OpenCV实践之路——行人检测

参考：http://blog.csdn.net/xingchenbingbuyu/article/details/51255253
参考：http://blog.csdn.net/garfielder007/article/details/50441048

---

行人检测是视觉领域很热也很有用的一个主题，特别是在无人驾驶中，行人检测的重要性不言而喻。
在之前进行了人脸检测之后，行人检测就显得简单多了。过程大致与人脸检测一样，都是先加载分类器，然后进行多尺度检测。

c++

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/gpu/gpu.hpp>//#include <stdio.h>using namespace cv;int main(int argc, char** argv){    Mat img;    vector<Rect> people;    img = imread("xingren.jpg",1);    //定义HOG对象，采用默认参数，或者按照下面的格式自己设置    HOGDescriptor defaultHog;        //(cv::Size(64, 128), cv::Size(16, 16), cv::Size(8, 8),                                 //cv::Size(8, 8),9, 1, -1,                                 //cv::HOGDescriptor::L2Hys, 0.2, true,                                 //cv::HOGDescriptor::DEFAULT_NLEVELS);    //设置SVM分类器，用默认分类器    defaultHog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());    //对图像进行多尺度行人检测，返回结果为矩形框    defaultHog.detectMultiScale(img, people,0,Size(8,8),Size(0,0),1.03,2);    //画长方形，框出行人    for (int i = 0; i < people.size(); i++)    {        Rect r = people[i];        rectangle(img, r.tl(), r.br(), Scalar(0, 0, 255), 3);    }    namedWindow("检测行人", CV_WINDOW_AUTOSIZE);    imshow("检测行人", img);    waitKey(0);    return 0;}

python

import cv2from imutils.object_detection import non_max_suppressionimport numpy as npimg=cv2.imread("./image/img.jpg")orig = img.copy()# 定义HOG对象，采用默认参数，或者按照下面的格式自己设置defaultHog=cv2.HOGDescriptor()# 设置SVM分类器，用默认分类器defaultHog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())# 对图像进行多尺度行人检测，返回结果为矩形框# people=defaultHog.detectMultiScale(img, 0,(8,8),(32,32),1.05,2)# detect people in the image(rects, weights) = defaultHog.detectMultiScale(img, winStride=(4, 4),     padding=(8, 8), scale=1.05)# 画长方形，框出行人# for i in range(len(people)):#     r=people[0][i]#     cv2.rectangle(img,(r[0],r[1]),(r[2],r[3]),(0,255,0),2,cv2.LINE_AA)for (x, y, w, h) in rects:    cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)for (xA, yA, xB, yB) in pick:    cv2.rectangle(img, (xA, yA), (xB, yB), (0, 255, 0), 2)cv2.imshow("Before NMS", orig)cv2.imshow("After NMS", img)cv2.waitKey(0)

---

参考：http://blog.csdn.net/garfielder007/article/details/50441048

# import the necessary packagesfrom __future__ import print_functionfrom imutils.object_detection import non_max_suppressionfrom imutils import pathsimport numpy as npimport argparseimport imutilsimport cv2# construct the argument parse and parse the arguments# ap = argparse.ArgumentParser()# ap.add_argument("-i", "--images", required=True, help="path to images directory")# args = vars(ap.parse_args())# initialize the HOG descriptor/person detectorhog = cv2.HOGDescriptor()hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())# loop over the image paths# for imagePath in paths.list_images(args["images"]):# load the image and resize it to (1) reduce detection time# and (2) improve detection accuracyimagePath="./image/img.jpg"image = cv2.imread(imagePath)image = imutils.resize(image, width=min(400, image.shape[1]))orig = image.copy()# detect people in the image(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),      padding=(8, 8), scale=1.05)# draw the original bounding boxesfor (x, y, w, h) in rects:     cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)# apply non-maxima suppression to the bounding boxes using a# fairly large overlap threshold to try to maintain overlapping# boxes that are still peoplerects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)# draw the final bounding boxesfor (xA, yA, xB, yB) in pick:     cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)# show some information on the number of bounding boxesfilename = imagePath[imagePath.rfind("/") + 1:]print("[INFO] {}: {} original boxes, {} after suppression".format(     filename, len(rects), len(pick)))# show the output imagescv2.imshow("Before NMS", orig)cv2.imshow("After NMS", image)cv2.waitKey(0)