新增7个模型，优化两个模型

AI.py

@@ -18,6 +18,7 @@ import torch.nn.functional as F
 from copy import deepcopy
 from scipy import interpolate
 import glob
+from loguru import logger
 
 def get_images_videos(impth, imageFixs=['.jpg','.JPG','.PNG','.png'],videoFixs=['.MP4','.mp4','.avi']):
     imgpaths=[];###获取文件里所有的图像
@@ -74,6 +75,17 @@ def score_filter_byClass(pdetections,score_para_2nd):
         if  score > score_th:
             ret.append(det)
     return ret
+# 按类过滤
+def filter_byClass(pdetections,allowedList):
+    ret=[]
+    for det in pdetections:
+        score,cls = det[4],det[5]
+        if int(cls) in allowedList:
+            ret.append(det)
+        elif str(int(cls)) in allowedList:
+            ret.append(det)
+
+    return ret
     
 def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False,'score_byClass':{x:0.1 for x in range(30)} }, font={ 'line_thickness':None, 'fontSize':None,'boxLine_thickness':None,'waterLineColor':(0,255,255),'waterLineWidth':3} ,segPar={'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True},mode='others',postPar=None):
 
@@ -118,12 +130,21 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
     img /= 255.0
     time01=time.time()
 
-
     if segmodel:
-        seg_pred,segstr = segmodel.eval(im0s[0] )
+        seg_pred, segstr = segmodel.eval(im0s[0])
-        segFlag=True
+        # 当不存在分割信息，无需做分类检测
+        # segFlag = True
+        logger.info("分割信息seg_prd: {}  数据类型：{} ", seg_pred, np.count_nonzero(seg_pred))
+        if not np.any(seg_pred != 0):
+            time_info = 'No SegMentInfo'
+            return [], time_info
+
     else:
-        seg_pred = None;segFlag=False;segstr='Not implemented'
+        # seg_pred = None;
+        # segFlag = False;
+        # segstr = 'Not implemented'
+        time_info = 'No SegMentInfo'
+        return [], time_info
 
     time1=time.time()  
     if   trtFlag_det:  
@@ -154,7 +175,8 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
         timeMixPost=':0 ms'
     #print('#### line121: segstr:%s    timeMixPost:%s    timeOut:%s'%( segstr.strip(), timeMixPost,timeOut  ))
     time_info = 'letterbox:%.1f, seg:%.1f , infer:%.1f,%s, seginfo:%s  ,timeMixPost:%s '%( (time01-time0)*1000, (time1-time01)*1000 ,(time2-time1)*1000,timeOut , segstr.strip(),timeMixPost )
-    #if mode=='highWay3.0':
+    if allowedList:
+        p_result[2] = filter_byClass(p_result[2],allowedList)
         
     print('-'*10,p_result[2])
     return p_result,time_info

conf/highWayCthc/labelnames.json

@@ -0,0 +1,3 @@
+{
+	"labelnames":["危化品","罐体","危险标识","普通车"]
+}

conf/highWayCthc/para.json

@@ -0,0 +1,3 @@
+{
+	"post_process":{ "name":"post_process","conf_thres":0.88,"iou_thres":0.45,"classes":5,"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]]    }
+}

conf/highWaySpill/para.json

@@ -0,0 +1,6 @@
+{
+	"post_process":{
+	       	"name":"post_process","conf_thres":0.5,"iou_thres":0.25,"classes":3,
+		"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[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]]
+	}
+}

conf/infraredPerson/labelnames.json

@@ -0,0 +1,3 @@
+{
+	"labelnames":["行人"]
+}

conf/infraredPerson/para.json

@@ -0,0 +1,7 @@
+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":1,"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]]    }
+
+
+}

conf/nightFireSmoke/labelnames.json

@@ -0,0 +1,3 @@
+{
+	"labelnames":["火","烟"]
+}

conf/nightFireSmoke/para.json

@@ -0,0 +1,7 @@
+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":1,"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]]    }
+
+
+}

conf/pannel/labelnames.json

@@ -0,0 +1,3 @@
+{
+	"labelnames":[  "光伏板","覆盖物","裂缝" ]
+}

conf/pannel/para.json

@@ -0,0 +1,6 @@
+{
+	"post_process":{
+	       	"name":"post_process","conf_thres":0.5,"iou_thres":0.25,"classes":3,
+		"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[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]]
+	}
+}

segutils/trafficUtils.py

@@ -466,7 +466,7 @@ def tracfficAccidentMixFunction(preds,seg_pred_mulcls,pars):
         #b_0 = box[1:5];b_0.insert(0,box[0]);b_0.append(box[5] )
         b_0 = box[0:4];b_0.insert(0,box[5]);b_0.append(box[4])
         det_coords_original.append( box )  
-        if int(box[5]) != 1: continue  
+        if int(box[5]) != pars['CarId'] or int(box[5]) != pars['CthcId']: continue
         det_coords.append(b_0)
     #print('##line957:',det_coords_original )
 
@@ -492,7 +492,7 @@ def tracfficAccidentMixFunction(preds,seg_pred_mulcls,pars):
             if bpoints[9]>pars['confThres']:
                 xyxy=bpoints[1:5];xyxy=[int(x) for x in xyxy]
                 
-                cls=9;conf=bpoints[9];
+                cls=pars['cls'];conf=bpoints[9];
                 box_acc = [*xyxy,conf,cls]
                 acc_det.append(box_acc)
                 #if cls  in allowedList: