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#!/usr/bin/env python
""" - Version 1.0 2012-02-11
Based on the OpenCV demo code
Extends the script which takes care of user input and image display
Created for the Pi Robot Project:
Copyright (c) 2011 Patrick Goebel. All rights reserved.
Modify by, this version can be used in opencv3.
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details at:
import rospy
import cv2
from ros2opencv3 import ROS2OpenCV3
class FaceDetector(ROS2OpenCV3):
def __init__(self, node_name):
super(FaceDetector, self).__init__(node_name)
# Get the paths to the cascade XML files for the Haar detectors.
# These are set in the launch file.
cascade_1 = rospy.get_param("~cascade_1", "")
cascade_2 = rospy.get_param("~cascade_2", "")
cascade_3 = rospy.get_param("~cascade_3", "")
# Initialize the Haar detectors using the cascade files
self.cascade_1 = cv2.CascadeClassifier(cascade_1)
self.cascade_2 = cv2.CascadeClassifier(cascade_2)
self.cascade_3 = cv2.CascadeClassifier(cascade_3)
# Set cascade parameters that tend to work well for faces.
# Can be overridden in launch file
self.haar_scaleFactor = rospy.get_param("~haar_scaleFactor", 1.3)
self.haar_minNeighbors = rospy.get_param("~haar_minNeighbors", 3)
self.haar_minSize = rospy.get_param("~haar_minSize", 30)
self.haar_maxSize = rospy.get_param("~haar_maxSize", 150)
# Store all parameters together for passing to the detector
self.haar_params = dict(scaleFactor = self.haar_scaleFactor,
minNeighbors = self.haar_minNeighbors,
minSize = (self.haar_minSize, self.haar_minSize),
maxSize = (self.haar_maxSize, self.haar_maxSize)
# Do we should text on the display?
self.show_text = rospy.get_param("~show_text", True)
# Intialize the detection box
self.detect_box = None
# Track the number of hits and misses
self.hits = 0
self.misses = 0
self.hit_rate = 0
def process_image(self, cv_image):
# Create a greyscale version of the image
grey = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
# Equalize the histogram to reduce lighting effects
grey = cv2.equalizeHist(grey)
# Attempt to detect a face
self.detect_box = self.detect_face(grey)
# Did we find one?
if self.detect_box is not None:
self.hits += 1
self.misses += 1
# Keep tabs on the hit rate so far
self.hit_rate = float(self.hits) / (self.hits + self.misses)
return cv_image
def detect_face(self, input_image):
# First check one of the frontal templates
if self.cascade_1:
faces = self.cascade_1.detectMultiScale(input_image, **self.haar_params)
# If that fails, check the profile template
if len(faces) == 0 and self.cascade_3:
faces = self.cascade_3.detectMultiScale(input_image, **self.haar_params)
# If that also fails, check a the other frontal template
if len(faces) == 0 and self.cascade_2:
faces = self.cascade_2.detectMultiScale(input_image, **self.haar_params)
# The faces variable holds a list of face boxes.
# If one or more faces are detected, return the first one.
if len(faces) > 0:
face_box = faces[0]
# If no faces were detected, print the "LOST FACE" message on the screen
if self.show_text:
font_face = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
cv2.putText(self.marker_image, "LOST FACE!",
(int(self.frame_size[0] * 0.65), int(self.frame_size[1] * 0.9)),
font_face, font_scale, (255, 50, 50))
face_box = None
# Display the hit rate so far
if self.show_text:
font_face = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
cv2.putText(self.marker_image, "Hit Rate: " +
str(trunc(self.hit_rate, 2)),
(20, int(self.frame_size[1] * 0.9)),
font_face, font_scale, (255, 255, 0))
return face_box
def trunc(f, n):
'''Truncates/pads a float f to n decimal places without rounding'''
slen = len('%.*f' % (n, f))
return float(str(f)[:slen])
if __name__ == '__main__':
node_name = "face_detector"
except KeyboardInterrupt:
print "Shutting down face detector node."
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