Face and Eye Detection in OpenCV
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The goal of object detection is to find an object of a pre-defined class in an image. In this post we will see how to use the Haar Classifier implemented in OpenCV in order to detect faces and eyes in a single image.
We are going to use two trained classifiers stored in two XML files:
- haarcascade_frontalface_default.xml - that you can find in the directory /data/haarcascades/ of your OpenCV installation
- haarcascade_eye.xml - that you can download from this website.
The first one is able to detect faces and the second one eyes. To use a trained classifier stored in a XML file we need to load it into memory using the function cv.Load() and call the function cv.HaarDetectObjects() to detect the objects. Let's see the snippet:
imcolor = cv.LoadImage('detectionimg.jpg') # input image # loading the classifiers haarFace = cv.Load('haarcascade_frontalface_default.xml') haarEyes = cv.Load('haarcascade_eye.xml') # running the classifiers storage = cv.CreateMemStorage() detectedFace = cv.HaarDetectObjects(imcolor, haarFace, storage) detectedEyes = cv.HaarDetectObjects(imcolor, haarEyes, storage) # draw a green rectangle where the face is detected if detectedFace: for face in detectedFace: cv.Rectangle(imcolor,(face,face), (face+face,face+face), cv.RGB(155, 255, 25),2) # draw a purple rectangle where the eye is detected if detectedEyes: for face in detectedEyes: cv.Rectangle(imcolor,(face,face), (face+face,face+face), cv.RGB(155, 55, 200),2) cv.NamedWindow('Face Detection', cv.CV_WINDOW_AUTOSIZE) cv.ShowImage('Face Detection', imcolor) cv.WaitKey()These images are produced running the script with two different inputs. The first one is obtained from an image that contains two faces and four eyes:
And the second one is obtained from an image that contains one face and two eyes (the shakira.jpg we used in the post about PCA):