FACE DETECTION Using Haar cascade in C.V.

In previous blog we discussed how Face Identification using Haar cascade classifier works(theory) . In today’s blog we will implement FACE DETECTION Using Haar cascade classifier in PYTHON using OpenCV library and haarcascade_frontalface_default.xml.

PREREQUISITES

1.) Download haarcascade_frontalface_default.xml [from repo link given below]

2.) pip install opencv-python

link to github Repo of my Computer Vision Tutorial

vaibhavhariramani/FaceDetection

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I.) CODE with explanation

I.1. Importing libraries

**import cv2**

I.2. Importing cascade files

The second step simply is to locate the pre-trained weights(cascade file). We will be using default pre-trained models to detect face, eyes and mouth. Depending on your version of Python, the files should be located in :

python3.7/site-packages/cv2/data

otherwise you can get all cascade files from our repo here:

Once identified, we’ll declare Cascade classifiers this way :

cascPath = "haarcascade/haarcascade\_frontalface\_default.xml"  
eyePath = "haarcascade/haarcascade\_eye.xml"  
smilePath = "haarcascade/haarcascade\_smile.xml" faceCascade = cv2.CascadeClassifier(cascPath)  
eyeCascade = cv2.CascadeClassifier(eyePath)  
smileCascade = cv2.CascadeClassifier(smilePath)

I.3. Load the image

Before implementing the real time face detection algorithm, let’s try a simple version on an image. We can start by loading a test image :

# Load the image
gray = cv2.imread(‘face\_detect\_test.jpeg’, 0)plt.figure(figsize=(12,8))
plt.imshow(gray, cmap=’gray’)
plt.show()

Test image

I.3. Detect face on an image

Now, we detect the face and we add a rectangle around it :

# Detect faces
faces = faceCascade.detectMultiScale(
  gray,
  scaleFactor=1.1,
  minNeighbors=5,
  flags=cv2.CASCADE\_SCALE\_IMAGE
)# For each face
for (x, y, w, h) in faces:
  # Draw rectangle around the face
  cv2.rectangle(gray, (x, y), (x+w, y+h), (255, 255, 255), 3)

Here is a list of the most common parameters of the detectMultiScale function :

• scaleFactor : Parameter specifying how much the image size is reduced at each image scale.
• minNeighbors : Parameter specifying how many neighbors each candidate rectangle should have to retain it.
• minSize : Minimum possible object size. Objects smaller than that are ignored.
• maxSize : Maximum possible object size. Objects larger than that are ignored.

Finally, display the result :

plt.figure(figsize=(12,8))
plt.imshow(gray, cmap=’gray’)
plt.show()

Face detection works well on our test image. Let’s move on to real time now !

I.4. Real time face detection

Let’s move on to the Python implementation of the live facial detection. The first step is to launch the camera, and capture the video. Then, we’ll transform the image to a gray scale image. This is used to reduce the dimension of the input image. Indeed, instead of 3 points per pixel describing Red, Green, Blue, we apply a simple linear transformation :

This is implemented by default in OpenCV.

video_capture = cv2.VideoCapture(0)while True:
  # Capture frame-by-frame
  ret, frame = video\_capture.read()
  gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY)

Now, we’ll use the faceCascade variable define above, which contains a pre-trained algorithm, and apply it to the gray scale image.

faces = faceCascade.detectMultiScale(
  gray,
  scaleFactor=1.1,
  minNeighbors=5,
  minSize=(30, 30),
  flags=cv2.CASCADE\_SCALE\_IMAGE
)

For each face detected, we’ll draw a rectangle around the face :

for (x, y, w, h) in faces:
  if w > 250 :
    cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 3)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = frame[y:y+h, x:x+w]

For each mouth detected, draw a rectangle around it :

smile = smileCascade.detectMultiScale(
  roi_gray,
  scaleFactor= 1.16,
  minNeighbors=35,
  minSize=(25, 25),
  flags=cv2.CASCADE_SCALE_IMAGE
)
for (sx, sy, sw, sh) in smile:
  cv2.rectangle(roi_color, (sh, sy), (sx+sw, sy+sh), (255, 0, 0), 2)
  cv2.putText(frame,’Smile’,(x + sx,y + sy), 1, 1, (0, 255, 0), 1)

For each eye detected, draw a rectangle around it :

eyes = eyeCascade.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
  cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
  cv2.putText(frame,’Eye’,(x + ex,y + ey), 1, 1, (0, 255, 0), 1)

Then, count the total number of faces, and display the overall image :

cv2.putText(frame,’Number of Faces : ‘ + str(len(faces)),(40, 40), font, 1,(255,0,0),2)
# Display the resulting frame
cv2.imshow(‘Video’, frame)

And implement an exit option when we want to stop the camera by pressing q :

if cv2.waitKey(1) & 0xFF == ord(‘q’):
  break

Finally, when everything is done, release the capture and destroy all windows. There are some troubles killing windows on Mac which might require killing Python from the Activity Manager later on.

video_capture.release()
cv2.destroyAllWindows()

I.5. Wrapping it up

I.6. Results

I will made a quick YouTube illustration of the face detection algorithm.

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