From 010f5c445a8acb9d28a1bc26a483b7bacc8a09e9 Mon Sep 17 00:00:00 2001 From: jiajie555 Date: Fri, 18 Apr 2025 14:41:53 +0800 Subject: [PATCH] =?UTF-8?q?=E5=9B=9E=E4=BC=A0=E6=95=B0=E6=8D=AE=E8=A7=A3?= =?UTF-8?q?=E6=9E=90=EF=BC=8C=E5=85=BC=E5=AE=B9v5=E5=92=8Cv10?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 74 + .idea/vcs.xml | 6 + .idea/workspace.xml | 131 ++ README.en.md | 36 + README.md | 7 + __init__.py | 0 __pycache__/event_time_specify.cpython-39.pyc | Bin 0 -> 6846 bytes __pycache__/export.cpython-312.pyc | Bin 0 -> 54366 bytes __pycache__/export.cpython-39.pyc | Bin 0 -> 31110 bytes __pycache__/imgs_inference.cpython-39.pyc | Bin 0 -> 12354 bytes __pycache__/move_detect.cpython-312.pyc | Bin 0 -> 10296 bytes __pycache__/move_detect.cpython-39.pyc | Bin 0 -> 6420 bytes __pycache__/pipeline_01.cpython-312.pyc | Bin 0 -> 14678 bytes __pycache__/pipeline_01.cpython-39.pyc | Bin 0 -> 8330 bytes 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a/.gitignore b/.gitignore new file mode 100644 index 0000000..f9e4e3c --- /dev/null +++ b/.gitignore @@ -0,0 +1,74 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json +*.cfg + +*.rar +*.pkl +*.pickle +*.npy +*.csv + + + +# for tracking --------------------------------------------------------------- +tracking/.git +tracking/bakeup +tracking/.gitignore +tracking/result/**/*.mp4 +tracking/result/**/*.png +tracking/data/boxes_imgs/* +tracking/data/trackfeats/* +tracking/data/tracks/* +tracking/data/handlocal/* +ckpts/* +doc + + + + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + + + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pth +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*.caffemodel +*_saved_model/ +*_web_model/ +*_openvino_model/ +*_paddle_model/ + + + + diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..35b88f6 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,131 @@ + + + + + + + + + + + + + + + + { + "associatedIndex": 2 +} + + + + { + "keyToString": { + "RunOnceActivity.OpenProjectViewOnStart": "true", + "RunOnceActivity.ShowReadmeOnStart": "true", + "WebServerToolWindowFactoryState": "true", + "node.js.detected.package.eslint": "true", + "node.js.detected.package.tslint": "true", + "node.js.selected.package.eslint": "(autodetect)", + "node.js.selected.package.tslint": "(autodetect)", + "settings.editor.selected.configurable": "com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable", + "vue.rearranger.settings.migration": "true" + } +} + + + + + + + + + + + + + + + + + + + 1744852597949 + + + + + + + + + + file://$PROJECT_DIR$/pipeline.py + 314 + + + + + + + + + \ No newline at end of file diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000..644a40d --- /dev/null +++ b/README.en.md @@ -0,0 +1,36 @@ +# YoloV5_track + +#### Description +{**When you're done, you can delete the content in this README and update the file with details for others getting started with your repository**} + +#### Software Architecture +Software architecture description + +#### Installation + +1. xxxx +2. xxxx +3. xxxx + +#### Instructions + +1. xxxx +2. xxxx +3. xxxx + +#### Contribution + +1. Fork the repository +2. Create Feat_xxx branch +3. Commit your code +4. Create Pull Request + + +#### Gitee Feature + +1. You can use Readme\_XXX.md to support different languages, such as Readme\_en.md, Readme\_zh.md +2. Gitee blog [blog.gitee.com](https://blog.gitee.com) +3. Explore open source project [https://gitee.com/explore](https://gitee.com/explore) +4. The most valuable open source project [GVP](https://gitee.com/gvp) +5. The manual of Gitee [https://gitee.com/help](https://gitee.com/help) +6. The most popular members [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) diff --git a/README.md b/README.md new file mode 100644 index 0000000..7eec15e --- /dev/null +++ b/README.md @@ -0,0 +1,7 @@ +说明： + 该代码仓库是yolov5_track文件夹下代码的备份，这里的yolov5代码来至：https://github.com/ultralytics/yolov5 + gitee地址为：https://gitee.com/nanjing-yimao-information/dettrack +核心模块： + track_reid.py实现： + 1. yolov5检测 + 2. 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zN$MOpRStlQ+llBX78a!xFI8}g(|EyF_ZG~xco&K8CK4ecQLH2q#qJ`NE-w3s)F&3G z&Phk}0gVPw>7PumF>{UD1J~5HP|LNA%(d6=y!iS{UwPvb zUwPy9m#(d43#t6I(c#hY;X@b+U*07mnAbvhnrt_fo&=MYcXa9VHG`(ShD7^sEs28673Afi^-v3gKrV zI7!S~f7H*)eT^(1Oz|`ap-`{$F3GK+@5udXSl)`-U7s}u3!F~-NjPasR&hV`L*r1f zOQoT2Xw(z2_YCamj_*w=@ z6Q9vYCMYW=?`D^whBToX?Lai(#6K?~^cN?i3&1$4mu_xz8t(E$k+JvDeB09YQ8_B< z>P#ZNfiqZ^cduxWB!tj6aDN#0ZRrx#Vh(jLhr$F!(g2W0Z{$qnO=q-H$tk4VXK7JH z2i=~Vwim$AQS^zmw0RsR37!+WDGm>2 zuy(S%jD?%ZmmY8d0>A$gexI*Shs~!H?uAl}qI&L~AxhxQs`vShOMav&ad_PChz9-y T>EGwO5b6(Ah4zP0: + _, fid0, bid0 = int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8]) + if f"{fid0}_{bid0}" in featdict.keys() and f"{fid}_{bid}" in featdict.keys(): + feat0 = featdict[f"{fid0}_{bid0}"] + feat1 = featdict[f"{fid}_{bid}"] + simi = 1 - np.maximum(0.0, cdist(feat0[None, :], feat1[None, :], "cosine"))[0][0] + + img = imgdict[f"{fid}_{bid}"] + imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png" + if simi is not None: + imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simi:.2f}.png" + + cv2.imwrite(imgpath, img) + + +def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None): + ''' + 当前 box 特征和该轨迹 smooth_feat 特征的相似度, yolo_resnet_tracker 函数中， + 采用该方式记录特征相似度 + ''' + for i in range(len(boxes)): + tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8]) + + key = f"{fid}_{bid}" + img = imgdict[key] + imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png" + if simidict is not None and key in simidict.keys(): + imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simidict[key]:.2f}.png" + + cv2.imwrite(imgpath, img) + + +def pipeline( + eventpath, + savepath, + SourceType, + weights, + YoloVersion="V5" + ): + ''' + eventpath: 单个事件的存储路径 + + ''' + optdict = {} + optdict["weights"] = weights + + if SourceType == "video": + vpaths = get_video_pairs(eventpath) + elif SourceType == "image": + vpaths = get_image_pairs(eventpath) + event_tracks = [] + + ## 构造购物事件字典 + evtname = Path(eventpath).stem + barcode = evtname.split('_')[-1] if len(evtname.split('_'))>=2 \ + and len(evtname.split('_')[-1])>=8 \ + and evtname.split('_')[-1].isdigit() else '' + '''事件结果存储文件夹''' + if not savepath: + savepath = Path(__file__).resolve().parents[0] / "events_result" + + savepath_pipeline = Path(savepath) / Path("Yolos_Tracking") / evtname + + + """ShoppingDict pickle 文件保存地址 """ + savepath_spdict = Path(savepath) / "ShoppingDict_pkfile" + if not savepath_spdict.exists(): + savepath_spdict.mkdir(parents=True, exist_ok=True) + pf_path = Path(savepath_spdict) / Path(str(evtname)+".pickle") + + # if pf_path.exists(): + # print(f"Pickle file have saved: {evtname}.pickle") + # return + + '''====================== 构造 ShoppingDict 模块 =======================''' + ShoppingDict = {"eventPath": eventpath, + "eventName": evtname, + "barcode": barcode, + "eventType": '', # "input", "output", "other" + "frontCamera": {}, + "backCamera": {}, + "one2n": [] # + } + yrtDict = {} + + + procpath = Path(eventpath).joinpath('process.data') + if procpath.is_file(): + SimiDict = read_similar(procpath) + ShoppingDict["one2n"] = SimiDict['one2n'] + + + for vpath in vpaths: + '''================= 1. 构造相机事件字典 =================''' + CameraEvent = {"cameraType": '', # "front", "back" + "videoPath": '', + "imagePaths": [], + "yoloResnetTracker": [], + "tracking": [], + } + + if isinstance(vpath, list): + CameraEvent["imagePaths"] = vpath + bname = os.path.basename(vpath[0]) + if not isinstance(vpath, list): + CameraEvent["videoPath"] = vpath + bname = os.path.basename(vpath).split('.')[0] + if bname.split('_')[0] == "0" or bname.find('back')>=0: + CameraEvent["cameraType"] = "back" + if bname.split('_')[0] == "1" or bname.find('front')>=0: + CameraEvent["cameraType"] = "front" + + '''================= 2. 事件结果存储文件夹 =================''' + if isinstance(vpath, list): + savepath_pipeline_imgs = savepath_pipeline / Path("images") + else: + savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem)) + + if not savepath_pipeline_imgs.exists(): + savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True) + + savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs") + if not savepath_pipeline_subimgs.exists(): + savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True) + + + '''================= 3. Yolo + Resnet + Tracker =================''' + optdict["source"] = vpath + optdict["save_dir"] = savepath_pipeline_imgs + optdict["is_save_img"] = True + optdict["is_save_video"] = True + + + if YoloVersion == "V5": + yrtOut = yolo_resnet_tracker(**optdict) + elif YoloVersion == "V10": + yrtOut = yolov10_resnet_tracker(**optdict) + + + yrtOut_save = [] + for frdict in yrtOut: + fr_dict = {} + for k, v in frdict.items(): + if k != "imgs": + fr_dict[k]=v + yrtOut_save.append(fr_dict) + CameraEvent["yoloResnetTracker"] = yrtOut_save + + # CameraEvent["yoloResnetTracker"] = yrtOut + + '''================= 4. tracking =================''' + '''(1) 生成用于 tracking 模块的 boxes、feats''' + bboxes = np.empty((0, 6), dtype=np.float64) + trackerboxes = np.empty((0, 9), dtype=np.float64) + trackefeats = {} + for frameDict in yrtOut: + tboxes = frameDict["tboxes"] + ffeats = frameDict["feats"] + + boxes = frameDict["bboxes"] + bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0) + trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0) + for i in range(len(tboxes)): + fid, bid = int(tboxes[i, 7]), int(tboxes[i, 8]) + trackefeats.update({f"{fid}_{bid}": ffeats[f"{fid}_{bid}"]}) + + + '''(2) tracking, 后摄''' + if CameraEvent["cameraType"] == "back": + vts = doBackTracks(trackerboxes, trackefeats) + vts.classify() + event_tracks.append(("back", vts)) + + CameraEvent["tracking"] = vts + ShoppingDict["backCamera"] = CameraEvent + + yrtDict["backyrt"] = yrtOut + + '''(2) tracking, 前摄''' + if CameraEvent["cameraType"] == "front": + vts = doFrontTracks(trackerboxes, trackefeats) + vts.classify() + event_tracks.append(("front", vts)) + + CameraEvent["tracking"] = vts + ShoppingDict["frontCamera"] = CameraEvent + + yrtDict["frontyrt"] = yrtOut + + '''========================== 保存模块 =================================''' + '''(1) 保存 ShoppingDict 事件''' + with open(str(pf_path), 'wb') as f: + pickle.dump(ShoppingDict, f) + + '''(2) 保存 Tracking 输出的运动轨迹子图，并记录相似度''' + for CamerType, vts in event_tracks: + if len(vts.tracks)==0: continue + if CamerType == 'front': + # yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"] + + yolos = yrtDict["frontyrt"] + ctype = 1 + if CamerType == 'back': + # yolos = ShoppingDict["backCamera"]["yoloResnetTracker"] + + yolos = yrtDict["backyrt"] + ctype = 0 + + imgdict, featdict, simidict = {}, {}, {} + for y in yolos: + imgdict.update(y["imgs"]) + featdict.update(y["feats"]) + simidict.update(y["featsimi"]) + + for track in vts.Residual: + if isinstance(track, np.ndarray): + save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype, featdict) + else: + save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_subimgs, ctype, featdict) + + '''(3) 轨迹显示与保存''' + illus = [None, None] + for CamerType, vts in event_tracks: + if len(vts.tracks)==0: continue + + if CamerType == 'front': + edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png") + + h, w = edgeline.shape[:2] + # nh, nw = h//2, w//2 + # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) + + img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) + illus[0] = img_tracking + + plt = plot_frameID_y2(vts) + plt.savefig(os.path.join(savepath_pipeline, "front_y2.png")) + + if CamerType == 'back': + edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png") + + h, w = edgeline.shape[:2] + # nh, nw = h//2, w//2 + # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) + + img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) + illus[1] = img_tracking + + illus = [im for im in illus if im is not None] + if len(illus): + img_cat = np.concatenate(illus, axis = 1) + if len(illus)==2: + H, W = img_cat.shape[:2] + cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3) + + trajpath = os.path.join(savepath_pipeline, "trajectory.png") + cv2.imwrite(trajpath, img_cat) + +def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", + source_type = "video", # video, image, + save_path = r"D:\work\result_pipeline", + yolo_ver = "V10", # V10, V5 + + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , + weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', + k=0 + ): + ''' + 运行函数 pipeline()，遍历事件文件夹，每个文件夹是一个事件 + ''' + parmDict = {} + parmDict["SourceType"] = source_type + parmDict["savepath"] = save_path + parmDict["YoloVersion"] = yolo_ver + if parmDict["YoloVersion"] == "V5": + parmDict["weights"] = weight_yolo_v5 + elif parmDict["YoloVersion"] == "V10": + parmDict["weights"] = weight_yolo_v10 + + evtdir = Path(evtdir) + errEvents = [] + for item in evtdir.iterdir(): + if item.is_dir(): + item = evtdir/Path("20250310-175352-741") + parmDict["eventpath"] = item + pipeline(**parmDict) + # try: + # pipeline(**parmDict) + # except Exception as e: + # errEvents.append(str(item)) + k+=1 + if k==1: + break + + errfile = os.path.join(parmDict["savepath"], 'error_events.txt') + with open(errfile, 'w', encoding='utf-8') as f: + for line in errEvents: + f.write(line + '\n') + +if __name__ == "__main__": + execute_pipeline() + + # spath_v10 = r"D:\work\result_pipeline_v10" + # spath_v5 = r"D:\work\result_pipeline_v5" + # execute_pipeline(save_path=spath_v10, yolo_ver="V10") + # execute_pipeline(save_path=spath_v5, yolo_ver="V5") + + datapath = r'/home/wqg/dataset/test_dataset/base_dataset/single_event/source/' + savepath = r'/home/wqg/dataset/pipeline/contrast/single_event_V5' + + + + + execute_pipeline(evtdir = datapath, + DataType = "raw", # raw, pkl + kk=1, + source_type = "video", # video, image, + save_path = savepath, + yolo_ver = "V10", # V10, V5 + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , + weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', + saveimages = False + ) + + + + + \ No newline at end of file diff --git a/bakeup/track_reid_20240515.py b/bakeup/track_reid_20240515.py new file mode 100644 index 0000000..5564cdc --- /dev/null +++ b/bakeup/track_reid_20240515.py @@ -0,0 +1,629 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import csv +import os +import platform +import sys +from pathlib import Path +import glob +import numpy as np +import pickle +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) +from utils.torch_utils import select_device, smart_inference_mode + +'''集成跟踪模块，输出跟踪结果文件 .npy''' +# from ultralytics.engine.results import Boxes # Results +# from ultralytics.utils import IterableSimpleNamespace, yaml_load +from tracking.utils.plotting import Annotator, colors +from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load, boxes_add_fid +from tracking.trackers import BOTSORT, BYTETracker +from tracking.utils.showtrack import drawtracks +from hands.hand_inference import hand_pose + +# ============================================================================= +# from tracking.trackers.reid.reid_interface import ReIDInterface +# from tracking.trackers.reid.config import config as ReIDConfig +# ReIDEncoder = ReIDInterface(ReIDConfig) +# ============================================================================= + +# tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml" + +def init_trackers(tracker_yaml = None, bs=1): + """ + Initialize trackers for object tracking during prediction. + """ + # tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml" + + TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT} + + cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml)) + trackers = [] + for _ in range(bs): + tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30) + trackers.append(tracker) + + return trackers + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s.pt', # model path or triton URL + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + + project=ROOT / 'runs/detect', # save results to project/name + name='exp', # save results to project/name + + tracker_yaml = "./tracking/trackers/cfg/botsort.yaml", + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_csv=False, # save results in CSV format + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidencesL + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride + data=ROOT / 'data/coco128.yaml', # dataset.yaml path +): + source = str(source) + # filename = os.path.split(source)[-1] + + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + save_dir = Path(project) / Path(source).stem + if save_dir.exists(): + print(Path(source).stem) + # return + + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + else: + save_dir.mkdir(parents=True, exist_ok=True) + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, dt = 0, (Profile(), Profile(), Profile()) + + tracker = init_trackers(tracker_yaml, bs)[0] + + handpose = hand_pose() + handlocals_dict = {} + + boxes_and_imgs = [] + track_boxes = np.empty((0, 9), dtype = np.float32) + det_boxes = np.empty((0, 9), dtype = np.float32) + + + features_dict = {} + for path, im, im0s, vid_cap, s in dataset: + if save_img and 'imgshow' not in locals().keys(): + imgshow = im0s.copy() + + ## ============================= tracking 功能只处理视频，writed by WQG + if dataset.mode == 'image': + continue + + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred = model(im, augment=augment, visualize=visualize) + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Define the path for the CSV file + # csv_path = save_dir / 'predictions.csv' + + # Create or append to the CSV file + # def write_to_csv(image_name, prediction, confidence): + # data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence} + # with open(csv_path, mode='a', newline='') as f: + # writer = csv.DictWriter(f, fieldnames=data.keys()) + # if not csv_path.is_file(): + # writer.writeheader() + # writer.writerow(data) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + im0_ant = im0.copy() + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + s += '%gx%g ' % im.shape[2:] # print string + + annotator = Annotator(im0_ant, line_width=line_thickness, example=str(names)) + + nd = len(det) + if nd: + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + + det = det.cpu().numpy() + det = np.concatenate([det[:, :4], np.arange(nd).reshape(-1, 1), det[:, 4:]], axis=-1) + + '''FeatFlag为相对于上一帧boxes，当前boxes是否为静止的标志。''' + # def static_estimate(box1, box2, TH1=8, TH2=12): + # dij_abs = max(np.abs(box1 - box2)) + # dij_euc = max([np.linalg.norm((box1[:2] - box2[:2])), + # np.linalg.norm((box1[2:4] - box2[2:4])) + # ]) + # if dij_abs < TH1 and dij_euc < TH2: + # return True + # else: + # return False + + # FeatFlag = [-1] * nd + # if len(boxes_and_imgs): + # detj = boxes_and_imgs[-1][0] + # frmj = boxes_and_imgs[-1][-1] + # for ii in range(nd): + # ## flag 中保存的是box索引 + # condt1 = frame-frmj==1 + # flag = [idx for jj, idx in enumerate(detj[:, 4]) if condt1 and static_estimate(det[ii, :4], detj[jj, :4])] + # if len(flag) == 1: + # FeatFlag[ii] = flag[0] + + + boxes_and_imgs.append((det, im0, frame)) + + ## ================================================================ writed by WQG + '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] + 这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变 + ''' + + det_tracking = Boxes(det, im0.shape).cpu().numpy() + tracks = tracker.update(det_tracking, im0) + + # detbox = [tlwh2tlbr(x._tlwh).tolist() + [x.track_id, x.score, x.cls, x.frame_id, x.idx] + # for x in tracker.tracked_stracks if x.is_activated] + if len(tracks): + ''' + tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] + 0 1 2 3 4 5 6 7 8 + ''' + tracks[:, 7] = dataset.frame + + + '''================== 1. 提取手势位置 ===================''' + # idx_0 = tracks[:, 6].astype(np.int_) == 0 + # hn = 0 + # for j, index in enumerate(idx_0): + # if index: + # track = tracks[j, :] + # hand_local, imgshow = handpose.get_hand_local(track, im0) + # handlocals_dict.update({int(track[7]): {int(track[8]): hand_local}}) + + # # '''yoloV5和手势检测的召回率并不一直，用hand_local代替tracks中手部的(x1, y1, x2, y2)，会使得两种坐标方式混淆''' + # # if hand_local: tracks[j, :4] = hand_local + + # hn += 1 + # cv2.imwrite(f"D:\DeepLearning\yolov5\hands\images\{Path(source).stem}_{int(track[7])}_{hn}.png", imgshow) + + + '''================== 2. 存储轨迹信息 ===================''' + track_boxes = np.concatenate([track_boxes, tracks], axis=0) + + # det_boxes = np.concatenate([det_boxes, detbox], axis=0) + '''================== 3. 存储轨迹 REID 特征 =============''' + + def crop_img(track, image): + tlbr = track.tlwh_to_tlbr(track._tlwh).astype(np.int_) + + H, W = image.shape[:2] + tlbr[0] = max(0, tlbr[0]) + tlbr[1] = max(0, tlbr[1]) + tlbr[2] = min(W - 1, tlbr[2]) + tlbr[3] = min(H - 1, tlbr[3]) + + img = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :] + # cv2.imwrite(f"./runs/imgs/{int(track.idx)}.png", img) + return img + + feat_dict_1 = {f'{int(x.idx)}_img': crop_img(x, im0) for x in tracker.tracked_stracks if x.is_activated} + feat_dict = {int(x.idx): x.curr_feat for x in tracker.tracked_stracks if x.is_activated} + feat_dict.update(feat_dict_1) + + features_dict.update({int(dataset.frame): feat_dict}) + + # det_anno = tracks.copy() + # else: + # idmark = -1 * np.ones([det.shape[0], 1]) + # det_anno = np.concatenate([det[:,:4], idmark, det[:, 4:]], axis=1) + + + + + for *xyxy, id, conf, cls, fid, bid in reversed(tracks): + name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)] + label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}') + + if id >=0 and cls==0: + color = colors(int(cls), True) + elif id >=0 and cls!=0: + color = colors(int(id), True) + else: + color = colors(19, True) # 19为调色板的最后一个元素 + + annotator.box_label(xyxy, label, color=color) + + # Save results (image and video with tracking) + im0 = annotator.result() + save_path_img, ext = os.path.splitext(save_path) + if save_img: + if dataset.mode == 'image': + imgpath = save_path_img + f"_{dataset}.png" + else: + imgpath = save_path_img + f"_{dataset.frame}.png" + + cv2.imwrite(Path(imgpath), im0) + + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + + ## ======================================================================== written by WQG + ## track_boxes: Array, [x1, y1, x2, y2, track_id, score, cls, frame_index, box_id] + + '''上面保存了检测结果是视频和图像，以下还保存五种类型的数据''' + filename = os.path.split(save_path_img)[-1] + # file, ext = os.path.splitext(filename) +# ============================================================================= +# fileElements = filename.split('_') +# if len(fileElements) == 6 and len(fileElements[3])==1: +# barcode = fileElements[1] +# camera = fileElements[3] +# elif len(fileElements) == 7 and len(fileElements[3])==1: +# barcode = fileElements[2] +# camera = fileElements[4] +# else: +# barcode = '' +# camera = '' +# ============================================================================= + + + '''======================== 1. save in './run/detect/' ====================''' + if source.find("front") >= 0: + carttemp = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png") + else: + carttemp = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png") + + + imgshow = drawtracks(track_boxes, carttemp) + showpath_1 = save_path_img + "_show.png" + cv2.imwrite(Path(showpath_1), imgshow) + + + '''======================== 2. save boxes and raw images ==================''' + # boxes_imgs_dir = Path('./tracking/data/boxes_imgs/') + # if not boxes_imgs_dir.exists(): + # boxes_imgs_dir.mkdir(parents=True, exist_ok=True) + # boxes_imgs_path = boxes_imgs_dir.joinpath(f'{filename}.pkl') + # with open(boxes_imgs_path, 'wb') as file: + # pickle.dump(boxes_and_imgs, file) + + '''======================== 3. save tracks data ===========================''' + tracks_dir = Path('./tracking/data/tracks/') + if not tracks_dir.exists(): + tracks_dir.mkdir(parents=True, exist_ok=True) + tracks_path = tracks_dir.joinpath(filename + "_track.npy") + np.save(tracks_path, track_boxes) + + detect_path = tracks_dir.joinpath(filename + "_detect.npy") + np.save(detect_path, det_boxes) + + '''======================== 4. save reid features data ====================''' + feats_dir = Path('./tracking/data/trackfeats/') + if not feats_dir.exists(): + feats_dir.mkdir(parents=True, exist_ok=True) + feats_path = feats_dir.joinpath(f'{filename}.pkl') + with open(feats_path, 'wb') as file: + pickle.dump(features_dict, file) + + '''======================== 5. save hand_local data ==================''' + # handlocal_dir = Path('./tracking/data/handlocal/') + # if not handlocal_dir.exists(): + # handlocal_dir.mkdir(parents=True, exist_ok=True) + # handlocal_path = handlocal_dir.joinpath(f'{filename}.pkl') + # with open(handlocal_path, 'wb') as file: + # pickle.dump(handlocals_dict, file) + + + + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + modelpath = ROOT / 'ckpts/best_yolov5m_250000.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt' + + '''datapath为视频文件目录或视频文件''' + datapath = r"D:/datasets/ym/videos/标记视频/" # ROOT/'data/videos', ROOT/'data/images' images + # datapath = r"D:\datasets\ym\highvalue\videos" + # datapath = r"D:/dcheng/videos/" + # modelpath = ROOT / 'ckpts/yolov5s.pt' + + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=modelpath, help='model path or triton URL') # 'yolov5s.pt', best_15000_0908.pt + parser.add_argument('--source', type=str, default=datapath, help='file/dir/URL/glob/screen/0(webcam)') # images, videos + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-csv', action='store_true', help='save results in CSV format') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + +def main_loop_folders(opt): + check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + + # path1 = r"D:\datasets\ym\videos\标记视频" + + path2 = r"D:\datasets\ym\永辉双摄视频\加购_前摄\videos_front" + # path3 = r"D:\datasets\ym\永辉双摄视频\加购_后摄\videos_back" + # path4 = r"D:\datasets\ym\永辉双摄视频\退购_前摄\videos_front" + # path5 = r"D:\datasets\ym\永辉双摄视频\退购_后摄\videos_back" + + path6 = r"D:\datasets\ym\测试数据20240328\front" + path7 = r"D:\datasets\ym\测试数据20240328\back" + + '''列表paths内的元素为视频文件夹，该文件夹下元素为视频文件''' + paths = [path2, path7] # [path1, path2, path3, path4, path5] + + optdict = vars(opt) + k1, k2 = 0, 0 + for p in paths: + files = [] + if os.path.isdir(p): + files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) + for file in files: + file = r"D:\datasets\ym\测试数据20240328\front\112954521-7dd5ddad-922a-427b-b59e-a593e95e6ff4_front.mp4" + + optdict["source"] = file + run(**optdict) + + k2 += 1 + if k2 == 1: + break + + elif os.path.isfile(p): + run(**optdict) + + k1 += 1 + if k1 == 1: + break + +def find_files_in_nested_dirs(root_dir): + all_files = [] + extensions = ['.mp4'] + for dirpath, dirnames, filenames in os.walk(root_dir): + for filename in filenames: + file, ext = os.path.splitext(filename) + if ext in extensions: + all_files.append(os.path.join(dirpath, filename)) + return all_files + +print('=======') + +def main(opt): + check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + + p = r"D:\datasets\ym\永辉测试数据_202404\20240402" + optdict = vars(opt) + files = [] + k = 0 + + all_files = find_files_in_nested_dirs(p) + + if os.path.isdir(p): + files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) + for file in files: + optdict["source"] = file + run(**optdict) + + k += 1 + if k == 2: + break + elif os.path.isfile(p): + run(**vars(opt)) + +def main_loop(opt): + check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + + optdict = vars(opt) + + # p = r"D:\datasets\ym\永辉测试数据_比对" + p = r"D:\datasets\ym\广告板遮挡测试\8" + # p = r"D:\datasets\ym\videos\标记视频" + # p = r"D:\datasets\ym\实验室测试" + + k = 0 + if os.path.isdir(p): + files = find_files_in_nested_dirs(p) + + files = [r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4", + r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_front_174037379.mp4" + ] + + files = [r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4"] + for file in files: + optdict["source"] = file + run(**optdict) + + k += 1 + if k == 2: + break + elif os.path.isfile(p): + optdict["source"] = p + run(**vars(opt)) + + + + +if __name__ == '__main__': + opt = parse_opt() + + # main_loop_folders(opt) + # main(opt) + main_loop(opt) + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/bclass.py b/bclass.py new file mode 100644 index 0000000..24cb443 --- /dev/null +++ b/bclass.py @@ -0,0 +1,50 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 15 16:23:03 2024 + +@author: ym +""" + +class CamEvent: + def __init__(self, datapath): + self.data_path = datapath + self.bboxes = None + self.bfeats = None + self.tboxes = None + self.tfeats = None + + + +class ShopEvent: + def __init__(self, eventpath, stdpath): + self.barcode = "" + self.event_path = eventpath + self.event_type = self.get_event_type(eventpath) + + self.FrontEvent = "" + self.BackEvent = "" + self.fusion_boxes = None + self.fusion_feats = None + self.stdfeats = self.get_stdfeats(stdpath) + self.weight = None + self.imu = None + + def get_event_type(self, eventpath): + pass + + + + def get_stdfeats(self, stdpath): + pass + + + + + + + + + + + + \ No newline at end of file diff --git a/benchmarks.py b/benchmarks.py new file mode 100644 index 0000000..b590ff6 --- /dev/null +++ b/benchmarks.py @@ -0,0 +1,174 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +""" +Run YOLOv5 benchmarks on all supported export formats + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT + +Usage: + $ python benchmarks.py --weights yolov5s.pt --img 640 +""" + +import argparse +import platform +import sys +import time +from pathlib import Path + +import pandas as pd + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import export +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from segment.val import run as val_seg +from utils import notebook_init +from utils.general import LOGGER, check_yaml, file_size, print_args +from utils.torch_utils import select_device +from val import run as val_det + + +def run( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + y, t = [], time.time() + device = select_device(device) + model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc. + for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU) + try: + assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported + assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML + if 'cpu' in device.type: + assert cpu, 'inference not supported on CPU' + if 'cuda' in device.type: + assert gpu, 'inference not supported on GPU' + + # Export + if f == '-': + w = weights # PyTorch format + else: + w = export.run(weights=weights, + imgsz=[imgsz], + include=[f], + batch_size=batch_size, + device=device, + half=half)[-1] # all others + assert suffix in str(w), 'export failed' + + # Validate + if model_type == SegmentationModel: + result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) + else: # DetectionModel: + result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) + speed = result[2][1] # times (preprocess, inference, postprocess) + y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference + except Exception as e: + if hard_fail: + assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}' + LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}') + y.append([name, None, None, None]) # mAP, t_inference + if pt_only and i == 0: + break # break after PyTorch + + # Print results + LOGGER.info('\n') + parse_opt() + notebook_init() # print system info + c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', ''] + py = pd.DataFrame(y, columns=c) + LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') + LOGGER.info(str(py if map else py.iloc[:, :2])) + if hard_fail and isinstance(hard_fail, str): + metrics = py['mAP50-95'].array # values to compare to floor + floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n + assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}' + return py + + +def test( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + y, t = [], time.time() + device = select_device(device) + for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable) + try: + w = weights if f == '-' else \ + export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights + assert suffix in str(w), 'export failed' + y.append([name, True]) + except Exception: + y.append([name, False]) # mAP, t_inference + + # Print results + LOGGER.info('\n') + parse_opt() + notebook_init() # print system info + py = pd.DataFrame(y, columns=['Format', 'Export']) + LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)') + LOGGER.info(str(py)) + return py + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--test', action='store_true', help='test exports only') + parser.add_argument('--pt-only', action='store_true', help='test PyTorch only') + parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + print_args(vars(opt)) + return opt + + +def main(opt): + test(**vars(opt)) if opt.test else run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/contrast/__init__.py b/contrast/__init__.py new file mode 100644 index 0000000..0cd4367 --- /dev/null +++ b/contrast/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Sep 26 08:53:58 2024 + +@author: ym +""" + diff --git a/contrast/__pycache__/__init__.cpython-312.pyc b/contrast/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b2010b8f55b0b36e10c582abb9c934542f077512 GIT binary patch literal 209 zcmX@j%ge<81dhSa(u08XV-N=h7@>^M96-i&h7^Vg`kg2nm|(<6cOV-N=!FakLaKwQiLBvKfn7*ZI688n%yjJTYOQWHy3Qxx*^ z6hbme6@pU>6pYLi3@ofnjjc>A6pRdvOt`ol5=%=m@{6n#Ds#E~G#PKP$H%ASC&$OH 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r"D:\contrast\stdlib\v11_test.json" + stdBarcode = [] + stdlib = {} + with open(stdFeaturePath, 'r', encoding='utf-8') as f: + data = json.load(f) + for dic in data['total']: + barcode = dic['key'] + feature = np.array(dic['value']) + stdBarcode.append(barcode) + stdlib[barcode] = feature + + return stdlib + +def get_eventlist_errortxt(evtpaths): + ''' + 读取一次测试中的错误事件 + ''' + text1 = "one_2_Small_n_Error.txt" + text2 = "one_2_Big_N_Error.txt" + events = [] + text = (text1, text2) + for txt in text: + txtfile = os.path.join(evtpaths, txt) + with open(txtfile, "r") as f: + lines = f.readlines() + for i, line in enumerate(lines): + line = line.strip() + if line: + fpath=os.path.join(evtpaths, line) + events.append(fpath) + + + + events = list(set(events)) + + return events + +def save_eventdata(): + evtpaths = r"/home/wqg/dataset/test_dataset/performence_dataset/" + events = get_eventlist_errortxt(evtpaths) + + '''定义当前事件存储地址及生成相应文件件''' + resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event" + for evtpath in events: + event = ShoppingEvent(evtpath) + save_data(event, resultPath) + + print(event.evtname) + + + +# def get_topk_percent(data, k): +# """ +# 获取数据中最大的 k% 的元素 +# """ +# # 将数据转换为 NumPy 数组 +# if isinstance(data, list): +# data = np.array(data) + +# percentile = np.percentile(data, 100-k) +# top_k_percent = data[data >= percentile] + +# return top_k_percent +# def cluster(data, thresh=0.15): +# # data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58]) +# # data = np.array([0.1, 0.13, 0.2, 0.3]) +# # data = np.array([0.1]) + +# if isinstance(data, list): +# data = np.array(data) + +# data1 = np.sort(data) +# cluter, Cluters, = [data1[0]], [] +# for i in range(1, len(data1)): +# if data1[i] - data1[i-1]< thresh: +# cluter.append(data1[i]) +# else: +# Cluters.append(cluter) +# cluter = [data1[i]] +# Cluters.append(cluter) + +# clt_center = [] +# for clt in Cluters: +# ## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中 +# # if len(clt)>=3: +# # clt_center.append(np.mean(clt)) +# clt_center.append(np.mean(clt)) + +# # print(clt_center) + +# return clt_center + +# def calsimi_vs_stdfeat_new(event, stdfeat): +# '''事件与标准库的对比策略 +# 该比对策略是否可以拓展到事件与事件的比对？ +# ''' + + +# def calsiml(feat1, feat2, topkp=75, cluth=0.15): +# '''轨迹样本和标准特征集样本相似度的选择策略''' +# matrix = 1 - cdist(feat1, feat2, 'cosine') +# simi_max = [] +# for i in range(len(matrix)): +# sim = np.mean(get_topk_percent(matrix[i, :], topkp)) +# simi_max.append(sim) +# cltc_max = cluster(simi_max, cluth) +# Simi = max(cltc_max) + +# ## cltc_max为空属于编程考虑不周，应予以排查解决 +# # if len(cltc_max): +# # Simi = max(cltc_max) +# # else: +# # Simi = 0 #不应该走到该处 + + +# return Simi + + +# front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 +# front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 +# for i in range(len(event.front_boxes)): +# front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0) +# front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0) + +# back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 +# back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 +# for i in range(len(event.back_boxes)): +# back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0) +# back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0) + +# if len(front_feats): +# front_simi = calsiml(front_feats, stdfeat) +# if len(back_feats): +# back_simi = calsiml(back_feats, stdfeat) + +# '''前后摄相似度融合策略''' +# if len(front_feats) and len(back_feats): +# diff_simi = abs(front_simi - back_simi) +# if diff_simi>0.15: +# Similar = max([front_simi, back_simi]) +# else: +# Similar = (front_simi+back_simi)/2 +# elif len(front_feats) and len(back_feats)==0: +# Similar = front_simi +# elif len(front_feats)==0 and len(back_feats): +# Similar = back_simi +# else: +# Similar = None # 在event.front_feats和event.back_feats同时为空时 + +# return Similar + + + + +def simi_matrix(): + evtpaths = r"/home/wqg/dataset/pipeline/contrast/single_event_V10/evtobjs/" + + stdfeatPath = r"/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/" + resultPath = r"/home/wqg/dataset/performence_dataset/result/" + + evt_paths, bcdSet = get_evtList(evtpaths) + + ## read std features + stdDict={} + evtDict = {} + for barcode in bcdSet: + stdpath = os.path.join(stdfeatPath, f"{barcode}.json") + if not os.path.isfile(stdpath): + continue + + with open(stdpath, 'r', encoding='utf-8') as f: + stddata = json.load(f) + feat = np.array(stddata["value"]) + stdDict[barcode] = feat + + for evtpath in evt_paths: + barcode = Path(evtpath).stem.split("_")[-1] + + if barcode not in stdDict.keys(): + continue + + # try: + # with open(evtpath, 'rb') as f: + # evtdata = pickle.load(f) + # except Exception as e: + # print(evtname) + + with open(evtpath, 'rb') as f: + event = pickle.load(f) + + stdfeat = stdDict[barcode] + + Similar = calsimi_vs_stdfeat_new(event, stdfeat) + + # 构造 boxes 子图存储路径 + subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg") + if not os.path.exists(subimgpath): + os.makedirs(subimgpath) + histpath = os.path.join(resultPath, "simi_hist") + if not os.path.exists(histpath): + os.makedirs(histpath) + + mean_values, max_values = [], [] + cameras = ('front', 'back') + fig, ax = plt.subplots(2, 3, figsize=(16, 9), dpi=100) + kpercent = 25 + for camera in cameras: + boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 + evtfeat = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + if camera == 'front': + for i in range(len(event.front_boxes)): + boxes = np.concatenate((boxes, event.front_boxes[i]), axis=0) + evtfeat = np.concatenate((evtfeat, event.front_feats[i]), axis=0) + imgpaths = event.front_imgpaths + + else: + for i in range(len(event.back_boxes)): + boxes = np.concatenate((boxes, event.back_boxes[i]), axis=0) + evtfeat = np.concatenate((evtfeat, event.back_feats[i]), axis=0) + imgpaths = event.back_imgpaths + + assert len(boxes)==len(evtfeat), f"Please check the Event: {event.evtname}" + if len(boxes)==0: continue + print(event.evtname) + + matrix = 1 - cdist(evtfeat, stdfeat, 'cosine') + simi_1d = matrix.flatten() + simi_mean = np.mean(matrix, axis=1) + # simi_max = np.max(matrix, axis=1) + + '''以相似度矩阵每一行最大的 k% 的相似度做均值计算''' + simi_max = [] + for i in range(len(matrix)): + sim = np.mean(get_topk_percent(matrix[i, :], kpercent)) + simi_max.append(sim) + + + mean_values.append(np.mean(matrix)) + max_values.append(np.mean(simi_max)) + + diff_max_mean = np.mean(simi_max) - np.mean(matrix) + + '''相似度统计特性图示''' + k =0 + if camera == 'front': k = 1 + + '''********************* 相似度全体数据 *********************''' + ax[k, 0].hist(simi_1d, bins=60, range=(-0.2, 1), edgecolor='black') + ax[k, 0].set_xlim([-0.2, 1]) + ax[k, 0].set_title(camera) + + _, y_max = ax[k, 0].get_ylim() # 获取y轴范围 + '''相似度变动范围''' + ax[k, 0].text(-0.1, 0.15*y_max, f"rng:{max(simi_1d)-min(simi_1d):.3f}", fontsize=18, color='b') + + '''********************* 均值********************************''' + ax[k, 1].hist(simi_mean, bins=24, range=(-0.2, 1), edgecolor='black') + ax[k, 1].set_xlim([-0.2, 1]) + ax[k, 1].set_title("mean") + _, y_max = ax[k, 1].get_ylim() # 获取y轴范围 + '''相似度变动范围''' + ax[k, 1].text(-0.1, 0.15*y_max, f"rng:{max(simi_mean)-min(simi_mean):.3f}", fontsize=18, color='b') + + + '''********************* 最大值 ******************************''' + ax[k, 2].hist(simi_max, bins=24, range=(-0.2, 1), edgecolor='black') + ax[k, 2].set_xlim([-0.2, 1]) + ax[k, 2].set_title("max") + _, y_max = ax[k, 2].get_ylim() # 获取y轴范围 + '''相似度变动范围''' + ax[k, 2].text(-0.1, 0.15*y_max, f"rng:{max(simi_max)-min(simi_max):.3f}", fontsize=18, color='b') + + + '''绘制聚类中心''' + cltc_mean = cluster(simi_mean) + for value in cltc_mean: + ax[k, 1].axvline(x=value, color='m', linestyle='--', linewidth=3) + + cltc_max = cluster(simi_max) + for value in cltc_max: + ax[k, 2].axvline(x=value, color='m', linestyle='--', linewidth=3) + + '''绘制相似度均值与最大值均值''' + ax[k, 1].axvline(x=np.mean(matrix), color='r', linestyle='-', linewidth=3) + ax[k, 2].axvline(x=np.mean(simi_max), color='g', linestyle='-', linewidth=3) + + '''绘制相似度最大值均值 - 均值''' + _, y_max = ax[k, 2].get_ylim() # 获取y轴范围 + ax[k, 2].text(-0.1, 0.05*y_max, f"g-r={diff_max_mean:.3f}", fontsize=18, color='m') + + plt.show() + + # for i, box in enumerate(boxes): + # x1, y1, x2, y2, tid, score, cls, fid, bid = box + # imgpath = imgpaths[int(fid-1)] + # image = cv2.imread(imgpath) + # subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :] + # camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_') + # subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID})_{simi_mean[i]:.3f}.png" + # imgpairs.append((subimgName, subimg)) + # spath = os.path.join(subimgpath, subimgName) + # cv2.imwrite(spath, subimg) + + # oldname = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png" + # oldpath = os.path.join(subimgpath, oldname) + # if os.path.exists(oldpath): + # os.remove(oldpath) + + + if len(mean_values)==2: + mean_diff = abs(mean_values[1]-mean_values[0]) + ax[0, 1].set_title(f"mean diff: {mean_diff:.3f}") + if len(max_values)==2: + max_diff = abs(max_values[1]-max_values[0]) + ax[0, 2].set_title(f"max diff: {max_diff:.3f}") + try: + fig.suptitle(f"Similar: {Similar:.3f}", fontsize=16) + except Exception as e: + print(e) + print(f"Similar: {Similar}") + pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png") + plt.savefig(pltpath) + + pltpath1 = os.path.join(histpath, f"{event.evtname}_.png") + plt.savefig(pltpath1) + + + plt.close() + + +def main(): + simi_matrix() + + + +if __name__ == "__main__": + main() + # cluster() + + + + + + + + + + diff --git a/contrast/feat_extract/__pycache__/config.cpython-312.pyc b/contrast/feat_extract/__pycache__/config.cpython-312.pyc new file mode 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