测试代码

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)

prt/.txt

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+1
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+T 
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+0
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+1
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+2
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+3
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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]
 
 

ultralytics/models/yolo/detect/val.py

@@ -148,9 +148,9 @@ class DetectionValidator(BaseValidator):
                 pred[:, 5] = 0
             
             # =======Targets 2 分类======
-            column = pred[:,5]
+            # column = pred[:,5]
-            condition2 = (column != 10)
+            # condition2 = (column != 10)
-            pred[:,5][condition2] = 0
+            # pred[:,5][condition2] = 0
             # ===========================
 
             predn = self._prepare_pred(pred, pbatch)

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: