# -*- coding: utf-8 -*-
"""
Created on Mon Sep 24 16:43:37 2018

@author: zy
"""

'''
HOG检测人
'''
import  cv2
import numpy as np

def is_inside(o,i):
    '''
    判断矩形o是不是在i矩形中
    
    args:
        o：矩形o  (x,y,w,h)
        i：矩形i  (x,y,w,h)
    '''
    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(img,person):
    '''
    在img图像上绘制矩形框person
    
    args:
        img：图像img
        person：人所在的边框位置 (x,y,w,h)
    '''
    x,y,w,h = person
    cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)
    

def detect_test():
    '''
    检测人
    '''
    img = cv2.imread('./image/person.jpg')
    rows,cols = img.shape[:2]
    sacle = 1.0
    #print('img',img.shape)
    img = cv2.resize(img,dsize=(int(cols*sacle),int(rows*sacle)))
    #print('img',img.shape)
    
    #创建HOG描述符对象
    #计算一个检测窗口特征向量维度：(64/8 - 1)*(128/8 - 1)*4*9 = 3780
    '''
    winSize = (64,128)
    blockSize = (16,16)    
    blockStride = (8,8)
    cellSize = (8,8)
    nbins = 9    
    hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins)  
    '''
    hog = cv2.HOGDescriptor()  
    #hist = hog.compute(img[0:128,0:64])   计算一个检测窗口的维度
    #print(hist.shape)
    detector = cv2.HOGDescriptor_getDefaultPeopleDetector()
    print('detector',type(detector),detector.shape)    
    hog.setSVMDetector(detector)


    #多尺度检测，found是一个数组，每一个元素都是对应一个矩形，即检测到的目标框
    found,w = hog.detectMultiScale(img)
    print('found',type(found),found.shape)
    
    #过滤一些矩形，如果矩形o在矩形i中，则过滤掉o
    found_filtered = []
    for ri,r in enumerate(found):
        for qi,q in enumerate(found):
            #r在q内？
            if ri != qi and is_inside(r,q):
                break
        else:
            found_filtered.append(r)
            
    for person in found_filtered:
        draw_person(img,person)
        
    cv2.imshow('img',img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
if __name__=='__main__':
    detect_test()