Python3+OpenCV实现简单交通标志识别

2023-03-13 10:56| 来源: 网络整理| 查看: 265

由于该项目是针对中小学生竞赛并且是第一次举行，所以识别的目标交通标志仅仅只有直行、右转、左转和停车让行。

数据集：https://pan.baidu.com/s/1sLl0CadEutv3PQXhmqpCXw 提取码：mp2x

源代码：https://github.com/ccxiao5/Traffic_sign_recognition

整体流程如下：

数据集收集（包括训练集和测试集的分类）图像预处理图像标注根据标注分割得到目标图像 HOG特征提取训练得到模型将模型带入识别算法进行识别

我的数据目录树。其中test_images/train_images是收集得到原始数据集。realTest/realTrain是预处理后的图像。dataTest/dataTrain是经过分类处理得到的图像，HogTest/HogTrain是通过XML标注后裁剪得到的图像。HogTest_affine/HogTrain_affine是经过仿射变换处理扩充的训练集和测试集。imgTest_hog.txt/imgTrain_hog.txt是测试集和训练集的Hog特征

一、图像处理

　　由于得到的数据集图像大小不一（如下），我们首先从中心区域裁剪并调整正方形图像的大小，然后将处理后的图像保存到realTrain和realTest里面。

图片名称对应关系如下：

img_label = { "000":"Speed_limit_5", "001":"Speed_limit_15", "002":"Speed_limit_30", "003":"Speed_limit_40", "004":"Speed_limit_50", "005":"Speed_limit_60", "006":"Speed_limit_70", "007":"Speed_limit_80", "008":"No straight or right turn", "009":"No straight or left turn", "010":"No straight", "011":"No left turn", "012":"Do not turn left and right", "013":"No right turn", "014":"No Overhead", "015":"No U-turn", "016":"No Motor vehicle", "017":"No whistle", "018":"Unrestricted speed_40", "019":"Unrestricted speed_50", "020":"Straight or turn right", "021":"Straight", "022":"Turn left", "023":"Turn left or turn right", "024":"Turn right", "025":"Drive on the left side of the road", "026":"Drive on the right side of the road", "027":"Driving around the island", "028":"Motor vehicle driving", "029":"Whistle", "030":"Non-motorized", "031":"U-turn", "032":"Left-right detour", "033":"traffic light", "034":"Drive cautiously", "035":"Caution Pedestrians", "036":"Attention non-motor vehicle", "037":"Mind the children", "038":"Sharp turn to the right", "039":"Sharp turn to the left", "040":"Downhill steep slope", "041":"Uphill steep slope", "042":"Go slow", "044":"Right T-shaped cross", "043":"Left T-shaped cross", "045":"village", "046":"Reverse detour", "047":"Railway crossing-1", "048":"construction", "049":"Continuous detour", "050":"Railway crossing-2", "051":"Accident-prone road section", "052":"stop", "053":"No passing", "054":"No Parking", "055":"No entry", "056":"Deceleration and concession", "057":"Stop For Check" } def center_crop(img_array, crop_size=-1, resize=-1, write_path=None): ##从中心区域裁剪并调整正方形图像的大小。 rows = img_array.shape[0] cols = img_array.shape[1] if crop_size==-1 or crop_size>max(rows,cols): crop_size = min(rows, cols) row_s = max(int((rows-crop_size)/2), 0) row_e = min(row_s+crop_size, rows) col_s = max(int((cols-crop_size)/2), 0) col_e = min(col_s+crop_size, cols) img_crop = img_array[row_s:row_e,col_s:col_e,] if resize>0: img_crop = cv2.resize(img_crop, (resize, resize)) if write_path is not None: cv2.imwrite(write_path, img_crop) return img_crop

　　然后根据得到的realTrain和realTest自动生成带有的xml文件

def write_img_to_xml(imgfile, xmlfile): img = cv2.imread(imgfile) img_folder, img_name = os.path.split(imgfile) img_height, img_width, img_depth = img.shape doc = Document() annotation = doc.createElement("annotation") doc.appendChild(annotation) folder = doc.createElement("folder") folder.appendChild(doc.createTextNode(img_folder)) annotation.appendChild(folder) filename = doc.createElement("filename") filename.appendChild(doc.createTextNode(img_name)) annotation.appendChild(filename) size = doc.createElement("size") annotation.appendChild(size) width = doc.createElement("width") width.appendChild(doc.createTextNode(str(img_width))) size.appendChild(width) height = doc.createElement("height") height.appendChild(doc.createTextNode(str(img_height))) size.appendChild(height) depth = doc.createElement("depth") depth.appendChild(doc.createTextNode(str(img_depth))) size.appendChild(depth) with open(xmlfile, "w") as f: doc.writexml(f, indent="\t", addindent="\t", newl="\n", encoding="utf-8") /home/xiao5/Desktop/Test2/data/realTest/PNGImages 000_1_0001_1_j.png 640 640 3

然后对realTrain和realTest的图片进行标注，向默认XML添加新的信息（矩形信息）。

PNGImages 021_1_0001_1_j.png C:\Users\xiao5\Desktop\realTest\PNGImages\021_1_0001_1_j.png Unknown 640 640 3 0 Straight Unspecified 0 0 13 22 573 580

处理完后利用我们添加的矩形将图片裁剪下来并且重命名进行分类。主要思路是：解析XML文档，根据标签进行分类，如果是直行、右转、左转、停止，那么就把它从原图中裁剪下来并重命名,如果没有那么就认为是负样本，其中在处理负样本的时候，我进行了颜色识别，把一张负样本图片根据颜色（红色、蓝色）裁剪成几张负样本，这样做的好处是：我们在进行交通标志的识别时，也是使用的颜色识别来选取到交通标志，我们从负样本中分割出来的相近颜色样本有利于负样本的训练，提高模型精度。

def produce_proposals(xml_dir, write_dir, square=False, min_size=30): ##返回proposal_num对象 proposal_num = {} for cls_name in classes_name: proposal_num[cls_name] = 0 index = 0 for xml_file in os.listdir(xml_dir): img_path, labels = parse_xml(os.path.join(xml_dir,xml_file)) img = cv2.imread(img_path) ##如果图片中没有出现定义的那几种交通标志就把它当成负样本 if len(labels) == 0: neg_proposal_num = produce_neg_proposals(img_path, write_dir, min_size, square, proposal_num["background"]) proposal_num["background"] = neg_proposal_num else: proposal_num = produce_pos_proposals(img_path, write_dir, labels, min_size, square=True, proposal_num=proposal_num) if index%100 == 0: print ("total xml file number = ", len(os.listdir(xml_dir)), "current xml file number = ", index) print ("proposal num = ", proposal_num) index += 1 return proposal_num

为了提高模型的精确度，还对目标图片（四类图片）进行仿射变换来扩充训练集。

def affine(img, delta_pix): rows, cols, _ = img.shape pts1 = np.float32([[0,0], [rows,0], [0, cols]]) pts2 = pts1 + delta_pix M = cv2.getAffineTransform(pts1, pts2) res = cv2.warpAffine(img, M, (rows, cols)) return res def affine_dir(img_dir, write_dir, max_delta_pix): img_names = os.listdir(img_dir) img_names = [img_name for img_name in img_names if img_name.split(".")[-1]=="png"] for index, img_name in enumerate(img_names): img = cv2.imread(os.path.join(img_dir,img_name)) save_name = os.path.join(write_dir, img_name.split(".")[0]+"f.png") delta_pix = np.float32(np.random.randint(-max_delta_pix,max_delta_pix+1,[3,2])) img_a = affine(img, delta_pix) cv2.imwrite(save_name, img_a)

二、HOG特征提取

处理好图片后分别对训练集和测试集进行特征提取得到imgTest_HOG.txt和imgTrain_HOG.txt

def hog_feature(img_array, resize=(64,64)): ##提取HOG特征 img = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY) img = cv2.resize(img, resize) bins = 9 cell_size = (8, 8) cpb = (2, 2) norm = "L2" features = ft.hog(img, orientations=bins, pixels_per_cell=cell_size, cells_per_block=cpb, block_norm=norm, transform_sqrt=True) return features def extra_hog_features_dir(img_dir, write_txt, resize=(64,64)): ##提取目录中所有图像HOG特征 img_names = os.listdir(img_dir) img_names = [os.path.join(img_dir, img_name) for img_name in img_names] if os.path.exists(write_txt): os.remove(write_txt) with open(write_txt, "a") as f: index = 0 for img_name in img_names: img_array = cv2.imread(img_name) features = hog_feature(img_array, resize) label_name = img_name.split("/")[-1].split("_")[0] label_num = img_label[label_name] row_data = img_name + "\t" + str(label_num) + "\t" for element in features: row_data = row_data + str(round(element,3)) + " " row_data = row_data + "\n" f.write(row_data) if index%100 == 0: print ("total image number = ", len(img_names), "current image number = ", index) index += 1 三、模型训练

利用得到的HOG特征进行训练模型得到svm_model.pkl

def load_hog_data(hog_txt): img_names = [] labels = [] hog_features = [] with open(hog_txt, "r") as f: data = f.readlines() for row_data in data: row_data = row_data.rstrip() img_path, label, hog_str = row_data.split("\t") img_name = img_path.split("/")[-1] hog_feature = hog_str.split(" ") hog_feature = [float(hog) for hog in hog_feature] #print "hog feature length = ", len(hog_feature) img_names.append(img_name) labels.append(label) hog_features.append(hog_feature) return img_names, np.array(labels), np.array(hog_features) def svm_train(hog_features, labels, save_path="./svm_model.pkl"): clf = SVC(C=10, tol=1e-3, probability = True) clf.fit(hog_features, labels) joblib.dump(clf, save_path) print ("finished.") 四、交通标志识别及实验测试

交通标志识别的流程：颜色识别得到阈值范围内的二值图、然后进行轮廓识别、剔除多余矩阵。

def preprocess_img(imgBGR): ##将图像由RGB模型转化成HSV模型 imgHSV = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2HSV) Bmin = np.array([110, 43, 46]) Bmax = np.array([124, 255, 255]) ##使用inrange(HSV,lower,upper)设置阈值去除背景颜色 img_Bbin = cv2.inRange(imgHSV,Bmin, Bmax) Rmin2 = np.array([165, 43, 46]) Rmax2 = np.array([180, 255, 255]) img_Rbin = cv2.inRange(imgHSV,Rmin2, Rmax2) img_bin = np.maximum(img_Bbin, img_Rbin) return img_bin ''' 提取轮廓,返回轮廓矩形框 ''' def contour_detect(img_bin, min_area=0, max_area=-1, wh_ratio=2.0): rects = [] ##检测轮廓，其中cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_NONE 存储所有的边界点 ##findContours返回三个值:第一个值返回img，第二个值返回轮廓信息，第三个返回相应轮廓的关系 contours, hierarchy= cv2.findContours(img_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) if len(contours) == 0: return rects max_area = img_bin.shape[0]*img_bin.shape[1] if max_area= min_area and area =Max_W*Max_H: Max_X,Max_Y,Max_W,Max_H=r proposal = img[Max_Y:(Max_Y+Max_H),Max_X:(Max_X+Max_W)]##用Numpy数组对图像像素进行访问时，应该先写图像高度所对应的坐标(y,row)，再写图像宽度对应的坐标(x,col)。 cv2.rectangle(img,(Max_X,Max_Y), (Max_X+Max_W,Max_Y+Max_H), (0,255,0), 2) cv2.imshow("proposal", proposal) cls_prop = hog_extra_and_svm_class(proposal, clf) cls_prop = np.round(cls_prop, 2) cls_num = np.argmax(cls_prop)##找到最大相似度的索引 if cls_names[cls_num] is not "background": print(cls_names[cls_num]) else: print("N/A") cv2.imshow('camera',img) cv2.waitKey(40) cv2.destroyAllWindows() cap.release()

【本文地址】

公司简介

联系我们