FACE DETECTION USING MEDIAPIPE AND PYTHON

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Packages to install : 

  1. pip install opencv-python.
  2. pip install mediapipe.

Program : 

# Face Detection using mediapipe with python
import cv2
import mediapipe as mp
import time

# Creating objects for detecting face
mpFaceDetection = mp.solutions.face_detection
mpDraw = mp.solutions.drawing_utils

faceDetection = mpFaceDetection.FaceDetection()

# Capturing video from the web cam
video = cv2.VideoCapture(0)

currentTime = 0
previousTime = 0

while True:
    success, img = video.read()
    # Converting BGR image to RGB image
    RGBimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # Getting landmark points and storing in multiHandLandmarks variable
    result = faceDetection.process(RGBimg)

    # This 'if' block will run when it's having the face landmark points
    if result.detections:
        for index, detection in enumerate(result.detections):
            score = detection.score[0] * 100
            boundingBox = detection.location_data.relative_bounding_box
            imgh, imgw, imgc = img.shape
            x, y, w, h = int(boundingBox.xmin * imgw), int(boundingBox.ymin * imgh), int(boundingBox.width * imgw), int(boundingBox.height * imgh)

            # creating rectangle box around the face
            cv2.rectangle(img, (x, y, w, h), (0, 255, 0), 2)
            cv2.putText(img, f"{int(score)}%", (x, y-15), cv2.FONT_HERSHEY_PLAIN, 2,(0, 255, 0), 2)

    # calculating frames per second
    currentTime = time.time()
    fps = 1 / (currentTime - previousTime)
    previousTime = currentTime
    imgShape = img.shape[0:2]

    # This will show the fps value on screen
    cv2.putText(img, f"FPS: {int(fps)}", ((imgShape[1]-imgShape[1])+20, imgShape[0] - 50), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 0), 2)
    cv2.imshow("Face Detection", img)

    key = cv2.waitKey(1)
    if key == 13:
        exit()

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