ltrb_proc_sec": 0.15,
  "ocr_proc_sec": 0.03,
  "status": "success"
}

🆓 FREE vs PAID Comparison

Feature FREE Test API Paid License
Requests/Day 1000 Unlimited
Speed ~0.5s (cloud) ~0.02s (local GPU)
Internet Required Yes No (offline OK)
Configuration None One-time setup
Regions All 5 All 5
Models All All
Price $0 Contact sales

🎓 Use Cases for Free API

  • ✅ Evaluation: Try before buying a license
  • ✅ Prototyping: Build POC applications
  • ✅ Testing: Test accuracy on your specific plates
  • ✅ Education: Learn ANPR/ALPR/LPR technology
  • ✅ Small Projects: Personal projects under 1000/day
  • ✅ Region Testing: Find which region works best
  • ✅ Model Comparison: Compare different model sizes

📈 When to Upgrade to Paid License?

Consider upgrading when you need:

  • 🚀 Unlimited requests (no daily limit)
  • ⚡ 10-100x faster processing (local GPU)
  • 🔒 Offline operation (no internet needed)
  • 🏢 Commercial deployment
  • 📹 Real-time video processing
  • 🎯 High-volume applications (>1000/day)

💡 Pro Tips

# 1. Use specific regions for best accuracy
ma-anpr test-api plate.jpg --region eup  # ✅ Better
ma-anpr test-api plate.jpg --region univ # ⚠️ OK but less accurate

# 2. Test different models to find best speed/accuracy balance
ma-anpr test-api plate.jpg --region eup --detector small_640p_fp32   # Faster
ma-anpr test-api plate.jpg --region eup --detector large_640p_fp32   # More accurate

# 3. Check remaining quota
ma-anpr test-api --check-quota

# 4. Get help
ma-anpr test-api --help

# 5. See all options
ma-anpr test-api --list-models

🔍 Troubleshooting

Rate limit exceeded?

# Wait until midnight UTC (resets daily)
# OR upgrade to paid license for unlimited requests

No plates detected?

# Try different detector models
ma-anpr test-api plate.jpg --region eup --detector large_640p_fp32

# Try universal region
ma-anpr test-api plate.jpg --region univ

Wrong text recognized?

# Make sure you're using correct region!
ma-anpr test-api plate.jpg --region kr   # For Korean plates
ma-anpr test-api plate.jpg --region eup  # For European plates

# Try larger OCR model
ma-anpr test-api plate.jpg --region eup --ocr large_fp32

📖 Complete Example Script

#!/usr/bin/env python3
"""
Free ANPR Test Script
Test license plate recognition with different regions and models
"""
import subprocess
import json

def test_anpr_free(image_path, region='eup', detector='medium_640p_fp32', ocr='medium_fp32'):
    """Test free ANPR API"""
    
    cmd = [
        'ma-anpr', 'test-api', image_path,
        '--region', region,
        '--detector', detector,
        '--ocr', ocr
    ]
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    
    if result.returncode == 0:
        try:
            data = json.loads(result.stdout)
            return data
        except:
            return result.stdout
    else:
        return {"error": result.stderr}

# Test European plate with different models
image = "eu-plate.jpg"

print("Testing different detector models...")
for detector in ['small_640p_fp32', 'medium_640p_fp32', 'large_640p_fp32']:
    result = test_anpr_free(image, 'eup', detector)
    print(f"{detector}: {result}")

print("\nTesting different regions...")
for region in ['eup', 'kr', 'na', 'univ']:
    result = test_anpr_free(image, region)
    print(f"{region}: {result}")

🎯 Real-World Example

# Parking lot monitoring (Europe)
ma-anpr test-api parking-cam.jpg --region eup

# Toll booth (USA)
ma-anpr test-api toll-booth.jpg --region na

# Security gate (Korea)
ma-anpr test-api security-cam.jpg --region kr

# Traffic enforcement (China)
ma-anpr test-api traffic.jpg --region cn

# Multi-national (airport parking)
ma-anpr test-api airport.jpg --region univ

🌟 Why Choose MareArts ANPR?

  • ✅ FREE tier available - Try before you buy!
  • ✅ State-of-the-art AI - Latest deep learning models
  • ✅ Multi-region support - Works worldwide
  • ✅ Fast processing - ~0.02s with GPU
  • ✅ Easy integration - Python, HTTP API, CLI
  • ✅ Regular updates - New models and features
  • ✅ Commercial ready - Production-grade quality

🚀 Get Started Now!

# Install (takes 10 seconds)
pip install marearts-anpr

# Test (takes 20 seconds)
ma-anpr test-api your-plate.jpg --region eup

# Celebrate! 🎉
# You just recognized your first license plate!

📞 Need More?

💬 What People Are Saying

"Finally, an ANPR API I can test without entering my credit card!" - Developer

"1000 requests/day is perfect for my small parking lot project." - Small Business Owner

"Tested all 5 regions before buying. Confident in my purchase!" - System Integrator

🎁 Summary

MareArts ANPR offers a completely FREE test API with 1000 requests per day. No credit card, no registration, no strings attached. Just install and start recognizing license plates!

  • ✅ Install: pip install marearts-anpr
  • ✅ Test: ma-anpr test-api plate.jpg --region eup
  • ✅ Evaluate: Try all regions and models
  • ✅ Upgrade: When ready for unlimited use

Start your ANPR/ALPR/LPR journey today - completely FREE! 🚗📸


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MareArts ANPR V14 - Easy 3-Method Integration (File, OpenCV, PIL)


🚀 MareArts ANPR V14 - Getting Started in 3 Easy Ways

Welcome to MareArts ANPR V14! Today I'll show you how to process license plates using three different methods: from files, OpenCV, or PIL. Plus, the new multi-region switching feature that saves memory.

📦 Quick Setup

pip install marearts-anpr
ma-anpr config  # Enter your credentials

🎯 Basic Usage - Three Input Methods

from marearts_anpr import ma_anpr_detector_v14, ma_anpr_ocr_v14
from marearts_anpr import marearts_anpr_from_image_file, marearts_anpr_from_cv2, marearts_anpr_from_pil
import cv2
from PIL import Image

# Initialize detector and OCR (once)
detector = ma_anpr_detector_v14(
    "medium_640p_fp32",
    user_name, serial_key, signature,
    backend="cpu",
    conf_thres=0.25
)

ocr = ma_anpr_ocr_v14("medium_fp32", "eup", user_name, serial_key, signature)

# Method 1: From file (easiest!)
result = marearts_anpr_from_image_file(detector, ocr, "plate.jpg")
print(result)

# Method 2: From OpenCV
img = cv2.imread("plate.jpg")
result = marearts_anpr_from_cv2(detector, ocr, img)
print(result)

# Method 3: From PIL
pil_img = Image.open("plate.jpg")
result = marearts_anpr_from_pil(detector, ocr, pil_img)
print(result)

🌍 NEW: Dynamic Region Switching (Saves 180MB!)

Previously, you needed separate OCR instances for each region. Now use set_region():

# Initialize once with any region
ocr = ma_anpr_ocr_v14("medium_fp32", "eup", user_name, serial_key, signature)

# Switch regions instantly!
ocr.set_region('eup')   # European plates
result = marearts_anpr_from_image_file(detector, ocr, "eu-plate.jpg")

ocr.set_region('kr')    # Korean plates  
result = marearts_anpr_from_image_file(detector, ocr, "kr-plate.jpg")

ocr.set_region('na')    # North American plates
result = marearts_anpr_from_image_file(detector, ocr, "us-plate.jpg")

ocr.set_region('cn')    # Chinese plates
ocr.set_region('univ')  # Universal

Memory savings: Single instance vs multiple = ~180MB saved per additional region!

📊 Available Regions

  • kr - Korean plates (123가4567)
  • eup - European plates (EU standards)
  • na - North American plates (USA, Canada, Mexico)
  • cn - Chinese plates (京A·12345)
  • univ - Universal (all regions, slightly lower accuracy)

🎨 Batch Processing

# Detect plates from multiple images
img1 = cv2.imread("plate1.jpg")
img2 = cv2.imread("plate2.jpg")

detections1 = detector.detector(img1)
detections2 = detector.detector(img2)

# Collect plate crops
plates = []
for det in detections1:
    bbox = det['bbox']
    crop = img1[int(bbox[1]):int(bbox[3]), int(bbox[0]):int(bbox[2])]
    plates.append(Image.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)))

for det in detections2:
    bbox = det['bbox']
    crop = img2[int(bbox[1]):int(bbox[3]), int(bbox[0]):int(bbox[2])]
    plates.append(Image.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)))

# Process all plates at once!
results = ocr.predict(plates)  # Pass list of images

for i, (text, conf) in enumerate(results):
    print(f"Plate {i+1}: {text} ({conf}%)")

🔧 Model Options

Detector models:

  • pico_640p_fp32 - Smallest, fastest
  • micro_640p_fp32
  • small_640p_fp32
  • medium_640p_fp32 - Recommended balance
  • large_640p_fp32 - Most accurate

OCR models:

  • pico_fp32 - Fastest
  • micro_fp32
  • small_fp32
  • medium_fp32 - Recommended
  • large_fp32 - Best accuracy

Backends:

  • cpu - Works everywhere
  • cuda - NVIDIA GPU (10-100x faster!)
  • directml - Windows GPU

📝 Complete Example

from marearts_anpr import ma_anpr_detector_v14, ma_anpr_ocr_v14
from marearts_anpr import marearts_anpr_from_image_file
import os

# Load credentials
user_name = os.getenv('MAREARTS_ANPR_USERNAME')
serial_key = os.getenv('MAREARTS_ANPR_SERIAL_KEY')
signature = os.getenv('MAREARTS_ANPR_SIGNATURE')

# Initialize models
detector = ma_anpr_detector_v14(
    "medium_640p_fp32",
    user_name, serial_key, signature,
    backend="cpu",
    conf_thres=0.25,
    iou_thres=0.5
)

ocr = ma_anpr_ocr_v14("medium_fp32", "eup", user_name, serial_key, signature)

# Process European plate
print("Processing European plate...")
result = marearts_anpr_from_image_file(detector, ocr, "eu-plate.jpg")
print(result)

# Switch to Korean region
ocr.set_region('kr')
print("\nProcessing Korean plate...")
result = marearts_anpr_from_image_file(detector, ocr, "kr-plate.jpg")
print(result)

💡 Key Takeaways

  • ✅ Three input methods: file, OpenCV, PIL
  • ✅ Dynamic region switching saves memory
  • ✅ Batch processing for efficiency
  • ✅ Multiple model sizes for different needs
  • ✅ GPU acceleration available

🔗 Try It Free!

No license yet? Try the free API (1000 requests/day):

ma-anpr test-api your-plate.jpg --region eup

Happy coding! 🚗📸

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