face_detections.py

import cv2
import datetime
import imutils
import numpy as np

protopath = "deploy.prototxt"
modelpath = "res10_300x300_ssd_iter_140000.caffemodel"
detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
# Only enable it if you are using OpenVino environment
# detector.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
# detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

def main():
    cap = cv2.VideoCapture('test_video.mp4')

    fps_start_time = datetime.datetime.now()
    fps = 0
    total_frames = 0

    while True:
        ret, frame = cap.read()
        frame = imutils.resize(frame, width=600)
        total_frames = total_frames + 1

        (H, W) = frame.shape[:2]

        face_blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0), False, False)

        detector.setInput(face_blob)
        face_detections = detector.forward()

        for i in np.arange(0, face_detections.shape[2]):
            confidence = face_detections[0, 0, i, 2]
            if confidence > 0.5:

                face_box = face_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
                (startX, startY, endX, endY) = face_box.astype("int")

                cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2)

        fps_end_time = datetime.datetime.now()
        time_diff = fps_end_time - fps_start_time
        if time_diff.seconds == 0:
            fps = 0.0
        else:
            fps = (total_frames / time_diff.seconds)

        fps_text = "FPS: {:.2f}".format(fps)

        cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)

        cv2.imshow("Application", frame)
        key = cv2.waitKey(1)
        if key == ord('q'):
            break

    cv2.destroyAllWindows()


main()