Classifier file opencv download

Here we learn to make our own image classifiers with a few commands and long yet simple python programs. The classification requires a large number of negative and positive images negatives do not contain the required object whereas the positives are the one that contain the object to be detected.

About negatives and positives are required. The python program converts the image to grayscale and a suitable size so that classifiers takes the optimum time to create. The size should not be very large as it takes larger time for the computer to process.

I took 50 by 50 size. Next we download the negative and positive images. You can find them online. Table of Contents. Save Article. Improve Article. Like Article. Last Updated : 18 Oct, Importing OpenCV package. Loading the required haar-cascade xml classifier file.

Applying the face detection method on the grayscale image. There is no data folder in the site-packages any more — Mohsen Sichani. MohsenSichani i used the python code to find the cv2 route and i did indeed find them on the data folder — Mr-Programs. CascadeClassifier cv2. These cascades seem to be somewhat timeless: e. Mona Jalal Mona Jalal Rohit Dhankar Rohit Dhankar 1, 14 14 silver badges 23 23 bronze badges. Kavishka Hirushan Kavishka Hirushan 21 4 4 bronze badges.

Manish Singh Manish Singh 1. Meir Gabay Meir Gabay 1, 1 1 gold badge 19 19 silver badges 24 24 bronze badges. The xml files can be accessed directly from cv2 like so cv2. Raikan 10 Raikan 10 37 5 5 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. That is a big gain. So now you take an image. Take each 24x24 window.

Apply features to it. Check if it is face or not. Isn't it a little inefficient and time consuming? Yes, it is. The authors have a good solution for that.

In an image, most of the image is non-face region. So it is a better idea to have a simple method to check if a window is not a face region. If it is not, discard it in a single shot, and don't process it again. Instead, focus on regions where there can be a face. This way, we spend more time checking possible face regions.

For this they introduced the concept of Cascade of Classifiers. Instead of applying all features on a window, the features are grouped into different stages of classifiers and applied one-by-one. Normally the first few stages will contain very many fewer features.

Which has so less relationship with the positive or negative training data. And for the recognition part, Machine learning is also just one option. And there are some papers about that.

If you decided to use the training data for recognition, please post a question then. OpenCV bundle contains some classifier files for face, eye, nose and body detection.

You will find it in the installer package. But for your custom object you need to build your own classifiers. How are we doing? Please help us improve Stack Overflow. Take our short survey.