vinakvm.blogg.se - Contour detect license plate python

Keras: Keras is easy to use and widely supported and used all over the world by python developers, Keras module makes deep learning about as simple as deep learning can be.

For this project, I have used version 4.1.0 of the OpenCV module.

OpenCV: OpenCV is a library of programming functions mainly aimed at real-time computer vision plus is open-source, fun to work with, and my personal favorite.

Haar cascade: It is a machine learning object detection algorithm used to identify objects in an image or video and is based on the concept of â€‹â€‹ features proposed by Paul Viola and Michael Jones in their esteemed paper titled “Rapid Object Detection using a Boosted Cascade of Simple Features” in the year 2001.

Python: aka swiss army knife of coding.

So, we need to perform the OCR (Optical Character Recognition) on it to detect the numbers and characters. Now, the new image which we obtained in the previous step is sure to have some characters which may be Numbers or Alphabets written on it. Again this can be done easily using the help of OpenCV module. This gets trickier if the image does not even have a car, in this case now, we will do an additional step to detect the car and then also the license plate.Īfter the successful detection of the License Plate, we now have to crop it out and save it as a new image on our server. Normally the detection algorithm is trained based on the position of the camera and also on the type of number plate used in that particular country, which may vary for different countries. The accuracy can be improved if we know the exact size, color, and approximate location of the number plate used on the cars. We will use the contour option which is present in the OpenCV to detect the rectangular objects which enable us to find the number plate. The first and primary step is to detect the License plate from the car. model.predict() Now, our goal is to detect number plates.

In the above figures, we can see that we have stored the model using the line below.Īnd also we make the prediction of the model of that image using the line command below. Using that data set we are going to train the model and perform the testing. The first step is to create a model for that we have 50 types of car bands and in each has 100 images.