Modified at 24/02/2023, eliminating several filters in the pipeline filters
LicensePlate_Yolov8_MaxFilters: recognition of car license plates that are detected by Yolov8 and recognized with pytesseract after processing with a pipeline of filters choosing the most repeated car license plate. In a test with 21 images, 17 hits are achieved
Requirements:
yolo must be installed, to do so follow the instructions indicated in: https://learnopencv.com/ultralytics-yolov8/#How-to-Use-YOLOv8?
In this case, the option to install on the computer itself was chosen.
pip install ultralytics
also must be installed the normal modules in computer vision: pytesseract, numpy, cv2, os, re, imutils, parabolic
Functioning:
Download the project to a folder on disk.
Download to that folder the roboflow files that will be used for training:
https://public.roboflow.com/object-detection/license-plates-us-eu/3
In that folder you should find the train and valid folders necessary to build the model
To ensure the version, the used roboflo file roboflow.zip is attached
Unzip the file with the test images test6Training.zip, taking into account when unzipping you can create a folder test6Training inside the test6Training folder,there should be only one test6Training folder, otherwise you will not find the test images
Model Train:
the train and valid folders of the roboflow folder, resulting from the unziping of robflow.zip, must be placed in the same directory where the execution program LicensePlateYolov8Train.py is located, according to the requirements indicated in license_data.yaml
run the program
LicensePlateYolov8Train.py
which only has 4 lines, but the line numbered 11 should indicate the full path where the license_data.yaml file is located.
Running from a simple laptop, the 100 epochs of the program will take a long time, but you can always pull the lid off the laptop and continue the next day. As obtaining best.pt is problematic, the one used in the project tests is attached, adjusting the route of instruction 15 in GetNumberSpanishLicensePlate_Yolov8_MaxFilters
As a result, inside the download folder, the directory runs\detect\trainN\weights( where in trainN, N indicates the last train directory created) in which the best.pt file is located, which is the base of the model and which is referenced in line 15 of the GetNumberSpanishLicensePlate_Yolov8_MaxFilters.py program (modify the route, the name of trainN, so that it points to the last train and best.pt created
As obtaining best.pt is problematic, the one used in the project tests is attached,it must be adjusted the route of instruction 15 in GetNumberSpanishLicensePlate_Yolov8_MaxFilters.py
Run the program.
GetNumberSpanishLicensePlate_Yolov8_MaxFilters.py
The car license plates and successes or failures through the different filters appear on the screen.
The LicenseResults.txt file lists the car license plates with their corresponding recognized ones.
In a test with 21 images, 18 hits are achieved, better than the 17 achieved with car licenses detected with yolo and manual labeling in the LicensePlate_Labeled_MaxFilters project https://github.com/ablanco1950/LicensePlate_Labeled_MaxFilters and better than with wpod_net https://github.com/ablanco1950/LicensePlate_Wpod-net_MaxFilters .
Also is attached GetNumberInternationalLicensePlate_Yolov8_MaxFiltersA.py, that tries to detect licenses plates in any format, and the hit rate is the same 70 hits in 117 than in https://github.com/ablanco1950/LicensePlate_Labeled_MaxFilters. In this case, the success rate drops because it is not possible to eliminate pytesseract outputs that do not comply with a certain format, so the correct outputs are not enough compared to the erroneous ones to establish the correct one.
References:
https://learnopencv.com/ultralytics-yolov8/#How-to-Use-YOLOv8?
https://public.roboflow.com/object-detection/license-plates-us-eu/3
https://docs.ultralytics.com/python/
https://medium.com/@alimustoofaa/how-to-load-model-yolov8-onnx-cv2-dnn-3e176cde16e6
Filters
https://gist.github.com/endolith/334196bac1cac45a4893#
https://stackoverflow.com/questions/46084476/radon-transformation-in-python
https://gist.github.com/endolith/255291#file-parabolic-py
https://learnopencv.com/otsu-thresholding-with-opencv/
https://towardsdatascience.com/image-enhancement-techniques-using-opencv-and-python-9191d5c30d45
https://blog.katastros.com/a?ID=01800-4bf623a1-3917-4d54-9b6a-775331ebaf05
https://programmerclick.com/article/89421544914/
https://anishgupta1005.medium.com/building-an-optical-character-recognizer-in-python-bbd09edfe438
https://datasmarts.net/es/como-usar-el-detector-de-puntos-clave-mser-en-opencv/
https://felipemeganha.medium.com/detecting-handwriting-regions-with-opencv-and-python-ff0b1050aa4e
Note: On 03/13/2023, the best results are obtained with the https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR project, which would replace this one.