python dlib ubuntu 人脸识别2 使用mmod_human_face_detector进行人脸检测

mmod_human_face_detector.dat是训练好的模型，可以从这里下载：

http://dlib.net/files/mmod_human_face_detector.dat.bz2

输入参数：

1. 模型dat文件

2.要检测的人脸图片

import sys
import dlib

if len(sys.argv) < 3:
    print(
        "Call this program like this:\n"
        "   ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg\n"
        "You can get the mmod_human_face_detector.dat file from:\n"
        "    http://dlib.net/files/mmod_human_face_detector.dat.bz2")
    exit()

cnn_face_detector = dlib.cnn_face_detection_model_v1(sys.argv[1])
win = dlib.image_window()

for f in sys.argv[2:]:
    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 = cnn_face_detector(img, 1)
    '''
    This detector returns a mmod_rectangles object. This object contains a list of mmod_rectangle objects.
    These objects can be accessed by simply iterating over the mmod_rectangles object
    The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score.
    
    It is also possible to pass a list of images to the detector.
        - like this: dets = cnn_face_detector([image list], upsample_num, batch_size = 128)

    In this case it will return a mmod_rectangless object.
    This object behaves just like a list of lists and can be iterated over.
    '''
    print("Number of faces detected: {}".format(len(dets)))
    for i, d in enumerate(dets):
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(
            i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))

    rects = dlib.rectangles()
    rects.extend([d.rect for d in dets])

    win.clear_overlay()
    win.set_image(img)
    win.add_overlay(rects)
    dlib.hit_enter_to_continue()