本文使用CPU来做运算，未使用GPU。练习项目，参考了网上部分资料。

        如果要用TensorFlow做检测，可以参考这里

使用GPU运行基于pytorch的yolov3代码的准备工作_little han的博客-CSDN博客文章浏览阅读943次。记录一下自己刚拿到带独显的电脑，如何成功使用上GPU跑程序的过程。List item环境：win10平台：pycharm代码是基于pytorch的yolo目标检测程序，是B站的一个up分享的，链接如下：https://www.bilibili.com/video/BV14f4y1q7ms1 下载安装cuda以及CUDNN教程参考：深度学习环境搭建（GPU）CUDA安装（完全版）注意：教程中cuda的安装地址选择系统默认值，否则容易出错，即系统找不到路径。在配置路径时，可以先检查自己的cudahttps://blog.csdn.net/weixin_42217041/article/details/118107802

        以下是代码，仅供参考：

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt#读取支持的类名文件coco.names
classNamesFile = './coco.names'
classNames = []
with open(classNamesFile,'rt') as f:classNames = f.read().rstrip('\n').split('\n')
classNum = len(classNames)
print('Total ' + str(classNum) + ' classes: ')
print(classNames)#置信度阈值
confidenceThreshold = 0.5
#非极大值抑制参数
NMSThreshold = 0.3
#Blob参数
targetWidth = 608
#yolov3的模型配置和权重
modelConfigFile = './yolov3.cfg'
modelWeightsFile = './yolov3.weights'
#读取dnn网络
net = cv.dnn.readNetFromDarknet(modelConfigFile, modelWeightsFile)
#设置网络偏好使用的后端和目标，这里使用CPU
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
#获取网络的层名
layerNames = net.getLayerNames()
#print("All layer names:")
#print(layerNames)
#获得未连接的输出层
#https://blog.csdn.net/weixin_43745234/article/details/124628811
outputLayerNames = [layerNames[i - 1] for i in net.getUnconnectedOutLayers()]
print(outputLayerNames)#读取视频
video = cv.VideoCapture('../../SampleVideos/Party.mp4')
width = video.get(cv.CAP_PROP_FRAME_WIDTH)
height = video.get(cv.CAP_PROP_FRAME_HEIGHT)
fps = video.get(cv.CAP_PROP_FPS)
fourcc = int(video.get(cv.CAP_PROP_FOURCC))
totalFrames = video.get(cv.CAP_PROP_FRAME_COUNT)
print("Video Properties: resolution - (", width, height, ") FPS - ", fps, " FOURCC - ", chr(fourcc&0xFF), chr((fourcc>>8)&0xFF), chr((fourcc>>16)&0xFF),chr((fourcc>>24)&0xFF), " Frame Count - ", totalFrames)def DebugOutputs(outputs):print("Length of Outputs:")print(len(outputs))for i in range(len(outputs)):print(outputs[i].shape)print(outputs[i][0])def FindObjects(outputs, img, confidenceThreshold):h,w,c = img.shapeprint(h,w,c)boxes = []classIds = []confidences = []for output in outputs:for detection in output:scores = detection[5:]classId = np.argmax(scores)confidence = scores[classId]#设置置信度阈值if confidence > confidenceThreshold:#yolov3的输出层的shape一般是一个二维数组(nBoxes, 85)#nBoxes 表示该层输出的边界框的数量#85列信息 表示每个边框相关的信息，比如边框位置（中心X，Y，长度和宽度W,H(百分比)），#        第五个值为该边框最有可能的物体分类ID号（confidence，置信度）#        剩下的80个位置的值是每一类物体的ID和为该物体的概率信息等#print(detection)#print(detection[0],detection[1],detection[2],detection[3],detection[4])boxWidth = int(detection[2] * w)boxHeight = int(detection[3] * h)boxX,boxY = int(detection[0] * w - boxWidth / 2),int(detection[1] * h - boxHeight / 2)boxes.append([boxX,boxY,boxWidth,boxHeight])classIds.append(classId)confidences.append(float(confidence))#print("Detected classes:")#for id in classIds:#print(classNames[id])#非极大值抑制indices = cv.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold)#print(indices)for index in indices:box = boxes[index]x,y,w,h = box[0],box[1],box[2],box[3]#print(box)#绘制边框和文字信息cv.rectangle(img, (x,y), (x+w,y+h), (0,255,0),2)text = '{}: {:.3f}'.format(classNames[classIds[index]], confidences[index])(text_w, text_h), baseline = cv.getTextSize(text, cv.FONT_HERSHEY_SIMPLEX, 0.5, 2)cv.rectangle(img, (x, y - text_h - baseline), (x + text_w, y), (255,0,255), -1)cv.putText(img, text, (x, y - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)while (True):ret,frame = video.read()if ret == False:break;#从Image创建blob#参考资料：https://blog.csdn.net/weixin_42216109/article/details/103010206blob = cv.dnn.blobFromImage(frame, 1/255, (targetWidth, targetWidth), [0,0,0])net.setInput(blob)#拿到输出层的结果outputs = net.forward(outputLayerNames)#DebugOutputs(outputs)FindObjects(outputs, frame, confidenceThreshold)cv.imshow('VideoPlayer', frame)if cv.waitKey(1) & 0xFF == ord('q'):break;video.release()
cv.destroyAllWindows()

运行结果，还不错：