内容简介:【 声明:版权所有,欢迎转载,请勿用于商业用途。 联系信箱:feixiaoxing @163.com】opencv大家用的很多,但是opencv的效率实在不敢恭维。所以,大家开始慢慢寻找其他的一些开源库,dlib就是不错的一个选择。当然,opencv也不是一无是处,现在主要用来进行基本图像数据的处理。dlib可以进行人脸检测、人脸旋转、人脸识别、视频检测等等,对于一般的场景来说,基本不会有很大的问题。1、安装opencv
【 声明:版权所有,欢迎转载,请勿用于商业用途。 联系信箱:feixiaoxing @163.com】
opencv大家用的很多,但是opencv的效率实在不敢恭维。所以,大家开始慢慢寻找其他的一些开源库,dlib就是不错的一个选择。当然,opencv也不是一无是处,现在主要用来进行基本图像数据的处理。dlib可以进行人脸检测、人脸旋转、人脸识别、视频检测等等,对于一般的场景来说,基本不会有很大的问题。
1、安装opencv
shell> sudo pip install opencv-python
2、安装dib
shell> sudo apt-get install libpython-dev
shell> sudo pip install dlib
因为这里dlib是需要进行 c语言 编译的,所以libpython-dev安装也是十分必要的
3、最简单的dlib应用
#!/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 numpy import sys import cv2 import 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]))
4、其他资源
以上所述就是小编给大家介绍的《随想录(dlib学习)》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
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