Detect the Faces in the Image using the Mediapipe Library

Face Detection with Mediapipe Library

So we have read the image as well now comes to the main task where we will use our face detection model and implement its functionality on our sample image for that we will be using the process() function from FaceDetection class. This function will return us the major six-coordinate for every face it will detect in the image/frame. Those six coordinates are as follows:

1. Right Eye
2. Left Eye
3. Nose Tip
4. Mouth Center
5. Right Ear Tragion
6. Left Ear Tragion

So after getting these 6 points we will be able to plot the bounding box on the image/frame but we will only plot two major key points so that the output is clear and there should be no extra points over the image which could make it congested.

face_detection_results = face_detection.process(sample_img[:,:,::-1])

if face_detection_results.detections:

    for face_no, face in enumerate(face_detection_results.detections):

        print(f'FACE NUMBER: {face_no+1}')
        print('==============================')

        print(f'FACE CONFIDENCE: {round(face.score[0], 2)}')

        face_data = face.location_data

        print(f'nFACE BOUNDING BOX:n{face_data.relative_bounding_box}')

        for i in range(2):

            print(f'{mp_face_detection.FaceKeyPoint(i).name}:')
            print(f'{face_data.relative_keypoints[mp_face_detection.FaceKeyPoint(i).value]}')

Output:

 

Code breakdown

1. Firstly we will change the format of the image from the BGR format to its RGB version.
2. Then, using the if condition we will first detect whether the face is found in the image or not for that we will be using the detection attribute of face_detection.
3. Then with the help of enumerate function and for loop, we will iterate over the faces found in the image.
4. Now we will print out the total number of faces found in the image.
5. We will also print out the confidence value i.e. how much our model is confident that the face is detected or not.
6. Then we will get the bounding box and major points which we will be displayed on the image.
7. Now note one thing in the inner for loop we are just iterating over 2 points because we only want to see the two points on the image from those major 6 points and then finally we will display those points – the normalized version of the coordinates.

Visualizing the Results

img_copy = sample_img[:,:,::-1].copy()

if face_detection_results.detections:

    for face_no, face in enumerate(face_detection_results.detections):

        mp_drawing.draw_detection(image=img_copy, detection=face, 
                                 keypoint_drawing_spec=mp_drawing.DrawingSpec(color=(255, 0, 0),
                                                                              thickness=2,
                                                                              circle_radius=2))
fig = plt.figure(figsize = [10, 10])

plt.title("Resultant Image");plt.axis('off');plt.imshow(img_copy);plt.show()

Output:

 

Code breakdown

1. First, we will create a copy of the image using copy the method so that we won’t lose the original preprocessed part of the image.
2. Then again we will first check if the faces are found or not then only we will proceed further.
3. If the faces were found then we will iterate over each face via for loop and enumerate the function.
4. Now instead of printing the points, we will draw the points on the sample image using the draw_detection function with relevant parameters as discussed.
5. Now we will set the size of the figure (here image) using Matplotlib’s figure function.
6. Finally, we will display the image with the bounding box and the points as well.

End Notes

1. The very first takeaway from this article is that we learned a different algorithm to detect the faces i.e. Mediapipe library to detect the faces.
2. We understood the process function which is the backbone of the media pipe face detection algorithm and we also had an in-depth discussion on the same.
   
3. And at the last, we have visualized the results as well and not only the face but also some landmarks like Eyes, ear, mouth, and the nose the results for the landmarks might not be that accurate for different angles but it is very handy for the frontal face.
   

Here’s the repo link to this article. Hope you liked my article on Face detection using the Mediapipe library. If you have any opinions or questions, then comment below.

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About the Author

Greeting to everyone, I’m currently working in TCS and previously, I worked as a Data Science Analyst in Zorba Consulting India. Along with full-time work, I’ve got an immense interest in the same field, i.e. Data Science, along with its other subsets of Artificial Intelligence such as Computer Vision, Machine Learning, and Deep learning; feel free to collaborate with me on any project on the domains mentioned above (LinkedIn).

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