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#!/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 a webcam stream using OpenCV.
# It is also meant to demonstrate that rgb images from Dlib can be used with opencv by just
# swapping the Red and Blue channels.
#
# You can run this program and see the detections from your webcam by executing the
# following command:
# ./opencv_face_detection.py
#
# 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
import cv2
detector = dlib.get_frontal_face_detector()
cam = cv2.VideoCapture(0)
color_green = (0,255,0)
line_width = 3
while True:
ret_val, img = cam.read()
rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dets = detector(rgb_image)
for det in dets:
cv2.rectangle(img,(det.left(), det.top()), (det.right(), det.bottom()), color_green, line_width)
cv2.imshow('my webcam', img)
if cv2.waitKey(1) == 27:
break # esc to quit
cv2.destroyAllWindows()
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