自学教程：python+opencv3.4.0 实现HOG+SVM行人检测的示例代码

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')

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()