1

AI.py

@@ -19,8 +19,6 @@ 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
     videopaths=[]###获取文件里所有的视频
@@ -137,15 +135,9 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
     time2=time.time()
 
 
-    
     p_result, timeOut = getDetectionsFromPreds(pred,img,im0s[0],conf_thres=conf_thres,iou_thres=iou_thres,ovlap_thres=ovlap_thres,padInfos=padInfos) 
-    #logger.info("获取视频p_result :{}, requestId:{}, names:{}", '+++' * 10, p_result[2],names)
-
     if score_byClass:
         p_result[2] = score_filter_byClass(p_result[2],score_byClass)
-    
-
-    #print('-'*10,p_result[2])
     #if  mode=='highWay3.0':
     #if segmodel:
     if segPar and  segPar['mixFunction']['function']:
@@ -153,7 +145,7 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
         mixFunction = segPar['mixFunction']['function'];H,W = im0s[0].shape[0:2]
         parMix = segPar['mixFunction']['pars'];#print('###line117:',parMix,p_result[2])
         parMix['imgSize'] = (W,H)
-        #print(' -----------line149:  ',p_result[2] ,'\n', seg_pred, parMix ,' sumpSeg:',np.sum(seg_pred))
+       #print(' -----------line110:  ',p_result[2] ,'\n', seg_pred)
         p_result[2] , timeMixPost= mixFunction(p_result[2], seg_pred, pars=parMix )
         #print(' -----------line112:  ',p_result[2] )
         p_result.append(seg_pred)
@@ -163,8 +155,8 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
     #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':
-    #print('line159:','-'*10,p_result[2])    
         
+    print('-'*10,p_result[2])
     return p_result,time_info
 def default_mix(predlist,par):
     return  predlist[0],''
@@ -194,7 +186,6 @@ def AI_process_N(im0s,modelList,postProcess):
     
     #ret就是混合处理后的结果
     ret = mixFunction( predsList, postProcess['pars'])   
-    
     return ret[0],timeInfos+ret[1]
     
 def getMaxScoreWords(detRets0):

DMPRUtils/DMPR_process.py

@@ -143,7 +143,6 @@ def detect_marking_points(detector, image, thresh, device,modelType='pth'):
 
     image_preprocess = preprocess_image(image).to(device)
     if modelType=='pth':
-        
         prediction = detector(image_preprocess)
         #print(prediction)
     elif  modelType=='trt':
@@ -197,16 +196,13 @@ def DMPR_process(img0, model, device, DMPRmodelPar):
     
     img, ratio, (dw, dh) = letterbox(img0, DMPRmodelPar['dmprimg_size'], auto=False)
     t1 = time.time()
-    #print('###line188:', height, width, img.shape)
     det = detect_marking_points(model, img, DMPRmodelPar['dmpr_thresh'], device,modelType=DMPRmodelPar['modelType'])
     t2 = time.time()
     if len(det):
         det[:, 1:3] = scale_coords2(img.shape[:2], det[:, 1:3], img0.shape)
         
-        
     t3 = time.time()
     timeInfos = 'dmpr:%1.f (lettbox:%.1f  dectect:%.1f scaleBack:%.1f) '%( (t3-t0)*1000,(t1-t0)*1000,(t2-t1)*1000,(t3-t2)*1000,   )
-
     return det,timeInfos
 
 

DMPRUtils/jointUtilbak.py

@@ -1,142 +0,0 @@
-import math
-
-import numpy as np
-import torch
-import time
-from loguru import logger
-
-
-def dmpr_yolo( yolo_det, dmpr_det,pars):
-    #if len(yolo_det)==0 or len(dmpr_det)==0:
-
-    #print('line11:\n',yolo_det, dmpr_det,pars)
-    time1=time.time()
-    if len(yolo_det)==0:
-        return yolo_det,' No yolo detections'
-        
-    img_shape = (pars['imgSize'][1],pars['imgSize'][0])
-    cls = pars['carCls']; scaleRatio = pars['scaleRatio']
-    illParkCls = pars['illCls'];border = pars['border']    
-    
-    yolo_det = np.array(yolo_det)
-    yolo_det_0 = yolo_det.copy()
-
-    
-    
-    
-    #print('-'*10,'line17：',yolo_det_0)
-
-    # 过滤在图像边界的box(防止出现类似一小半车辆的情况)
-     
-    x_c = (yolo_det[:, 0] + yolo_det[:, 2]) / 2
-    y_c = (yolo_det[:, 1] + yolo_det[:, 3]) / 2
-    tmp = (x_c >= border) & (x_c <= (img_shape[1] - border)) & (y_c >= border) & (y_c <= (img_shape[0] - border))
-    yolo_det = yolo_det[tmp]
-    
-
-    # 创建yolo_det_clone内容为x1, y1, x2, y2, conf, cls, unlabel (unlabel代表该类是否需要忽略，0：不忽略 其他：忽略)
-    logger.info("标记 :{}, yolo_det:{}, dmpr_det:{}", '+++'* 10, yolo_det, dmpr_det)
-    yolo_det_clone = yolo_det.copy()
-    tmp_0_tensor = np.zeros([len(yolo_det), 1])
-    yolo_det_clone = np.concatenate([yolo_det_clone, tmp_0_tensor], axis=1)
-
-    # cls为需要计算的类别
-    yolo_det = yolo_det[yolo_det[:, -1] == cls]
-
-    # new_yolo_det为膨胀后数据，内容为x1, y1, x2, y2, flag (flag代表膨胀后车位内是否包含角点 且 与角点方向差值小于90度, 其值为第一个满足条件的角点索引)
-    new_yolo_det = np.zeros([len(yolo_det), 7])
-
-    # yolo框膨胀，长的边两边各膨胀0.4倍总长，短的边两边各膨胀0.2倍总长
-    x_length = yolo_det[:, 2] - yolo_det[:, 0]      #x2-x1
-    y_length = yolo_det[:, 3] - yolo_det[:, 1]     #y2-y1
-
-    # x, y哪个方向差值大哪个方向膨胀的多
-    x_dilate_coefficient = ((x_length > y_length) + 1)*scaleRatio
-    y_dilate_coefficient = ((~(x_length > y_length)) + 1)*scaleRatio
-
-   
-    logger.info("膨胀前标记 :{}, yolo_det:{}", '+++'*10, new_yolo_det)
-   # 原始框中心点x_c, y_c
-    new_yolo_det[:, 5] = (yolo_det[:, 0] + yolo_det[:, 2]) / 2
-    new_yolo_det[:, 6] = (yolo_det[:, 1] + yolo_det[:, 3]) / 2
-
-    # 膨胀
-    new_yolo_det[:, 0] = np.round(yolo_det[:, 0] - x_dilate_coefficient * x_length).clip(0, img_shape[1])   #x1 膨胀
-    new_yolo_det[:, 1] = np.round(yolo_det[:, 1] - y_dilate_coefficient * y_length).clip(0, img_shape[0])   #y1 膨胀
-    new_yolo_det[:, 2] = np.round(yolo_det[:, 2] + x_dilate_coefficient * x_length).clip(0, img_shape[1])   #x2 膨胀
-    new_yolo_det[:, 3] = np.round(yolo_det[:, 3] + y_dilate_coefficient * y_length).clip(0, img_shape[0])   #y2 膨胀
-    
-    m, n = new_yolo_det.size, dmpr_det.size
-
-    logger.info("膨胀后标记 :{}, yolo_det:{}， m:{}, n:{}", '+++'*10, new_yolo_det,m,n)
-    if not m or not n:
-        #print('##line47 original yolo_det_clone:',yolo_det_clone)
-        yolo_det_clone[np.logical_and( yolo_det_clone[:,-1]==0,yolo_det_clone[:,-2]==cls),-2] = illParkCls
-         
-        #yolo_det_clone[yolo_det_clone[:, -1] == 0 & yolo_det_clone[:, -2==cls] , -2] = illParkCls
-        return yolo_det_clone[:,0:6], ' no cars or T/L corners'
-
-    new_yolo = new_yolo_det[:, np.newaxis, :].repeat(dmpr_det.shape[0], 1)      # 扩展为 (m , n, 5)
-    dmpr_det = dmpr_det[np.newaxis, ...].repeat(new_yolo_det.shape[0], 0)
-    yolo_dmpr = np.concatenate((new_yolo, dmpr_det), axis=2)                    #  (m, n, 10)
-
-    logger.info("膨胀后标记 :{}, yolo_dmpr:{}", '+++' * 10, yolo_dmpr)
-    x_p, y_p = yolo_dmpr[..., 8], yolo_dmpr[..., 9]
-    x1, y1, x2, y2 = yolo_dmpr[..., 0], yolo_dmpr[..., 1], yolo_dmpr[..., 2], yolo_dmpr[..., 3]
-    x_c, y_c = yolo_dmpr[..., 5], yolo_dmpr[..., 6]
-
-    direction1 = np.arctan2(y_c - y_p, x_c - x_p) / math.pi * 180
-    direction2 = yolo_dmpr[..., 10] / math.pi * 180
-    direction3 = direction2 + 90                    # L形角点另外一个方向
-    direction3[direction3 > 180] -= 360
-    ang_diff = direction1 - direction2
-    ang_diff2 = direction1 - direction3
-
-    # 判断膨胀后yolo框包含角点关系 && 包含角点的时候计算水平框中心点与角点的角度关系
-    # direction ∈ （-180， 180） 若角差大于180，需算补角
-    # T形角点比较一个方向，L形角点比较两个方向
-    mask = (x_p >= x1) & (x_p <= x2) & (y_p >= y1) & (y_p <= y2) & \
-           (((yolo_dmpr[..., 11] <= 0.5) &                          # T形角点情况
-                (((ang_diff >= -90) & (ang_diff <= 90)) | ((ang_diff > 180) & ((360 - ang_diff) <= 90)) |
-                    (((ang_diff) < -180) & ((360 + ang_diff) <= 90)))) |
-            ((yolo_dmpr[..., 11] > 0.5) &                            # L形角点情况
-                (((ang_diff >= -90) & (ang_diff <= 90)) | ((ang_diff > 180) & ((360 - ang_diff) <= 90)) |
-                    (((ang_diff) < -180) & ((360 + ang_diff) <= 90))) &
-                (((ang_diff2 >= -90) & (ang_diff2 <= 90)) | ((ang_diff2 > 180) & ((360 - ang_diff2) <= 90)) |
-                    (((ang_diff2) < -180) & ((360 + ang_diff2) <= 90)))))
-    logger.info("省略 :{}, mask1:{}", '+++' * 10, mask)
-    res = np.sum(mask, axis=1)
-    logger.info("省略 :{}, mask2:{}", '+++' * 10, res)
-    logger.info("省略 :{}, yolo_det_clone:{}", '+++' * 10, yolo_det_clone)
-    yolo_det_clone[yolo_det_clone[:, -2] == cls, -1] = res
-    #print('##line69 original yolo_det_clone:',yolo_det_clone)
-    #yolo_det_clone[yolo_det_clone[:, -1] == 0, -2] = illParkCls
-    logger.info("省略:{}, yolo_det_clone:{}", '+++' * 10, yolo_det_clone)
-    #print('-'*20,'--line78',yolo_det_clone)
-    yolo_det_clone[  np.logical_and( yolo_det_clone[:,-1]==0,yolo_det_clone[:,-2]==cls) ,-2 ] = illParkCls
-    #print('-'*20,'--line80:',yolo_det_clone)
-    yolo_det_clone = yolo_det_clone[:,0:6]
-    time2=time.time()
-    
-    return np.array(yolo_det_clone), 'dmpr_yolo:%.1f'%( (time2-time1)*1000 )
-def stdc_yolo(stdc_det, yolo_det):
-    im = np.uint8(stdc_det)
-    x_c = ((yolo_det[:, 0] + yolo_det[:, 2]) // 2).astype(int)
-    y_c = ((yolo_det[:, 1] + yolo_det[:, 3]) // 2).astype(int)
-
-    yolo_filted = yolo_det[im[y_c, x_c] == 0]
-   
-    return yolo_filted
-    
-def dmpr_yolo_stdc(predsList,pars):
-    if len(predsList)==2:
-        yolo_det, dmpr_det = predsList[0:2]
-    else:    
-        yolo_det, dmpr_det,stdc_det = predsList[0:3]
-    if len(yolo_det)==0:
-        return  yolo_det,' No yolo detections'
-    if isinstance(yolo_det,list):
-        yolo_det = np.array(yolo_det)
-    if   len(predsList)>2:  
-        yolo_det = stdc_yolo(stdc_det, yolo_det)
-    return dmpr_yolo(yolo_det, dmpr_det,pars)

DMPRUtils/yolo_net.py

@@ -56,7 +56,8 @@ class Detect(nn.Module):
         #         y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
         #         z.append(y.view(bs, -1, self.no))
 
-        prediction = self.m[1](x[1])
+        prediction = self.m[1](x[1]) #40*40
+        #prediction = self.m[0](x[0]) #80*80
         point_pred, angle_pred = torch.split(prediction, 4, dim=1)
         point_pred = torch.sigmoid(point_pred)
         angle_pred = torch.tanh(angle_pred)

conf/OCR_Ch/360CC_config.yaml

@@ -1,61 +0,0 @@
-GPUID: 0
-WORKERS: 1
-PRINT_FREQ: 10
-SAVE_FREQ: 10
-PIN_MEMORY: False
-OUTPUT_DIR: 'output'
-
-CUDNN:
-  BENCHMARK: True
-  DETERMINISTIC: False
-  ENABLED: True
-
-DATASET:
-  DATASET: 360CC
-  ROOT: "../textGenerator/dataset/dataset9/images"
-  CHAR_FILE: '../textGenerator/dataset/dataset9/chars.txt'
-  JSON_FILE: {'train': '../textGenerator/dataset/dataset9/train.txt', 'val': '../textGenerator/dataset/dataset9/val.txt'}
-#  JSON_FILE: {'train': 'H:/DL-DATASET/360M/train.txt', 'val': 'H:/DL-DATASET/360M/test.txt'}
-  SCALE_FACTOR: 0.25
-  ROT_FACTOR: 30
-  STD: 0.193
-  MEAN: 0.588
-  ALPHABETS: ''
-
-TRAIN:
-  BATCH_SIZE_PER_GPU: 32
-  SHUFFLE: True
-  BEGIN_EPOCH: 0
-  END_EPOCH: 100
-  RESUME:
-    IS_RESUME: False
-    FILE: 'output/360CC/crnn/2023-04-27-13-01/checkpoints/checkpoint_99_acc_0.5030.pth'
-  OPTIMIZER: 'adam'
-  LR: 0.0001
-  WD: 0.0
-  LR_STEP: [60, 80]
-  LR_FACTOR: 0.1
-  MOMENTUM: 0.0
-  NESTEROV: False
-  RMSPROP_ALPHA:
-  RMSPROP_CENTERED:
-  FINETUNE:
-    IS_FINETUNE: False
-    FINETUNE_CHECKPOINIT: 'output/checkpoints/mixed_second_finetune_acc_97P7.pth'
-    FREEZE: true
-
-TEST:
-  BATCH_SIZE_PER_GPU: 16
-  SHUFFLE: True  # for random test rather than test on the whole validation set
-  NUM_TEST_BATCH: 1000
-  NUM_TEST_DISP: 10
-
-MODEL:
-  NAME: 'crnn'
-  IMAGE_SIZE:
-    OW: 160 # origial width: 280
-    H: 32
-    W: 160   # resized width: 160
-  NUM_CLASSES: 0
-  NUM_HIDDEN: 256
-

conf/OCR_Ch/chars.txt

@@ -1,92 +0,0 @@
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-°
-!
-"
-#
-$
-%
-&
-'
-(
-)
-*
-+
-,
--
-.
-/
-:
-;
-?
-@
-[
-\
-]
-^
-_
-`
-{
-|
-}
-~

conf/channel2/labelnames.json

@@ -1,5 +1,5 @@
 {
-	"labelnames_实际":["国旗","浮标","船名","船只","未挂国旗船只" ],
+	"labelnames_实际":["国旗","浮标","船名","船只","未挂国旗船只","未封仓船只" ],
-        "labelnames":[  "国旗","浮标","船名","船只","未挂国旗船只" ],
+        "labelnames":[  "国旗","浮标","船名","船只","未挂国旗船只","未封仓船只" ],
 	"labelIndexs":["SL040", "SL041","SL042","SL043","SL044"]
 }

conf/cityMangement3/labelnames.json

@@ -1,4 +1,4 @@
 {
-	"labelnames":["车辆","垃圾","商贩","违停"],
+	"labelnames":["车辆","垃圾","商贩","违停","占道经营","裸土"],
 	"labelIndexs":["SL01","SL02","SL03","SL04"]
 }

conf/firework/labelnames.json

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

conf/firework/para.json

@@ -0,0 +1,7 @@
+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"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/labelnames.json

@@ -0,0 +1,3 @@
+{
+	"labelnames":["抛洒物","车辆"]
+}

conf/riverT/labelnames.json

@@ -0,0 +1,5 @@
+{
+	"labelnames":[   "漂浮物","垃圾","排口","非法建筑","非法种植","水生植物","游泳人员","钓鱼人员","船只","蓝藻"]  ,
+	"labelIndexs":[  "SL04","SL05","SL06","SL07","SL08","SL09","SL10","SL11","SL12","SL13"   ],
+	"labelOrders":[0,1,2,3,4,5,6,7,8,9]
+}

conf/riverT/para.json

@@ -0,0 +1,7 @@
+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.3,"ovlap_thres_crossCategory":0.65,"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/rubbish/labelnames.json

@@ -0,0 +1,3 @@
+{
+	"labelnames":["建筑垃圾","白色垃圾","其他垃圾"]
+}

conf/rubbish/para.json

@@ -0,0 +1,7 @@
+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"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/smartSite/para.json

@@ -0,0 +1,7 @@
+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":13,"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/smogfire/labelnames_bak.json

@@ -1,4 +0,0 @@
-{
-	"labelnames":["烟雾","火焰"],
-	"labelIndexs":["SL01","SL02"]
-}

models/yolo.py

@@ -42,10 +42,9 @@ class Detect(nn.Module):
         z = []  # inference output
         self.training |= self.export
         for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
+            x[i] = self.m[i](x[i])  # convi
             bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
             x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
             if not self.training:  # inference
                 if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                     self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

readme.md

@@ -127,5 +127,3 @@
   2.在yolov5模型中增加了“云朵”类别，减少”烟雾“的误识别，但”云朵“并未输出，在后处理的时候就已经过滤了。
   3.增加了后处理函数，在“行人”的基础上，判断他们之间的距离，群定是否是人群。主要有两个参数：
     'crowdThreshold':判断是否是人群时人的数量，'distancePersonScale':人与人之间的距离/人的身高
-2025.04.25
-  1.这个仓库把权重移出去了，放在pth分支下。