?#?import?the?necessary?packages
from?__future__?import?print_function??#確保代碼同時在Python2.7和Python3上兼容
from?imutils.object_detection?import?non_max_suppression
from?imutils?import?paths
import?numpy?as?np
import?argparse
import?imutils???#安裝庫pip?install?imutils?；pip?install?--upgrade?imutils更新版本大于v0.3.1
import?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（））

#?初始化我們的行人檢測器
hog?=?cv2.HOGDescriptor（）???#初始化方向梯度直方圖描述子
hog.setSVMDetector（cv2.HOGDescriptor_getDefaultPeopleDetector（））??#設(shè)置支持向量機(jī)（Support?Vector?Machine）使得它成為一個預(yù)先訓(xùn)練好了的行人檢測器
#到這里，我們的OpenCV行人檢測器已經(jīng)完全載入了，我們只需要把它應(yīng)用到一些圖像上
#---------------------------------------------------------------------------------------------------------
image_Path=“./images“;
#?loop?over?the?image?paths
for?imagePath?in?paths.list_images（image_Path）:?#args[“images“]
????#?load?the?image?and?resize?it?to?（1）?reduce?detection?time
????#?and?（2）?improve?detection?accuracy
????image?=?cv2.imread（imagePath）
????image?=?imutils.resize（image?width=min（400?image.shape[1]））
????orig?=?image.copy（）
????‘‘‘
????構(gòu)造了一個尺度scale=1.05的圖像金字塔，以及一個分別在x方向和y方向步長為（44）像素大小的滑窗
????scale的尺度設(shè)置得越大，在圖像金字塔中層的數(shù)目就越少，相應(yīng)的檢測速度就越快，但是尺度太大會導(dǎo)致行人出現(xiàn)漏檢；
????同樣的，如果scale設(shè)置得太小，將會急劇的增加圖像金字塔的層數(shù)，這樣不僅耗費計算資源，而且還會急劇地增加檢測過程
????中出現(xiàn)的假陽數(shù)目（也就是不是行人的被檢測成行人）。這表明，scale是在行人檢測過程中它是一個重要的參數(shù)，
????需要對scale進(jìn)行調(diào)參。我會在后面的文章中對detectMultiScale中的每個參數(shù)做些調(diào)研。
????‘‘‘
#?detect?people?in?the?image：
????（rects?weights）?=?hog.detectMultiScale（image?winStride=（4?4）
????????padding=（8?8）?scale=1.05）

????#?draw?the?original?bounding?boxes
????for?（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?people
????#應(yīng)用非極大抑制方法，通過設(shè)置一個閾值來抑制那些重疊的邊框
????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）

????#?draw?the?final?bounding?boxes
????for?（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?boxes
????filename?=?imagePath[imagePath.rfind（“/“）?+?1:]
????print（“[INFO]?{}:?{}?original?boxes?{}?after?suppression“.format（
????????filename?len（rects）?len（pick）））

????#?show?the?output?images
????cv2.imshow（“Before?NMS“?orig）
????cv2.imshow（“After?NMS“?image）
????cv2.waitKey（0）

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2019-03-01?17:56??PedestrianDetection\
?????目錄???????????0??2019-03-01?17:56??PedestrianDetection\.idea\
?????文件?????????183??2018-01-21?14:41??PedestrianDetection\.idea\misc.xml
?????文件?????????290??2018-01-21?14:40??PedestrianDetection\.idea\modules.xml
?????文件?????????455??2018-01-21?14:41??PedestrianDetection\.idea\PedestrianDetection.iml
?????文件???????10650??2018-01-22?09:32??PedestrianDetection\.idea\workspace.xml
?????文件??????????39??2018-12-18?18:04??PedestrianDetection\1.txt
?????文件????????3361??2018-12-10?19:27??PedestrianDetection\detect.py
?????目錄???????????0??2018-12-18?18:07??PedestrianDetection\image\
?????目錄???????????0??2019-03-01?17:56??PedestrianDetection\images\
?????文件???????98918??2018-12-18?17:41??PedestrianDetection\images\person_1.jpg
?????文件???????23582??2018-12-18?17:40??PedestrianDetection\images\person_10.jpg
?????文件??????150943??2018-12-18?17:56??PedestrianDetection\images\person_11.jpg
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_12.bmp
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_13.bmp
?????文件??????204593??2018-12-18?17:58??PedestrianDetection\images\person_14.jpeg
?????文件??????103383??2018-12-18?18:00??PedestrianDetection\images\person_15.jpeg
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_16.bmp
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_2.bmp
?????文件???????22728??2018-12-18?17:53??PedestrianDetection\images\person_220.jpg
?????文件??????921654??2003-05-20?13:37??PedestrianDetection\images\person_221.bmp
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_3.bmp
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_302.bmp
?????文件??????921654??2003-05-20?13:44??PedestrianDetection\images\person_390.bmp
?????文件??????945906??2018-12-18?17:47??PedestrianDetection\images\person_4.JPG
?????文件???????37042??2018-12-18?17:46??PedestrianDetection\images\person_5.jpg
?????文件???????71234??2018-12-18?17:47??PedestrianDetection\images\person_6.jpg
?????文件???????83034??2018-12-18?17:36??PedestrianDetection\images\person_7.jpg
?????文件???????30150??2018-12-18?17:50??PedestrianDetection\images\person_8.jpg
?????文件??????921654??2003-05-20?13:43??PedestrianDetection\images\person_9.bmp
?????文件????????3511??2018-12-18?18:06??PedestrianDetection\untitled1.py
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