843 lines
45 KiB
Python
843 lines
45 KiB
Python
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import sys, yaml
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from easydict import EasyDict as edict
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from concurrent.futures import ThreadPoolExecutor
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sys.path.extend(['..','../AIlib2' ])
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from AI import AI_process,AI_process_forest,get_postProcess_para,get_postProcess_para_dic,ocr_process,AI_det_track,AI_det_track_batch
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import cv2,os,time
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from segutils.segmodel import SegModel
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from segutils.segmodel import SegModel
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from segutils.trafficUtils import tracfficAccidentMixFunction
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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,save_problem_images,riverDetSegMixProcess
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from ocrUtils.ocrUtils import CTCLabelConverter,AlignCollate
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from trackUtils.sort import Sort,track_draw_boxAndTrace,track_draw_trace_boxes,moving_average_wang,drawBoxTraceSimplied
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from trackUtils.sort_obb import OBB_Sort,obbTohbb,track_draw_all_boxes,track_draw_trace
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from obbUtils.shipUtils import OBB_infer,OBB_tracker,draw_obb,OBB_tracker_batch
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from utilsK.noParkingUtils import mixNoParking_road_postprocess
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from obbUtils.load_obb_model import load_model_decoder_OBB
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import numpy as np
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import torch,glob
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import tensorrt as trt
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from utilsK.masterUtils import get_needed_objectsIndex
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from copy import deepcopy
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from scipy import interpolate
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from utilsK.drownUtils import mixDrowing_water_postprocess
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#import warnings
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#warnings.filterwarnings("error")
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def view_bar(num, total,time1,prefix='prefix'):
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rate = num / total
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time_n=time.time()
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rate_num = int(rate * 30)
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rate_nums = np.round(rate * 100)
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r = '\r %s %d / %d [%s%s] %.2f s'%(prefix,num,total, ">" * rate_num, " " * (30 - rate_num), time_n-time1 )
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sys.stdout.write(r)
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sys.stdout.flush()
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'''
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多线程
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'''
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def process_v1(frame):
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#try:
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print('demo.py beging to :',frame[8])
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time00 = time.time()
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H,W,C = frame[0][0].shape
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p_result,timeOut = AI_process(frame[0],frame[1],frame[2],frame[3],frame[4],frame[5],objectPar=frame[6],font=frame[7],segPar=frame[9],mode=frame[10],postPar=frame[11])
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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('images/results/',frame[8] ) ,image_array)
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bname = frame[8].split('.')[0]
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if len(p_result)==5:
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image_mask = p_result[4]
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cv2.imwrite(os.path.join('images/results/',bname+'_mask.png' ) , (image_mask).astype(np.uint8))
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boxes=p_result[2]
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with open( os.path.join('images/results/',bname+'.txt' ),'w' ) as fp:
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for box in boxes:
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box_str=[str(x) for x in box]
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out_str=','.join(box_str)+'\n'
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fp.write(out_str)
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time22 = time.time()
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print('%s,%d*%d,AI-process: %.1f,image save:%.1f , %s'%(frame[8],H,W, (time11 - time00) * 1000.0, (time22-time11)*1000.0,timeOut), boxes)
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return 'success'
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#except Exception as e:
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# return 'failed:'+str(e)
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def process_video(video,par0,mode='detSeg'):
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cap=cv2.VideoCapture(video)
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if not cap.isOpened():
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print('#####error url:',video)
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return False
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bname=os.path.basename(video).split('.')[0]
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fps = int(cap.get(cv2.CAP_PROP_FPS)+0.5)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH )+0.5)
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)+0.5)
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framecnt=int(cap.get(7)+0.5)
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save_path_AI = os.path.join(par0['outpth'],os.path.basename(video))
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problem_image_dir= os.path.join( par0['outpth'], 'probleImages' )
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os.makedirs(problem_image_dir,exist_ok=True)
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vid_writer_AI = cv2.VideoWriter(save_path_AI, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width,height))
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num=0
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iframe=0;post_results=[];fpsample=30*10
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imgarray_list = []; iframe_list = []
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patch_cnt = par0['trackPar']['patchCnt']
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##windowsize 对逐帧插值后的结果做平滑，windowsize为平滑的长度,没隔det_cnt帧做一次跟踪。
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trackPar={'det_cnt':10,'windowsize':29 }
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##track_det_result_update= np.empty((0,8)) ###每100帧跑出来的结果，放在track_det_result_update，只保留当前100帧里有的tracker Id.
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while cap.isOpened():
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ret, imgarray = cap.read() #读取摄像头画面
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iframe +=1
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if not ret:break
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if mode=='detSeg':
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p_result,timeOut = AI_process([imgarray],par0['model'],par0['segmodel'],par0['names'],par0['label_arraylist'],par0['rainbows'],objectPar=par0['objectPar'],font=par0['digitFont'],segPar=par0['segPar'])
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elif mode == 'track':
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#sampleCount=10
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imgarray_list.append( imgarray )
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iframe_list.append(iframe )
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if iframe%patch_cnt==0:
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time_patch0 = time.time()
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retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
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#print('###line111:',retResults[2])
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###需要保存成一个二维list，每一个list是一帧检测结果。
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###track_det_result 内容格式：x1, y1, x2, y2, conf, cls,iframe,trackId
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time_patch2 = time.time()
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frame_min = iframe_list[0];frame_max=iframe_list[-1]
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for iiframe in range(frame_min,frame_max+1):
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img_draw = imgarray_list[ iiframe- frame_min ]
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img_draw = drawBoxTraceSimplied(retResults[1] ,iiframe, img_draw,rainbows=par0['drawPar']['rainbows'],boxFlag=True,traceFlag=True,names=par0['drawPar']['names'] )
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ret = vid_writer_AI.write(img_draw)
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view_bar(iiframe, framecnt,time.time(),prefix=os.path.basename(video))
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imgarray_list=[];iframe_list=[]
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elif mode =='obbTrack':
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imgarray_list.append( imgarray )
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iframe_list.append(iframe )
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if iframe%patch_cnt==0:
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time_patch0 = time.time()
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track_det_results, timeInfos = OBB_tracker_batch(imgarray_list,iframe_list,par0['modelPar'],par0['obbModelPar'],par0['sort_tracker'],par0['trackPar'],segPar=None)
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print( timeInfos )
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#对结果画图
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track_det_np = track_det_results[1]
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frame_min = iframe_list[0];frame_max=iframe_list[-1]
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for iiframe in range(frame_min,frame_max+1):
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img_draw = imgarray_list[ iiframe- frame_min ]
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if len( track_det_results[2][ iiframe- frame_min]) > 0:
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img_draw = draw_obb( track_det_results[2][iiframe- frame_min ] ,img_draw,par0['drawPar'])
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if True:
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frameIdex=12;trackIdex=13;
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boxes_oneFrame = track_det_np[ track_det_np[:,frameIdex]==iiframe ]
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###在某一帧上，画上轨迹
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track_ids = boxes_oneFrame[:,trackIdex].tolist()
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boxes_before_oneFrame = track_det_np[ track_det_np[:,frameIdex]<=iiframe ]
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for trackId in track_ids:
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boxes_before_oneFrame_oneId = boxes_before_oneFrame[boxes_before_oneFrame[:,trackIdex]==trackId]
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xcs = boxes_before_oneFrame_oneId[:,8]
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ycs = boxes_before_oneFrame_oneId[:,9]
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[cv2.line(img_draw, ( int(xcs[i]) , int(ycs[i]) ),
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( int(xcs[i+1]),int(ycs[i+1]) ),(255,0,0), thickness=2)
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for i,_ in enumerate(xcs) if i < len(xcs)-1 ]
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ret = vid_writer_AI.write(img_draw)
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#sys.exit(0)
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#print('vide writer ret:',ret)
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imgarray_list=[];iframe_list=[]
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view_bar(iframe, framecnt,time.time(),prefix=os.path.basename(video))
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else:
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p_result,timeOut = AI_process_forest([imgarray],par0['model'],par0['segmodel'],par0['names'],par0['label_arraylist'],par0['rainbows'],par0['half'],par0['device'],par0['conf_thres'], par0['iou_thres'],par0['allowedList'],font=par0['digitFont'],trtFlag_det=par0['trtFlag_det'])
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if mode not in [ 'track','obbTrack']:
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image_array = p_result[1];num+=1
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ret = vid_writer_AI.write(image_array)
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view_bar(num, framecnt,time.time(),prefix=os.path.basename(video))
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##每隔 fpsample帧处理一次，如果有问题就保存图片
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if (iframe % fpsample == 0) and (len(post_results)>0) :
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parImage=save_problem_images(post_results,iframe,par0['names'],streamName=bname,outImaDir=problem_image_dir,imageTxtFile=False)
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post_results=[]
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if len(p_result[2] )>0:
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post_results.append(p_result)
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vid_writer_AI.release();
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def det_track_demo(business, videopaths):
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'''
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跟踪参数说明：
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'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100}
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sort_max_age--跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
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sort_min_hits--每隔目标连续出现的次数，超过这个次数才认为是一个目标。
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sort_iou_thresh--检测最小的置信度。
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det_cnt--每隔几次做一个跟踪和检测，默认10。
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windowsize--轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。
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patchCnt--每次送入图像的数量，不宜少于100帧。
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'''
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''' 以下是基于检测和分割的跟踪模型，分割用来修正检测的结果'''
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####河道巡检的跟踪模型参数
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if opt['business'] == 'river' or opt['business'] == 'river2' :
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par={
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'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
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'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
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'gpuname':'2080Ti',###显卡名称
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'max_workers':1, ###并行线程数
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'half':True,
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'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
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'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
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'seg_nclass':2,###分割模型类别数目，默认2类
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'segRegionCnt':0,###分割模型结果需要保留的等值线数目
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'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,#分割模型预处理参数
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'mixFunction':{'function':riverDetSegMixProcess,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}} #分割和检测混合处理的函数
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},
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'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
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'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
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'postFile': '../AIlib2/weights/conf/%s/para.json'%( opt['business'] ),###后处理参数文件
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'txtFontSize':80,###文本字符的大小
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'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
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#'testImgPath':'images/videos/river',###测试图像的位置
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'testImgPath':'images/tt',###测试图像的位置
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'testOutPath':'images/results/',###输出测试图像位置
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}
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if opt['business'] == 'highWay2':
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par={
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'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
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'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
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'half':True,
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'gpuname':'3090',###显卡名称
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'max_workers':1, ###并行线程数
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'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
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#'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
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'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
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'seg_nclass':3,###分割模型类别数目，默认2类
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'segRegionCnt':2,###分割模型结果需要保留的等值线数目
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'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
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'mixFunction':{'function':tracfficAccidentMixFunction,
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'pars':{ 'RoadArea': 16000, 'vehicleArea': 10, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75,'radius': 50 , 'roundness': 1.0, 'cls': 9, 'vehicleFactor': 0.1,'cls':9, 'confThres':0.25,'roadIou':0.6,'vehicleFlag':False,'distanceFlag': False }
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}
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},
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'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
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'mode':'highWay3.0',
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'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
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'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
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'txtFontSize':20,###文本字符的大小
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'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':0.5,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
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#'testImgPath':'images/trafficAccident/8.png',###测试图像的位置
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'testImgPath':'images/noParking/',###测试图像的位置
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|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
|
|||
|
|
if opt['business'] == 'noParking':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
|
|||
|
|
'half':True,
|
|||
|
|
'gpuname':'3090',###显卡名称
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
#'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
|||
|
|
'seg_nclass':4,###分割模型类别数目，默认2类
|
|||
|
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
|||
|
|
'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
|
|||
|
|
'mixFunction':{'function':mixNoParking_road_postprocess,
|
|||
|
|
'pars':
|
|||
|
|
#{ 'roundness': 0.3, 'cls': 9, 'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000, }
|
|||
|
|
|
|||
|
|
{'RoadArea': 16000, 'roadVehicleAngle': 15,'radius': 50, 'distanceFlag': False, 'vehicleFlag': False}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'mode':'highWay3.0',
|
|||
|
|
'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/%s/para.json'%('highWay2' ),###后处理参数文件
|
|||
|
|
'txtFontSize':20,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
|
|||
|
|
'testImgPath':'images/noParking/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
|
|||
|
|
if opt['business'] == 'drowning':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
|
|||
|
|
'half':True,
|
|||
|
|
'gpuname':'3090',###显卡名称
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
#'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
|||
|
|
'seg_nclass':2,###分割模型类别数目，默认2类
|
|||
|
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
|||
|
|
'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
|
|||
|
|
'mixFunction':{'function':mixDrowing_water_postprocess,
|
|||
|
|
'pars':{ }
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/%s/para.json'%('highWay2' ),###后处理参数文件
|
|||
|
|
'txtFontSize':20,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
|
|||
|
|
'testImgPath':'images/drowning/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
|
|||
|
|
|
|||
|
|
|
|||
|
|
''' 以下是基于检测的跟踪模型，只有检测没有分割 '''
|
|||
|
|
if opt['business'] == 'forest2':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/forest2/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':opt['gpu'],###显卡名称
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'half':True,
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
#'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ],
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###分割模型类别数目，默认2类
|
|||
|
|
'segRegionCnt':0,###分割模型结果需要保留的等值线数目
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/forest/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':80,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'../AIdemo2/images/forest2/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
###车辆巡检参数
|
|||
|
|
if opt['business'] == 'vehicle':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/vehicle/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':'2080T',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###分割模型类别数目，默认2类
|
|||
|
|
'segRegionCnt':0,###分割模型结果需要保留的等值线数目
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/vehicle/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'images/videos/vehicle/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
###行人检测模型
|
|||
|
|
if opt['business'] == 'pedestrian':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/pedestrian/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':'2080T',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###分割模型类别数目，默认2类
|
|||
|
|
'segRegionCnt':0,###分割模型结果需要保留的等值线数目
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/pedestrian/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'../AIdemo2/images/pedestrian/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
if opt['business'] == 'smogfire':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/smogfire/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':'2080T',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###没有分割模型，此处不用
|
|||
|
|
'segRegionCnt':0,###没有分割模型，此处不用
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/smogfire/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'../AIdemo2/images/smogfire/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
###钓鱼游泳检测
|
|||
|
|
if opt['business'] == 'AnglerSwimmer':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/AnglerSwimmer/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':'2080T',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###没有分割模型，此处不用
|
|||
|
|
'segRegionCnt':0,###没有分割模型，此处不用
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/AnglerSwimmer/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'../AIdemo2/images/AnglerSwimmer/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
###航道应急，做落水人员检测, channelEmergency
|
|||
|
|
if opt['business'] == 'channelEmergency':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/channelEmergency/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':'2080T',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
#'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###没有分割模型，此处不用
|
|||
|
|
'segRegionCnt':0,###没有分割模型，此处不用
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/channelEmergency/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'../AIdemo2/images/channelEmergency/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
###乡村路违法种植
|
|||
|
|
if opt['business'] == 'countryRoad':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/countryRoad/labelnames.json", ###检测类别对照表
|
|||
|
|
'gpuname':'2080T',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###没有分割模型，此处不用
|
|||
|
|
'segRegionCnt':0,###没有分割模型，此处不用
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/countryRoad/para.json',###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'../AIdemo2/images/countryRoad/',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
###城管项目，检测城市垃圾和车辆
|
|||
|
|
if opt['business'] == 'cityMangement':
|
|||
|
|
par={
|
|||
|
|
'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
|
|||
|
|
'gpuname':'2080Ti',###显卡名称
|
|||
|
|
'half':True,
|
|||
|
|
'max_workers':1, ###并行线程数
|
|||
|
|
'trtFlag_det':True,###检测模型是否采用TRT
|
|||
|
|
'trtFlag_seg':False,###分割模型是否采用TRT
|
|||
|
|
'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'seg_nclass':2,###没有分割模型，此处不用
|
|||
|
|
'segRegionCnt':0,###没有分割模型，此处不用
|
|||
|
|
'segPar':None,###分割模型预处理参数
|
|||
|
|
'Segweights' : None,###分割模型权重位置
|
|||
|
|
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business']),###后处理参数文件
|
|||
|
|
'txtFontSize':40,###文本字符的大小
|
|||
|
|
'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
|
|||
|
|
'testImgPath':'images/cityMangement',###测试图像的位置
|
|||
|
|
'testOutPath':'images/results/',###输出测试图像位置
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
par['trtFlag_det']=True if par['Detweights'].endswith('.engine') else False
|
|||
|
|
if par['Segweights']:
|
|||
|
|
par['segPar']['trtFlag_seg']=True if par['Segweights'].endswith('.engine') else False
|
|||
|
|
|
|||
|
|
##使用森林，道路模型,business 控制['forest','road']
|
|||
|
|
##预先设置的参数
|
|||
|
|
gpuname=par['gpuname']#如果用trt就需要此参数，只能是"3090" "2080Ti"
|
|||
|
|
device_=par['device'] ##选定模型，可选 cpu,'0','1'
|
|||
|
|
|
|||
|
|
|
|||
|
|
device = select_device(device_)
|
|||
|
|
half = device.type != 'cpu' # half precision only supported on CUDA
|
|||
|
|
trtFlag_det=par['trtFlag_det'] ###是否采用TRT模型加速
|
|||
|
|
|
|||
|
|
##以下参数目前不可改
|
|||
|
|
imageW=1080 ####道路模型
|
|||
|
|
digitFont= par['digitFont']
|
|||
|
|
|
|||
|
|
|
|||
|
|
####加载检测模型
|
|||
|
|
if trtFlag_det:
|
|||
|
|
Detweights=par['Detweights']
|
|||
|
|
logger = trt.Logger(trt.Logger.ERROR)
|
|||
|
|
with open(Detweights, "rb") as f, trt.Runtime(logger) as runtime:
|
|||
|
|
model=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
|
|||
|
|
print('####load TRT model :%s'%(Detweights))
|
|||
|
|
else:
|
|||
|
|
Detweights=par['Detweights']
|
|||
|
|
model = attempt_load(Detweights, map_location=device) # load FP32 model
|
|||
|
|
if half: model.half()
|
|||
|
|
|
|||
|
|
####加载分割模型
|
|||
|
|
seg_nclass = par['seg_nclass']
|
|||
|
|
segPar=par['segPar']
|
|||
|
|
if par['Segweights']:
|
|||
|
|
if par['segPar']['trtFlag_seg']:
|
|||
|
|
Segweights = par['Segweights']
|
|||
|
|
logger = trt.Logger(trt.Logger.ERROR)
|
|||
|
|
with open(Segweights, "rb") as f, trt.Runtime(logger) as runtime:
|
|||
|
|
segmodel=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
|
|||
|
|
print('############locad seg model trt success: ',Segweights)
|
|||
|
|
else:
|
|||
|
|
Segweights = par['Segweights']
|
|||
|
|
segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
|
|||
|
|
print('############locad seg model pth success:',Segweights)
|
|||
|
|
else:
|
|||
|
|
segmodel=None
|
|||
|
|
|
|||
|
|
|
|||
|
|
trackPar=par['trackPar']
|
|||
|
|
sort_tracker = Sort(max_age=trackPar['sort_max_age'],
|
|||
|
|
min_hits=trackPar['sort_min_hits'],
|
|||
|
|
iou_threshold=trackPar['sort_iou_thresh'])
|
|||
|
|
|
|||
|
|
|
|||
|
|
labelnames = par['labelnames']
|
|||
|
|
postFile= par['postFile']
|
|||
|
|
print( Detweights,labelnames )
|
|||
|
|
conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
|
|||
|
|
|
|||
|
|
detPostPar = get_postProcess_para_dic(postFile)
|
|||
|
|
conf_thres,iou_thres,classes,rainbows = detPostPar["conf_thres"],detPostPar["iou_thres"],detPostPar["classes"],detPostPar["rainbows"]
|
|||
|
|
if 'ovlap_thres_crossCategory' in detPostPar.keys(): iou2nd=detPostPar['ovlap_thres_crossCategory']
|
|||
|
|
else:iou2nd = None
|
|||
|
|
|
|||
|
|
|
|||
|
|
####模型选择参数用如下：
|
|||
|
|
mode_paras=par['detModelpara']
|
|||
|
|
|
|||
|
|
allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
|
|||
|
|
#slopeIndex = par['slopeIndex']
|
|||
|
|
##只加载检测模型，准备好显示字符
|
|||
|
|
|
|||
|
|
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=par['txtFontSize'],fontpath="../AIlib2/conf/platech.ttf")
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
##图像测试和视频
|
|||
|
|
outpth = par['testOutPath']
|
|||
|
|
impth = par['testImgPath']
|
|||
|
|
imgpaths=[]###获取文件里所有的图像
|
|||
|
|
videopaths=videopaths###获取文件里所有的视频
|
|||
|
|
img_postfixs = ['.jpg','.JPG','.PNG','.png'];
|
|||
|
|
vides_postfixs= ['.MP4','.mp4','.avi']
|
|||
|
|
if os.path.isdir(impth):
|
|||
|
|
for postfix in img_postfixs:
|
|||
|
|
imgpaths.extend(glob.glob('%s/*%s'%(impth,postfix )) )
|
|||
|
|
for postfix in ['.MP4','.mp4','.avi']:
|
|||
|
|
videopaths.extend(glob.glob('%s/*%s'%(impth,postfix )) )
|
|||
|
|
else:
|
|||
|
|
postfix = os.path.splitext(impth)[-1]
|
|||
|
|
if postfix in img_postfixs: imgpaths=[ impth ]
|
|||
|
|
if postfix in vides_postfixs: videopaths = [impth ]
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
imgpaths.sort()
|
|||
|
|
|
|||
|
|
|
|||
|
|
modelPar={ 'det_Model': model,'seg_Model':segmodel }
|
|||
|
|
|
|||
|
|
processPar={'half':par['half'],'device':device,'conf_thres':conf_thres,'iou_thres':iou_thres,'trtFlag_det':trtFlag_det,'iou2nd':iou2nd}
|
|||
|
|
drawPar={'names':names,'label_arraylist':label_arraylist,'rainbows':rainbows,'font': par['digitFont'],'allowedList':allowedList}
|
|||
|
|
|
|||
|
|
for i in range(len(imgpaths)):
|
|||
|
|
#for i in range(2):
|
|||
|
|
#imgpath = os.path.join(impth, folders[i])
|
|||
|
|
imgpath = imgpaths[i]
|
|||
|
|
bname = os.path.basename(imgpath )
|
|||
|
|
im0s=[cv2.imread(imgpath)]
|
|||
|
|
time00 = time.time()
|
|||
|
|
retResults,timeOut = AI_det_track_batch(im0s, [i] ,modelPar,processPar,sort_tracker ,trackPar,segPar)
|
|||
|
|
#print('###line627:',retResults[2])
|
|||
|
|
#retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
|
|||
|
|
if len(retResults[1])>0:
|
|||
|
|
retResults[0][0] = drawBoxTraceSimplied(retResults[1],i, retResults[0][0],rainbows=rainbows,boxFlag=True,traceFlag=False,names=drawPar['names'])
|
|||
|
|
time11 = time.time()
|
|||
|
|
image_array = retResults[0][0]
|
|||
|
|
'''
|
|||
|
|
|
|||
|
|
返回值retResults[2] --list,其中每一个元素为一个list，表示每一帧的检测结果，每一个结果是由多个list构成，每个list表示一个框，格式为[ cls , x0 ,y0 ,x1 ,y1 ,conf,ifrmae,trackId ]
|
|||
|
|
--etc. retResults[2][j][k]表示第j帧的第k个框。
|
|||
|
|
'''
|
|||
|
|
cv2.imwrite( os.path.join( outpth,bname ) ,image_array )
|
|||
|
|
|
|||
|
|
print('----image:%s, process:%s ( %s ),save:%s'%(bname,(time11-time00) * 1000, timeOut,(time.time() - time11) * 1000) )
|
|||
|
|
|
|||
|
|
##process video
|
|||
|
|
|
|||
|
|
print('##begin to process videos, total %d videos'%( len(videopaths)))
|
|||
|
|
for i,video in enumerate(videopaths):
|
|||
|
|
print('process video%d :%s '%(i,video))
|
|||
|
|
par0={'modelPar':modelPar,'processPar':processPar,'drawPar':drawPar,'outpth':par['testOutPath'], 'sort_tracker':sort_tracker,'trackPar':trackPar,'segPar':segPar}
|
|||
|
|
process_video(video,par0,mode='track')
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def OCR_demo2(opt):
|
|||
|
|
from ocrUtils2 import crnn_model
|
|||
|
|
from ocrUtils2.ocrUtils import get_cfg,recognition_ocr,strLabelConverter
|
|||
|
|
|
|||
|
|
if opt['business'] == 'ocr2':
|
|||
|
|
par={
|
|||
|
|
'image_dir':'images/ocr_en',
|
|||
|
|
'outtxt':'images/results',
|
|||
|
|
'weights':'../AIlib2/weights/conf/ocr2/crnn_448X32.pth',
|
|||
|
|
|
|||
|
|
#'weights':'../weights/2080Ti/AIlib2/ocr2/crnn_2080Ti_fp16_448X32.engine',
|
|||
|
|
'device':'cuda:0',
|
|||
|
|
'cfg':'../AIlib2/weights/conf/ocr2/360CC_config.yaml',
|
|||
|
|
|
|||
|
|
'char_file':'../AIlib2/weights/conf/ocr2/chars.txt',
|
|||
|
|
'imgH':32,
|
|||
|
|
'imgW':448,
|
|||
|
|
'workers':1
|
|||
|
|
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
image_dir=par['image_dir']
|
|||
|
|
outtxt=par['outtxt']
|
|||
|
|
workers=par['workers']
|
|||
|
|
weights= par['weights']
|
|||
|
|
device=par['device']
|
|||
|
|
|
|||
|
|
char_file=par['char_file']
|
|||
|
|
imgH=par['imgH']
|
|||
|
|
imgW=par['imgW']
|
|||
|
|
cfg = par['cfg']
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
config = get_cfg(cfg, char_file)
|
|||
|
|
|
|||
|
|
|
|||
|
|
par['contextFlag']=False
|
|||
|
|
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
|
|||
|
|
if weights.endswith('.pth'):
|
|||
|
|
model = crnn_model.get_crnn(config,weights=weights).to(device)
|
|||
|
|
par['model_mode']='pth'
|
|||
|
|
else:
|
|||
|
|
logger = trt.Logger(trt.Logger.ERROR)
|
|||
|
|
with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
|
|||
|
|
model = runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
|
|||
|
|
print('#####load TRT file:',weights,'success #####')
|
|||
|
|
context = model.create_execution_context()
|
|||
|
|
par['model_mode']='trt';par['contextFlag']=context
|
|||
|
|
|
|||
|
|
converter = strLabelConverter(config.DATASET.ALPHABETS)
|
|||
|
|
|
|||
|
|
img_urls=glob.glob('%s/*.jpg'%( image_dir ))
|
|||
|
|
img_urls.extend( glob.glob('%s/*.png'%( image_dir )) )
|
|||
|
|
cnt=len(img_urls)
|
|||
|
|
print('%s has %d images'%(image_dir ,len(img_urls) ) )
|
|||
|
|
# 准备数据
|
|||
|
|
parList=[]
|
|||
|
|
for i in range(cnt):
|
|||
|
|
img_patch=cv2.imread( img_urls[i] , cv2.IMREAD_GRAYSCALE)
|
|||
|
|
started = time.time()
|
|||
|
|
img = cv2.imread(img_urls[i])
|
|||
|
|
sim_pred = recognition_ocr(config, img, model, converter, device,par=par)
|
|||
|
|
finished = time.time()
|
|||
|
|
print('{0}: elapsed time: {1} prd:{2} '.format( os.path.basename( img_urls[i] ), finished - started, sim_pred ))
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def OBB_track_demo(opt):
|
|||
|
|
###倾斜框（OBB）的ship目标检测
|
|||
|
|
'''
|
|||
|
|
par={
|
|||
|
|
'model_size':(608,608), #width,height
|
|||
|
|
'K':100, #Maximum of objects'
|
|||
|
|
'conf_thresh':0.18,##Confidence threshold, 0.1 for general evaluation
|
|||
|
|
'device':"cuda:0",
|
|||
|
|
|
|||
|
|
'down_ratio':4,'num_classes':15,
|
|||
|
|
#'weights':'../AIlib2/weights/conf/ship2/obb_608X608.engine',
|
|||
|
|
'weights':'../weights/%s/AIlib2/%s/obb_608X608_%s_fp16.engine'%(opt['gpu'],opt['business'],opt['gpu']),
|
|||
|
|
'dataset':'dota',
|
|||
|
|
'test_dir': '/mnt/thsw2/DSP2/videos/obbShips',
|
|||
|
|
'outpth': 'images/results',
|
|||
|
|
'half': False,
|
|||
|
|
'mean':(0.5, 0.5, 0.5),
|
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|
|
'std':(1, 1, 1),
|
|||
|
|
'model_size':(608,608),##width,height
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'heads': {'hm': None,'wh': 10,'reg': 2,'cls_theta': 1},
|
|||
|
|
'decoder':None,
|
|||
|
|
'test_flag':True,
|
|||
|
|
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
|
|||
|
|
'drawBox':True,#####是否画框
|
|||
|
|
'digitWordFont': { 'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0,'label_location':'leftTop'},
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business'] ), ###检测类别对照表
|
|||
|
|
}
|
|||
|
|
'''
|
|||
|
|
par={
|
|||
|
|
|
|||
|
|
'obbModelPar':{
|
|||
|
|
'model_size':(608,608),'K':100,'conf_thresh':0.3, 'down_ratio':4,'num_classes':15,'dataset':'dota',
|
|||
|
|
'heads': {'hm': None,'wh': 10,'reg': 2,'cls_theta': 1},
|
|||
|
|
'mean':(0.5, 0.5, 0.5),'std':(1, 1, 1), 'half': False,'decoder':None,
|
|||
|
|
'weights':'../weights/%s/AIlib2/%s/obb_608X608_%s_fp16.engine'%(opt['gpu'],opt['business'],opt['gpu']),
|
|||
|
|
|
|||
|
|
},
|
|||
|
|
'outpth': 'images/results',
|
|||
|
|
'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
|
|||
|
|
'device':"cuda:0",
|
|||
|
|
|
|||
|
|
#'test_dir': '/mnt/thsw2/DSP2/videos/obbShips/DJI_20230208110806_0001_W_6M.MP4',
|
|||
|
|
'test_dir':'/mnt/thsw2/DSP2/videos/obbShips/freighter2.mp4',
|
|||
|
|
|
|||
|
|
'test_flag':True,
|
|||
|
|
'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
|
|||
|
|
'drawBox':True,#####是否画框
|
|||
|
|
'drawPar': { 'digitWordFont' :{'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0,'label_location':'leftTop'}} ,
|
|||
|
|
'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business'] ), ###检测类别对照表
|
|||
|
|
}
|
|||
|
|
#par['model_size'],par['mean'],par['std'],par['half'],par['saveType'],par['heads'],par['labelnames'],par['decoder'],par['down_ratio'],par['drawBox']
|
|||
|
|
#par['rainbows'],par['label_array'],par['digitWordFont']
|
|||
|
|
|
|||
|
|
obbModelPar = par['obbModelPar']
|
|||
|
|
####加载模型
|
|||
|
|
model,decoder2=load_model_decoder_OBB(obbModelPar)
|
|||
|
|
obbModelPar['decoder']=decoder2
|
|||
|
|
|
|||
|
|
names=get_labelnames(par['labelnames']);obbModelPar['labelnames']=names
|
|||
|
|
|
|||
|
|
_,_,_,rainbows=get_postProcess_para(par['postFile']);par['drawPar']['rainbows']=rainbows
|
|||
|
|
|
|||
|
|
|
|||
|
|
label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['drawPar']['digitWordFont']['wordSize'],fontpath="../AIlib2/conf/platech.ttf")
|
|||
|
|
#par['label_array']=label_arraylist
|
|||
|
|
|
|||
|
|
trackPar=par['trackPar']
|
|||
|
|
sort_tracker = OBB_Sort(max_age=trackPar['sort_max_age'],
|
|||
|
|
min_hits=trackPar['sort_min_hits'],
|
|||
|
|
iou_threshold=trackPar['sort_iou_thresh'])
|
|||
|
|
|
|||
|
|
|
|||
|
|
##图像测试和视频
|
|||
|
|
impth = par['test_dir']
|
|||
|
|
img_urls=[]###获取文件里所有的图像
|
|||
|
|
video_urls=[]###获取文件里所有的视频
|
|||
|
|
img_postfixs = ['.jpg','.JPG','.PNG','.png'];
|
|||
|
|
vides_postfixs= ['.MP4','.mp4','.avi']
|
|||
|
|
if os.path.isdir(impth):
|
|||
|
|
for postfix in img_postfixs:
|
|||
|
|
img_urls.extend(glob.glob('%s/*%s'%(impth,postfix )) )
|
|||
|
|
for postfix in ['.MP4','.mp4','.avi']:
|
|||
|
|
video_urls.extend(glob.glob('%s/*%s'%(impth,postfix )) )
|
|||
|
|
else:
|
|||
|
|
postfix = os.path.splitext(impth)[-1]
|
|||
|
|
if postfix in img_postfixs: img_urls=[ impth ]
|
|||
|
|
if postfix in vides_postfixs: video_urls = [impth ]
|
|||
|
|
|
|||
|
|
parIn = {'obbModelPar':obbModelPar,'modelPar':{'obbmodel': model},'sort_tracker':sort_tracker,'outpth':par['outpth'],'trackPar':trackPar,'drawPar':par['drawPar']}
|
|||
|
|
par['drawPar']['label_array']=label_arraylist
|
|||
|
|
for img_url in img_urls:
|
|||
|
|
#print(img_url)
|
|||
|
|
ori_image=cv2.imread(img_url)
|
|||
|
|
|
|||
|
|
#ori_image_list,infos = OBB_infer(model,ori_image,obbModelPar)
|
|||
|
|
|
|||
|
|
ori_image_list,infos = OBB_tracker_batch([ori_image],[0],parIn['modelPar'],parIn['obbModelPar'],None,parIn['trackPar'],None)
|
|||
|
|
|
|||
|
|
ori_image_list[1] = draw_obb(ori_image_list[2] ,ori_image_list[1],par['drawPar'])
|
|||
|
|
|
|||
|
|
imgName = os.path.basename(img_url)
|
|||
|
|
saveFile = os.path.join(par['outpth'], imgName)
|
|||
|
|
ret=cv2.imwrite(saveFile, ori_image_list[1])
|
|||
|
|
if not ret:
|
|||
|
|
print(saveFile, ' not created ')
|
|||
|
|
print( os.path.basename(img_url),':',infos,ori_image_list[2])
|
|||
|
|
|
|||
|
|
###处理视频
|
|||
|
|
|
|||
|
|
for video_url in video_urls:
|
|||
|
|
process_video(video_url, parIn ,mode='obbTrack')
|
|||
|
|
|
|||
|
|
if __name__=="__main__":
|
|||
|
|
|
|||
|
|
#jkm_demo()
|
|||
|
|
#businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2']
|
|||
|
|
businessAll = ['river2']
|
|||
|
|
videopaths = ['/home/th/tuo_heng/dev/DJI_20211229100908_0002_S.mp4']
|
|||
|
|
for busi in businessAll:
|
|||
|
|
print('-'*40,'beg to test:',busi,'-'*40)
|
|||
|
|
opt={'gpu':'2080Ti','business':busi}
|
|||
|
|
if busi in ['ship2']:
|
|||
|
|
OBB_track_demo(opt)
|
|||
|
|
else:
|
|||
|
|
#if opt['business'] in ['river','highWay2','noParking','drowning','']:
|
|||
|
|
det_track_demo(opt, videopaths)
|