OpenCV 检测人

import cv2
import numpy as np
def is_inside(o,i):
    ox,oy,ow,oh = o
    ix,iy,iw,ih = i
    return ox>ix and oy>iy and ox+ow <ix+iw and oy+oh < iy+ih
def draw_person(image,person):
    x,y,w,h = person
    cv2.rectangle(img, (x,y),(x+w,y+h),(0,255,255),2)

img =cv2.imread("6.jpg")
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
found,w = hog.detectMultiScale(img)
found_filtered = []
for ri, r in enumerate(found):
    for qi,q in enumerate(found):
        if ri != qi and is_inside(r,q):
            break
    else:
        print(w[ri])
        found_filtered.append(r)
for person in found_filtered:
#for person in found:
    draw_person(img,person)
print("找到%d个人"%len(found_filtered))
cv2.imshow("people detection", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

'''
detectMultiScale(...) method of cv2.HOGDescriptor instance
    detectMultiScale(img[, hitThreshold[, winStride[, padding[, scale[, finalThreshold[, useMeanshiftGrouping]]]]]]) -> foundLocations, foundWeights
    .   @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
    .       of rectangles.
    .       @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
    .       @param foundLocations Vector of rectangles where each rectangle contains the detected object.
    .       @param foundWeights Vector that will contain confidence values for each detected object.
    .       @param hitThreshold Threshold for the distance between features and SVM classifying plane.
    .       Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
    .       But if the free coefficient is omitted (which is allowed), you can specify it manually here.
    .       @param winStride Window stride. It must be a multiple of block stride.
    .       @param padding Padding
    .       @param scale Coefficient of the detection window increase.
    .       @param finalThreshold Final threshold
    .       @param useMeanshiftGrouping indicates grouping algorithm
<1>img：源图像。
<2>foundlocations：检测出的物体的边缘。
<3>foundWeights: 检测窗口得分
<4>hit_threshold：阀值，特征向量和SVM划分超平面的距离，大于这个值的才作为目标返回。
<4>win_stride：窗口步长，必须是block步长的整数倍。
<5>padding：图片边缘补齐参数，gpu版本必须是(0,0)。
<6>scale0：检测窗口增长参数。
<7>finalThreshold：检测结果聚类参数<8>useMeanshiftGrouping:聚类方式选择的参数

'''