| #!/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 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])) | |