本项目基于pyqt5开发，驱动两款工业相机：Basler acA2040、HIKVISION GiGE，获取图像后使用PaddleX库搭建目标检测、图像分割、图像分类功能。
SoftwareofIndustrialCameraUsePaddle
data: (超参数配置：类别、置信度、像素 设定)
gigetype1.json
gigetype2,json
MVImport (GiGE相机的依赖文件)
plaforms (生成exe所需库文件)
software (服务依赖)
UI：
startwindow.py (开始界面)
mainwindow_one.py (单相机主界面)
mainwindow_two.py (多相机主界面)
configCamera.py (相机配置界面)
cameraImgs.py (用于存放相机图像的文件，可以直接import这个文件中的CameraImgs类，使用getImg方法就可以得到相机的图像。)
infer.py (预测)
main.py (单相机工程的入口文件，包含操作软件主界面的逻辑函数)
main_one.py (多相机工程的入口文件，包含操作软件主界面的逻辑函数)
openGigeCamera.py (打开GIGE相机配置界面的文件，其中包含操作相机配置界面的逻辑函数，例如：在配置界面的Combox中显示相机的列表)
openUSBCamera.py (打开USB相机配置界面的文件，其中包含操作相机配置界面的逻辑函数，例如：在配置界面的Combox中显示相机的列表)
start.py (启动主程序)
visualizeimg.py (可视化)
- 本项目支持单相机、多相机两种模式可供选择。
# 安装依赖
cd ./SoftwareofIndustrialCameraUsePaddle
pip install -r requirements.txt -i https://mirror.baidu.com/pypi/simple# 启动主程序
python start.py
- 单机此操作界面按钮进入主界面，主界面中两个下拉列表可供选择相机品牌（海康相机/Basler相机）及序号。
- 打开相机显示图片的函数：
openGigeCamera.py
def open_camera(self):
self.thread_camera = threading.Thread(target=self.showImgThread)
self.thread_camera.start()- 加载模型：
infer.py
def loadmodel1(self):
with open("./data/gigetype1.json",'r',encoding='utf8') as fp:
modelconfigs = json.load(fp)
gige1modelPath= modelconfigs['model_path']
if(not gige1modelPath== ''):
self.model_gige1= pdx.load_model(gige1modelPath)
def loadmodel2(self):
with open("./data/gigetype2.json",'r',encoding='utf8')as fp:
modelconfigs = json.load(fp)
gige2modelPath= modelconfigs["model_path"]
if(not gige2modelPath== ''):
self.model_gige2= pdx.load_model(gige2modelPath)- 设置显示识别结果：
cameraImgs.py
@staticmethod
def setInferImg(flag,img):
'''
:param flag: 3->usb的图 1->gige1 2-> gige2
:return: None
'''
if flag == 3:
CameraImgs.USBCameraInferImg = img
elif flag == 1:
CameraImgs.GIGECameraInferImg_1 = img
elif flag == 2:
CameraImgs.GIGECameraInferImg_2 = img
@staticmethod
def getInferImg(flag):
if flag == 3:
return CameraImgs.USBCameraInferImg
elif flag == 1:
return CameraImgs.GIGECameraInferImg_1
elif flag == 2:
return CameraImgs.GIGECameraInferImg_2
@staticmethod
def setinfer_flag1(flag1):
CameraImgs.infer_flag1=flag1
@staticmethod
def setinfer_flag2(flag2):
CameraImgs.infer_flag2=flag2
@staticmethod
def getinfer_flag1():
return CameraImgs.infer_flag1
@staticmethod
def getinfer_flag2():
return CameraImgs.infer_flag2 - confidence和bbox大小的限定部分代码：
bisualizeimg
default_font_size = max(np.sqrt(height * width) // 90, 10 // scale)
linewidth = max(default_font_size / 4, 1)
labels = list()
for dt in np.array(results):
if dt['category'] not in labels:
labels.append(dt['category'])
color_map = get_color_map_list(256)
keep_results = []
areas = []
if cameratype =="gige1":
with open("./data/gigetype1.json",'r',encoding='utf8')as fp:
modelconfigs = json.load(fp)
elif cameratype =="gige2":
with open("./data/gigetype2.json",'r',encoding='utf8')as fp:
modelconfigs = json.load(fp)
threshold = 0.5
pix_w = 0
pix_h = 0
for dt in np.array(results):
cname, bbox, score = dt['category'], dt['bbox'], dt['score']
for i in range(0,len(modelconfigs)):
if cname in modelconfigs['confidence_set_pix'][i]['cname']:
threshold = modelconfigs['confidence_set_pix'][i]['confidence']
pix_w = modelconfigs['confidence_set_pix'][i]['set_pix_w']
pix_h = modelconfigs['confidence_set_pix'][i]['set_pix_h']
break
if score < threshold:
continue
if bbox[2]< pix_w or bbox[3]<pix_h:
continue- RPC模式暂时隐藏：
infer.py
# def detectmode1rpc(self):
# if(self.servermode==1): #采用服务器模式预测
# infer_flag = CameraImgs.getinfer_flag1()
# while(infer_flag==1):
# infer_flag = CameraImgs.getinfer_flag1()
# start = time.time()
# img = CameraImgs.getImg(3)
# conn = grpc.insecure_channel(_HOST+':'+_PORT)
# str = base64.b64encode(img)
# client = data_pb2_grpc.FormatDataStub(channel=conn)
# response = client.DoFormat(data_pb2.actionrequest(img=str,modeltype='1',threshold=0.5))
# strimg = response.img
# decode_img = base64.b64decode(strimg)
# resultimg = np.frombuffer(decode_img,dtype=np.uint8)
# resultimg = np.reshape(resultimg,(480,640,3))./data/gigetype1.json
confidence_set_pix 是置信度、矩形框/像素大小设定；model_path是模型路径设定。
其中 confidence_set_pix 中 cname 为类型、confidence 为置信度、set_pix_w 为矩形框/像素宽、set_pix_h 为矩形框/像素高值。
**注：**类别可自行添加，此项目以5类为例。
{"confidence_set_pix":
[{"cname": "person",
"confidence": 0.5,
"set_pix_w": 128,
"set_pix_h": 128},
{"cname": "bicycle",
"confidence": 0.5,
"set_pix_w": 128,
"set_pix_h": 128},
{"cname": "car",
"confidence": 0.5,
"set_pix_w": 128,
"set_pix_h": 128},
{"cname": "mouse",
"confidence": 0.5,
"set_pix_w": 128,
"set_pix_h": 128},
{"cname": "cup",
"confidence": 0.2,
"set_pix_w": 20,
"set_pix_h": 20}],
"model_path": "E:/Work/Paddle/20201107/softwareOfPaddlePaddle/yolov3_mobilenetv1_coco"}
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