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56 lines
2.0 KiB
Python
Executable File
56 lines
2.0 KiB
Python
Executable File
#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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# This example program shows how to find frontal human faces in a webcam stream using OpenCV.
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# It is also meant to demonstrate that rgb images from Dlib can be used with opencv by just
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# swapping the Red and Blue channels.
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#
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# You can run this program and see the detections from your webcam by executing the
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# following command:
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# ./opencv_face_detection.py
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#
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# This face detector is made using the now classic Histogram of Oriented
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# Gradients (HOG) feature combined with a linear classifier, an image
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# pyramid, and sliding window detection scheme. This type of object detector
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# is fairly general and capable of detecting many types of semi-rigid objects
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# in addition to human faces. Therefore, if you are interested in making
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# your own object detectors then read the train_object_detector.py example
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# program.
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#
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake installed. On Ubuntu, this can be done easily by running the
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# command:
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# sudo apt-get install cmake
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#
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# Also note that this example requires Numpy which can be installed
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# via the command:
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# pip install numpy
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import sys
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import dlib
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import cv2
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detector = dlib.get_frontal_face_detector()
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cam = cv2.VideoCapture(0)
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color_green = (0,255,0)
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line_width = 3
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while True:
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ret_val, img = cam.read()
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rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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dets = detector(rgb_image)
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for det in dets:
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cv2.rectangle(img,(det.left(), det.top()), (det.right(), det.bottom()), color_green, line_width)
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cv2.imshow('my webcam', img)
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if cv2.waitKey(1) == 27:
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break # esc to quit
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cv2.destroyAllWindows()
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