[Ubuntu] [Python] Face detection comparison between dlib and OpenCV

I compared the face detection of dlib and OpenCV. It's a video I see from time to time, but I wanted to try it myself, so give it a try.

dlib is more accurate in terms of orientation, OpenCV feels faster (thanks to Adaboost? If it is to be used in business, we will investigate it in more detail.

Linux is easier to install dlib, so I'm doing it with Ubutntu. Even on Windows, if you include dlib, it should work with the same source. .. .. ??

The video is below. Red is detection by OpenCV and blue is detection by dlib. https://www.youtube.com/watch?v=SQTXLfwlPjQ

The source code is below. To make it work, put the OpenCV trained data in the same directory as the py file. Must be placed. →./data/haarcascades/haarcascade_frontalface_alt.xml

#!/usr/bin/env python
# -*- coding: utf-8 -*-

'''
face_landmark_detector.py.

Usage:
  face_landmark_detector.py [<video source>] [<resize rate>]
'''

import sys
import dlib
import cv2
import time
import copy

try:
    fn = sys.argv[1]
    if fn.isdigit() == True:
        fn = int(fn)
except:
    fn = 0

try:
    resize_rate = sys.argv[2]
    resize_rate = int(resize_rate)
except:
    resize_rate = 1

# Dlib
detector = dlib.get_frontal_face_detector()

# OpenCV
cascade_fn = "./data/haarcascades/haarcascade_frontalface_alt.xml"
cascade = cv2.CascadeClassifier(cascade_fn)

video_input = cv2.VideoCapture(fn)

total_frame_count = 0
face_detection_frame_count_dlib = 0
face_detection_frame_count_opencv = 0

while(video_input.isOpened() == True):
    total_frame_count += 1

    ret, frame = video_input.read()
    temp_frame = copy.deepcopy(frame)

    #Target frame reduction to reduce processing load (when argument is specified)
    height, width = frame.shape[:2]
    temp_frame = cv2.resize(frame, (int(width/resize_rate), int(height/resize_rate)))

    #Face detection(dlib)
    start = time.time()
    dets = detector(temp_frame, 1)
    elapsed_time_dlib = time.time() - start

    if len(dets) > 0:
        face_detection_frame_count_dlib += 1

    #Detection result drawing (dlib)
    for k, d in enumerate(dets):
        cv2.rectangle(frame, (int(d.left() * resize_rate), int(d.top() * resize_rate)), \
            (int(d.right() * resize_rate), int(d.bottom() * resize_rate)), (255, 0, 0), -1)

    #Face detection(opencv)
    gray_image = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2GRAY)
    gray_image = cv2.equalizeHist(gray_image)

    start = time.time()
    rects = cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
    if len(rects) == 0:
        rects = []
    else:
        rects[:,2:] += rects[:,:2]
    elapsed_time_opencv = time.time() - start


    if len(rects) > 0:
        face_detection_frame_count_opencv += 1

    #Detection result drawing (OpenCV)
    for x1, y1, x2, y2 in rects:
        cv2.putText(frame, "OpenCV", (int(x1 * resize_rate), int(y1 * resize_rate)), cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), thickness = 2)
        cv2.rectangle(frame, (int(x1 * resize_rate), int(y1 * resize_rate)), (int(x2 * resize_rate), int(y2 * resize_rate)), (0, 0, 255), -1)
    #Detection result drawing (dlib)
    for k, d in enumerate(dets):
        cv2.putText(frame, "Dlib", (int(d.left() * resize_rate), int(d.top() * resize_rate)), cv2.FONT_HERSHEY_PLAIN, 2.0, (255, 0, 0), thickness = 2)
        cv2.rectangle(frame, (int(d.left() * resize_rate), int(d.top() * resize_rate)), \
            (int(d.right() * resize_rate), int(d.bottom() * resize_rate)), (255, 0, 0), 2)

    print ("face detect(dlib) processing time:{0}".format(elapsed_time_dlib)) + "[sec]"
    print ("face detect(opencv) processing time:{0}".format(elapsed_time_opencv)) + "[sec]"
    print ("face detect(dlib) success count:" + '%06d' % face_detection_frame_count_dlib + "/" + '%06d' % total_frame_count)
    print ("face detect(opencv) success count:" + '%06d' % face_detection_frame_count_opencv + "/" + '%06d' % total_frame_count)
    print

    cv2.imshow('face detector vs', frame)

    c = cv2.waitKey(50) & 0xFF

    if c==27: # ESC
        break

video_input.release()
cv2.destroyAllWindows()

that's all.