update

event_time_specify.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Oct 10 11:01:39 2024
+
+@author: ym
+"""
+import os
+import numpy as np
+# from matplotlib.pylab import mpl 
+# mpl.use('Qt5Agg')
+import matplotlib.pyplot as plt
+from move_detect import MoveDetect
+
+
+import sys
+sys.path.append(r"D:\DetectTracking")
+
+# from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_weight_timeConsuming
+
+
+from tracking.utils.read_data import read_weight_timeConsuming
+
+def str_to_float_arr(s):
+    # 移除字符串末尾的逗号（如果存在）  
+    if s.endswith(','):  
+        s = s[:-1]  
+      
+    # 使用split()方法分割字符串，然后将每个元素转化为float  
+    float_array = [float(x) for x in s.split(",")]
+    return float_array 
+
+def find_samebox_in_array(arr, target):
+
+    for i, st in enumerate(arr):
+        if st[:4] == target[:4]:
+            return i
+    return -1
+
+def array2frame(bboxes):
+    frameID = np.sort(np.unique(bboxes[:, 7].astype(int)))    
+    # frame_ids = bboxes[:, frameID].astype(int)
+    fboxes, ttamps = [], []
+    for fid in frameID: 
+        idx = np.where(bboxes[:, 7] == fid)[0]
+        box = bboxes[idx, :]
+
+        fboxes.append(box)
+        ttamps.append(int(box[0, 9]))
+        
+    frameTstamp = np.concatenate((frameID[:,None], np.array(ttamps)[:,None]), axis=1)
+    
+    return fboxes, frameTstamp
+
+
+def extract_data_1(datapath):
+    '''
+    要求每一帧（包括最后一帧）输出数据后有一空行作为分割行，该分割行为标志行
+    '''
+    
+    trackerboxes = np.empty((0, 10), dtype=np.float64)
+    trackerfeats = np.empty((0, 256), dtype=np.float64)
+    
+    boxes, feats, tboxes, tfeats = [], [], [], []
+    timestamp = -1
+    
+    newframe = False
+    with open(datapath, 'r', encoding='utf-8') as lines:
+        for line in lines:
+            if line.find("CameraId")>=0: 
+                newframe = True
+                timestamp, frameId = [int(ln.split(":")[1]) for ln in line.split(",")[1:]]
+                # boxes, feats, tboxes, tfeats = [], [], [], []
+
+
+            if line.find("box:") >= 0 and line.find("output_box:") < 0:
+                line = line.strip()
+                box = line[line.find("box:") + 4:].strip()
+                # if len(box)==6:
+                boxes.append(str_to_float_arr(box))
+                
+            if line.find("feat:") >= 0:
+                line = line.strip()
+                feat = line[line.find("feat:") + 5:].strip()
+                # if len(feat)==256:
+                feats.append(str_to_float_arr(feat))
+                
+            if line.find("output_box:") >= 0:
+                line = line.strip()
+                # 确保 boxes 和 feats 一一对应，并可以保证 tboxes 和 tfeats 一一对应
+                if len(boxes)==0 or len(boxes)!=len(feats):
+                    continue
+                
+                box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
+                
+                box.append(timestamp)
+                index = find_samebox_in_array(boxes, box)
+                if index >= 0:
+                    tboxes.append(box)  # 去掉'output_box:'并去除可能的空白字符
+                    
+                    # feat_f = str_to_float_arr(input_feats[index])
+                    feat_f = feats[index]
+                    norm_f = np.linalg.norm(feat_f) 
+                    feat_f = feat_f / norm_f
+                    tfeats.append(feat_f)
+                    
+            '''标志行(空行)判断'''
+            condt = line.find("timestamp")<0 and line.find("box:")<0 and line.find("feat:")<0
+            if condt and  newframe:
+                if len(tboxes) and len(tfeats): 
+                    trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
+                    trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
+      
+                timestamp = -1
+                boxes, feats, tboxes, tfeats = [], [], [], []
+                newframe = False
+                
+    return trackerboxes, trackerfeats
+
+        
+def devide_motion_state(tboxes, width):
+    '''frameTstamp: 用于标记当前相机视野内用购物车运动状态变化'''
+
+    periods = []
+    if len(tboxes) < width: 
+        return periods
+        
+    fboxes, frameTstamp = array2frame(tboxes)
+
+    fnum = len(frameTstamp)
+    if fnum < width: return periods
+
+    state = np.zeros((fnum, 2), dtype=np.int64)
+    frameState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64)
+    
+    mtrackFid = {}
+    '''frameState 标记由图像判断的购物车状态：0: 静止，1: 运动'''
+    for idx in range(width, fnum+1):
+        lboxes = np.concatenate(fboxes[idx-width:idx], axis = 0)
+
+        md = MoveDetect(lboxes)
+        md.classify()
+        
+        # if idx==60:
+        #     print('a')
+        
+        ## track.during 二元素组, 表征在该时间片段内，轨迹 track 的起止时间，数值用 boxes[:, 7]      
+        for track in md.track_motion:
+            if track.cls == 0: continue
+            
+            
+        
+            f1, f2 = track.during
+            
+            
+            
+            idx1 = set(np.where(frameState[:,0] >= f1)[0])
+            idx2 = set(np.where(frameState[:,0] <= f2)[0])
+            idx3 = list(idx1.intersection(idx2))
+            
+            if track.tid not in mtrackFid:
+                mtrackFid[track.tid] = set(idx3)
+            else:
+                mtrackFid[track.tid] = mtrackFid[track.tid].union(set(idx3))
+
+            frameState[idx-1, 3] = 1
+            frameState[idx3, 2] = 1
+            
+        '''状态变化输出'''
+    
+    for tid, fid in mtrackFid.items():
+        fstate = np.zeros((fnum, 1), dtype=np.int64)
+        fstate[list(fid), 0] = tid
+        
+        frameState = np.concatenate((frameState, fstate), axis = 1).astype(np.int64)
+        
+        
+        
+    
+    
+    
+    return frameState
+
+
+def state_measure(periods, weights, spath=None):
+    '''两种状态：static、motion, 
+    (t0, t1)
+        t0: static ----> motion
+        t1: motion ----> static
+    '''
+    
+    PrevState = 'static'
+    CuurState = 'static'
+    
+    camtype_0, frstate_0 =  periods[0]
+    camtype_1, frstate_1 =  periods[1]
+
+    '''计算总时间区间: tmin, tmax, during'''
+    tmin_w, tmax_w = np.min(weights[:, 0]), np.max(weights[:, 0])
+    tmin_0, tmax_0 = np.min(frstate_0[:, 1]), np.max(frstate_0[:, 1])
+    tmin_1, tmax_1 = np.min(frstate_1[:, 1]), np.max(frstate_1[:, 1])
+    
+    tmin = min([tmin_w, tmin_0, tmin_1])
+    tmax = max([tmax_w, tmax_0, tmax_1])
+    
+    # for ctype, tboxes, _ in tracker_boxes:  
+    #     t_min, t_max = np.min(tboxes[:, 9]), np.max(tboxes[:, 9])
+    #     if t_min<tmin:
+    #         tmin = t_min    
+    #     if t_max>tmax:
+    #         tmax = t_max
+    # during = tmax - tmin
+    
+
+
+    fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
+    
+    ax1.plot(weights[:, 0] - tmin, weights[:, 1], 'bo-', linewidth=1, markersize=4)
+    # ax1.set_xlim([0, during])
+    ax1.set_title('Weight (g)')
+    
+    ax2.plot(frstate_0[:, 1] - tmin, frstate_0[:, 2], 'rx-', linewidth=1, markersize=8)
+    ax2.plot(frstate_0[:, 1] - tmin, frstate_0[:, 3], 'bo-', linewidth=1, markersize=4)
+    # ax2.set_xlim([0, during])
+    ax2.set_title(f'Camera: {int(camtype_0)}')
+    
+    ax3.plot(frstate_1[:, 1] - tmin, frstate_1[:, 2], 'rx-', linewidth=1, markersize=8)
+    ax3.plot(frstate_1[:, 1] - tmin, frstate_1[:, 3], 'bo-', linewidth=1, markersize=4)
+    ax3.set_title(f'Camera: {int(camtype_1)}')
+    
+    if spath:
+        plt.savefig(spath)
+    plt.show()
+
+def read_yolo_weight_data(eventdir):
+    filepaths = []
+    for filename in os.listdir(eventdir):
+        file, ext = os.path.splitext(filename)
+        if ext =='.data':
+            filepath = os.path.join(eventdir, filename)
+            filepaths.append(filepath)
+            
+    if len(filepaths) != 5:
+        return
+
+    tracker_boxes = []
+    WeightDict, SensorDict, ProcessTimeDict = {}, {}, {}
+    for filepath in filepaths:
+        filename = os.path.basename(filepath)
+        
+        if filename.find('_track.data')>0:
+            CamerType = filename.split('_')[0]  
+            trackerboxes, trackerfeats = extract_data_1(filepath)
+            tracker_boxes.append((CamerType, trackerboxes, trackerfeats))
+            
+        if filename.find('process.data')==0:
+            WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(filepath)
+
+    '''====================重力信号处理===================='''
+    weights = [(float(t), w) for t, w in WeightDict.items()]
+    weights = np.array(weights)
+
+    return tracker_boxes, weights
+
+
+def main():
+    eventdir = r"\\192.168.1.28\share\测试_202406\0819\images\20240817-192549-6940120c-634c-481b-97a6-65042729f86b_null"
+    
+    tracker_boxes, weights = read_yolo_weight_data(eventdir)
+    
+    '''====================图像运动分析===================='''
+    win_width = 12
+    periods = []
+    for ctype, tboxes, _ in tracker_boxes: 
+        period = devide_motion_state(tboxes, win_width)
+        
+        periods.append((ctype, period))
+        print('done!')
+    
+    '''===============重力、图像信息融合==================='''
+    state_measure(periods, weights)
+    
+    
+if __name__ == "__main__":
+    main()
+    
+    
+    
+    
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