Opencv Q&A_3

2022/03/07

基于opencv实现的物体识别

准备工作

使用的是训练好的模型，需提前准备好：

配置文件：ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt

配重文件：frozen_inference_graph.pb

coco数据集的标注文件：coco.names

https://github.com/Fafa-DL/Opencv-project/tree/main/CVZone/05%20Object%20Detection%20OpenCV%20(MobileNet%20SSD)

代码

import cv2 as cv


coco_path = 'D:\work\\automation\Skill\Python\pythonWORK\cv\object_detect\Object_Detection_Files\coco.names'
# img_path = 'D:\work\\automation\Skill\Python\pythonWORK\cv\material\lena.png'
config_path = 'D:\work\\automation\Skill\Python\pythonWORK\cv\object_detect\Object_Detection_Files\ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
weights_path = 'D:\work\\automation\Skill\Python\pythonWORK\cv\object_detect\Object_Detection_Files\\frozen_inference_graph.pb'
detect_thres = 0.6  #检测阈值 越高越严格
nms_thres = 0.3     #nms阈值，越低越严格


with open(coco_path,'r') as f:      #读取coco数据集
    className = f.read()
    className = list(className.split('\n'))

###以下配置后续深入学习，当前仅做使用即可
net = cv.dnn_DetectionModel(weights_path,config_path)   #创建dnn_detect对象(权重路径，配置路径)
net.setInputSize(320,320)
net.setInputScale(1.0/127.5)
net.setInputMean((127.5,127.5,127.5))
net.setInputSwapRB(True)



cap = cv.VideoCapture(0)
cap.set(3,640)
cap.set(4,480)
cap.set(10,150) #亮度



while True:
    flag, img = cap.read()
    classIds, confs, bbox = net.detect(img, confThreshold=0.5)  # 返回依次为：识别结果序号，准确率，左上角坐标和右下角坐标
    if len(classIds) != 0:      #当检测数据不为空时才进行数据处理
        bbox = bbox.tolist()    #numpy.array转化为list
        confs = list(map(float, confs.flatten()))  #多维降一维，numpy.float全部映射为float，map转为list
        classIds_t = cv.dnn.NMSBoxes(bbox,confs,detect_thres,nms_thres)     #非极大抑制留下识别最好的，输出序号(坐标，识别准确率)

        for i in classIds_t:
            i = i[0]        #classIds_t是每个元素都带有[]的列表，去除列表
            cv.rectangle(img, bbox[i], color=(0, 255, 0), thickness=2)
            cv.putText(img, className[classIds[i][0] - 1] + '  ' + str(round(confs[i] * 100, 2)), (bbox[i][0] + 2, bbox[i][1] + 25),
                       cv.FONT_HERSHEY_SIMPLEX, 1,color=(0, 255, 0), thickness=2)   #classIds第一号元素序号为1，需减1
    cv.imshow('img', img)
    cv.waitKey(1)