opencv+yolov3实现目标检测

- 日理万妓 2022-11-13 04:20 424阅读 0赞

效果图  
![20210325163047725.png_pic_center][]

![2021032516351188.png_pic_center][] ![20210325164204392.png_pic_center][]

代码/font>  
语言：python

import cv2 as cv
    import argparse
    import numpy as np
    
    # Initialize the parameters
    confThreshold = 0.25  # Confidence threshold
    nmsThreshold = 0.4  # Non-maximum suppression threshold
    inpWidth = 320  # Width of network's input image
    inpHeight = 320  # Height of network's input image
    
    # Give the configuration and weight files for the model and load the network using them.
    modelConfiguration = "Yolo-Fastest-voc/yolo-fastest-xl.cfg"
    modelWeights = "Yolo-Fastest-voc/yolo-fastest-xl.weights"
    # Load names of classes
    classesFile = "voc.names"
    classes = None
    with open(classesFile, 'rt') as f:
        classes = f.read().rstrip('\n').split('\n')
    colors = [np.random.randint(0, 255, size=3).tolist() for _ in range(len(classes))]
    
    # Get the names of the output layers
    def getOutputsNames(net):
        # Get the names of all the layers in the network
        layersNames = net.getLayerNames()
        # print(dir(net))
        # Get the names of the output layers, i.e. the layers with unconnected outputs
        return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    
    # Draw the predicted bounding box
    def drawPred(classId, conf, left, top, right, bottom):
        # Draw a bounding box.
        cv.rectangle(frame, (left, top), (right, bottom), (0,0,255), thickness=4)
    
        label = '%.2f' % conf
    
        # Get the label for the class name and its confidence
        if classes:
            assert (classId < len(classes))
            label = '%s:%s' % (classes[classId], label)
    
        # Display the label at the top of the bounding box
        labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
        top = max(top, labelSize[1])
        # cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine), (255,255,255), cv.FILLED)
        cv.putText(frame, label, (left, top-10), cv.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), thickness=2)
    
    # Remove the bounding boxes with low confidence using non-maxima suppression
    def postprocess(frame, outs):
        frameHeight = frame.shape[0]
        frameWidth = frame.shape[1]
    
        classIds = []
        confidences = []
        boxes = []
        # Scan through all the bounding boxes output from the network and keep only the
        # ones with high confidence scores. Assign the box's class label as the class with the highest score.
        classIds = []
        confidences = []
        boxes = []
        for out in outs:
            for detection in out:
                scores = detection[5:]
                classId = np.argmax(scores)
                confidence = scores[classId]
                if confidence > confThreshold:
                    center_x = int(detection[0] * frameWidth)
                    center_y = int(detection[1] * frameHeight)
                    width = int(detection[2] * frameWidth)
                    height = int(detection[3] * frameHeight)
                    left = int(center_x - width / 2)
                    top = int(center_y - height / 2)
                    classIds.append(classId)
                    confidences.append(float(confidence))
                    boxes.append([left, top, width, height])
    
        # Perform non maximum suppression to eliminate redundant overlapping boxes with
        # lower confidences.
        indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
        for i in indices:
            i = i[0]
            box = boxes[i]
            left = box[0]
            top = box[1]
            width = box[2]
            height = box[3]
            drawPred(classIds[i], confidences[i], left, top, left + width, top + height)
    
    if __name__=='__main__':
        parser = argparse.ArgumentParser(description='Object Detection using YOLO in OPENCV')
        parser.add_argument('--image', type=str, default='person.jpg', help='Path to image file.')
        args = parser.parse_args()
    
        net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
        net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
        # Process inputs
        frame = cv.imread(args.image)
    
        # Create a 4D blob from a frame.
        blob = cv.dnn.blobFromImage(frame, 1/255.0, (inpWidth, inpHeight), [0, 0, 0], swapRB=False, crop=False)
    
        # Sets the input to the network
        net.setInput(blob)
    
        # Runs the forward pass to get output of the output layers
        outs = net.forward(getOutputsNames(net))
     
        # Remove the bounding boxes with low confidence
        postprocess(frame, outs)
    
        # Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
        t, _ = net.getPerfProfile()
        label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
        cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
    
        winName = 'Deep learning object detection in OpenCV'
        cv.namedWindow(winName,0)
        cv.imshow(winName, frame)
        cv.waitKey(0)
        cv.destroyAllWindows()

代码的中图 存放位置 是和资源代码一个文件夹。路径为相对路径  
parser.add\_argument(’–image’, type=str, default=‘person.jpg’, help=‘Path to image file.’)

所有代码见个人资源：  
[https://download.csdn.net/download/KOBEYU652453/13082584][https_download.csdn.net_download_KOBEYU652453_13082584]

![在这里插入图片描述][20210325163858833.jpg_pic_center]  
作者：电气余登武  
![2021032516425296.jpg_pic_center][]

[20210325163047725.png_pic_center]: /images/20221022/348da21cddbd4b999b1c79cc5996f6b6.png
[2021032516351188.png_pic_center]: https://img-blog.csdnimg.cn/2021032516351188.png#pic_center
[20210325164204392.png_pic_center]: /images/20221022/960f308c42ea4a9584e169d50c3db33f.png
[https_download.csdn.net_download_KOBEYU652453_13082584]: https://download.csdn.net/download/KOBEYU652453/13082584
[20210325163858833.jpg_pic_center]: https://img-blog.csdnimg.cn/20210325163858833.jpg#pic_center
[2021032516425296.jpg_pic_center]: https://img-blog.csdnimg.cn/2021032516425296.jpg#pic_center