modify 1:1 比对方式

contrast/utils/event.py

@@ -0,0 +1,179 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov 26 17:35:05 2024
+
+@author: ym
+"""
+import os
+import numpy as np
+from pathlib import Path
+
+import sys
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
+
+IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
+VID_FORMAT = ['.mp4', '.avi']
+
+class Event:
+    def __init__(self, eventpath, stype="data"):  
+        '''stype: str, 'video', 'image', 'data', '''
+        
+        self.eventpath = eventpath
+        self.evtname = str(Path(eventpath).stem)
+        self.barcode = ''
+        self.evtType = ''
+        
+        '''=========== path of image and video =========== '''
+        self.back_videopath = ''
+        self.front_videopath = ''
+        self.back_imgpaths = []
+        self.front_imgpaths = []
+        
+        '''=========== process.data ==============================='''
+        self.one2one = None
+        self.one2n = None
+        
+        '''=========== 0/1_track.data ============================='''
+        self.back_yolobboxes = np.empty((0, 6), dtype=np.float64)
+        self.back_yolofeats = np.empty((0, 256), dtype=np.float64)
+        self.back_trackerboxes = np.empty((0, 9), dtype=np.float64)
+        self.back_trackerfeats = np.empty((0, 256), dtype=np.float64)
+        self.back_trackingboxes = np.empty((0, 9), dtype=np.float64)
+        self.back_trackingfeats = np.empty((0, 256), dtype=np.float64)
+        
+        self.front_yolobboxes = np.empty((0, 6), dtype=np.float64)
+        self.front_yolofeats = np.empty((0, 256), dtype=np.float64)
+        self.front_trackerboxes = np.empty((0, 9), dtype=np.float64)
+        self.front_trackerfeats = np.empty((0, 256), dtype=np.float64)
+        self.front_trackingboxes = np.empty((0, 9), dtype=np.float64)
+        self.front_trackingfeats = np.empty((0, 256), dtype=np.float64)
+
+        '''=========== 0/1_tracking_output.data ==================='''
+        self.back_boxes = np.empty((0, 9), dtype=np.float64)
+        self.front_boxes = np.empty((0, 9), dtype=np.float64)
+        self.back_feats = np.empty((0, 256), dtype=np.float64)
+        self.front_feats = np.empty((0, 256), dtype=np.float64)
+        self.feats_compose = np.empty((0, 256), dtype=np.float64)
+        self.feats_select = np.empty((0, 256), dtype=np.float64)
+
+        if stype=="data":
+            self.from_datafile(eventpath)
+
+        if stype=="video":
+            self.from_video(eventpath)
+        
+        if stype=="image":
+            self.from_image(eventpath)
+        
+    def from_datafile(self, eventpath):
+        evtList = self.evtname.split('_')
+        if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
+            self.barcode = evtList[-1]
+        if len(evtList)==3 and evtList[-1]== evtList[-2]:
+            self.evtType = 'input'
+        else:
+            self.evtType = 'other'
+   
+        '''================ path of image ============='''
+        frontImgs, frontFid = [], []
+        backImgs, backFid = [], []
+        for imgname in os.listdir(eventpath):
+            name, ext = os.path.splitext(imgname)
+            if ext not in IMG_FORMAT or name.find('frameId') < 0: continue
+            if len(name.split('_')) != 3 and not name.split('_')[3].isdigit(): continue
+        
+            CamerType = name.split('_')[0]
+            frameId = int(name.split('_')[3])
+            imgpath = os.path.join(eventpath, imgname)
+            if CamerType == '0':
+                backImgs.append(imgpath)
+                backFid.append(frameId)
+            if CamerType == '1':
+                frontImgs.append(imgpath)
+                frontFid.append(frameId)
+        ## 生成依据帧 ID 排序的前后摄图像地址列表
+        frontIdx = np.argsort(np.array(frontFid))
+        backIdx = np.argsort(np.array(backFid))
+        self.front_imgpaths = [frontImgs[i] for i in frontIdx]
+        self.back_imgpaths = [backImgs[i] for i in backIdx]
+        
+        
+        '''================ path of video ============='''
+        for vidname in os.listdir(eventpath):
+            name, ext = os.path.splitext(vidname)
+            if ext not in VID_FORMAT: continue
+            vidpath = os.path.join(eventpath, vidname)
+            
+            CamerType = name.split('_')[0]
+            if CamerType == '0':
+                self.back_videopath = vidpath
+            if CamerType == '1':
+                self.front_videopath = vidpath
+                
+        '''================ process.data ============='''
+        procpath = Path(eventpath).joinpath('process.data')
+        if procpath.is_file():
+            SimiDict = read_similar(procpath)
+            self.one2one = SimiDict['one2one']
+            self.one2n = SimiDict['one2n']
+            
+
+        '''=========== 0/1_track.data & 0/1_tracking_output.data ======='''
+        for dataname in os.listdir(eventpath):
+            datapath = os.path.join(eventpath, dataname)
+            if not os.path.isfile(datapath): continue
+            CamerType = dataname.split('_')[0]
+            
+            '''========== 0/1_track.data =========='''
+            if dataname.find("_track.data")>0:
+                bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
+                if CamerType == '0':
+                    self.back_yolobboxes = bboxes
+                    self.back_yolofeats = ffeats
+                    self.back_trackerboxes = trackerboxes
+                    self.back_trackerfeats = tracker_feat_dict
+                    self.back_trackingboxes = trackingboxes
+                    self.back_trackingfeats = tracking_feat_dict
+                if CamerType == '1':
+                    self.front_yolobboxes = bboxes
+                    self.front_yolofeats = ffeats
+                    self.front_trackerboxes = trackerboxes
+                    self.front_trackerfeats = tracker_feat_dict
+                    self.front_trackingboxes = trackingboxes
+                    self.front_trackingfeats = tracking_feat_dict
+
+            '''========== 0/1_tracking_output.data =========='''
+            if dataname.find("_tracking_output.data")>0:
+                tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)    
+                if CamerType == '0':
+                    self.back_boxes = tracking_output_boxes
+                    self.back_feats = tracking_output_feats
+                elif CamerType == '1':
+                    self.front_boxes = tracking_output_boxes
+                    self.front_feats = tracking_output_feats        
+        self.select_feat()
+        self.compose_feats()
+
+        
+    def compose_feats(self):
+        '''事件的特征集成'''
+        feats_compose = np.empty((0, 256), dtype=np.float64)
+        if len(self.front_feats):
+            feats_compose = np.concatenate((feats_compose, self.front_feats), axis=0)
+        if len(self.back_feats):
+            feats_compose = np.concatenate((feats_compose, self.back_feats), axis=0)
+        self.feats_compose = feats_compose
+        
+    def select_feats(self):
+        '''事件的特征选择'''
+        if len(self.front_feats):
+            self.feats_select = self.front_feats
+        else:
+            self.feats_select = self.back_feats
+        
+
+
+            
+    
+