90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
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import cv2,os,time
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from models.experimental import attempt_load
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from segutils.segmodel import SegModel,get_largest_contours
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from utils.torch_utils import select_device
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from utilsK.queRiver import get_labelnames,get_label_arrays,post_process_
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from utils.datasets import letterbox
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import numpy as np
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import torch
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def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,half=True,device=' cuda:0',conf_thres=0.25, iou_thres=0.45,allowedList=[0,1,2,3]):
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#输入参数
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# im0s---原始图像列表
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# model---检测模型，segmodel---分割模型
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#输出：两个元素（列表，字符）构成的元组，[im0s[0],im0,det_xywh,iframe],strout
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# [im0s[0],im0,det_xywh,iframe]中，
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# im0s[0]--原始图像，im0--AI处理后的图像,iframe--帧号/暂时不需用到。
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# det_xywh--检测结果，是一个列表。
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# 其中每一个元素表示一个目标构成如：[float(cls_c), xc,yc,w,h, float(conf_c)]
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# #cls_c--类别，如0,1,2,3; xc,yc,w,h--中心点坐标及宽；conf_c--得分, 取值范围在0-1之间
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# #strout---统计AI处理个环节的时间
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# Letterbox
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img = [letterbox(x, 640, auto=True, stride=32)[0] for x in im0s]
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# Stack
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img = np.stack(img, 0)
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# Convert
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img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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seg_pred,segstr = segmodel.eval(im0s[0] )
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pred = model(img,augment=False)[0]
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datas = [[''], img, im0s, None,pred,seg_pred,10]
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p_result,timeOut = post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,10,object_config=allowedList)
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return p_result,timeOut
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def main():
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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 = "weights/yolov5/class5/best_5classes.pt"
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seg_nclass = 2
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Segweights = "weights/BiSeNet/checkpoint.pth"
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conf_thres,iou_thres,classes= 0.25,0.45,5
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labelnames = "weights/yolov5/class5/labelnames.json"
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rainbows = [ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
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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="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/examples/'
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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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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)
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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('----process:%s'%(folders[i]), (time.time() - time11) * 1000)
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if __name__=="__main__":
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main()
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