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Face and Eye Detection in OpenCV

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.

(Note: this article is part of a series (1,2) on object detection with OpenCV in Python. --Ed.)

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.RGB(155, 255, 25),2)

# draw a purple rectangle where the eye is detected
if detectedEyes:
 for face in detectedEyes:
               cv.RGB(155, 55, 200),2)

cv.NamedWindow('Face Detection', cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage('Face Detection', imcolor) 
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):


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