13.基于opencv-python+yolov3实现实时视频目标检测

import numpy
import cv2
import time

coco='F:/coco.names'
# 读取模型和权重
configPath='F:/yolo.cfg'
weightPath='F:/yolov3.weights'
CONFIDENCE = 0.2  # 过滤弱检测的最小概率
THRESHOLD = 0.4  # 非最大值抑制阈值
net = cv2.dnn.readNetFromDarknet(configPath, weightPath)
clicked = False

def onMouse(event, x, y, flags, param):
    global clicked
    if event == cv2.EVENT_LBUTTONUP:
        clicked = True

cameraCapture = cv2.VideoCapture(0)  # 打开编号为0的摄像头
cv2.namedWindow('img')  # 给视频框命名
cv2.setMouseCallback('img', onMouse)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1 and not clicked:  # 当循环没结束，并且剩余的帧数大于零时进行下面的程序
    # 加载图片、转为blob格式、送入网络输入层
    blobImg = cv2.dnn.blobFromImage(frame, 1.0 / 255.0, (416, 416), None, True,
                                False)  # # net需要的输入是blob格式的，用blobFromImage这个函数来转格式
    net.setInput(blobImg)  # # 调用setInput函数将图片送入输入层

    # 获取网络输出层信息（所有输出层的名字），设定并前向传播
    outInfo = net.getUnconnectedOutLayersNames()  # # 前面的yolov3架构也讲了，yolo在每个scale都有输出，outInfo是每个scale的名字信息，供net.forward使用
    layerOutputs = net.forward(outInfo)  # 得到各个输出层的、各个检测框等信息，是二维结构。

    # 拿到图片尺寸
    (H, W) = frame.shape[:2]
    # 过滤layerOutputs
    # layerOutputs的第1维的元素内容: [center_x, center_y, width, height, objectness, N-class score data]
    # 过滤后的结果放入：
    boxes = []  # 所有边界框（各层结果放一起）
    confidences = []  # 所有置信度
    classIDs = []  # 所有分类ID

    # # 1）过滤掉置信度低的框框
    for out in layerOutputs:  # 各个输出层
        for detection in out:  # 各个框框
            # 拿到置信度
            scores = detection[5:]  # 各个类别的置信度
            classID = numpy.argmax(scores)  # 最高置信度的id即为分类id
            confidence = scores[classID]  # 拿到置信度

            # 根据置信度筛查
            if confidence > CONFIDENCE:
                box = detection[0:4] * numpy.array([W, H, W, H])  # 将边界框放会图片尺寸
                (centerX, centerY, width, height) = box.astype("int")
                x = int(centerX - (width / 2))
                y = int(centerY - (height / 2))
                boxes.append([x, y, int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)

    # # 2）应用非最大值抑制(non-maxima suppression，nms)进一步筛掉
    idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD)  # boxes中，保留的box的索引index存入idxs
    # 得到labels列表
    with open(coco, 'rt') as f:
        labels = f.read().rstrip('\n').split('\n')
    # 应用检测结果
    numpy.random.seed(42)
    COLORS = numpy.random.randint(0, 255, size=(len(labels), 3),
                                  dtype="uint8")  # 框框显示颜色，每一类有不同的颜色，每种颜色都是由RGB三个值组成的，所以size为(len(labels), 3)
    if len(idxs) > 0:
        for i in idxs.flatten():  # indxs是二维的，第0维是输出层，所以这里把它展平成1维
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])

            color = [int(c) for c in COLORS[classIDs[i]]]
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)  # 线条粗细为2px
            text = "{}: {:.4f}".format(labels[classIDs[i]], confidences[i])
            cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color,
                        2)  # cv.FONT_HERSHEY_SIMPLEX字体风格、0.5字体大小、粗细2px
    cv2.imshow('img', frame)
    success, frame = cameraCapture.read()  # 摄像头获取下一帧
cv2.destroyWindow('img')
cameraCapture.release()

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-47pk8gzi-1662134393335)(https://git.poker/quinhua/pics/blob/main/markdown/bhgtfdgtfrdcd.webp?raw=true)]