先看程序实现效果

1. 设置环境

IDE：PyCharm
Python：3.8
新建 project： Yolov3

安装依赖 Package, (路径: 菜单 PyCharm > Preferences > Project: Yolov3 > Python Interpreter)

numpy
opencv-python

2. 下载对象名字coco.names, 参数文件cfg，和权重文件 weights

coco.names 下载
https://github.com/pjreddie/darknet/blob/master/data/coco.names

cfg和weights，这里会下载两套，一套是精准预测YOLOv3-320，一套是速度比较快不那么精准YOLOv3-tiny。下载请点击下面的链接，注意名字要命名为

yolov3-320.cfg
yolov3-320.weights
yolov3-tiny.cfg
yolov3-tiny.weights

Model	Train	Test	mAP	FLOPS	FPS	Cfg	Weights
YOLOv3-320	COCO trainval	test-dev	51.5	38.97 Bn	45	cfg	weights
YOLOv3-416	COCO trainval	test-dev	55.3	65.86 Bn	35	cfg	weights
YOLOv3-608	COCO trainval	test-dev	57.9	140.69 Bn	20	cfg	weights
YOLOv3-tiny	COCO trainval	test-dev	33.1	5.56 Bn	220	cfg	weights

3. 代码实现

3.1 开发摄像头，持续读取图像

import cv2
import numpy as npcap = cv2.VideoCapture(0)with open(classesFile, 'rt') as f:classNames = f.read().rstrip('\n').split('\n')while True:success, img = cap.read()cv2.imshow('Image', img)cv2.waitKey(1)

3.2 读取可以识别的物体coco.names, 权重和参数

import cv2
import numpy as npcap = cv2.VideoCapture(0)classesFile = 'coco.names'
classNames = []
with open(classesFile, 'rt') as f:classNames = f.read().rstrip('\n').split('\n')
# print(classNames)
# print(len(classNames))modelConfiguration = 'yolov3-320.cfg'
modelWeights = 'yolov3-320.weights'# modelConfiguration = 'yolov3-tiny.cfg'
# modelWeights = 'yolov3-tiny.weights'net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

3.3 cnn神经网络前向传播，计算预测值，有3个输出layers

import cv2
import numpy as npcap = cv2.VideoCapture(0)
whT = 320
confThreshold = 0.5
nmsThreshold = 0.3# ...while True:success, img = cap.read()blob = cv2.dnn.blobFromImage(img, 1/255, (whT, whT), [0,0,0], 1, crop=False)net.setInput(blob)layerNames = net.getLayerNames()# print(layerNames)outputNames = [layerNames[i[0]-1] for i in net.getUnconnectedOutLayers()]# print(outputNames)# print(net.getUnconnectedOutLayers())outputs = net.forward(outputNames)# print(outputs[0].shape)# print(outputs[1].shape)# print(outputs[2].shape)# print(outputs[0][0])#...cv2.imshow('Image', img)cv2.waitKey(1)

三个layers 输出参数shape如下：

(300, 85)
(1200, 85)
(4800, 85)

80个数据分类：
位置(0~3)：cx,cy,w,h
检测到物体的概率(4)：confidence
每一个物体的概率比如(5~79)：比如car 0.93

3.4 检测物体，去除概率比较低的检测，只留下最大的，添加物体框，物体名字，概率

def findObjects(outputs, img):hT, wT, cT = img.shapebbox = []classIds = []confs = []for output in outputs:for det in output:scores = det[5:]classId = np.argmax(scores)confidence = scores[classId]if confidence > confThreshold:w, h = int(det[2]*wT), int(det[3]*hT)x, y = int((det[0]*wT) - w/2), int((det[1]*hT) - h/2)bbox.append([x,y,w,h])classIds.append(classId)confs.append(float(confidence))#print(len(bbox))indices = cv2.dnn.NMSBoxes(bbox,confs,confThreshold,nmsThreshold)# print(indices)for i in indices:i = i[0]box = bbox[i]x,y,w,h = box[0], box[1], box[2], box[3]cv2.rectangle(img,(x,y),(x+w, y+h),(255,0,255),2)cv2.putText(img,f'{classNames[classIds[i]].upper()} {int(confs[i]*100)}%', (x,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6,(255,0,255),2)

3.5 为了更快检测，可以替换参数pkg和权重weights为tiny

 modelConfiguration = 'yolov3-tiny.cfg'modelWeights = 'yolov3-tiny.weights'

4. 完整代码如下

import cv2
import numpy as npcap = cv2.VideoCapture(0)
whT = 320
confThreshold = 0.5
nmsThreshold = 0.3classesFile = 'coco.names'
classNames = []
with open(classesFile, 'rt') as f:classNames = f.read().rstrip('\n').split('\n')
# print(classNames)
# print(len(classNames))modelConfiguration = 'yolov3-320.cfg'
modelWeights = 'yolov3-320.weights'# modelConfiguration = 'yolov3-tiny.cfg'
# modelWeights = 'yolov3-tiny.weights'net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)def findObjects(outputs, img):hT, wT, cT = img.shapebbox = []classIds = []confs = []for output in outputs:for det in output:scores = det[5:]classId = np.argmax(scores)confidence = scores[classId]if confidence > confThreshold:w, h = int(det[2]*wT), int(det[3]*hT)x, y = int((det[0]*wT) - w/2), int((det[1]*hT) - h/2)bbox.append([x,y,w,h])classIds.append(classId)confs.append(float(confidence))#print(len(bbox))indices = cv2.dnn.NMSBoxes(bbox,confs,confThreshold,nmsThreshold)# print(indices)for i in indices:i = i[0]box = bbox[i]x,y,w,h = box[0], box[1], box[2], box[3]cv2.rectangle(img,(x,y),(x+w, y+h),(255,0,255),2)cv2.putText(img,f'{classNames[classIds[i]].upper()} {int(confs[i]*100)}%', (x,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6,(255,0,255),2)while True:success, img = cap.read()blob = cv2.dnn.blobFromImage(img, 1/255, (whT, whT), [0,0,0], 1, crop=False)net.setInput(blob)layerNames = net.getLayerNames()# print(layerNames)outputNames = [layerNames[i[0]-1] for i in net.getUnconnectedOutLayers()]# print(outputNames)# print(net.getUnconnectedOutLayers())outputs = net.forward(outputNames)# print(outputs[0].shape)# print(outputs[1].shape)# print(outputs[2].shape)# print(outputs[0][0])findObjects(outputs, img)cv2.imshow('Image', img)cv2.waitKey(1)

参考

https://www.youtube.com/watch?v=GGeF_3QOHGE&ab_channel=Murtaza%27sWorkshop-RoboticsandAI
https://www.youtube.com/watch?v=9AycYn9gj1U&ab_channel=Murtaza%27sWorkshop-RoboticsandAI
https://www.youtube.com/watch?v=xK4li3jinSw&ab_channel=Murtaza%27sWorkshop-RoboticsandAI
https://www.youtube.com/watch?v=xK4li3jinSw&ab_channel=Murtaza%27sWorkshop-RoboticsandAI
https://pjreddie.com/darknet/yolo/

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