参照opencv官网例程写了一个基于python的行人检测程序，实现了和自带检测器基本一致的检测效果。

网址 ：https://docs.opencv.org/3.4.0/d5/d77/train_HOG_8cpp-example.html

opencv版本：3.4.0

训练集和opencv官方用了同一个，可以从http://pascal.inrialpes.fr/data/human/下载，在网页的最下方“here（970MB处）”，用迅雷下载比较快（500kB/s）。训练集文件比较乱，需要仔细阅读下载首页的文字介绍。注意pos文件夹下的png图片属性，它们用opencv无法直接打开，linux系统下也无法显示，需要用matlab读取图片->保存才行，很奇怪的操作。

代码如下，尽可能与opencv官方例程保持一致，但省略了很多不是很关键的东西。训练一次大概需要十几分钟

import cv2import numpy as npimport random def load_images(dirname, amout = 9999): img_list = [] file = open(dirname) img_name = file.readline() while img_name != '': # 文件尾 img_name = dirname.rsplit(r'/', 1)[0] + r'/' + img_name.split('/', 1)[1].strip('\n') img_list.append(cv2.imread(img_name)) img_name = file.readline() amout -= 1 if amout <= 0: # 控制读取图片的数量 break return img_list # 从每一张没有人的原始图片中随机裁出10张64*128的图片作为负样本def sample_neg(full_neg_lst, neg_list, size): random.seed(1) width, height = size[1], size[0] for i in range(len(full_neg_lst)): for j in range(10): y = int(random.random() * (len(full_neg_lst[i]) - height)) x = int(random.random() * (len(full_neg_lst[i][0]) - width)) neg_list.append(full_neg_lst[i][y:y + height, x:x + width]) return neg_list # wsize: 处理图片大小，通常64*128; 输入图片尺寸>= wsizedef computeHOGs(img_lst, gradient_lst, wsize=(128, 64)): hog = cv2.HOGDescriptor() # hog.winSize = wsize for i in range(len(img_lst)): if img_lst[i].shape[1] >= wsize[1] and img_lst[i].shape[0] >= wsize[0]: roi = img_lst[i][(img_lst[i].shape[0] - wsize[0]) // 2: (img_lst[i].shape[0] - wsize[0]) // 2 + wsize[0], \ (img_lst[i].shape[1] - wsize[1]) // 2: (img_lst[i].shape[1] - wsize[1]) // 2 + wsize[1]] gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) gradient_lst.append(hog.compute(gray)) # return gradient_lst def get_svm_detector(svm): sv = svm.getSupportVectors() rho, _, _ = svm.getDecisionFunction(0) sv = np.transpose(sv) return np.append(sv, [[-rho]], 0) # 主程序# 第一步：计算HOG特征neg_list = []pos_list = []gradient_lst = []labels = []hard_neg_list = []svm = cv2.ml.SVM_create()pos_list = load_images(r'G:/python_project/INRIAPerson/96X160H96/Train/pos.lst')full_neg_lst = load_images(r'G:/python_project/INRIAPerson/train_64x128_H96/neg.lst')sample_neg(full_neg_lst, neg_list, [128, 64])print(len(neg_list))computeHOGs(pos_list, gradient_lst)[labels.append(+1) for _ in range(len(pos_list))]computeHOGs(neg_list, gradient_lst)[labels.append(-1) for _ in range(len(neg_list))] # 第二步：训练SVMsvm.setCoef0(0)svm.setCoef0(0.0)svm.setDegree(3)criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 1000, 1e-3)svm.setTermCriteria(criteria)svm.setGamma(0)svm.setKernel(cv2.ml.SVM_LINEAR)svm.setNu(0.5)svm.setP(0.1) # for EPSILON_SVR, epsilon in loss function?svm.setC(0.01) # From paper, soft classifiersvm.setType(cv2.ml.SVM_EPS_SVR) # C_SVC # EPSILON_SVR # may be also NU_SVR # do regression tasksvm.train(np.array(gradient_lst), cv2.ml.ROW_SAMPLE, np.array(labels)) # 第三步：加入识别错误的样本，进行第二轮训练# 参考 http://masikkk.com/article/SVM-HOG-HardExample/hog = cv2.HOGDescriptor()hard_neg_list.clear()hog.setSVMDetector(get_svm_detector(svm))for i in range(len(full_neg_lst)): rects, wei = hog.detectMultiScale(full_neg_lst[i], winStride=(4, 4),padding=(8, 8), scale=1.05) for (x,y,w,h) in rects: hardExample = full_neg_lst[i][y:y+h, x:x+w] hard_neg_list.append(cv2.resize(hardExample,(64,128)))computeHOGs(hard_neg_list, gradient_lst)[labels.append(-1) for _ in range(len(hard_neg_list))]svm.train(np.array(gradient_lst), cv2.ml.ROW_SAMPLE, np.array(labels)) # 第四步：保存训练结果hog.setSVMDetector(get_svm_detector(svm))hog.save('myHogDector.bin')

以下是测试代码：

import cv2import numpy as np hog = cv2.HOGDescriptor()hog.load('myHogDector.bin')cap = cv2.VideoCapture(0)while True: ok, img = cap.read() rects, wei = hog.detectMultiScale(img, winStride=(4, 4),padding=(8, 8), scale=1.05) for (x, y, w, h) in rects: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2) cv2.imshow('a', img) if cv2.waitKey(1)&0xff == 27: # esc键 breakcv2.destroyAllWindows()

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