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#!/usr/bin/python
"""
This program is demonstration for face and object detection using haar-like features.
The program finds faces in a camera image or video stream and displays a red box around them.
Original C implementation by: ?
Python implementation by: Roman Stanchak, James Bowman
Updated: Copyright (c) 2016, Tal Regev.
"""
import sys
import os
from optparse import OptionParser
import rospy
import sensor_msgs.msg
from cv_bridge import CvBridge
import cv2
import numpy
# Parameters for haar detection
# From the API:
# The default parameters (scale_factor=2, min_neighbors=3, flags=0) are tuned
# for accurate yet slow object detection. For a faster operation on real video
# images the settings are:
# scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING,
# min_size=<minimum possible face size
min_size = (10, 10)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
if __name__ == '__main__':
# TODO add this file in the repository and make it relative to this python script. (not all people will run this from linux)
haarfile = '/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml'
parser = OptionParser(usage = "usage: %prog [options]")
parser.add_option("-c", "--cascade", action="store", dest="cascade", type="str", help="Haar cascade file, default %default", default = haarfile)
parser.add_option("-t", "--topic", action="store", dest="topic", type="str", help="Topic to find a face on, default %default", default = '/camera/rgb/image_raw')
parser.add_option("-ct", "--ctopic", action="store", dest="ctopic", type="str", help="Compressed topic to find a face on, default %default", default = '/camera/rgb/image/compressed')
(options, args) = parser.parse_args()
cascade = cv2.CascadeClassifier()
cascade.load(options.cascade)
br = CvBridge()
def detect_and_draw(imgmsg):
img = br.imgmsg_to_cv2(imgmsg, "bgr8")
# allocate temporary images
new_size = (int(img.shape[1] / image_scale), int(img.shape[0] / image_scale))
# convert color input image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# scale input image for faster processing
small_img = cv2.resize(gray, new_size, interpolation = cv2.INTER_LINEAR)
small_img = cv2.equalizeHist(small_img)
if(cascade):
faces = cascade.detectMultiScale(small_img, haar_scale, min_neighbors, haar_flags, min_size)
if faces is not None:
for (x, y, w, h) in faces:
# the input to detectMultiScale was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv2.rectangle(img, pt1, pt2, (255, 0, 0), 3, 8, 0)
cv2.imshow("result", img)
cv2.waitKey(6)
def compressed_detect_and_draw(compressed_imgmsg):
img = br.compressed_imgmsg_to_cv2(compressed_imgmsg, "bgr8")
# allocate temporary images
new_size = (int(img.shape[1] / image_scale), int(img.shape[0] / image_scale))
# convert color input image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# scale input image for faster processing
small_img = cv2.resize(gray, new_size, interpolation = cv2.INTER_LINEAR)
small_img = cv2.equalizeHist(small_img)
if(cascade):
faces = cascade.detectMultiScale(small_img, haar_scale, min_neighbors, haar_flags, min_size)
if faces is not None:
for (x, y, w, h) in faces:
# the input to detectMultiScale was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv2.rectangle(img, pt1, pt2, (255, 0, 0), 3, 8, 0)
cv2.imshow("compressed_result", img)
cv2.waitKey(6)
rospy.init_node('rosfacedetect')
rospy.Subscriber(options.topic, sensor_msgs.msg.Image, detect_and_draw)
rospy.Subscriber(options.ctopic, sensor_msgs.msg.CompressedImage, compressed_detect_and_draw)
rospy.spin()
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