live_face_detection_dnn.py

import cv2
import numpy as np

# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
prototxt_path = "weights/deploy.prototxt.txt"
# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"

# load Caffe model
model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)

cap = cv2.VideoCapture(0)

while True:

    # read the desired image
    _, image = cap.read()
    # get width and height of the image
    h, w = image.shape[:2]

        # preprocess the image: resize and performs mean subtraction
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
    # set the image into the input of the neural network
    model.setInput(blob)
    # perform inference and get the result
    output = np.squeeze(model.forward())
    font_scale = 1.0
    for i in range(0, output.shape[0]):
        # get the confidence
        confidence = output[i, 2]
        # if confidence is above 50%, then draw the surrounding box
        if confidence > 0.5:
            # get the surrounding box cordinates and upscale them to original image
            box = output[i, 3:7] * np.array([w, h, w, h])
            # convert to integers
            start_x, start_y, end_x, end_y = box.astype(np.int)
            # draw the rectangle surrounding the face
            cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
            # draw text as well
            cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2)
    # show the image
    cv2.imshow("image", image)
    if cv2.waitKey(1) == ord("q"):
        break

cv2.destroyAllWindows()
cap.release()