From 3d364aa0bdcb42db19b08b52aedc2ece28a79568 Mon Sep 17 00:00:00 2001 From: lichen Date: Tue, 24 Jun 2025 16:57:16 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B5=8B=E8=AF=95=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- gift_demo.py | 97 +++++++++++++++++++ prt/.txt | 40 ++++++++ ultralytics/data/utils.py | 2 +- ultralytics/models/yolo/detect/val.py | 6 +- ultralytics/utils/metrics_confusion_visual.py | 2 +- 5 files changed, 142 insertions(+), 5 deletions(-) create mode 100644 gift_demo.py diff --git a/gift_demo.py b/gift_demo.py new file mode 100644 index 0000000..c16f3f6 --- /dev/null +++ b/gift_demo.py @@ -0,0 +1,97 @@ +import argparse +import os +import time +from ultralytics import YOLOv10 +import cv2 +import torch + +from ultralytics.utils.show_trace_pr import ShowPR +# from trace_detect import run, _init_model +import numpy as np +image_ext = [".jpg", ".jpeg", ".webp", ".bmp", ".png"] +video_ext = ["mp4", "mov", "avi", "mkv"] + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--demo", default="image", help="demo type, eg. image, video and webcam" + ) + parser.add_argument("--config", default='../config/gift-1.5x.yml', help="model config file path") + parser.add_argument("--model", default='../ckpts/nanodet_m_1.5x/model_best/nanodet_model_best.pth', + help="model file path") + parser.add_argument("--path", default="../../data_center/gift/objectdet/test/images", + help="path to images or video") + parser.add_argument("--camid", type=int, default=0, help="webcam demo camera id") + parser.add_argument( + "--save_result", + default="../../data_center/gift/objectdet/test/result", + help="whether to save the inference result of image/video", + ) + args = parser.parse_args() + return args + +def get_image_list(path): + image_names = [] + for maindir, subdir, file_name_list in os.walk(path): + for filename in file_name_list: + apath = os.path.join(maindir, filename) + ext = os.path.splitext(apath)[1] + if ext in image_ext: + image_names.append(apath) + return image_names + + +def _init(): + model = YOLOv10('runs/detect/train/weights/best_gift_v10n.pt') + return model + + +def get_trace_event(model, path): + res_single = [] + if os.path.isdir(path): + files = get_image_list(path) + else: + files = [path] + files.sort() + for image_name in files: + # all_box = run(model=model, stride=stride, pt=pt, source=image_name) + # print(image_name) + all_box = model.predict(image_name, save=False, imgsz=[224, 224], conf=0.1) + # print(all_box[0].boxes.conf) + all_box = np.array(all_box[0].boxes.conf.cpu()) + if len(all_box) == 0: + res_single.append(0) + else: + res_single.append(all_box[-1]) + # if sum(res_single) == 0 and (not "commodity" in path): + # with open('err.txt', 'w') as f: + # f.write(path+'\n') + return res_single + + +def main(path): + model = _init() + tags, result_all = [], [] + classify = ['commodity', 'gift'] + for cla in classify: + pre_pth = os.sep.join([path, cla]) + for root, dirs, files in os.walk(pre_pth): + if not dirs: + if cla == 'commodity': + tags.append(0) + else: + tags.append(1) + res_single = get_trace_event(model, root) + result_all.append(res_single) + spr = ShowPR(tags, result_all, title_name='yolov10n') + # spr.change_precValue() + spr.get_pic() + + +if __name__ == "__main__": + # path = '../data_center/gift/trace_subimgs/d50' # 间距为50时 + # path = '../data_center/gift/trace_subimgs/actual_test' # 永辉超市实测 + path = '../data_center/gift/gift_test' #yolov10单图测试 + # path = '../data_center/gift/trace_subimgs/tracluster' # tracluster方法过滤 + main(path) \ No newline at end of file diff --git a/prt/.txt b/prt/.txt index e5ae645..e1fb2da 100644 --- a/prt/.txt +++ b/prt/.txt @@ -82,3 +82,43 @@ R 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 T 0.060 0.063 0.068 0.071 0.077 0.081 0.087 0.091 0.097 0.104 0.109 0.116 0.124 0.130 0.140 0.150 0.160 0.171 0.184 0.196 0.212 0.228 0.247 0.271 0.291 0.315 0.341 0.367 0.411 0.481 R 0.918 0.917 0.914 0.912 0.908 0.905 0.903 0.902 0.900 0.898 0.895 0.893 0.890 0.888 0.885 0.881 0.879 0.879 0.876 0.873 0.869 0.866 0.862 0.857 0.853 0.849 0.843 0.835 0.824 0.809 +0 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 +T 0.060 0.063 0.068 0.071 0.077 0.081 0.087 0.091 0.097 0.104 0.109 0.116 0.124 0.130 0.140 0.150 0.160 0.171 0.184 0.196 0.212 0.228 0.247 0.271 0.291 0.315 0.341 0.367 0.411 0.481 +R 0.918 0.917 0.914 0.912 0.908 0.905 0.903 0.902 0.900 0.898 0.895 0.893 0.890 0.888 0.885 0.881 0.879 0.879 0.876 0.873 0.869 0.866 0.862 0.857 0.853 0.849 0.843 0.835 0.824 0.809 +0 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 +T 0.060 0.063 0.068 0.071 0.077 0.081 0.087 0.091 0.097 0.104 0.109 0.116 0.124 0.130 0.140 0.150 0.160 0.171 0.184 0.196 0.212 0.228 0.247 0.271 0.291 0.315 0.341 0.367 0.411 0.481 +R 0.918 0.917 0.914 0.912 0.908 0.905 0.903 0.902 0.900 0.898 0.895 0.893 0.890 0.888 0.885 0.881 0.879 0.879 0.876 0.873 0.869 0.866 0.862 0.857 0.853 0.849 0.843 0.835 0.824 0.809 +0 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.960 0.970 0.980 0.990 +T 0.939 0.939 0.939 0.939 0.939 0.940 0.941 0.942 0.942 0.942 0.942 0.943 0.943 0.946 0.947 0.947 0.947 0.949 0.949 0.949 0.949 0.951 0.951 0.951 0.951 0.958 0.958 0.958 0.958 +R 0.069 0.069 0.069 0.069 0.069 0.057 0.047 0.042 0.042 0.042 0.042 0.036 0.036 0.018 0.016 0.016 0.016 0.010 0.010 0.010 0.010 0.008 0.008 0.008 0.008 0.002 0.002 0.002 0.002 +1 +P +T +R +2 +P +T +R +3 +P +T +R +0 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 +T 0.013 0.014 0.015 0.018 0.020 0.021 0.023 0.025 0.027 0.030 0.033 0.037 0.041 0.046 0.052 0.059 0.065 0.079 0.090 0.104 0.122 0.134 0.168 0.214 0.261 0.310 0.358 0.432 0.553 0.617 +R 0.950 0.950 0.950 0.947 0.945 0.945 0.945 0.944 0.944 0.939 0.938 0.938 0.938 0.936 0.935 0.934 0.932 0.927 0.925 0.924 0.923 0.921 0.917 0.912 0.905 0.901 0.899 0.887 0.867 0.856 +1 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 +T 0.036 0.039 0.041 0.045 0.049 0.052 0.056 0.060 0.066 0.070 0.077 0.084 0.092 0.102 0.108 0.116 0.129 0.142 0.155 0.174 0.188 0.202 0.226 0.255 0.289 0.317 0.349 0.410 0.473 0.515 +R 0.934 0.934 0.934 0.933 0.931 0.931 0.930 0.928 0.928 0.927 0.927 0.925 0.924 0.923 0.923 0.921 0.919 0.917 0.916 0.914 0.911 0.908 0.905 0.903 0.899 0.897 0.890 0.877 0.871 0.866 +2 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 +T 0.190 0.196 0.211 0.218 0.228 0.233 0.244 0.251 0.264 0.275 0.289 0.294 0.300 0.319 0.337 0.343 0.358 0.367 0.383 0.404 0.429 0.498 0.514 0.555 0.566 0.609 0.647 0.662 0.675 0.694 +R 0.643 0.640 0.629 0.623 0.617 0.613 0.606 0.603 0.593 0.585 0.574 0.571 0.568 0.559 0.545 0.537 0.522 0.513 0.504 0.493 0.487 0.436 0.422 0.400 0.397 0.375 0.349 0.342 0.329 0.316 +3 +P 0.700 0.710 0.720 0.730 0.740 0.750 0.760 0.770 0.780 0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900 0.910 0.920 0.930 0.940 0.950 0.960 0.970 0.980 0.990 +T 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.006 0.006 0.006 0.007 0.007 0.008 0.009 0.009 0.010 0.013 0.018 0.021 0.023 0.026 0.028 0.035 0.038 0.039 0.042 0.049 0.052 0.065 0.088 +R 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 0.984 diff --git a/ultralytics/data/utils.py b/ultralytics/data/utils.py index 3085d97..eb35cd4 100644 --- a/ultralytics/data/utils.py +++ b/ultralytics/data/utils.py @@ -43,7 +43,7 @@ PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_ def img2label_paths(img_paths): """Define label paths as a function of image paths.""" # sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings - sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels_class2{os.sep}" # /images/, /labels/ substrings + sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths] diff --git a/ultralytics/models/yolo/detect/val.py b/ultralytics/models/yolo/detect/val.py index 77d06cc..464b903 100644 --- a/ultralytics/models/yolo/detect/val.py +++ b/ultralytics/models/yolo/detect/val.py @@ -148,9 +148,9 @@ class DetectionValidator(BaseValidator): pred[:, 5] = 0 # =======Targets 2 分类====== - column = pred[:,5] - condition2 = (column != 10) - pred[:,5][condition2] = 0 + # column = pred[:,5] + # condition2 = (column != 10) + # pred[:,5][condition2] = 0 # =========================== predn = self._prepare_pred(pred, pbatch) diff --git a/ultralytics/utils/metrics_confusion_visual.py b/ultralytics/utils/metrics_confusion_visual.py index d81dbd0..d53012a 100644 --- a/ultralytics/utils/metrics_confusion_visual.py +++ b/ultralytics/utils/metrics_confusion_visual.py @@ -389,7 +389,7 @@ class ConfusionMatrix: cls = detect_cur_cpu[5] c = int(cls) label = f'{names[c]} {conf:.2f} iou:{float(iou):.2f}' - print(">>>>>>>>>>>>>>>>>>> label: {} C: {}".format(label, c)) + # print(">>>>>>>>>>>>>>>>>>> label: {} C: {}".format(label, c)) if fp_flag: annotator.box_label(xyxy, label, color=(125, 0, 125)) ##fp iou匹配上，类别错误 紫色框 else: