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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ANSI Common Lisp
Paul Graham / Prentice Hall / 1995-11-12 / USD 116.40
For use as a core text supplement in any course covering common LISP such as Artificial Intelligence or Concepts of Programming Languages. Teaching students new and more powerful ways of thinking abo......一起来看看 《ANSI Common Lisp》 这本书的介绍吧!