Ravindra Naik

Here is how you can implement Face detection with Python and  OpenCV  in less than 25 lines of code: Face detection with Python and OpenCV Install OpenCV. Now download the code from  repo Now Let's break down the code # Get user supplied values imagePath   =   sys . argv [ 1 ] cascPath   =   sys . argv [ 2 ] The above lines takes image and cascade as input. The default cascade will help in detecting image with OpenCV. # Create the haar cascade faceCascade   =   cv2 . CascadeClassifier ( cascPath ) Now we create a cascade, this loads the face cascade into memory for its use. Cascade is just an XML which contains data to detect faces. # Read the image image   =   cv2 . imread ( imagePath ) gray   =   cv2 . cvtColor ( image ,   cv2 . COLOR_BGR2GRAY ) Here we read the image and convert it into Grayscale. A lot of operations in OpenCV are done in GrayScale. # Detect faces in th...

Face detection with OpenCV OpenCV is the most popular library for computer vision. Originally written in C/C++, it now provides bindings for Python. OpenCV uses machine learning algorithms to search for faces within a picture. For something as complicated as a face, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns/features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called  classifiers . For something like a face, you might have 6,000 or more classifiers, all of which must match for a face to be detected (within error limits, of course). But therein lies the problem: For face detection, the algorithm starts at the top left of a picture and moves down across small blocks of data, looking at each block, constantly asking, “ Is this a face? … Is this a face? … Is this a face ?...