255 lines
10 KiB
Python
255 lines
10 KiB
Python
import sys
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sys.path.extend(['..','../AIlib' ])
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from AI import AI_process,AI_process_v2,AI_process_forest,get_postProcess_para
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import cv2,os,time
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from segutils.segmodel import SegModel
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from segutils.segmodel_trt import SegModel_STDC_trt
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from segutils.trtUtils import DetectMultiBackend
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from models.experimental import attempt_load
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from utils.torch_utils import select_device
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from utilsK.queRiver import get_labelnames,get_label_arrays
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import numpy as np
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import torch
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from utilsK.masterUtils import get_needed_objectsIndex
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def river_demo_v2():
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##预先设置的参数
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device_='0' ##选定模型，可选 cpu,'0','1'
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###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
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##以下参数目前不可改
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gpuname='3090'###3090,2080Ti. 只能是其中之一
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Detweights = "../AIlib/weights/yolov5/class8/bestcao_%s_fp16.engine"%(gpuname) ##升级后的检测模型
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labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
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seg_nclass = 2
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#Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
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Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640_%s_fp16.engine'%(gpuname) ##升级的分割模型
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postFile= '../AIlib/conf/para.json'
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digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3}
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conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
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####模型选择参数用如下：
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mode_paras=[
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{"id":"0","config":{"k1":"v1","k2":"v2"}},
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{"id":"1","config":{"k1":"v1","k2":"v2"}},
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{"id":"2","config":{"k1":"v1","k2":"v2"}},
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{"id":"3","config":{"k1":"v1","k2":"v2"}},
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{"id":"4","config":{"k1":"v1","k2":"v2"}},
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{"id":"5","config":{"k1":"v1","k2":"v2"}},
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{"id":"6","config":{"k1":"v1","k2":"v2"}},
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{"id":"7","config":{"k1":"v1","k2":"v2"}},
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]
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allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
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#allowedList=[0,1,2,3]
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##加载模型，准备好显示字符
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device = select_device(device_)
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names=get_labelnames(labelnames)
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label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
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half = device.type != 'cpu' # half precision only supported on CUDA
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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)
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model = DetectMultiBackend(Detweights, device=device)
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##图像测试
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#url='images/examples/20220624_响水河_12300_1621.jpg'
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impth = 'images/slope/'
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outpth = 'images/results/'
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folders = os.listdir(impth)
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for i in range(len(folders)):
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imgpath = os.path.join(impth, folders[i])
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im0s=[cv2.imread(imgpath)]
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H,W,C = im0s[0].shape
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time00 = time.time()
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p_result,timeOut = AI_process_v2(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
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time11 = time.time()
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image_array = p_result[1]
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cv2.imwrite( os.path.join( outpth,folders[i] ) ,image_array )
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print('%s,%d*%d,AI-process: %.1f, %s'%(folders[i],H,W, (time11 - time00) * 1000,timeOut))
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def river_demo():
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##预先设置的参数
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device_='1' ##选定模型，可选 cpu,'0','1'
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##以下参数目前不可改
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#Detweights = "../AIlib/weights/yolov5/class5/best_5classes.pt"
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#labelnames = "../AIlib/weights/yolov5/class5/labelnames.json"
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Detweights = "../AIlib/weights/yolov5/class8/bestcao.pt" ##升级后的检测模型
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labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
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seg_nclass = 2
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#Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
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Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640.pth' ##升级的分割模型
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postFile= '../AIlib/conf/para.json'
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digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3}
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conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
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####模型选择参数用如下：
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mode_paras=[
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{"id":"0","config":{"k1":"v1","k2":"v2"}},
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{"id":"1","config":{"k1":"v1","k2":"v2"}},
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{"id":"2","config":{"k1":"v1","k2":"v2"}},
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{"id":"3","config":{"k1":"v1","k2":"v2"}},
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{"id":"4","config":{"k1":"v1","k2":"v2"}},
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{"id":"5","config":{"k1":"v1","k2":"v2"}},
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{"id":"6","config":{"k1":"v1","k2":"v2"}},
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{"id":"7","config":{"k1":"v1","k2":"v2"}},
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]
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allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
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#allowedList=[0,1,2,3]
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##加载模型，准备好显示字符
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device = select_device(device_)
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names=get_labelnames(labelnames)
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label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
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half = device.type != 'cpu' # half precision only supported on CUDA
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model = attempt_load(Detweights, map_location=device) # load FP32 model
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if half: model.half()
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segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
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##图像测试
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#url='images/examples/20220624_响水河_12300_1621.jpg'
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impth = 'images/slope/'
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outpth = 'images/results/'
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folders = os.listdir(impth)
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for i in range(len(folders)):
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imgpath = os.path.join(impth, folders[i])
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im0s=[cv2.imread(imgpath)]
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H,W,C = im0s[0].shape
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time00 = time.time()
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p_result,timeOut = AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
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time11 = time.time()
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image_array = p_result[1]
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cv2.imwrite( os.path.join( outpth,folders[i] ) ,image_array )
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print('%s,%d*%d,AI-process: %.1f, %s'%(folders[i],H,W, (time11 - time00) * 1000,timeOut))
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def road_forest_demo(business ):
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##使用森林，道路模型,business 控制['forest','road']
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##预先设置的参数
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device_='1' ##选定模型，可选 cpu,'0','1'
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##以下参数目前不可改
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#business='forest';imageW=4916 ####森林模型
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#business='road';
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imageW=1536 ####道路模型
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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
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Detweights="../AIlib/weights/%s/best.pt"%(business)
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labelnames = "../AIlib/weights/%s/labelnames.json"%(business)
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postFile= '../AIlib/conf/para.json'
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print( Detweights,labelnames )
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conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
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####模型选择参数用如下：
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mode_paras=[
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{
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"id":"0",
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"config":{
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"k1":"v1",
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"k2":"v2"
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}
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},
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{
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"id":"1",
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"config":{
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"k1":"v1",
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"k2":"v2"
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}
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}
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]
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allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
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#allowedList=[0,1,2,3]
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print('####line108###')
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##只加载检测模型，准备好显示字符
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device = select_device(device_)
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names=get_labelnames(labelnames)
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#imageW=4915;###默认是1920，在森林巡检的高清图像中是4920
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outfontsize=int(imageW/1920*40);###
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label_arraylist = get_label_arrays(names,rainbows,outfontsize=outfontsize,fontpath="../AIlib/conf/platech.ttf")
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half = device.type != 'cpu' # half precision only supported on CUDA
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model = attempt_load(Detweights, map_location=device) # load FP32 model
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if half: model.half()
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segmodel = None
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##图像测试
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#url='images/examples/20220624_响水河_12300_1621.jpg'
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impth = 'images/%s/'%(business)
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outpth = 'images/results/'
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folders = os.listdir(impth)
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folders.sort()
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for i in range(len(folders)):
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#for i in range(2):
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imgpath = os.path.join(impth, folders[i])
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im0s=[cv2.imread(imgpath)]
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time00 = time.time()
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#使用不同的函数。每一个领域采用一个函数
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p_result,timeOut = AI_process_forest(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
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time11 = time.time()
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image_array = p_result[1]
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cv2.imwrite( os.path.join( outpth,folders[i] ) ,image_array )
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print('----image:%s, process:%s ,save:%s, %s'%(folders[i],(time11-time00) * 1000, (time.time() - time11) * 1000,timeOut) )
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def jkm_demo():
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from utilsK.jkmUtils import pre_process,post_process,get_return_data
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img_type = 'plate' ## code,plate
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par={'code':{'weights':'../AIlib/weights/jkm/health_yolov5s_v3.jit','img_type':'code','nc':10 },
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'plate':{'weights':'../AIlib/weights/jkm/plate_yolov5s_v3.jit','img_type':'plate','nc':1 },
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'conf_thres': 0.4,
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'iou_thres':0.45,
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'device':'cuda:0',
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'plate_dilate':(0.5,0.1)
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}
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###加载模型
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device = torch.device(par['device'])
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jit_weights = par['code']['weights']
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model = torch.jit.load(jit_weights)
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jit_weights = par['plate']['weights']
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model_plate = torch.jit.load(jit_weights)
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imgd='images/plate'
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imgpaths = os.listdir(imgd)
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for imgp in imgpaths[0:]:
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#imgp = 'plate_IMG_20221030_100612.jpg'
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imgpath = os.path.join(imgd,imgp)
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im0 = cv2.imread(imgpath) #读取数据
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img ,padInfos = pre_process(im0,device) ##预处理
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if img_type=='code': pred = model(img) ##模型推理
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else: pred = model_plate(img)
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boxes = post_process(pred,padInfos,device,conf_thres= par['conf_thres'], iou_thres= par['iou_thres'],nc=par[img_type]['nc']) #后处理
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dataBack=get_return_data(im0,boxes,modelType=img_type,plate_dilate=par['plate_dilate'])
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print(imgp,boxes,dataBack['type'])
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for key in dataBack.keys():
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if isinstance(dataBack[key],list):
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cv2.imwrite( 'images/results/%s_%s.jpg'%( imgp.replace('.jpg','').replace('.png',''),key),dataBack[key][0] ) ###返回值： dataBack
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if __name__=="__main__":
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river_demo_v2()
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#road_forest_demo('forest' )
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#jkm_demo()
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