python+opencv3.4.0 实现HOG+SVM行人检测的示例代码
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2022-06-24 11:40:49
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_ho...
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 cv2 import numpy as np import 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>= wsize def 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svm svm.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 classifier svm.settype(cv2.ml.svm_eps_svr) # c_svc # epsilon_svr # may be also nu_svr # do regression task svm.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 cv2 import 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 break cv2.destroyallwindows()
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