License Plate Recognition-Server

This solutions stands for ANPR/ALPR(Automatic Number/License Plate Recognition), LPR, vehicle number plate recognition, plate number reader, vehicle scanner, vehicle detection for server SDKs

KBY-AI's LPR solutions utilize artificial intelligence and machine learning to greatly surpass legacy solutions. Now, in real-time, users can receive a vehicle's license plate number.

Automatic license-plate recognition (ALPR) is a technology that uses OCR(optical character recognition) on images to read vehicle registration plates. It can use existing closed-circuit television, road-rule enforcement cameras, or cameras specifically designed for the task. ALPR can be used by police forces around the world for law enforcement purposes, including to check if a vehicle is registered or licensed. It is also used for electronic toll collection on pay-per-use roads and as a method of cataloguing the movements of traffic, for example by highways agencies.

Features

• Vehicle License Plate Reader

• OCR Based License Number Parser

• Vehicle Detection & Tracking

• Export License Plate Coordinate

• Support Car, Truck, Bus, etc

• Support Various License Plates From 180+ Counrtries

License

We offer lifetime license(perpetual license) based on machine ID for every server(Windows, Linux). The license is available for one-time payment. In other words, once you purchase license from me, you can use our SDK permanently.

To request a license, please contact us:

Email: [e-mail]

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Resources

GitHub - kby-ai/Automatic-License-Plate-Recognition-Docker: This stands for Automatic Number/License Plate Recognition, ALPR, ANPR, LPR, Vehicle Number Plate Recognition, Vehicle Detection and Vehicle TrackingGitHub

https://hub.docker.com/r/kbyai/license-plate-recognition

1. Linux

• CPU: 2 cores or more (Recommended: 2 cores)

• RAM: 4 GB or more (Recommended: 8 GB)

• HDD: 4 GB or more (Recommended: 8 GB)

• OS: Windows 7 or later

• Architecture: x64

2. Windows

• CPU: 2 cores or more (Recommended: 2 cores)

• RAM: 4 GB or more (Recommended: 8 GB)

• HDD: 4 GB or more (Recommended: 8 GB)

• OS: Ubuntu 20.04 or later

• Architecture: x64

Import SDK

from alprsdk import initSDK, getLicensePlate, getMachineCode, freeLicenseResults, setActivation

Initializing SDK

• Obtain the machine code to activate and request a license.

# Python code example
machineCode = getMachineCode()
print("\nmachineCode: ", machineCode.decode('utf-8')) 

• Activate the SDK using the license key.

# Python code example
ret = setActivation(license.encode('utf-8'))
print("\nactivation: ", ret)

• Initializing SDK

# Python code example
ret = initSDK()

• Once ret value is zero, SDK can get work started.

APIs

1. Getting License Number & Coordinate

The SDK provides a single API for getting license plate number and its coordinate(x, y, width, height). The function can be used as follows:

# Python code example
recog_array = (c_int * 1024)()  # Assuming a maximum of 256 rectangles
license_plate_ptr = POINTER(c_char_p)()
cnt = getLicensePlate(img_byte, len(img_byte), byref(license_plate_ptr), recog_array)

• recog_array: coordinate(x, y, width, height).

• img_byte: image data in binary format.

• license_plate_ptr: pointer to variable with license plate number.

2. Analyzing the result from SDK

Result values from SDK inference can be analyzed as follows.

# Python code example
license_plate = [license_plate_ptr[i].decode('utf-8') for i in range(cnt)]
rectangles = [
(recog_array[i * 4], recog_array[i * 4 + 1], recog_array[i * 4 + 2], recog_array[i * 4 + 3])
for i in range(cnt)]

freeLicenseResults(license_plate_ptr, cnt)

print("number: ", cnt, rectangles, license_plate)

• cnt: the number of detected license plate.

• rectangles: list of coordinate.

• license plate: list of license number.

3. Free Memory

freeLicenseResults(license_plate_ptr, cnt)