river_demo/demo.py

import sys
sys.path.extend(['..','../AIlib' ])

from AI import AI_process,AI_process_v2,AI_process_forest,get_postProcess_para
import cv2,os,time
from segutils.segmodel import SegModel
from segutils.segmodel_trt import SegModel_STDC_trt
from segutils.trtUtils import DetectMultiBackend
from models.experimental import attempt_load
from utils.torch_utils import select_device
from utilsK.queRiver import get_labelnames,get_label_arrays
import numpy as np
import torch
from utilsK.masterUtils import get_needed_objectsIndex
def river_demo_v2():
    ##预先设置的参数
    device_='0'  ##选定模型，可选 cpu,'0','1'
    ###注意TRT模型生成时，就需要对应cuda device，下面的trt文件是cuda:0生成的，device只能是0  
    ##以下参数目前不可改
    
    gpuname='3090'
    Detweights = "../AIlib/weights/yolov5/class8/bestcao_%s_fp16.engine"%(gpuname) ##升级后的检测模型
    labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表

    
    seg_nclass = 2 
    #Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
    Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640_%s_fp16.engine'%(gpuname) ##升级的分割模型
    
    postFile= '../AIlib/conf/para.json'
    digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3} 
    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
   
    ####模型选择参数用如下：
    mode_paras=[
        {"id":"0","config":{"k1":"v1","k2":"v2"}},
        {"id":"1","config":{"k1":"v1","k2":"v2"}},
        {"id":"2","config":{"k1":"v1","k2":"v2"}},
        {"id":"3","config":{"k1":"v1","k2":"v2"}},
        {"id":"4","config":{"k1":"v1","k2":"v2"}},
        {"id":"5","config":{"k1":"v1","k2":"v2"}},
        {"id":"6","config":{"k1":"v1","k2":"v2"}},
        {"id":"7","config":{"k1":"v1","k2":"v2"}},
    ]

    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
    #allowedList=[0,1,2,3]
    ##加载模型，准备好显示字符
    device = select_device(device_)
    names=get_labelnames(labelnames)
    
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
    half = device.type != 'cpu'  # half precision only supported on CUDA
    
    segmodel = SegModel_STDC_trt(weights=Segweights,modelsize=(640,360),std=(0.229, 0.224, 0.225),mean=(0.485, 0.456, 0.406),device=device)
    model = DetectMultiBackend(Detweights, device=device)
    
    ##图像测试
    #url='images/examples/20220624_响水河_12300_1621.jpg'
    impth = 'images/slope/'
    outpth = 'images/results/'
    folders = os.listdir(impth)
    for i in range(len(folders)):
        imgpath = os.path.join(impth, folders[i])
        im0s=[cv2.imread(imgpath)]
        H,W,C = im0s[0].shape
        time00 = time.time()
        p_result,timeOut = AI_process_v2(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
        time11 = time.time()
        image_array = p_result[1]
        cv2.imwrite(  os.path.join( outpth,folders[i] ) ,image_array )
        print('%s,%d*%d,AI-process: %.1f, %s'%(folders[i],H,W, (time11 - time00) * 1000,timeOut))
        
def river_demo():
    ##预先设置的参数
    device_='1'  ##选定模型，可选 cpu,'0','1'
     
    ##以下参数目前不可改
    #Detweights = "../AIlib/weights/yolov5/class5/best_5classes.pt" 
    #labelnames = "../AIlib/weights/yolov5/class5/labelnames.json"
    
    Detweights = "../AIlib/weights/yolov5/class8/bestcao.pt" ##升级后的检测模型
    labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
    
    seg_nclass = 2 
    #Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
    Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640.pth' ##升级的分割模型
    
    postFile= '../AIlib/conf/para.json'
    digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3} 
    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
   
    ####模型选择参数用如下：
    mode_paras=[
        {"id":"0","config":{"k1":"v1","k2":"v2"}},
        {"id":"1","config":{"k1":"v1","k2":"v2"}},
        {"id":"2","config":{"k1":"v1","k2":"v2"}},
        {"id":"3","config":{"k1":"v1","k2":"v2"}},
        {"id":"4","config":{"k1":"v1","k2":"v2"}},
        {"id":"5","config":{"k1":"v1","k2":"v2"}},
        {"id":"6","config":{"k1":"v1","k2":"v2"}},
        {"id":"7","config":{"k1":"v1","k2":"v2"}},
    ]

    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
    #allowedList=[0,1,2,3]
    
    

    ##加载模型，准备好显示字符
    device = select_device(device_)
    names=get_labelnames(labelnames)
    
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
    half = device.type != 'cpu'  # half precision only supported on CUDA
    model = attempt_load(Detweights, map_location=device)  # load FP32 model
    if half: model.half()
    
    segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
    
    
    
    ##图像测试
    #url='images/examples/20220624_响水河_12300_1621.jpg'
    impth = 'images/slope/'
    outpth = 'images/results/'
    folders = os.listdir(impth)
    for i in range(len(folders)):
        imgpath = os.path.join(impth, folders[i])
        im0s=[cv2.imread(imgpath)]
        H,W,C = im0s[0].shape
        time00 = time.time()
        p_result,timeOut = AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
        time11 = time.time()
        image_array = p_result[1]
        cv2.imwrite(  os.path.join( outpth,folders[i] ) ,image_array )
        print('%s,%d*%d,AI-process: %.1f, %s'%(folders[i],H,W, (time11 - time00) * 1000,timeOut))
        
def road_forest_demo(business ):

    ##使用森林，道路模型,business 控制['forest','road']
    ##预先设置的参数
    device_='1'  ##选定模型，可选 cpu,'0','1'
    
    
    
    ##以下参数目前不可改
    #business='forest';imageW=4916  ####森林模型
    #business='road';
    imageW=1536     ####道路模型
    digitFont= { 'line_thickness':2, 'fontSize':1.0} ###数字显示的线宽度，大小; 如果都是None，则采用默认大小
    Detweights="../AIlib/weights/%s/best.pt"%(business)
    labelnames = "../AIlib/weights/%s/labelnames.json"%(business)

    postFile= '../AIlib/conf/para.json'
    print( Detweights,labelnames  ) 
    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
    
    ####模型选择参数用如下：
    mode_paras=[
        {
            "id":"0",
            "config":{
            "k1":"v1",
            "k2":"v2"
            }
        },
        {
            "id":"1",
            "config":{
            "k1":"v1",
            "k2":"v2"
            }
        }
    ]

    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
    #allowedList=[0,1,2,3]
    
    print('####line108###')

    ##只加载检测模型，准备好显示字符
    device = select_device(device_)
    names=get_labelnames(labelnames)
    #imageW=4915;###默认是1920，在森林巡检的高清图像中是4920
    outfontsize=int(imageW/1920*40);###
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=outfontsize,fontpath="../AIlib/conf/platech.ttf")
    half = device.type != 'cpu'  # half precision only supported on CUDA
    model = attempt_load(Detweights, map_location=device)  # load FP32 model
    if half: model.half()
    segmodel = None 
    
    
    
    ##图像测试
    #url='images/examples/20220624_响水河_12300_1621.jpg'
    impth = 'images/%s/'%(business)
    outpth = 'images/results/'
    folders = os.listdir(impth)
    folders.sort()
    for i in range(len(folders)):
    #for i in range(2):
        imgpath = os.path.join(impth, folders[i])
        im0s=[cv2.imread(imgpath)]
        time00 = time.time()
        #使用不同的函数。每一个领域采用一个函数
        p_result,timeOut = AI_process_forest(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
        time11 = time.time()
        image_array = p_result[1]
        cv2.imwrite(  os.path.join( outpth,folders[i] ) ,image_array )
        
        print('----image:%s, process:%s ,save:%s, %s'%(folders[i],(time11-time00) * 1000, (time.time() - time11) * 1000,timeOut) )

    
def jkm_demo():
    from utilsK.jkmUtils import pre_process,post_process,get_return_data
    img_type = 'plate' ## code,plate
    par={'code':{'weights':'../AIlib/weights/jkm/health_yolov5s_v3.jit','img_type':'code','nc':10 },
        'plate':{'weights':'../AIlib/weights/jkm/plate_yolov5s_v3.jit','img_type':'plate','nc':1 },
        'conf_thres': 0.4,
        'iou_thres':0.45,
        'device':'cuda:0',
        'plate_dilate':(0.5,0.1)
    }
     ###加载模型
    device = torch.device(par['device'])
    jit_weights = par['code']['weights']
    model = torch.jit.load(jit_weights)

    jit_weights =  par['plate']['weights']
    model_plate = torch.jit.load(jit_weights)

    imgd='images/plate'
    imgpaths = os.listdir(imgd)
    for imgp in imgpaths[0:]:
        #imgp = 'plate_IMG_20221030_100612.jpg'
        imgpath = os.path.join(imgd,imgp)
        im0 = cv2.imread(imgpath) #读取数据
        img ,padInfos = pre_process(im0,device) ##预处理
        if img_type=='code':  pred = model(img) ##模型推理
        else: pred = model_plate(img)
        boxes = post_process(pred,padInfos,device,conf_thres= par['conf_thres'], iou_thres= par['iou_thres'],nc=par[img_type]['nc'])  #后处理
        dataBack=get_return_data(im0,boxes,modelType=img_type,plate_dilate=par['plate_dilate'])
        print(imgp,boxes,dataBack['type'])
        for key in dataBack.keys():
            if isinstance(dataBack[key],list):
                cv2.imwrite(  'images/results/%s_%s.jpg'%( imgp.replace('.jpg','').replace('.png',''),key),dataBack[key][0] )    ###返回值： dataBack

        
if __name__=="__main__":
    river_demo_v2()
    #road_forest_demo('forest'  )
    #jkm_demo()