回传数据解析，兼容v5和v10

tracking/utils/__init__.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec 31 17:06:34 2023
+
+@author: ym
+"""
+from .proBoxes import Boxes, boxes_add_fid
+from .iterYaml import IterableSimpleNamespace, yaml_load
+
+
+__all__ = "IterableSimpleNamespace", "yaml_load", "Boxes", "boxes_add_fid"

tracking/utils/annotator.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep 20 14:21:13 2023
+
+@author: ym
+"""
+import cv2
+
+# import sys
+# sys.path.append(r"D:\DeepLearning\yolov5")
+# from ultralytics.utils.plotting import Annotator, colors
+
+from .plotting import Annotator, colors
+
+class TrackAnnotator(Annotator): 
+    
+    def plotting_track(self, track, names='abc'):
+        """
+        track：[x, y, w, h, track_id, score, cls, frame_index]
+        
+        boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+                0   1   2   3      4        5     6       7           8
+        id：跟踪id，从 1 开始计数，
+        frame_index: 帧索引，从 1 开始计数
+        cls：类别编号，从 0 开始计数，用作 names 的 key 值
+        """
+        
+        if track.size==0: return
+    
+        id, cls = track[0, 4],  track[0, 6]
+        if id >=0 and cls==0:
+            color = colors(int(cls), True)
+        elif id >=0 and cls!=0:
+            color = colors(int(id), True)   # 不存在 id = 0，不会和上面产生冲突
+        else:
+            color = colors(19, True)     # 19为调色板的最后一个元素        
+        
+        nb = track.shape[0]
+        for i in range(nb):
+            if i == 0: 
+                # label = f'{int(track[i, 4])}:({int(track[i, 7])})'
+                label = f'ID_{int(track[i, 4])}'
+            elif i == nb-1:
+                label = ''
+                # label = f'{int(track[i, 4])}:({int(track[i, 7])})&{int(nb)}'
+            else:
+                label = ''
+                
+            self.circle_label(track[i, :], label, color=color)
+    
+    
+    def circle_label(self, track,  label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
+        """
+        绘制选定 track 的轨迹
+        """
+        if track.size==0: return
+        
+        x, y = int((track[0]+track[2])/2), int((track[1]+track[3])/2)
+        
+        cv2.circle(self.im, (x, y), 6, color, 2)
+        # txt_color = (0,0,0)
+        
+        if label:
+            tf = max(self.lw - 1, 1)  # font thickness
+            w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
+            outside = x + w <= self.im.shape[1]-3
+            # p2 = x + w, y - h - 3 if outside else y + h + 3
+            # cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
+            
+            cv2.putText(self.im,
+                        label, (x-10 if outside else x-w+2,  y-20),
+                        0,
+                        # self.lw / 3,
+                        self.lw/2,
+                        txt_color,
+                        thickness=tf,
+                        lineType=cv2.LINE_AA)
+            
+            
+
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            

tracking/utils/drawtracks.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jan 15 15:26:38 2024
+
+@author: ym
+"""
+import numpy as np
+import cv2
+import os
+from pathlib import Path
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+from tracking.utils.annotator import TrackAnnotator
+from tracking.utils.plotting import colors
+
+
+
+def plot_frameID_y2(vts):
+    # boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+    #         0,  1,  2,  3,    4,        5,    6,      7,         8
+    markers = ['o', 'v', '^', '<', '>', 's', 'p', 'P','*', '+', 'x', 'X', 'd', 'D', 'H']  
+    colors = ['b', 'g', 'c', 'm', 'y', ]
+    
+    bboxes = vts.bboxes
+    maxfid = max(vts.bboxes[:, 7])
+    
+    CART_HIGH_THRESH1 = 430
+    TRACK_STATIC_THRESH = 8
+
+    fig = plt.figure(figsize=(16, 12))
+    gs = fig.add_gridspec(2, 1,  left=0.1, right=0.9, bottom=0.1, top=0.9,
+                      wspace=0.05, hspace=0.15)
+    # ax1, ax2 = axs
+    
+    ax1 = fig.add_subplot(gs[0,0])
+    ax2 = fig.add_subplot(gs[1,0])
+    ax1.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 )
+    ax2.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 )
+    
+    
+    hands = [t for t in vts.Hands if not t.isHandStatic]
+    
+    tracks = vts.join_tracks(vts.Residual, hands)
+    
+    for i, track in enumerate(vts.tracks):
+        boxes = track.boxes
+        cls, tid = track.cls, track.tid
+        
+        y2, fids = boxes[:, 3], boxes[:, 7]
+        if cls==0:
+            ax1.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}")
+        else:
+            ax1.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)], 
+                       s=50, label = f"ID_{tid}")
+            # hist, bins = np.histogram(1280-y2, bins='auto') 
+        ax1.set_ylim([-50, 1350])  
+    
+    for i, track in enumerate(tracks):
+        boxes = track.boxes
+        cls, tid = track.cls, track.tid
+        
+        y2, fids = boxes[:, 3], boxes[:, 7]
+        if cls==0:
+            ax2.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}")
+        else:
+            ax2.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)], 
+                       s=50, label = f"ID_{tid}")
+            # hist, bins = np.histogram(1280-y2, bins='auto') 
+        ax2.set_ylim([-50, 1350])    
+         
+    
+    ax1.grid(True), ax1.set_xlim(0, maxfid+5), ax1.set_title('y2')
+    ax1.legend()
+    ax2.grid(True), ax2.set_xlim(0, maxfid+5), ax2.set_title('y2')
+    ax2.legend()
+    # plt.show()
+    
+    
+    
+    
+    return plt
+
+
+def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
+    '''显示四种类型结果'''
+    # file, ext = os.path.splitext(filename)
+    # edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
+    # edgeline2 = edgeline1.copy()
+    # edgeline = np.concatenate((edgeline1, edgeline2), exis = 1)
+    
+    if not isinstance(save_dir, Path): save_dir = Path(save_dir)
+    
+    ''' all tracks 中心轨迹'''
+    img1, img2 = edgeline.copy(), edgeline.copy()
+    img1 = drawTrack(vts.tracks, img1)
+    img2 = drawTrack(vts.Residual, img2)
+    
+    imgcat = np.concatenate((img1, img2), axis = 1)
+    H, W = imgcat.shape[:2]
+    cv2.line(imgcat, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
+    
+    
+    # imgpth = save_dir.joinpath(f"{file}_show.png")
+    # cv2.imwrite(str(imgpth), img)
+
+    if not draw5p:
+        return imgcat
+    ## yi  write piclke change
+    ''' tracks 5点轨迹'''
+    # trackpth = save_dir / Path("trajectory")
+    # if not trackpth.exists():
+    #     trackpth.mkdir(parents=True, exist_ok=True)
+    # for track in vts.tracks:
+    #     # if track.cls != 0:
+    #     img = edgeline.copy()
+    #     img = draw5points(track, img)
+    #
+    #     pth = trackpth.joinpath(f"{file}_{track.tid}_.png")
+    #     cv2.imwrite(str(pth), img)
+        
+    # for track in vts.Residual:
+    #     # if track.cls != 0:
+    #     img = edgeline.copy()
+    #     img = draw5points(track, img)
+
+    #     pth = trackpth.joinpath(f"{file}_{track.tid}_.png")        
+    #     cv2.imwrite(str(pth), img)
+        
+    return imgcat
+    
+
+# =============================================================================
+#     '''3. moving tracks 中心轨迹'''
+#     filename2 = f"{file}_show_r.png"
+#     img = edgeline.copy()
+#     img = drawTrack(vts.Residual, img)
+#     pth = save_dir.joinpath(filename2)
+#     cv2.imwrite(pth, img)
+# =============================================================================
+    
+
+    
+    '''5. tracks 时序：trajmin、trajmax、arearate、incartrate'''
+    # plt = drawtracefeat(vts)
+    # pth = save_dir.joinpath(f"{file}_x.png")
+    # plt.savefig(pth)
+    # plt.close('all')
+    
+
+
+        
+def drawFeatures(allvts, save_dir):
+    # [trajlen_min, trajdist_max, trajlen_rate, trajist_rate]]
+        
+    feats = [track.TrajFeat for vts in allvts for track in vts.tracks]
+    feats = np.array(feats)
+    fig, ax = plt.subplots()
+    ax.scatter(feats[:,3], feats[:, 1], s=10) 
+    # ax.set_xlim(0, 2)
+    # ax.set_ylim(0, 100)
+    ax.grid(True)
+    
+    plt.show()
+    
+    
+    pth = save_dir.joinpath("scatter.png")    
+    plt.savefig(pth) 
+    plt.close('all')    
+
+
+
+def drawtracefeat(vts):   
+    '''
+        需要对曲线进行特征提取和分类
+        boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+                0   1   2   3      4        5     6       7           8
+    '''
+    # matplotlib.use('Agg')
+    fid = vts.frameid
+    fid1, fid2 = min(fid), max(fid)
+    
+    fig, axs = plt.subplots(2, 2,figsize=(18, 8))
+    kernel = [0.15, 0.7, 0.15]
+    for i, track in enumerate(vts.tracks):
+        boxes = track.boxes
+        tid = int(track.tid)
+        cls = int(track.cls)
+        posState = track.posState
+        
+        if track.frnum>=5:
+            x1 = boxes[1:, 7]
+            y1 = track.trajmin
+            x11 = [i for i in range(int(min(x1)), int(max(x1)+1))]
+            y11 = np.interp(x11, x1, y1)
+            y11[1:-1] = np.convolve(y11, kernel, 'valid')
+            
+            x3 = boxes[1:, 7]
+            y3 = track.trajmax
+            x33 = [i for i in range(int(min(x3)), int(max(x3)+1))]
+            y33 = np.interp(x33, x3, y3)
+            y33[1:-1] = np.convolve(y33, kernel, 'valid')
+            
+            
+            x2 = boxes[:, 7]
+            # y2 = track.Area/max(track.Area) - min(track.Area/max(track.Area))
+            y2 = track.Area/max(track.Area)
+            x22 = [i for i in range(int(min(x2)), int(max(x2)+1))]
+            y22 = np.interp(x22, x2, y2)
+            y22[1:-1] = np.convolve(y22, kernel, 'valid')
+            
+            x4 = boxes[:, 7]
+            y4 = track.incartrates
+            x44 = [i for i in range(int(min(x4)), int(max(x4)+1))]
+            y44 = np.interp(x44, x4, y4)
+            y44[1:-1] = np.convolve(y44, kernel, 'valid')
+            
+             
+        elif track.frnum>=2:
+            x11 = boxes[1:, 7]
+            y11 = track.trajmin
+            x33 = boxes[1:, 7]
+            y33 = track.trajmax
+            
+            x22 = boxes[:, 7]
+            # y22 = track.Area/max(track.Area) - min(track.Area/max(track.Area))
+            y22 = track.Area/max(track.Area)
+            x44 = boxes[:, 7]
+            y44 = track.incartrates
+        else:
+            continue
+
+        # cls!=0, max(y)>20
+        if cls!=0 and cls!=9 and posState>=2 and max(y11)>10 and max(y33)>10 and max(y22>0.1):                         
+            axs[0, 0].plot(x11, y11, label=f"ID_{tid}") 
+            axs[0, 0].legend()
+            # axs[0].set_ylim(0, 100)
+            
+            axs[0, 1].plot(x22, y22, label=f"ID_{tid}")
+            axs[0, 1].legend()
+            
+            axs[1, 0].plot(x33, y33, label=f"ID_{tid}")
+            axs[1, 0].legend()
+            
+            axs[1, 1].plot(x44, y44, label=f"ID_{tid}")
+            axs[1, 1].legend()
+            
+    
+    axs[0, 0].grid(True), axs[0, 0].set_xlim(fid1, fid2+10), axs[0, 0].set_title('trajmin')
+    axs[0, 1].grid(True), axs[0, 1].set_xlim(fid1, fid2+10), axs[0, 1].set_title('arearate')
+    axs[1, 0].grid(True), axs[1, 0].set_xlim(fid1, fid2+10), axs[1, 0].set_title('trajmax')
+    axs[1, 1].grid(True), axs[1, 1].set_xlim(fid1, fid2+10), axs[1, 1].set_ylim(-0.1, 1.1)
+    axs[1, 1].set_title('incartrate')
+
+    # pth = save_dir.joinpath(f"{file}_show_x.png")
+    # plt.savefig(pth)
+    
+    # plt.savefig(f"./result/cls11_80212_time/{file}_show_x.png")
+    # plt.show()
+
+    return plt    
+
+def draw5points(track, img):
+    """
+    显示中心点、4角点的轨迹，以及轨迹 features
+    """
+    colorx = np.array([[255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255],
+                       [0, 0, 255], [0, 255, 0], [255, 51, 255], [102, 178, 255], [51, 153, 255],[255, 153, 153], 
+                       [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51], 
+                       [255, 102, 255], [153, 204, 255], [255, 0, 0], [255, 255, 255]], dtype=np.uint8)
+    color = ((0, 0, 255), (255, 128, 0))
+    
+    # img = cv2.imread("./shopcart/cart_tempt/edgeline.png")
+    boxes = track.boxes
+    cornpoints = track.cornpoints
+
+    trajlens = [int(t) for t in track.trajlens]
+    trajdist = [int(t) for t in track.trajdist]
+    
+    if len(track.trajmin):
+        trajstd = np.std(track.trajmin)
+    else:
+        trajstd = 0
+    
+    
+    trajlen_min, trajlen_max, trajdist_min,  trajdist_max, trajlen_rate, trajdist_rate = track.TrajFeat
+    
+    for i in range(boxes.shape[0]):
+        cv2.circle(img, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 6, (255, 255, 255), 2)         
+        cv2.circle(img, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), 6, (255, 0, 255), 2)
+        cv2.circle(img, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), 6, (0, 255, 0), 2)
+        cv2.circle(img, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), 6, (64, 128, 255), 2)
+        cv2.circle(img, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), 6, (255, 128, 64), 2)
+
+    
+    
+    label_0 = f"ID: {track.tid}, Class: {track.cls}"
+    label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}"
+    label_2 = f"trajdist: {trajdist}: trajdist_max: {int(trajdist_max)}"
+    label_3 = "trajlen_min/trajlen_max: {:.2f}/{:.2f} = {:.2f}".format(trajlen_min, trajlen_max, trajlen_rate)
+    label_4 = "trajdist_min/mwh         : {:.2f}/{:.2f} = {:.2f}".format(trajdist_min, track.mwh, trajdist_rate)
+    label_5 = "std(trajmin)             : {:.2f}".format(trajstd)
+    label_6 = "PCA(variance_ratio)      : "
+    label_7 = "Rect W&H&Ratio           : "
+    
+    label_8 = ""
+    # label_8 = "IOU of incart/maxbox/minbox: {:.2f}, {:.2f}, {:.2f}".format(
+    #           track.feature_ious[0], track.feature_ious[3], track.feature_ious[4])
+    
+    
+    '''=============== 最小轨迹长度索引 ===================='''
+    trajlens = [int(t) for t in track.trajrects_wh]
+    if track.isCornpoint:
+        idx = 0
+    else:
+        idx = trajlens.index(min(trajlens))
+    
+    
+    '''=============== PCA ===================='''
+    if trajlens[idx] > 12:
+        X = cornpoints[:, 2*idx:2*(idx+1)]
+        
+        pca = PCA()
+        pca.fit(X)
+        label_6 = "PCA(variance_ratio): {:.2f}".format(pca.explained_variance_ratio_[0])
+        # if sum(np.isnan(pca.explained_variance_ratio_)) == 0:
+        for i, (comp, var) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
+            pt1 = (pca.mean_ - comp*var*200).astype(np.int64)
+            pt2 = (pca.mean_ + comp*var*200).astype(np.int64)
+            cv2.line(img, pt1, pt2, color=color[i], thickness=2)
+    
+    '''=============== RECT ====================''' 
+    rect = track.trajrects[idx]
+    box = cv2.boxPoints(rect)
+    box = np.int0(box)
+    cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
+    label_7 = "Rect W&H&Ratio: {}, {}, {:.2f}".format(int(rect[1][0]), int(rect[1][1]), min(rect[1])/(max(rect[1])+0.001))
+
+
+    '''=============== 显示文字 ====================''' 
+    # label = [label_0, label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8]
+    # w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0] 
+    # for i in range(len(label)):
+    #     cv2.putText(img, label[i], (20, int((i+1)*1.1*h)), 0, 1,
+    #                 [int(x) for x in colorx[i]], 2, lineType=cv2.LINE_AA)
+   
+    # pth = save_dir.joinpath(f"{file}_{track.tid}.png")
+    # cv2.imwrite(pth, img)
+    
+    '''撰写专利需要，生成黑白图像'''
+    # imgbt = cv2.bitwise_not(img)
+    # for i in range(box.shape[0]):
+    #     cv2.circle(imgbt, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 14, (0, 0, 0), 2)         
+    #     cv2.drawMarker(imgbt, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), color= (0, 0, 0), markerType=3, markerSize = 30, thickness=2)
+    #     cv2.drawMarker(imgbt, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), color= (0, 0, 0), markerType=4, markerSize = 30, thickness=2)
+    #     cv2.drawMarker(imgbt, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), color= (0, 0, 0), markerType=5, markerSize = 30, thickness=2)
+    #     cv2.drawMarker(imgbt, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), color= (0, 0, 0), markerType=6, markerSize = 30, thickness=2)
+    # cv2.imwrite(pth + f"/zhuanli/{file}_{track.tid}.png", imgbt)
+    
+    return img
+
+def drawTrack(tracks, img):
+    # img = cv2.imread("./shopcart/cart_tempt/edgeline.png")
+    annotator = TrackAnnotator(img, line_width=2)
+    
+    for track in tracks:
+        if isinstance(track, np.ndarray):
+            annotator.plotting_track(track)
+        else:
+            annotator.plotting_track(track.boxes)
+    img = annotator.result()    
+    
+    # pth = save_dir.joinpath(f"{filename}")
+    # cv2.imwrite(pth, img)  
+
+    return img    
+
+
+
+
+if __name__ == "__main__":
+    
+    y = np.array([5.0, 20, 40, 41, 42, 55, 56])
+    
+    
+
+

tracking/utils/gen.py

@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 16 10:36:38 2024
+
+@author: ym
+"""
+import contextlib
+import time
+
+class Profile(contextlib.ContextDecorator):
+    # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
+    def __init__(self, t=0.0):
+        self.t = t
+        # self.cuda = torch.cuda.is_available()
+
+    def __enter__(self):
+        self.start = self.time()
+        return self
+
+    def __exit__(self, type, value, traceback):
+        self.dt = self.time() - self.start  # delta-time
+        self.t += self.dt  # accumulate dt
+
+    def time(self):
+        # if self.cuda:
+        #     torch.cuda.synchronize()
+        return time.time()

tracking/utils/iterYaml.py

@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec 31 17:07:09 2023
+
+@author: ym
+"""
+
+from pathlib import Path
+from types import SimpleNamespace
+import re
+import yaml
+
+class IterableSimpleNamespace(SimpleNamespace):
+    """
+    Ultralytics IterableSimpleNamespace is an extension class of SimpleNamespace that adds iterable functionality and
+    enables usage with dict() and for loops.
+    """
+
+    def __iter__(self):
+        """Return an iterator of key-value pairs from the namespace's attributes."""
+        return iter(vars(self).items())
+
+    def __str__(self):
+        """Return a human-readable string representation of the object."""
+        return '\n'.join(f'{k}={v}' for k, v in vars(self).items())
+
+    def __getattr__(self, attr):
+        """Custom attribute access error message with helpful information."""
+        name = self.__class__.__name__
+        raise AttributeError(f"""
+            '{name}' object has no attribute '{attr}'. This may be caused by a modified or out of date ultralytics
+            'default.yaml' file.\nPlease update your code with 'pip install -U ultralytics' and if necessary replace
+            DEFAULT_CFG_PATH with the latest version from
+            https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/default.yaml
+            """)
+
+    def get(self, key, default=None):
+        """Return the value of the specified key if it exists; otherwise, return the default value."""
+        return getattr(self, key, default)
+
+
+
+def yaml_load(file='data.yaml', append_filename=False):
+    """
+    Load YAML data from a file.
+
+    Args:
+        file (str, optional): File name. Default is 'data.yaml'.
+        append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.
+
+    Returns:
+        (dict): YAML data and file name.
+    """
+    assert Path(file).suffix in ('.yaml', '.yml'), f'Attempting to load non-YAML file {file} with yaml_load()'
+    with open(file, errors='ignore', encoding='utf-8') as f:
+        s = f.read()  # string
+
+        # Remove special characters
+        if not s.isprintable():
+            s = re.sub(r'[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]+', '', s)
+
+        # Add YAML filename to dict and return
+        data = yaml.safe_load(s) or {}  # always return a dict (yaml.safe_load() may return None for empty files)
+        if append_filename:
+            data['yaml_file'] = str(file)
+        return data

tracking/utils/mergetrack.py

@@ -0,0 +1,213 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb 23 11:04:48 2024
+
+@author: ym
+"""
+import numpy as np
+import cv2
+from scipy.spatial.distance import cdist
+# from trackers.utils import matching
+
+# TracksDict
+def readDict(boxes, TracksDict):
+    feats = []
+    for i in range(boxes.shape[0]):   
+        tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
+        
+        trackdict = TracksDict[f"frame_{fid}"]
+        if "feats" in trackdict:
+            feat = trackdict["feats"][bid]
+            feats.append(feat)
+        
+        if "boxes" in trackdict:
+            box = trackdict["boxes"][bid]
+            assert (box[:4].astype(int) == boxes[i, :4].astype(int)).all(), f"Please check: frame_{fid}"
+        
+        if "imgs" in trackdict:
+            img = trackdict["imgs"][bid]
+            cv2.imwrite(f'./data/imgs/{tid}_{fid}_{bid}.png', img)
+    
+    return np.asarray(feats, dtype=np.float32)
+
+
+
+def track_equal_track(atrack, btrack):
+    # boxes: [x, y, w, h, track_id, score, cls, frame_index, box_index]
+    #         0  1  2  3       4      5     6        7          8
+    aboxes = atrack.boxes
+    bboxes = btrack.boxes
+    
+    afeat = atrack.features
+    bfeat = btrack.features
+    
+    # afeat = readDict(aboxes, TracksDict)
+    # bfeat = readDict(bboxes, TracksDict)
+
+    
+    ''' 1. 判断轨迹在时序上是否有交集 '''
+    afids = aboxes[:, 7].astype(np.int_)
+    bfids = bboxes[:, 7].astype(np.int_)
+    # 帧索引交集
+    interfid = set(afids).intersection(set(bfids))
+    
+    # 或者直接判断帧索引是否有交集，返回 Ture or False
+    # interfid = set(afids).isdisjoint(set(bfids))
+    
+    if len(interfid):
+        return False
+    
+    ''' 2. 轨迹特征相似度判断'''
+    feat = np.concatenate((afeat, bfeat), axis=0)
+    
+    emb_simil = 1 - np.maximum(0.0, cdist(feat, feat, 'cosine'))
+    emb_ = 1 - np.maximum(0.0, cdist(np.mean(afeat, axis=0)[None, :], np.mean(bfeat, axis=0)[None, :], 'cosine'))/2
+    
+    if emb_[0, 0]<0.66:
+        return False
+    
+    ''' 3. 轨迹空间iou'''
+    alabel = np.array([0] * afids.size, dtype=np.int_)
+    blabel = np.array([1] * bfids.size, dtype=np.int_)
+    
+    label = np.concatenate((alabel, blabel), axis=0)
+    fids = np.concatenate((afids, bfids), axis=0)
+    indices = np.argsort(fids)
+    idx_pair = []
+    for i in range(len(indices)-1):
+        idx1, idx2 = indices[i], indices[i+1]
+        if label[idx1] != label[idx2] and fids[idx2] - fids[idx1] <= 3:
+            if label[idx1] == 0:
+                a_idx = idx1
+                b_idx = idx2-alabel.size
+            else:
+                a_idx = idx2
+                b_idx = idx1-alabel.size 
+            
+            idx_pair.append((a_idx, b_idx))
+    
+    ious = []
+    embs = []
+    for a, b in idx_pair:
+        abox, bbox = aboxes[a, :], bboxes[b, :]
+        
+        af, bf = afeat[a, :], bfeat[b, :]
+        
+        emb_ab = 1 - np.maximum(0.0, cdist(af[None, :], bf[None, :], 'cosine'))
+        
+        
+        xa1, ya1 = abox[0] - abox[2]/2, abox[1] - abox[3]/2 
+        xa2, ya2 = abox[0] + abox[2]/2, abox[1] + abox[3]/2
+        
+        xb1, yb1 = bbox[0] - bbox[2]/2, bbox[1] - bbox[3]/2
+        xb2, yb2 = bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2 
+        
+        
+        inter = (np.minimum(xb2, xa2) - np.maximum(xb1, xa1)).clip(0) * \
+                     (np.minimum(yb2, ya2) - np.maximum(yb1, ya1)).clip(0)
+                     
+        # Union Area
+        box1_area = abox[2] * abox[3]
+        box2_area = bbox[2] * bbox[3]
+        union = box1_area + box2_area - inter + 1e-6
+
+        ious.append(inter/union)
+        embs.append(emb_ab[0, 0])
+    
+    ''' 4. 和同一手部关联，如何将该代码和 iou 部分相融合,需进一步完善'''   
+    # ahands = np.array(atrack.Hands)
+    # bhands = np.array(btrack.Hands)
+    # ahids = ahands[:, 0]
+    # bhids = bhands[:, 0]
+    # interhid = set(ahids).intersection(set(bhids))
+    # for hid in interhid:
+    #     aidx = ahands[:, 0] == hid
+    #     bidx = bhands[:, 0] == hid
+        
+    #     ahfids = ahids[aidx, 1]
+    #     bhfids = bhids[bidx, 1]
+    
+    
+    
+
+    cont = False if len(interfid) else True    # fid 无交集
+    cont1 = all(emb > 0.5 for emb in embs)
+    cont2 = all(iou > 0.5 for iou in ious)
+    # cont = cont and cont2 and cont3
+    
+    cont = cont and cont1 and cont2
+
+    
+    return cont
+
+
+
+def track_equal_str(atrack, btrack):
+    if atrack == btrack:
+        return True
+    else:
+        return False
+
+
+def merge_track(Residual):
+    out_list = []
+    alist = [t for t in Residual]
+    while alist:
+        atrack = alist[0]
+        cur_list = []
+        cur_list.append(atrack)
+        alist.pop(0)
+        
+        blist = [b for b in alist]
+        alist = []
+        for btrack in blist:
+            if track_equal_str(atrack, btrack):
+                cur_list.append(btrack)
+            else:
+                alist.append(btrack)
+                
+        out_list.append(cur_list)
+    return out_list
+    
+def main():
+    Residual = ['a', 'b', 'c', 'd', 'a', 'b', 'c', 'b', 'c', 'd']
+    out_list = merge_track(Residual)
+    
+    print(Residual)
+    print(out_list)
+
+if __name__ == "__main__": 
+    
+    main()
+
+
+
+
+# =============================================================================
+# for i, atrack in enumerate(input_list):
+#     cur_list = []
+#     cur_list.append(atrack)
+#     del input_list[i]
+#     
+#     for j, btrack in enumerate(input_list):
+#         if track_equal(atrack, btrack):
+#             cur_list.append(btrack)
+#             del input_list[j]
+#     
+#     out_list.append(cur_list)
+# =============================================================================
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+   

tracking/utils/plotting.py

@@ -0,0 +1,364 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import contextlib
+import math
+import warnings
+from pathlib import Path
+import os
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from PIL import __version__ as pil_version
+# from utils.general import increment_path
+
+# from ultralytics.utils import LOGGER, TryExcept, ops, plt_settings, threaded
+
+# from .checks import check_font, check_version, is_ascii
+# from .files import increment_path
+
+
+class Colors:
+    """
+    Ultralytics default color palette https://ultralytics.com/.
+
+    This class provides methods to work with the Ultralytics color palette, including converting hex color codes to
+    RGB values.
+
+    Attributes:
+        palette (list of tuple): List of RGB color values.
+        n (int): The number of colors in the palette.
+        pose_palette (np.array): A specific color palette array with dtype np.uint8.
+    """
+
+    def __init__(self):
+        """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
+        hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
+                '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
+        
+        self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
+        self.n = len(self.palette)
+        self.pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
+                                      [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
+                                      [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
+                                      [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]],
+                                     dtype=np.uint8)
+
+    def __call__(self, i, bgr=False):
+        """Converts hex color codes to RGB values."""
+        c = self.palette[int(i) % self.n]
+        return (c[2], c[1], c[0]) if bgr else c
+
+    @staticmethod
+    def hex2rgb(h):
+        """Converts hex color codes to RGB values (i.e. default PIL order)."""
+        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
+
+
+colors = Colors()  # create instance for 'from utils.plots import colors'
+
+
+class Annotator:
+    """
+    Ultralytics Annotator for train/val mosaics and JPGs and predictions annotations.
+
+    Attributes:
+        im (Image.Image or numpy array): The image to annotate.
+        pil (bool): Whether to use PIL or cv2 for drawing annotations.
+        font (ImageFont.truetype or ImageFont.load_default): Font used for text annotations.
+        lw (float): Line width for drawing.
+        skeleton (List[List[int]]): Skeleton structure for keypoints.
+        limb_color (List[int]): Color palette for limbs.
+        kpt_color (List[int]): Color palette for keypoints.
+    """
+
+    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
+        """Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
+        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
+        self.im = im
+        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
+        # Pose
+        self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
+                         [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
+
+        self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
+        self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
+
+    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
+        """Add one xyxy box to image with label."""
+        if isinstance(box, torch.Tensor):
+            box = box.tolist()
+
+        p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+        cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
+        if label:
+            tf = max(self.lw - 1, 1)  # font thickness
+            w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
+            outside = p1[1] - h >= 3
+            p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
+            cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
+            cv2.putText(self.im,
+                        label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+                        0,
+                        self.lw / 3,
+                        txt_color,
+                        thickness=tf,
+                        lineType=cv2.LINE_AA)
+
+    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
+        """
+        Plot masks on image.
+
+        Args:
+            masks (tensor): Predicted masks on cuda, shape: [n, h, w]
+            colors (List[List[Int]]): Colors for predicted masks, [[r, g, b] * n]
+            im_gpu (tensor): Image is in cuda, shape: [3, h, w], range: [0, 1]
+            alpha (float): Mask transparency: 0.0 fully transparent, 1.0 opaque
+            retina_masks (bool): Whether to use high resolution masks or not. Defaults to False.
+        """
+        if self.pil:
+            # Convert to numpy first
+            self.im = np.asarray(self.im).copy()
+        if len(masks) == 0:
+            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
+        if im_gpu.device != masks.device:
+            im_gpu = im_gpu.to(masks.device)
+        colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0  # shape(n,3)
+        colors = colors[:, None, None]  # shape(n,1,1,3)
+        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
+        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
+
+        inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
+        mcs = masks_color.max(dim=0).values  # shape(n,h,w,3)
+
+        im_gpu = im_gpu.flip(dims=[0])  # flip channel
+        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
+        im_gpu = im_gpu * inv_alph_masks[-1] + mcs
+        im_mask = (im_gpu * 255)
+        im_mask_np = im_mask.byte().cpu().numpy()
+        self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
+        if self.pil:
+            # Convert im back to PIL and update draw
+            self.fromarray(self.im)
+
+    def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
+        """
+        Plot keypoints on the image.
+
+        Args:
+            kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
+            shape (tuple): Image shape as a tuple (h, w), where h is the height and w is the width.
+            radius (int, optional): Radius of the drawn keypoints. Default is 5.
+            kpt_line (bool, optional): If True, the function will draw lines connecting keypoints
+                                       for human pose. Default is True.
+
+        Note: `kpt_line=True` currently only supports human pose plotting.
+        """
+        if self.pil:
+            # Convert to numpy first
+            self.im = np.asarray(self.im).copy()
+        nkpt, ndim = kpts.shape
+        is_pose = nkpt == 17 and ndim == 3
+        kpt_line &= is_pose  # `kpt_line=True` for now only supports human pose plotting
+        for i, k in enumerate(kpts):
+            color_k = [int(x) for x in self.kpt_color[i]] if is_pose else colors(i)
+            x_coord, y_coord = k[0], k[1]
+            if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
+                if len(k) == 3:
+                    conf = k[2]
+                    if conf < 0.5:
+                        continue
+                cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
+
+        if kpt_line:
+            ndim = kpts.shape[-1]
+            for i, sk in enumerate(self.skeleton):
+                pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
+                pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
+                if ndim == 3:
+                    conf1 = kpts[(sk[0] - 1), 2]
+                    conf2 = kpts[(sk[1] - 1), 2]
+                    if conf1 < 0.5 or conf2 < 0.5:
+                        continue
+                if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
+                    continue
+                if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
+                    continue
+                cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
+        if self.pil:
+            # Convert im back to PIL and update draw
+            self.fromarray(self.im)
+
+    def rectangle(self, xy, fill=None, outline=None, width=1):
+        """Add rectangle to image (PIL-only)."""
+        self.draw.rectangle(xy, fill, outline, width)
+
+    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top', box_style=False):
+        """Adds text to an image using PIL or cv2."""
+        if anchor == 'bottom':  # start y from font bottom
+            w, h = self.font.getsize(text)  # text width, height
+            xy[1] += 1 - h
+        if self.pil:
+            if box_style:
+                w, h = self.font.getsize(text)
+                self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
+                # Using `txt_color` for background and draw fg with white color
+                txt_color = (255, 255, 255)
+            if '\n' in text:
+                lines = text.split('\n')
+                _, h = self.font.getsize(text)
+                for line in lines:
+                    self.draw.text(xy, line, fill=txt_color, font=self.font)
+                    xy[1] += h
+            else:
+                self.draw.text(xy, text, fill=txt_color, font=self.font)
+        else:
+            if box_style:
+                tf = max(self.lw - 1, 1)  # font thickness
+                w, h = cv2.getTextSize(text, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
+                outside = xy[1] - h >= 3
+                p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
+                cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA)  # filled
+                # Using `txt_color` for background and draw fg with white color
+                txt_color = (255, 255, 255)
+            tf = max(self.lw - 1, 1)  # font thickness
+            cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
+
+    def fromarray(self, im):
+        """Update self.im from a numpy array."""
+        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+        self.draw = ImageDraw.Draw(self.im)
+
+    def result(self):
+        """Return annotated image as array."""
+        return np.asarray(self.im)
+
+
+def scale_image(masks, im0_shape, ratio_pad=None):
+    """
+    Takes a mask, and resizes it to the original image size
+
+    Args:
+        masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
+        im0_shape (tuple): the original image shape
+        ratio_pad (tuple): the ratio of the padding to the original image.
+
+    Returns:
+        masks (torch.Tensor): The masks that are being returned.
+    """
+    # Rescale coordinates (xyxy) from im1_shape to im0_shape
+    im1_shape = masks.shape
+    if im1_shape[:2] == im0_shape[:2]:
+        return masks
+    if ratio_pad is None:  # calculate from im0_shape
+        gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1])  # gain  = old / new
+        pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+    top, left = int(pad[1]), int(pad[0])  # y, x
+    bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
+
+    if len(masks.shape) < 2:
+        raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
+    masks = masks[top:bottom, left:right]
+    masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
+    if len(masks.shape) == 2:
+        masks = masks[:, :, None]
+
+    return masks
+
+
+def boxing_img(det, img, line_width=3):
+    annotator = Annotator(img, line_width)
+    for *xyxy, id, conf, cls, _, _ in reversed(det):
+        label = (f'id:{int(id)} '+str(int(cls)) +f' {conf:.2f}')                           
+        if cls==0:
+            color = colors(int(cls), True)
+        else:
+            color = colors(int(id), True)
+        annotator.box_label(xyxy, label, color=color)
+            
+    # Save results (image and video with tracking)
+    imgx = annotator.result()
+    
+    return imgx
+
+def array2list(bboxes):
+    track_fids = np.unique(bboxes[:, 7].astype(int))
+    track_fids.sort()
+    
+    lboxes = []
+    for f_id in track_fids:
+        # print(f"The ID is: {t_id}")
+        idx = np.where(bboxes[:, 7] == f_id)[0]
+        box = bboxes[idx, :]  
+        lboxes.append(box)
+        
+        assert len(set(box[:, 4])) == len(box), "Please check!!!"
+        
+    return lboxes
+
+
+def get_subimgs(imgs, tracks, scale=2):
+    bboxes = []
+    if len(tracks):
+        bboxes = array2list(tracks)
+    
+    subimgs = []
+    for i, boxes in enumerate(bboxes):
+        fid = int(boxes[0, 7])
+        
+        for *xyxy, tid, conf, cls, fid, bid in boxes:
+            pt2 = [p/scale for p in xyxy]
+            x1, y1, x2, y2 = (int(pt2[0]), int(pt2[1])), (int(pt2[2]), int(pt2[3]))
+            subimgs.append((int(fid), int(bid), imgs[fid-1][y1:y2, x1:x2]))
+    
+    return subimgs
+
+def draw_tracking_boxes(imgs, tracks, scale=2):
+    '''需要确保 imgs 覆盖tracks中的帧ID数
+        tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+                0   1   2   3      4        5     6       7           8
+       关键：
+         (1) imgs中的次序和 track 中的 fid 对应
+         (2) img 尺度小对于xyxy减半
+       
+    '''
+    
+
+    bboxes = []
+    if len(tracks):
+        bboxes = array2list(tracks)
+    
+    # if len(bboxes)!=len(imgs):
+    #     return False, imgs
+    
+    annimgs = []
+    for i, boxes in enumerate(bboxes):
+        fid = int(boxes[0, 7])
+        annotator = Annotator(imgs[fid-1].copy())
+        for *xyxy, tid, conf, cls, fid, bid in boxes:
+            label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
+            
+            if cls==0:
+                color = colors(int(cls), True)
+            elif tid>0 and cls!=0:
+                color = colors(int(tid), True)
+            else:
+                color = colors(19, True)     # 19为调色板的最后一个元素
+            
+            pt2 = [p/scale for p in xyxy]
+            annotator.box_label(pt2, label, color=color)
+
+        img = annotator.result()
+        annimgs.append((int(fid), img))
+        
+    return annimgs
+
+
+
+
+    
+

tracking/utils/proBoxes.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec 31 17:14:37 2023
+
+@author: ym
+"""
+import numpy as np
+
+
+class Boxes:
+    def __init__(self, boxes, orig_shape=None) -> None:
+        """Initialize the Boxes class."""
+        if boxes.ndim == 1:
+            boxes = boxes[None, :]
+        m, n = boxes.shape
+        assert n in (6, 7), f'expected `n` in [6, 7], but got {n}'  # xyxy, track_id, conf, cls
+        
+        '''对每一个box进行编号，利用该编号可以索引对应 feature'''
+        self.data = np.concatenate([boxes[:, :4], np.arange(m).reshape(-1, 1), boxes[:, 4:]], axis=-1)
+        
+        self.orig_shape = orig_shape
+    
+    def cpu(self):
+        """Return a copy of the tensor on CPU memory."""
+        return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape)
+    def numpy(self):
+        """Return a copy of the tensor as a numpy array."""
+        return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape)
+    
+    @property
+    def xyxy(self):
+        """Return the boxes in xyxy format."""
+        return self.data[:, :4]
+    
+    @property
+    def xyxyb(self):
+        """Return the boxes in xyxyb format."""    
+        return self.data[:, :5]
+
+    @property
+    def conf(self):
+        """Return the confidence values of the boxes."""
+        return self.data[:, -2]
+
+    @property
+    def cls(self):
+        """Return the class values of the boxes."""
+        return self.data[:, -1]
+
+
+
+# def boxes_add_fid(tboxes):
+#     '''
+#         将 bboxes 对应的帧索引添加到 boxes 最后一列
+#     Return：
+#         bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index]
+#     '''
+    
+#     bboxes = np.empty((0, 8), dtype = np.float32)  
+#     for tbox, f in tboxes:
+#         data = tbox.numpy()
+        
+#         frame = f * np.ones([data.shape[0], 1])
+#         bbox = np.concatenate([data, frame], axis=1)
+#         bboxes = np.concatenate([bboxes, bbox], axis=0)
+    
+#     return bboxes
+
+
+def boxes_add_fid(tboxes):
+    '''
+        将 bboxes 对应的帧索引添加到 boxes 最后一列
+    Return：
+        bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index]
+    '''
+    
+    bboxes = np.empty((0, 8), dtype = np.float32)  
+    for data, f in tboxes:
+        frame = f * np.ones([data.shape[0], 1])
+        bbox = np.concatenate([data, frame], axis=1)
+        bboxes = np.concatenate([bboxes, bbox], axis=0)
+    
+    return bboxes
+
+
+
+
+
+
+
+
+
+
+
+

tracking/utils/processboxes.py

@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep 20 14:28:20 2023
+
+@author: ym
+"""
+import numpy as np
+from scipy.spatial.distance import cdist
+
+
+def boxes_add_fid(tboxes):
+    '''
+        将 bboxes 对应的帧索引添加到 boxes 最后一列
+    Return：
+        bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index]
+    '''
+    
+    bboxes = np.empty((0, 8), dtype = np.float32)  
+    for tbox, f in tboxes:
+        data = tbox.numpy()
+        
+        frame = f * np.ones([data.shape[0], 1])
+        bbox = np.concatenate([data, frame], axis=1)
+        bboxes = np.concatenate([bboxes, bbox], axis=0)
+    
+    return bboxes
+
+
+    
+def array2list(bboxes):
+    '''
+        将 bboxes 变换为 track 列表
+        bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index]
+    Return：
+        lboxes：列表，列表中元素具有同一 track_id，xywh 格式
+                [x, y, w, h, track_id, score, cls, frame_index]        
+    '''
+    track_ids = set(bboxes[:, 4])
+    lboxes = []
+    for t_id in track_ids:
+        idx = np.where(bboxes[:, 4] == t_id)[0]
+        box = bboxes[idx, :]
+        
+        x = (box[:, 0] + box[:, 2]) / 2
+        y = (box[:, 1] + box[:, 3]) / 2
+        
+        # box: [x, y, w, h, track_id, score, cls, frame_index]
+        box[:, 2] = box[:, 2] - box[:, 0]    
+        box[:, 3] = box[:, 3] - box[:, 1]
+        box[:, 0] = x
+        box[:, 1] = y
+  
+        lboxes.append(box)
+    
+    return lboxes
+
+
+def max_dist_track(tboxes):
+    
+    '''
+        计算 tboxes 中最大dist的 track
+    Return：     
+    '''
+    
+    max_track_dist, max_dist = 0, 0
+    for track in tboxes:
+        box = track[:, :4].astype(int)
+        
+        dist = cdist(box[:, :2], box[:, :2])
+        
+        dm = np.max(dist)
+        if dm > max_dist:
+            max_dist = dm 
+            max_track = track.copy()
+            max_track_dist = dist.copy()
+            
+            
+            # 同一 track_id 中目标中心移动最大距离的索引（ix1, ix2）
+            indx, indy = np.where(dist == dm)
+            ix1,  ix2 = indx[0], indy[0]
+            # 确保 ix1 < ix2，索引 ix1 是开始时的视频
+            if ix1 > ix2: ix1, ix2 = ix2, ix1
+            
+# =============================================================================
+# # =============================================================================
+# # 逻辑分析
+# # =============================================================================
+#     Scanzone = ((0, int(Height/4)), (int(2*Weight/3), Weight))
+#     if max_track.shape[0] > 10:    
+#     
+#         # max_track 视频序列的第一帧索引 idx1
+#         frame_1 = int(min(max_track[:, 7]))
+#         idx1 = np.where(max_track[:, 7] == frame_1)[0][0]
+#         
+#         # max_track 视频序列的最后一帧索引 idx2
+#         frame_2 = int(max(max_track[:, 7]))
+#         idx2 = np.where(max_track[:, 7] == frame_2)[0][0]
+#         
+#         # max_track 视频序列的第一帧目标位置中心 (x1, y1)
+#         x1, y1 = max_track[idx1, :2]
+#         
+#         # max_track 视频序列的第最后一帧目标位置中心 (x2, y2)
+#         x2, y2 = max_track[idx2, :2]
+#             
+#         
+#         # track序列第一帧和最后一帧的距离，该距离和 mx_dist 不是一个概念
+#         dist_1_2 = max_track_dist[idx1, idx2]
+#         
+#         if max_dist < 3 * Height/10:
+#             State = Uncertain
+#         
+#         elif y1 > y2:
+#             State = TakeOut
+#             
+#         elif y1 < y2:
+#             State = PutIn
+# =============================================================================
+    return max_track, max_dist

tracking/utils/read_data.py

@@ -0,0 +1,750 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jul  5 13:59:21 2024
+       函数                           读取文件
+    extract_data()                 0/1_track.data
+  read_tracking_output()       0/1_tracking_output.data
+   read_similar()                  process.data
+   
+@author: ym
+"""
+import numpy as np
+import re
+import os
+from collections import OrderedDict
+import warnings
+import matplotlib.pyplot as plt
+
+
+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 array2list(boxes, feats):
+    '''boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
+    
+    trackID = np.unique(boxes[:, 4].astype(int))
+    
+    track_ids = boxes[:, 4].astype(int)
+    lboxes = []
+    for t_id in trackID: 
+        idx = np.where(track_ids == t_id)[0]
+        box = boxes[idx, :] 
+        feat = feats[idx, :]
+                 
+        assert len(set(box[:, 7])) == len(box), "Please check!!!"
+        lboxes.append(box)
+    
+    return lboxes
+
+def extract_data(datapath):
+    '''
+    0/1_track.data 数据读取
+    对于特征，需要构造两个对象：
+    (1) tracking输出 boxes，依据 (fid, bid) 找到对应的 feats, tracker_feat_dict 实现较为方便
+    (2) frameDictList 中元素为 frameDict，输出同一接口
+    '''
+    bboxes, ffeats = [], []
+    
+    trackerboxes = np.empty((0, 9), dtype=np.float64)
+    trackerfeats = np.empty((0, 256), dtype=np.float64)
+    
+    boxes, feats, tboxes, tfeats = [], [], [], []
+    timestamps, frameIds = [], []
+    with open(datapath, 'r', encoding='utf-8') as lines:
+        for line in lines:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+            if not line:  # 跳过空行  
+                continue
+             
+            if line.find("CameraId")>=0:
+                if len(boxes): bboxes.append(np.array(boxes))
+                if len(feats): ffeats.append(np.array(feats))
+                if len(tboxes): 
+                    trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
+                if len(tfeats):
+                    trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
+                    
+                timestamp, frameId = [int(ln.split(":")[1]) for ln in line.split(",")[1:]]    
+                timestamps.append(timestamp)
+                frameIds.append(frameId)
+     
+                boxes, feats, tboxes, tfeats = [], [], [], []
+            
+            if line.find("box:") >= 0 and line.find("output_box:")<0 and line.find("out_boxes")<0:
+                box = line[line.find("box:") + 4:].strip()
+                # if len(box)==6:
+                boxes.append(str_to_float_arr(box))
+                
+            if line.find("feat:") >= 0:
+                feat = line[line.find("feat:") + 5:].strip()
+                # if len(feat)==256:
+                feats.append(str_to_float_arr(feat))
+                    
+            if line.find("output_box:") >= 0:
+                assert(len(boxes)>=0 and len(boxes)==len(feats)), f"{datapath}, {datapath}, len(boxes)!=len(feats)"
+                
+                box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
+                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)
+                    
+                    
+                
+    if len(boxes): bboxes.append(np.array(boxes))
+    if len(feats): ffeats.append(np.array(feats))
+    if len(tboxes): trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
+    if len(tfeats): trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
+    
+    assert(len(bboxes)==len(ffeats)), "Error at Yolo output!"
+    assert(len(trackerboxes)==len(trackerfeats)), "Error at tracker output!"
+    
+    ## 生成帧为单位的特征列表
+    tracker_feats = {}  
+    frmIDs = np.sort(np.unique(trackerboxes[:, 7].astype(int)))
+    for fid in frmIDs:
+        idx = np.where(trackerboxes[:, 7] == fid)[0]
+        boxes = trackerboxes[idx, :]
+        feats = trackerfeats[idx, :]
+        
+        for i in range(len(boxes)):
+            f, b = int(boxes[i, 7]), int(boxes[i, 8])
+            tracker_feats.update({f"{f}_{b}": feats[i, :]})  
+    
+    boxes, trackingboxes= [], []
+    tracking_flag = False    
+    with open(datapath, 'r', encoding='utf-8') as lines:
+        for line in lines:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+            if not line:  # 跳过空行  
+                tracking_flag = False
+                continue
+            
+            if tracking_flag:
+                if line.find("tracking_") >= 0:
+                    tracking_flag = False
+                else:
+                    box = str_to_float_arr(line)
+                    boxes.append(box)
+            if line.find("tracking_") >= 0:
+                tracking_flag = True
+                if len(boxes):
+                    trackingboxes.append(np.array(boxes))
+                boxes = []
+    
+    if len(boxes): 
+        trackingboxes.append(np.array(boxes))
+        
+    # tracking_feat_dict = {}
+    # try:
+    #     for i, boxes in enumerate(trackingboxes):
+    #         for box in boxes:
+    #             tid, fid, bid = int(box[4]), int(box[7]), int(box[8])
+    #             if f"track_{tid}" not in tracking_feat_dict:
+    #                 tracking_feat_dict[f"track_{tid}"]= {"feats": {}}
+    #             tracking_feat_dict[f"track_{tid}"]["feats"].update({f"{fid}_{bid}": tracker_feat_dict[f"frame_{fid}"]["feats"][bid]})
+    # except Exception as e:
+    #     print(f'Path: {datapath}, tracking_feat_dict can not be structured correcttly, Error: {e}') 
+    
+    # tracker_feat_dict = {}
+    # tracker_feat_dict["timestamps"] = timestamps
+    # tracker_feat_dict["frameIds"] = frameIds
+ 
+    # for i in range(len(trackerboxes)):
+    #     fid, bid = int(trackerboxes[i, 7]), int(trackerboxes[i, 8])
+    #     if f"frame_{fid}" not in tracker_feat_dict:
+    #         tracker_feat_dict[f"frame_{fid}"]= {"feats": {}}
+    #     tracker_feat_dict[f"frame_{fid}"]["feats"].update({bid: trackerfeats[i, :]})
+    
+    trackingfeats = []
+    try:
+        for i, boxes in enumerate(trackingboxes):
+            feats = []
+            for box in boxes:
+                fid, bid = int(box[7]), int(box[8])
+                feat = tracker_feats[f"{fid}_{bid}"]
+                feats.append(feat)
+            trackingfeats.append(np.array(feats))
+    except Exception as e:
+        print(f'Path: {datapath}, trackingfeats can not be structured correcttly, Error: {e}') 
+
+    # return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, trackingfeats
+    return bboxes, ffeats, trackerboxes, tracker_feats, trackingboxes, trackingfeats
+
+
+
+
+def extract_data_realtime(datapath):
+    boxes, feats = [], []
+    tracker_feats = {}
+    with open(datapath, 'r', encoding='utf-8') as lines:
+        for line in lines:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+            if not line:  # 跳过空行  
+                continue
+            
+            if line.endswith(','):  
+                line = line[:-1]  
+            ftlist = [float(x) for x in line.split()]
+            
+            if len(ftlist) != 265:
+                continue
+            boxes.append(ftlist[:9])
+            feats.append(ftlist[9:])
+    
+    
+    trackerboxes = np.array(boxes)
+    trackerfeats = np.array(feats)
+    
+    if len(trackerboxes)==0 or len(trackerboxes) != len(trackerfeats):
+        return np.array([]), {}
+
+    frmIDs = np.sort(np.unique(trackerboxes[:, 7].astype(int)))
+    for fid in frmIDs:
+        idx = np.where(trackerboxes[:, 7] == fid)[0]
+        box = trackerboxes[idx, :]
+        feat = trackerfeats[idx, :]
+        
+        for i in range(len(box)):
+            f, b = int(box[i, 7]), int(box[i, 8])
+            tracker_feats.update({f"{f}_{b}": feat[i, :]})       
+    return trackerboxes, tracker_feats
+    
+    
+def read_tracking_output_realtime(datapath):
+
+    trackingboxes, trackingfeats = [], []
+    tracking_outboxes, tracking_outfeats  = [], []
+    with open(datapath, 'r', encoding='utf-8') as lines:
+        boxes, feats = [], []
+        Flag = False
+        for line in lines:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+            if not line:  # 跳过空行  
+                continue
+            if line.endswith(','):  
+                line = line[:-1] 
+            
+            ftlist = [float(x) for x in line.split()]
+            if len(ftlist) != 265: continue
+            
+            Flag = all(elem == 0 for elem in ftlist)
+            if Flag: 
+                trackingboxes.append(np.array(boxes))
+                trackingfeats.append(np.array(feats))
+                boxes, feats = [], []
+                continue
+            
+            boxes.append(ftlist[:9])
+            feats.append(ftlist[9:])
+    if len(boxes):
+        trackingboxes.append(np.array(boxes))
+        trackingfeats.append(np.array(feats))
+    if len(trackingboxes):
+        tracking_outboxes = trackingboxes[:1]
+        tracking_outfeats = trackingfeats[:1]
+       
+    return trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats        
+        
+
+def read_tracking_output(filepath):
+    '''
+    0/1_tracking_output.data 数据读取
+    '''
+    
+    boxes = []
+    feats = []
+    if not os.path.isfile(filepath):
+        return boxes, feats
+        
+    with open(filepath, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符
+            
+            if not line:  
+                continue
+            if line.find("gift")>0: continue
+            
+            if line.endswith(','):  
+                line = line[:-1]
+                
+            data = np.array([float(x) for x in line.split(",")])
+            if data.size == 9:
+                boxes.append(data)
+            if data.size == 256:
+                feats.append(data)
+
+
+    if len(feats) != len(boxes) or len(boxes)==0:
+        return [], []
+    
+    return [np.array(boxes)], [np.array(feats)] 
+
+
+def read_process(filePath):
+    timeDict = {}
+    with open(filePath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clines = [line.strip().replace("'", '').replace('"', '') for line in lines]
+        for i, line in enumerate(clines):
+            line = line.strip()
+            if line.endswith(','):  
+                line = line[:-1]
+            if not line: continue
+        
+            lnList = line.split(":")  
+            if line.find("eventStart")>=0:
+                timeDict["eventStart"] = int(lnList[-1])
+            
+            if line.find("eventEnd")>=0:
+                timeDict["eventEnd"] = int(lnList[-1])
+            
+            if line.find("weightValue")>=0:
+                timeDict["weightValue"] = int(lnList[-1])    
+    return timeDict
+
+
+def read_similar(filePath):
+    '''1:n时 Dict['type']字段提取和非全实时不一致，无 "=" 字符 '''
+    
+    SimiDict = {}
+    SimiDict['one2one'] = []
+    SimiDict['one2SN'] = []
+    SimiDict['one2n'] = []
+    SimiDict['algroStartToEnd'] = -1
+    
+    with open(filePath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]  
+        one2one_list, one2SN_list, one2n_list = [], [], []
+        
+        Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False
+        for i, line in enumerate(clean_lines):
+            line = line.strip()
+            if line.endswith(','):  
+                line = line[:-1]
+            Dict = {}
+
+            if not line:
+                if len(one2one_list): SimiDict['one2one'] = one2one_list
+                if len(one2SN_list): SimiDict['one2SN'] = one2SN_list
+                if len(one2n_list): SimiDict['one2n'] = one2n_list
+                
+                
+                one2one_list, one2SN_list, one2n_list = [], [], []
+                Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False
+                continue
+            
+            if line.find("algroStartToEnd")>=0:
+                alg_during = line.split(':')[1].strip()
+                SimiDict['algroStartToEnd'] = int(alg_during)
+                
+            
+            
+                
+            if line.find('oneToOne')>=0:
+                Flag_1to1, Flag_1toSN, Flag_1ton = True, False,False
+                continue
+            if line.find('oneToSN')>=0:
+                Flag_1to1, Flag_1toSN, Flag_1ton = False, True, False
+                continue
+            if line.find('oneTon')>=0:
+                Flag_1to1, Flag_1toSN, Flag_1ton = False, False, True
+                continue
+            
+            
+            
+            if Flag_1to1:
+                barcode = line.split(',')[0].strip()
+                value = line.split(',')[1].split(':')[1].strip()
+                Dict['barcode'] = barcode
+                Dict['similar'] = float(value)
+                one2one_list.append(Dict)
+                continue
+            
+            if Flag_1toSN:
+                barcode = line.split(',')[0].strip()
+                value = line.split(',')[1].split(':')[1].strip()
+                Dict['barcode'] = barcode
+                Dict['similar'] = float(value)
+                one2SN_list.append(Dict)
+                continue
+
+            if Flag_1ton:
+                label = line.split(':')[0].strip()
+                value = line.split(':')[1].strip()
+                Dict['barcode'] = ''
+                if label.find("_") > 0:
+                    bcd = label.split('_')[-1]
+                    if len(bcd)>=8 and bcd.isdigit():
+                        Dict['barcode'] = bcd
+                        
+
+
+                Dict['event'] = label
+                Dict['similar'] = float(value.split(',')[0])
+                
+                if value.find("=")>0:
+                    Dict['type'] = value.split('=')[-1]
+                else:
+                    Dict['type'] = value.split(',')[-1]
+                one2n_list.append(Dict)
+
+        if len(one2one_list): SimiDict['one2one'] = one2one_list
+        if len(one2n_list): SimiDict['one2n'] = one2n_list
+        if len(one2SN_list): SimiDict['one2SN'] = one2SN_list
+    
+    return SimiDict
+
+def read_weight_sensor(filepath):
+    WeightDict = OrderedDict()
+    with open(filepath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]  
+        for i, line in enumerate(clean_lines):
+            line = line.strip()
+            
+            if line.find(':') < 0: continue
+            if line.find("Weight") >= 0: 
+                label = "Weight"
+                continue
+ 
+            
+            keyword = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            if label == "Weight":    
+                vdata = [float(s) for s in value.split(',') if len(s) and s.isdigit()]
+                WeightDict[keyword] = vdata[-1]
+                
+                
+    weights = [(float(t), w) for t, w in WeightDict.items()]
+    weights = np.array(weights).astype(np.int64)
+    
+    return weights
+
+def read_weight_timeConsuming(filePth):
+    WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict()
+    
+    with open(filePth, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        # label = ''
+        for i, line in enumerate(lines):
+            line = line.strip()
+            
+            if line.find(':') < 0: continue
+            if line.find("Weight") >= 0: 
+                label = "Weight"
+                continue
+            if line.find("Sensor") >= 0: 
+                label = "Sensor"
+                continue
+            if line.find("processTime") >= 0: 
+                label = "ProcessTime"
+                continue
+            
+            keyword = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            if label == "Weight":
+                WeightDict[keyword] = float(value.strip(','))
+            if label == "Sensor":
+                SensorDict[keyword] = [float(s) for s in value.split(',') if len(s)]
+            if label == "ProcessTime":
+                ProcessTimeDict[keyword] = float(value.strip(','))
+  
+    # print("Done!")
+    return WeightDict, SensorDict, ProcessTimeDict
+
+
+    
+
+def read_deletedBarcode_file(filePath):
+    with open(filePath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+
+        split_flag, all_list = False, []
+        dict, barcode_list, similarity_list =  {}, [], []
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
+        
+        for i, line in enumerate(clean_lines):
+            if line.endswith(','):  
+                line = line[:-1]
+                
+            stripped_line = line.strip()
+            if not stripped_line:
+                if len(barcode_list): dict['barcode'] = barcode_list
+                if len(similarity_list): dict['similarity'] = similarity_list
+                if len(dict): all_list.append(dict)
+                
+                split_flag = False
+                dict, barcode_list, similarity_list =  {}, [], []
+                continue
+            
+            if line.find(':')<0: continue
+            label = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            if label == 'SeqDir':
+                dict['SeqDir'] = value
+                dict['filetype'] = "deletedBarcode"
+            if label == 'Deleted':
+                dict['Deleted'] = value
+            if label == 'List':
+                split_flag = True
+                continue
+            if split_flag:
+                barcode_list.append(label)
+                similarity_list.append(value)
+        
+        if len(barcode_list): dict['barcode'] = barcode_list
+        if len(similarity_list): dict['similarity'] = similarity_list
+        if len(dict): all_list.append(dict)
+    return all_list
+
+
+def read_returnGoods_file(filePath):
+    '''
+    20241030开始，原 deletedBarcode.txt 中数据格式修改为 returnGoods.txt，读数方式随之变化
+    '''
+    
+    with open(filePath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
+
+        all_list = []
+        split_flag, dict = False, {} 
+        barcode_list, similarity_list = [], []
+        event_list, type_list = [], []
+        
+        
+        for i, line in enumerate(clean_lines):
+            stripped_line = line.strip()
+            if line.endswith(','):  
+                line = line[:-1]
+            
+            if not stripped_line:
+                if len(barcode_list): dict['barcode'] = barcode_list
+                if len(similarity_list): dict['similarity'] = similarity_list
+                if len(event_list): dict['event'] = event_list
+                if len(type_list): dict['type'] = type_list
+                
+                if len(dict) and  dict['SeqDir'].find('*')<0: 
+                    all_list.append(dict)
+                
+                split_flag, dict = False, {} 
+                barcode_list, similarity_list = [], []
+                event_list, type_list = [], []
+                continue
+            
+            if line.find(':')<0: continue
+            if line.find('1:n')==0: continue
+        
+            label = line.split(':')[0].strip()
+            value = line.split(':')[1].strip()
+    
+            if label == 'SeqDir':
+                dict['SeqDir'] = value
+                dict['Deleted'] = value.split('_')[-1]
+                dict['filetype'] = "returnGoods"
+            if label == 'List':
+                split_flag = True
+                continue
+            if split_flag:
+                bcd = label.split('_')[-1]
+                if len(bcd)<8: continue
+                # event_list.append(label + '_' + bcd)
+                event_list.append(label)
+                barcode_list.append(bcd)
+                similarity_list.append(value.split(',')[0])
+                type_list.append(value.split('=')[-1])
+        
+            
+        if len(barcode_list): dict['barcode'] = barcode_list
+        if len(similarity_list): dict['similarity'] = similarity_list
+        if len(event_list): dict['event'] = event_list
+        if len(type_list): dict['type'] = type_list
+        if len(dict) and  dict['SeqDir'].find('*')<0: 
+            all_list.append(dict)
+    
+    return all_list
+        
+    
+def analyse_simi(simi_list, one2n_flag=False):
+    dict_ = {}
+    # maxSimi_dict = sorted(simi_list, key=lambda x: x['similar'], reverse=True)[0]
+
+    for i, simi_dict in enumerate(simi_list):
+        if one2n_flag:
+            event = simi_dict['event']
+            simi = simi_dict['similar']
+            # print(f"barcode:{barcode}, simi:{simi}")
+            dict_[event] = simi
+        else:
+            barcode = simi_dict['barcode']
+            simi = simi_dict['similar']
+            # print(f"barcode:{barcode}, simi:{simi}")
+            dict_[barcode] = simi
+    return dict_
+def get_process_csv_data(csv_path, event_path):
+    dict_data = {}
+    event = event_path.name
+    process_file = event_path / 'process.data'
+    # print('**********process_file', process_file)
+    simidct = read_similar(process_file)
+    # print('simidct', simidct)
+    ###========================解析每个相似度======================
+    one2one_list = simidct['one2one']
+    one2SN_list = simidct['one2SN']
+    one2n_list = simidct['one2n']
+    # print('one2one_list', one2one_list)
+
+
+    if len(one2one_list):
+        one2one_dict = analyse_simi(one2one_list)
+        dict_data['one2one'] = str(list(one2one_dict.values())[0])
+        # print('one2one_dict', one2one_dict)
+    if len(one2SN_list):
+        one2SN_dict = analyse_simi(one2SN_list)
+        dict_data['one2SN'] = one2SN_dict
+        # print('one2SN_dict', one2SN_dict)
+
+    if len(one2n_list):
+        one2n_dict = analyse_simi(one2n_list, one2n_flag=True)
+        dict_data['one2n'] = one2n_dict
+        # print('one2n_dict', one2n_dict)
+    algroStartToEnd = str(simidct['algroStartToEnd'])
+    dict_data['algroStartToEnd'] = algroStartToEnd
+    ###=========================================================
+
+    ####人工标注初始化excel文件中已有
+    # barcode = read_csv(csv_path, event)
+    # dict_data['barcode'] = barcode
+    return dict_data
+
+            
+                
+def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
+    
+    wtime, wdata = [], []
+    stime, sdata = [], []
+    for key, value in WeightDict.items():
+        wtime.append(int(key))
+        wdata.append(value)
+    
+    for key, value in SensorDict.items():
+        if len(value) != 9: continue
+        
+        stime.append(int(key))
+        sdata.append(np.array(value))
+        
+    static_range = []
+    dynamic_range = []
+    windth = 8
+    nw = len(wdata)
+    assert(nw) >= 8, "The num of weight data is less than 8!"
+    
+    # i1, i2 = 0, 7
+    # while i2 < nw:
+    #     data = wdata[i1:(i2+1)]
+    #     max(data) - min(data)
+    #     if i2<7:
+    #         i1 = 0
+    #     else:
+    #         i1 = i2-windth
+
+    min_t = min(wtime + stime)
+    wtime = [t-min_t for t in wtime]
+    stime = [t-min_t for t in stime]
+    
+    max_t = max(wtime + stime)
+    
+    fig = plt.figure(figsize=(16, 12))
+    gs = fig.add_gridspec(2, 1,  left=0.1, right=0.9, bottom=0.1, top=0.9,
+                      wspace=0.05, hspace=0.15)
+    # ax1, ax2 = axs
+    
+    ax1 = fig.add_subplot(gs[0,0])
+    ax2 = fig.add_subplot(gs[1,0])
+    
+    ax1.plot(wtime, wdata, 'b--', linewidth=2 )
+    for i in range(9):
+        ydata = [s[i] for s in sdata]
+        ax2.plot(stime, ydata, linewidth=2 )
+    
+    ax1.grid(True), ax1.set_xlim(0, max_t), ax1.set_title('Weight')
+    ax1.set_label("(Time: ms)")
+    # ax1.legend()
+    
+    ax2.grid(True), ax2.set_xlim(0, max_t), ax2.set_title('IMU')
+    # ax2.legend()
+    
+    plt.show()
+    
+    
+    
+        
+    
+    
+def test_process(file_path):
+    WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
+    plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
+    
+    
+def main():
+    files_path = r'\\192.168.1.28\share\测试_202406\0814\0814\20240814-102227-62264578-a720-4eb9-b95e-cb8be009aa98_null'  
+    k = 0
+    for filename in os.listdir(files_path):    
+        filename = 'process.data'
+        
+        file_path = os.path.join(files_path, filename)
+        if os.path.isfile(file_path) and filename.find("track.data")>0:
+            extract_data(file_path)
+        
+        if os.path.isfile(file_path) and filename.find("process.data")>=0:
+            test_process(file_path)
+        
+        k += 1
+        if k == 1:
+            break
+        
+def main1():
+    fpath = r'\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1209永辉超市测试\20241209-155924-117e1941-70f8-4287-8de1-4866868548a6_6926475209967\process.data'
+    simidct = read_similar(fpath)
+    print(simidct)
+          
+
+if __name__ == "__main__":
+    main1()
+
+
+
+
+
+
+
+
+
+
+
+
+

tracking/utils/read_pipeline_data.py

@@ -0,0 +1,250 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 21 15:25:23 2024
+读取 Pipeline 各模块的数据，主代码由 马晓慧 完成
+
+@author: ieemoo-zl003
+"""
+
+import os
+import numpy as np
+  
+# 替换为你的目录路径  
+files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'  
+
+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 = np.array([float(x) for x in s.split(",")]) 
+    return float_array
+
+def extract_tracker_input_boxes_feats(file_name):
+    boxes = []
+    feats = []
+    with open(file_name, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+              
+            # 跳过空行  
+            if not line:  
+                continue  
+              
+            # 检查是否以'box:'或'feat:'开始  
+            if line.find("box:") >= 0 and line.find("output_box:") < 0:  
+                box = line[line.find("box:") + 4:].strip()
+                boxes.append(str_to_float_arr(box))  # 去掉'box:'并去除可能的空白字符  
+
+            if line.find("feat:") >= 0:
+                feat = line[line.find("feat:") + 5:].strip()
+                feats.append(str_to_float_arr(feat))  # 去掉'box:'并去除可能的空白字符 
+            
+    return np.array(boxes), np.array(feats)
+
+def find_string_in_array(arr, target):  
+    """  
+    在字符串数组中找到目标字符串对应的行（索引）。  
+  
+    参数:  
+    arr -- 字符串数组  
+    target -- 要查找的目标字符串  
+  
+    返回:  
+    目标字符串在数组中的索引。如果未找到，则返回-1。  
+    """  
+    tg = [float(t) for k, t in enumerate(target.split(',')) if k<4][:4]
+    for i, st in enumerate(arr): 
+        st = [float(s) for k, s in enumerate(target.split(',')) if k<4][:4]
+        
+        if st == tg:
+            return i
+        
+        # if st[:20] == target[:20]:  
+        #     return i  
+    return -1 
+
+def find_samebox_in_array(arr, target):
+
+    for i, st in enumerate(arr):
+        if all(st[:4] == target[:4]):
+            return i
+    return -1
+    
+    
+    
+
+
+
+def extract_tracker_output_boxes_feats(read_file_name):
+    
+    input_boxes, input_feats = extract_tracker_input_boxes_feats(read_file_name)
+    
+    boxes = []
+    feats = []
+    with open(read_file_name, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+              
+            # 跳过空行  
+            if not line:  
+                continue  
+              
+            # 检查是否以'output_box:'开始  
+            if line.find("output_box:") >= 0:
+                box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
+                boxes.append(box)  # 去掉'output_box:'并去除可能的空白字符  
+                index = find_samebox_in_array(input_boxes, box)
+                if index >= 0:
+                    # feat_f = str_to_float_arr(input_feats[index])
+                    feat_f = input_feats[index]
+                    norm_f = np.linalg.norm(feat_f) 
+                    feat_f = feat_f / norm_f
+                    feats.append(feat_f)
+    return input_boxes, input_feats, np.array(boxes), np.array(feats)
+
+def extract_tracking_output_boxes_feats(read_file_name):
+    tracker_boxes, tracker_feats, input_boxes, input_feats = extract_tracker_output_boxes_feats(read_file_name)
+    boxes = []
+    feats = []
+    
+    tracking_flag = False
+    with open(read_file_name, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+              
+            # 跳过空行  
+            if not line:  
+                continue  
+            
+            if tracking_flag:
+                if line.find("tracking_") >= 0:
+                    tracking_flag = False
+                else:
+                    box = str_to_float_arr(line)
+                    boxes.append(box)
+                    index = find_samebox_in_array(input_boxes, box)
+                    if index >= 0:
+                        feats.append(input_feats[index])
+            # 检查是否以tracking_'开始  
+            if line.find("tracking_") >= 0:
+                tracking_flag = True
+
+    assert(len(tracker_boxes)==len(tracker_feats)), "Error at Yolo output"
+    assert(len(input_boxes)==len(input_feats)), "Error at tracker output"
+    assert(len(boxes)==len(feats)), "Error at tracking output"            
+
+    return tracker_boxes, tracker_feats, input_boxes, input_feats, np.array(boxes), np.array(feats)
+
+def read_tracking_input(datapath):
+    with open(datapath, 'r') as file:  
+        lines = file.readlines()
+    
+    data = []
+    for line in lines:
+        data.append([s for s in line.split(',') if len(s)>=3])
+        # data.append([float(s) for s in line.split(',') if len(s)>=3])
+
+    # data = np.array(data, dtype = np.float32)
+    try:    
+        data = np.array(data, dtype = np.float32)
+    except Exception as e:
+        data = np.array([], dtype = np.float32)
+        print('DataError for func: read_tracking_input()')
+        
+    
+    return data
+
+
+
+def read_tracker_input(datapath):
+    with open(datapath, 'r') as file:  
+        lines = file.readlines()
+    Videos = []
+    FrameBoxes, FrameFeats = [], []
+    boxes, feats = [], []
+    
+    timestamp = []
+    t1 = None
+    for line in lines: 
+        if line.find('CameraId') >= 0: 
+            t = int(line.split(',')[1].split(':')[1])
+            timestamp.append(t)
+     
+            if len(boxes) and len(feats):
+                FrameBoxes.append(np.array(boxes, dtype = np.float32))
+                FrameFeats.append(np.array(feats, dtype = np.float32))
+                boxes, feats = [], []
+            
+            if t1 and t - t1 > 1e3:
+                Videos.append((FrameBoxes, FrameFeats))    
+                FrameBoxes, FrameFeats = [], []
+            t1 = int(line.split(',')[1].split(':')[1])
+            
+        if line.find('box') >= 0:
+            box = line.split(':', )[1].split(',')[:-1]
+            boxes.append(box)
+            
+        
+        if line.find('feat') >= 0:
+            feat = line.split(':', )[1].split(',')[:-1]
+            feats.append(feat)
+
+    FrameBoxes.append(np.array(boxes, dtype = np.float32))
+    FrameFeats.append(np.array(feats, dtype = np.float32))  
+    Videos.append((FrameBoxes, FrameFeats)) 
+    
+    # TimeStamp = np.array(timestamp, dtype = np.int64) 
+    # DimesDiff = np.diff((TimeStamp))
+    # sorted_indices = np.argsort(TimeStamp)
+    # TimeStamp_sorted = TimeStamp[sorted_indices]
+    # DimesDiff_sorted = np.diff((TimeStamp_sorted))
+    
+    return Videos
+
+
+
+def main():
+    files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'  
+    
+    # 遍历目录下的所有文件和目录  
+    for filename in os.listdir(files_path):  
+        # 构造完整的文件路径  
+        file_path = os.path.join(files_path, filename)
+        if os.path.isfile(file_path) and filename.find("track.data")>0:
+            tracker_boxes, tracker_feats, tracking_boxes, tracking_feats, output_boxes, output_feats = extract_tracking_output_boxes_feats(file_path)
+ 
+            print("Done")
+            
+        
+if __name__ == "__main__":
+    main()     
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        

tracking/utils/rename.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun  8 09:51:59 2024
+
+@author: ym
+"""
+import os
+
+def main():
+    directory = r'D:\DetectTracking\runs\detect'  
+    directory = r'D:\DetectTracking\tracking\result\tracks'
+
+    suffix = '_'
+    
+    for root, dirs, files in os.walk(directory):    
+        for name in dirs:
+            old_name = os.path.join(root, name)
+            new_name = os.path.join(root, f"{name}{suffix}")  
+            try:
+                os.rename(old_name, new_name)
+            except Exception as e:
+                print(f"Failed to rename directory '{old_name}': {e}")
+                
+        for name in files:
+            old_name = os.path.join(root, name)
+            file, ext = os.path.splitext(name)
+            new_name = os.path.join(root, f"{file}{suffix}{ext}")
+            try:
+                os.rename(old_name, new_name)
+            except Exception as e:
+                print(f"Failed to rename file '{old_name}': {e}")
+    
+
+if __name__ == "__main__":
+    main()

tracking/utils/showtrack.py

@@ -0,0 +1,361 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  5 10:01:11 2023
+
+@author: ym
+"""
+
+import numpy as np
+import os
+import cv2
+import sys
+
+from scipy.spatial.distance import cdist
+import matplotlib
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+# from ultralytics.utils.plotting import Annotator, colors
+
+
+from .annotator import TrackAnnotator
+# from .processboxes import array2list
+
+# boxes Format: [x1, y1, x2, y2, track_id, score, cls, frame_index]
+pth = r"D:/DeepLearning/yolov5/tracking/"
+colors = np.array([[255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255],
+                              [0, 0, 255], [0, 255, 0], [255, 51, 255], [102, 178, 255], [51, 153, 255],
+                              [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
+                              [51, 255, 51], [255, 102, 255], [153, 204, 255], [255, 0, 0], [255, 255, 255]],
+                             dtype=np.uint8)
+
+def array2list(bboxes):
+    '''
+        将 bboxes 变换为 track 列表
+        bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+    Return：
+        lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
+                [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]        
+    '''
+    tids = set(bboxes[:, 4])
+    track_ids = bboxes[:, 4].astype(int)
+    lboxes = []
+    for t_id in tids: 
+        # print(f"The ID is: {t_id}")
+        idx = np.where(track_ids == t_id)[0]
+        box = bboxes[idx, :]     
+
+        lboxes.append(box)
+    
+    return lboxes 
+
+
+
+def draw5points(bboxes, file):
+    """
+    显示中心点、4角点的轨迹，以及轨迹 features
+    """
+    image = cv2.imread(pth + r"/shopcart/cart_tempt/edgeline.png")
+    
+    imgx = image.copy()
+    annotator = TrackAnnotator(imgx, line_width=2)
+
+    lboxes = array2list(bboxes)
+    
+    
+    for k in range(len(lboxes)):
+        boxes = lboxes[k]
+        
+        cls = int(boxes[0, 6])
+        tid = int(boxes[0, 4])
+        
+        # print(tid)
+        
+        frnum = boxes.shape[0]
+        cornpoints = np.zeros((frnum, 10))
+
+        cornpoints[:,0] = (boxes[:, 0] + boxes[:, 2]) / 2
+        cornpoints[:,1] = (boxes[:, 1] + boxes[:, 3]) / 2
+        cornpoints[:,2], cornpoints[:,3] = boxes[:, 0], boxes[:, 1]
+        cornpoints[:,4], cornpoints[:,5] = boxes[:, 2], boxes[:, 1]
+        cornpoints[:,6], cornpoints[:,7] = boxes[:, 0], boxes[:, 3]
+        cornpoints[:,8], cornpoints[:,9] = boxes[:, 2], boxes[:, 3]
+    
+        
+        x1, y1, x2, y2 = cornpoints[:,2],cornpoints[:,3],cornpoints[:,8],cornpoints[:,9]
+        BoundPixel = 10
+        BoundThresh = 0.4
+        cont1 = sum(abs(x1)<BoundPixel) / frnum > BoundThresh
+        cont2 = sum(abs(y1)<BoundPixel) / frnum > BoundThresh
+        cont3 = sum(abs(x2-1024)<BoundPixel) / frnum > BoundThresh
+        cont4 = sum(abs(y2-1280)<BoundPixel) / frnum > BoundThresh
+        isImgBorder = False
+        if cont1 or cont2 or cont3 or cont4:
+            isImgBorder = True
+
+# =============================================================================
+#         '''情况1: 在商品运动过程中，商品检测框始终左下角点和图像左下角点重合, 用中心点代替''' 
+#         lfcn_dist = np.linalg.norm(cornpoints[:, 6:8] - [0, 1280], axis=1)
+#         idx1 = lfcn_dist<10
+#         if sum(idx1)/frnum > 0.5:
+#             cornpoints[:, 6:8] = cornpoints[:, 0:2]
+#         
+#         '''情况2: 在商品运动过程中，商品检测框始终右下角点和图像右下角点重合, 用中心点代替''' 
+#         rtcn_dist = np.linalg.norm(cornpoints[:, 8:10] - [1024, 1280], axis=1)
+#         idx2 = rtcn_dist<10
+#         if sum(idx2)/frnum > 0.5:
+#             cornpoints[:, 8:10] = cornpoints[:, 0:2]
+# =============================================================================
+        
+    
+        mwh = (np.mean(boxes[:, 2]) + np.mean(boxes[:, 3]))/2
+        
+        trajectory = []
+        trajlens = []
+        trajdist = []
+        for k in range(5):
+            traj = np.linalg.norm(np.diff(cornpoints[:, 2*k:2*(k+1)], axis = 0), axis=1)
+            trajlen = np.sum(traj)
+            ptdist = np.max(cdist(cornpoints[:, 2*k:2*(k+1)], cornpoints[:, 2*k:2*(k+1)]))
+            
+            trajectory.append(traj)
+            trajlens.append(trajlen)
+            trajdist.append(ptdist)
+        
+        if not isImgBorder:
+            idx = trajlens.index(min(trajlens))
+            
+            trajmin = trajectory[idx]
+            trajlen_min = min(trajlens)
+            trajdist_min = min(trajdist)
+        else:
+            trajmin = trajectory[0]
+            trajlen_min = trajlens[0]
+            trajdist_min = trajdist[0]
+  
+        
+        '''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
+        trajlen_rate = trajlen_min/(max(trajlens)+0.0001)    
+        
+        '''最小轨迹欧氏距离/目标框尺度均值'''
+        trajdist_rate = trajdist_min/(mwh+0.0001)
+        
+        
+        
+        
+        # idx = trajlens.index(min(trajlens))
+        # trajmin = trajectory[idx]
+        
+        # '''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
+        # trajlen_rate = min(trajlens)/(max(trajlens)+0.0001)   
+        
+        # '''最小轨迹欧氏距离，越小，代表运动幅度越小'''
+        # trajdist_min = min(trajdist)
+        
+        # '''最小轨迹欧氏距离 / 目标框尺度均值'''
+        # mindist_rate = min(trajdist)/(mwh+0.0001)
+        
+
+        
+        
+        img = image.copy()
+        for i in range(boxes.shape[0]):
+            cv2.circle(img, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 6, (255, 255, 255), 2)         
+            cv2.circle(img, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), 6, (255, 0, 255), 2)
+            cv2.circle(img, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), 6, (0, 255, 0), 2)
+            cv2.circle(img, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), 6, (64, 128, 255), 2)
+            cv2.circle(img, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), 6, (255, 128, 64), 2)
+        
+        # if frnum>=3:
+        #     cntpoints = cornpoints[:, 0:2].astype(np.int64)
+        #     rect = cv2.minAreaRect(cntpoints)
+        #     box = cv2.boxPoints(rect)
+        #     box = np.int0(box)
+        #     cv2.drawContours(img, [box], 0, (255, 0, 0), 2)
+        
+        # img1 = image.copy()
+        # for i in range(boxes.shape[0]-1):
+        #     pt1 = cornpoints[i, :].astype(np.int64)
+        #     pt2 = cornpoints[i+1, :].astype(np.int64)
+        #     cv2.line(img1, pt1, pt2, color=(255, 255, 255), thickness=2)
+        # gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+        # _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+        # contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            
+            
+         
+        color = ((0, 0, 255), (255, 128, 0))
+        label_6 = "PCA(singular_values_)    : "
+        label_7 = "Rect    : "
+        
+        if frnum>=3:
+            if isImgBorder:
+                X = cornpoints[:, 0:2]
+            else:
+                X = cornpoints[:, 2*idx:2*(idx+1)]
+            
+            pca = PCA()
+            pca.fit(X)
+            label_6 = "PCA(variance_ratio)  : {:.2f}".format(pca.explained_variance_ratio_[0])
+            for i, (comp, var) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
+                pt1 = (pca.mean_ - comp*var*200).astype(np.int64)
+                pt2 = (pca.mean_ + comp*var*200).astype(np.int64)
+                cv2.line(img, pt1, pt2, color=color[i], thickness=2)
+                
+            rect = cv2.minAreaRect(X.astype(np.int64))
+            box = cv2.boxPoints(rect)
+            box = np.int0(box)
+            cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
+            label_7 = "Rect W&H&Ratio: {}, {}, {:.2f}".format(int(rect[1][0]), int(rect[1][1]), min(rect[1])/(max(rect[1])+0.001))
+
+        
+        
+        '''撰写专利需要，生成黑白图像'''
+        # imgbt = cv2.bitwise_not(img)
+        # for i in range(box.shape[0]):
+        #     cv2.circle(imgbt, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 14, (0, 0, 0), 2)         
+        #     cv2.drawMarker(imgbt, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), color= (0, 0, 0), markerType=3, markerSize = 30, thickness=2)
+        #     cv2.drawMarker(imgbt, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), color= (0, 0, 0), markerType=4, markerSize = 30, thickness=2)
+        #     cv2.drawMarker(imgbt, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), color= (0, 0, 0), markerType=5, markerSize = 30, thickness=2)
+        #     cv2.drawMarker(imgbt, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), color= (0, 0, 0), markerType=6, markerSize = 30, thickness=2)
+        # cv2.imwrite(pth + f"/zhuanli/{file}_{tid}.png", imgbt)
+
+        if len(trajmin):
+            trajstd = np.std(trajmin)
+        else:
+            trajstd = 0
+            
+                
+        
+        trajlens = [int(t) for t in trajlens]
+        trajdist = [int(t) for t in trajdist]
+        label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}"
+        label_2 = f"trajdist: {trajdist}: trajdist_min: {int(trajdist_min)}"
+        label_3 = "trajlen_min/max(trajlens): {:.2f}/{} = {:.2f}".format(trajlen_min, max(trajlens), trajlen_rate)
+        label_4 = "trajdist_min/mwh         : {:.2f}/{} = {:.2f}".format(trajdist_min, int(mwh), trajdist_rate)
+        label_5 = "std(trajmin)             : {:.2f}".format(trajstd)
+        
+        label = [label_1, label_2, label_3, label_4, label_5, label_6, label_7]
+        word = 'abc'
+        w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0] 
+        for i in range(len(label)):
+            # color = [int(x) for x in colors[i]]
+            cv2.putText(img,
+                        label[i], 
+                        (20, int(50+(i+1)*1.2*h)),
+                        0,
+                        1,
+                        [int(x) for x in colors[i]],
+                        2,
+                        lineType=cv2.LINE_AA)
+        cv2.imwrite(pth + f"/result/cls11_80212/{file}_{tid}.png", img)
+
+
+       
+        
+        
+def drawtracks(bboxes, imgshow=None):
+    
+    """
+    Inputs
+        bboxes: 原始检测跟踪后的结果，变换为 tboxes
+        image：只用于获取图像的（Width, Height）
+    Outputs:
+        imgshow
+    """
+    if imgshow is None:
+        edgeline = cv2.imread(pth + r"/shopcart/cart_tempt/edgeline.png")
+        # edgeline = cv2.bitwise_not(edgeline)
+        H, W = edgeline.shape[0:2]
+        imgshow= np.zeros((H, W, 3), np.uint8)
+    
+    if 'edgeline' in locals().keys():
+        imgshow = cv2.add(imgshow, edgeline)
+    
+    ## ==== list，其中元素格式: [x, y, w, h, track_id, score, cls, frame_index]
+
+    tboxes = array2list(bboxes) 
+    
+    # imgshow = cv2.bitwise_not(imgshow)
+    
+    annotator = TrackAnnotator(imgshow, line_width=2)
+    for boxes in tboxes:
+        annotator.plotting_track(boxes)
+        
+    imgshow = annotator.result()
+        
+    return imgshow
+
+
+
+def writefilename():
+    npydir = r"D:\DeepLearning\yolov5\runs\boxes"
+    files = [f.split('.')[0] for f in os.listdir(npydir)]
+    with open('data.txt', 'w') as f:
+        [f.write(f"{file}:\n") for file in files]
+    
+    print("len(files)")
+    # for filename in os.listdir(npydir):
+    #     file, ext = os.path.splitext(filename)
+    
+
+def main():
+    npydir = r"D:\DeepLearning\yolov5\runs\boxes"
+    k = 0
+    fields = []
+    
+    for filename in os.listdir(npydir):
+        # filename = "加购_快速置入_12.npy"
+        print(filename)
+        
+        file, ext = os.path.splitext(filename)
+        filepath = os.path.join(npydir, filename)
+        
+        try:
+            bboxes = np.load(filepath)
+            
+            imgshow = drawtracks(bboxes, file)
+            draw5points(bboxes, file)
+            
+            cv2.imwrite(pth + f"/result/cls11_80212/{file}_show.png", imgshow)
+        except Exception as e:
+            # print(str(e))
+            pass
+        
+        # k += 1
+        # if k == 1:
+        #     break  
+            
+
+if __name__ == "__main__": 
+    
+    main()
+    
+    # writefilename()
+    
+        
+        
+
+    
+    
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

tracking/utils/videot.py

@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep 20 14:10:09 2023
+
+@author: ym
+"""
+
+import numpy as np
+import os
+import cv2
+
+# from pathlib import Path
+# import math
+# import sys
+# from scipy.spatial.distance import cdist
+
+VideoFormat = ['.mp4', '.avi', '.ts']
+def video2imgs(videof, imgdir):
+    cap = cv2.VideoCapture(videof)
+    i = 0
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        imgp = os.path.join(imgdir, f"{i}.png")
+        i += 1
+        cv2.imwrite(imgp, frame)
+        
+        if i == 400:
+            break
+    cap.release()
+    
+    print(os.path.basename(videof) + f" haved resolved")
+
+def videosave(bboxes, videopath="100_1688009697927.mp4"):
+
+    cap = cv2.VideoCapture(videopath)
+    
+    fps = int(cap.get(cv2.CAP_PROP_FPS))  # integer required, floats produce error in MP4 codec
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    ## =========================================== 在当前模块地址下存储图像和视频
+    path = os.path.split(os.path.realpath(__file__))[0]
+    _, filename = os.path.split(videopath)
+    file, ext = os.path.splitext(filename)
+    
+    ## ======================================================== 视频保存设置
+    fourcc = cv2.VideoWriter_fourcc(*'MP4V')
+    save_video_path = os.path.join(path, "{}_show_1.mp4".format(file))    
+    vid_writer = cv2.VideoWriter(save_video_path, fourcc, fps, (width, height))
+    
+    ## ======================================================== 图像保存路径设置
+    save_img_path = os.path.join(path, "{}_show".format(file))
+    if not os.path.exists(save_img_path):
+        os.makedirs(save_img_path)
+    
+    cout = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if ret:
+            
+            idx = np.where(bboxes[:, 7] == cout)[0]
+            box = bboxes[idx, 0:4].astype(int)
+            for i in range(box.shape[0]):
+                x1, y1 = box[i, :2]
+                x2, y2 = box[i, 2:4]
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 125, 255), 2)
+                
+                
+            cv2.imwrite(os.path.join(save_img_path, "{}.png".format(cout)), frame)
+            vid_writer.write(frame)
+            
+            cout += 1
+        else:
+            print("end!!!!!!!!!!!!!!!!!!!")
+            break
+    
+    vid_writer.release()
+    cap.release()
+    
+def main():
+    videopath = r'D:\videos'
+    savepath = r'D:\videos'
+    # video2imgs(videopath, savepath)
+    k = 0
+    for filename in os.listdir(videopath):
+        # filename = "20240929-155533.ts"
+        
+        file, ext = os.path.splitext(filename)
+        if ext not in VideoFormat:
+            continue
+        
+        basename = os.path.basename(videopath)
+        imgbase = basename + '-&-' + file
+        imgdir = os.path.join(savepath, imgbase)
+        if not os.path.exists(imgdir):
+            os.mkdir(imgdir)
+
+        videof = os.path.join(videopath, filename)
+        video2imgs(videof, imgdir)
+        
+        # k += 1
+        # if k == 1:
+        #     break
+
+    
+    
+if __name__ == '__main__':
+    main()
+