Find the face in a picture — OpenCV example for beginners

To begin, we need to download the Haar cascade files from the following github link — https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml

NOTE — Make sure that the downloaded XML file (haarcascade_frontalface_default.xml) and the input image is present in the directory from which the code is running.

About haarcascade_frontalface_default.xml file

This is basically a XML (extensible markup language) file containing generic information about features of a face. The following code uses this information to draw a rectangle around the detected face.

Let’s use this image to check the working of our code. Download it here.

Trial Image

THE CODE

First we will import all the required modules

import cv2
import numpy as np
import math

In next step, the image is extracted from its location and stored in ‘img’ variable

img=cv2.imread("pic1.jpg")

and then the cascade file is also loaded into ‘face_cascade’ variable

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Now come very important two lines of code-

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

The first line converts the image from one color space to another i.e from BGR to grayscale in this case.

The second line uses the CascadeClassifier::detectMultiScale() function which detects objects of different sizes in the input image and the detected objects are returned as a list of rectangles. More information on this module can be found here .

In the next 4 lines, we’re finding faces, their sizes, drawing rectangles, and noting the region of interest(ROI).

for (x,y,w,h) in faces:
   cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
   roi_gray = gray[y:y+h, x:x+w]
   roi_color = img[y:y+h, x:x+w]

In above code, (x,y) are the starting indices of the rectangle and (w,h) are the width and height that when added to (x,y) form the rectangle. The second line draws rectangles on the input image.

Following code is used to display the image with the detected faces -

cv2.imshow("Image",img)
k = cv2.waitKey(30) & 0xff
if k == 27:
   break

The first line displays the image in a new window named “Image”.

As seen, the last 8 lines of code are written inside an infinite loop (while 1:).

k = cv2.waitKey(30) & 0xff
if k == 27:
   break

Above code displays the image until you press the “Esc” key (breaks the infinite loop).

Final Code

import cv2
import numpy as np
import math
img=cv2.imread("name_of_image.extension")
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
while 1:
   for (x,y,w,h) in faces:
      cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
      roi_gray = gray[y:y+h, x:x+w]
      roi_color = img[y:y+h, x:x+w]
   cv2.imshow("Image",img)
   k = cv2.waitKey(30) & 0xff
   if k == 27:
      break
cv2.destroyAllWindows()

Output of the program

Output for Trail Image

As we can clearly see from the above output, it does not give perfect result.It left the guy in dark blue shirt and triange on guy which at back is not perfect.

So lets try our code with one more image.

Download this image -

Second Trail Image

Now run the code with this image. Make sure XML file, image and code are in same working directory.

Output for second trail Image

After running the program we find that it again left the girl at right bottom of the picture.

In my personal opinian it is because of XML file we downloaded from github in the beginning. For more accurate results we have to modify that XML file so that it can capture face more accurately.

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