opencv：使用dlib进行人脸检测

pip install dlib
pip install opencv-python

import cv2import sysimport dlib

detector = dlib.get_frontal_face_detector()  # init detectorimg_file = sys.argv[1]
img = cv2.imread(img_file)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to gray img to speedfaces = detector(img_gray, 1)  # detect input img, para 1 means 1 times upsamlefor face in faces:  # may be many faces in one image
    print(face)
    y1 = face.bottom()  # detect box bottom y value
    y2 = face.top()  # top y value
    x1 = face.left()  # left x value
    x2 = face.right()  # right x value
    print(x1, x2, y1, y2)    # add detect box in image
    cv2.rectangle(img,(int(x1),int(y1)),(int(x2),int(y2)),(0,255,0),3)
    
cv2.imshow('new.jpg', img)
cv2.waitKey(0)

python test.py image1

[(161, 247) (546, 632)]161 546 632 247

#!/usr/bin/python# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt##   This example program shows how to find frontal human faces in an image.  In#   particular, it shows how you can take a list of images from the command#   line and display each on the screen with red boxes overlaid on each human#   face.##   The examples/faces folder contains some jpg images of people.  You can run#   this program on them and see the detections by executing the#   following command:#       ./face_detector.py ../examples/faces/*.jpg##   This face detector is made using the now classic Histogram of Oriented#   Gradients (HOG) feature combined with a linear classifier, an image#   pyramid, and sliding window detection scheme.  This type of object detector#   is fairly general and capable of detecting many types of semi-rigid objects#   in addition to human faces.  Therefore, if you are interested in making#   your own object detectors then read the train_object_detector.py example#   program.  ### COMPILING/INSTALLING THE DLIB PYTHON INTERFACE#   You can install dlib using the command:#       pip install dlib##   Alternatively, if you want to compile dlib yourself then go into the dlib#   root folder and run:#       python setup.py install##   Compiling dlib should work on any operating system so long as you have#   CMake installed.  On Ubuntu, this can be done easily by running the#   command:#       sudo apt-get install cmake##   Also note that this example requires Numpy which can be installed#   via the command:#       pip install numpyimport sysimport dlib

detector = dlib.get_frontal_face_detector()
win = dlib.image_window()for f in sys.argv[1:]:
    print("Processing file: {}".format(f))
    img = dlib.load_rgb_image(f)    # The 1 in the second argument indicates that we should upsample the image
    # 1 time.  This will make everything bigger and allow us to detect more
    # faces.
    dets = detector(img, 1)
    print("Number of faces detected: {}".format(len(dets)))    for i, d in enumerate(dets):
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
            i, d.left(), d.top(), d.right(), d.bottom()))

    win.clear_overlay()
    win.set_image(img)
    win.add_overlay(dets)
    dlib.hit_enter_to_continue()# Finally, if you really want to you can ask the detector to tell you the score# for each detection.  The score is bigger for more confident detections.# The third argument to run is an optional adjustment to the detection threshold,# where a negative value will return more detections and a positive value fewer.# Also, the idx tells you which of the face sub-detectors matched.  This can be# used to broadly identify faces in different orientations.if (len(sys.argv[1:]) > 0):
    img = dlib.load_rgb_image(sys.argv[1])
    dets, scores, idx = detector.run(img, 1, -1)    for i, d in enumerate(dets):
        print("Detection {}, score: {}, face_type:{}".format(
            d, scores[i], idx[i]))