Merge branch 'master' of https://gitee.com/nanjing-yimao-information/ieemoo-ai-gift

datasets_preprocess/split_train_val_.py

@@ -7,6 +7,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument('--img_path', default='/home/lc/data_center/gift/v2/images', type=str,
                     help='input xml label path')  # 图片存放地址
 # 数据集的划分，地址选择自己数据下的ImageSets/Main
+# parser.add_argument('--txt_path', default='/home/lc/data_center/gift/yolov10_data/Main', type=str, help='output txt label path')
 parser.add_argument('--txt_path', default='/home/lc/data_center/gift/yolov10_data/Main', type=str,
                     help='output txt label path')
 opt = parser.parse_args()

datasets_preprocess/voc_label_.py

@@ -5,7 +5,7 @@ from os import getcwd
 
 sets = ['train', 'val', 'test']
 
-classes = ['tag', 'bandage']
+classes = ['tag', 'bandage', 'word', 'package']
 
 
 def convert(size, box):
@@ -51,8 +51,9 @@ def convert_annotation(image_id, imgname_list, label_path, Annotation_path, imag
                     cls_id = classes.index(cls)
                     xmlbox = obj.find('bndbox')
                     b = (
-                    float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
-                    float(xmlbox.find('ymax').text))
+                        float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text),
+                        float(xmlbox.find('ymin').text),
+                        float(xmlbox.find('ymax').text))
 
                     b1, b2, b3, b4 = b
                     # 标注越界修正

ultralytics/cfg/models/v10/yolov10s.yaml

@@ -2,11 +2,12 @@
 nc: 80 # number of classes
 scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
   # [depth, width, max_channels]
-  s: [0.33, 0.50, 1024]
+   s: [0.33, 0.50, 1024]
+#  s: [0.33, 0.375, 1024]
 
 backbone:
   # [from, repeats, module, args]
-  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1
   - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
   - [-1, 3, C2f, [128, True]]
   - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8

ultralytics/models/yolo/detect/val_confusion_confusion_visual_0621.py

@@ -0,0 +1,337 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import os
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+
+from ultralytics.data import build_dataloader, build_yolo_dataset, converter
+from ultralytics.engine.validator import BaseValidator
+from ultralytics.utils import LOGGER, ops
+from ultralytics.utils.checks import check_requirements
+# from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
+from ultralytics.utils.metrics_confusion_visual import ConfusionMatrix, DetMetrics, box_iou
+from ultralytics.utils.plotting import output_to_target, plot_images, Colors
+
+###  val时可视化图片增加
+from ultralytics.utils.plotting import Annotator, Colors
+colors = Colors()
+
+
+class DetectionValidator(BaseValidator):
+    """
+    A class extending the BaseValidator class for validation based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.detect import DetectionValidator
+
+        args = dict(model='yolov8n.pt', data='coco8.yaml')
+        validator = DetectionValidator(args=args)
+        validator()
+        ```
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """Initialize detection model with necessary variables and settings."""
+        super().__init__(dataloader, save_dir, pbar, args, _callbacks)
+        self.nt_per_class = None
+        self.is_coco = False
+        self.class_map = None
+        self.args.task = "detect"
+        self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
+        self.iouv = torch.linspace(0.5, 0.95, 10)  # IoU vector for mAP@0.5:0.95
+        self.niou = self.iouv.numel()
+        self.lb = []  # for autolabelling
+
+    def preprocess(self, batch):
+        """Preprocesses batch of images for YOLO training."""
+        batch["img"] = batch["img"].to(self.device, non_blocking=True)
+        batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
+        for k in ["batch_idx", "cls", "bboxes"]:
+            batch[k] = batch[k].to(self.device)
+
+        if self.args.save_hybrid:
+            height, width = batch["img"].shape[2:]
+            nb = len(batch["img"])
+            bboxes = batch["bboxes"] * torch.tensor((width, height, width, height), device=self.device)
+            self.lb = (
+                [
+                    torch.cat([batch["cls"][batch["batch_idx"] == i], bboxes[batch["batch_idx"] == i]], dim=-1)
+                    for i in range(nb)
+                ]
+                if self.args.save_hybrid
+                else []
+            )  # for autolabelling
+
+        return batch
+
+    def init_metrics(self, model):
+        """Initialize evaluation metrics for YOLO."""
+        val = self.data.get(self.args.split, "")  # validation path
+        self.is_coco = isinstance(val, str) and "coco" in val and val.endswith(f"{os.sep}val2017.txt")  # is COCO
+        self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(1000))
+        self.args.save_json |= self.is_coco  # run on final val if training COCO
+        self.names = model.names
+        self.nc = len(model.names)
+        self.metrics.names = self.names
+        self.metrics.plot = self.args.plots
+        self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf)
+        self.seen = 0
+        self.jdict = []
+        self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[])
+
+    def get_desc(self):
+        """Return a formatted string summarizing class metrics of YOLO model."""
+        return ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)")
+
+    def postprocess(self, preds):
+        """Apply Non-maximum suppression to prediction outputs."""
+        return ops.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            labels=self.lb,
+            multi_label=True,
+            agnostic=self.args.single_cls,
+            max_det=self.args.max_det,
+        )
+
+    def _prepare_batch(self, si, batch):
+        """Prepares a batch of images and annotations for validation."""
+        idx = batch["batch_idx"] == si
+        cls = batch["cls"][idx].squeeze(-1)
+        bbox = batch["bboxes"][idx]
+        ori_shape = batch["ori_shape"][si]
+        imgsz = batch["img"].shape[2:]
+        ratio_pad = batch["ratio_pad"][si]
+        if len(cls):
+            bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]  # target boxes
+            ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad)  # native-space labels
+        return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
+
+    def _prepare_pred(self, pred, pbatch):
+        """Prepares a batch of images and annotations for validation."""
+        predn = pred.clone()
+        ops.scale_boxes(
+            pbatch["imgsz"], predn[:, :4], pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]
+        )  # native-space pred
+        return predn
+
+    def update_metrics(self, preds, batch):
+        """Metrics."""
+        for si, pred in enumerate(preds):
+            self.seen += 1
+            npr = len(pred)
+            stat = dict(
+                conf=torch.zeros(0, device=self.device),
+                pred_cls=torch.zeros(0, device=self.device),
+                tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
+            )
+            pbatch = self._prepare_batch(si, batch)
+            cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
+            nl = len(cls)
+            stat["target_cls"] = cls
+            if npr == 0:
+                if nl:
+                    for k in self.stats.keys():
+                        self.stats[k].append(stat[k])
+                    if self.args.plots:
+                        self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
+                continue
+
+            # Predictions
+            if self.args.single_cls:
+                pred[:, 5] = 0
+            predn = self._prepare_pred(pred, pbatch)
+            stat["conf"] = predn[:, 4]
+            stat["pred_cls"] = predn[:, 5]
+
+            # Evaluate
+            if nl:
+                stat["tp"] = self._process_batch(predn, bbox, cls)
+                # ####===========增加匹配结果返回==================
+                # stat["tp"], matches, iou_list = self._process_batch(predn, bbox, cls)  ### 生成gt和pred box匹配
+                # colors = Colors()
+                # if len(matches) > 0:  ## 有匹配结果
+                #     print('len(match)', len(matches))
+                #     indl = matches[:, 0]  ## label index
+                #     indp = matches[:, 1]  ## pred index
+                #     # print('img', img)
+                #     # img_name = batch['im_file']
+                #     # print('img_name', img_name[0])
+                #     # img = cv2.imread(img_name[0])
+                #     img = cv2.imread(batch['im_file'][0])
+                #     # annotator = Annotator(img, line_width=3)
+                #     annotator = Annotator(img, line_width=3, font_size=3, pil=True, example=self.names)
+                #     for ind, (*xyxy, conf, p_cls) in enumerate(predn):
+                #         if ind in indp:
+                #             p_ind = list(indp).index(ind)  ## ind在match中的索引
+                #             t_ind = indl[p_ind]
+                #             iou = iou_list[t_ind, p_ind]
+                #             conf_c = conf.cpu().item()
+                #             label = self.names[int(p_cls)] + str(conf_c) + '_iou' + str(f'{iou:.2f}')
+                #             annotator.box_label(xyxy, label, color=(128, 0, 128))
+                #
+                #     img = annotator.result()
+                #     path_save = 'tp'
+                #     os.makedirs(path_save, exist_ok=True)
+                #     save_path1 = os.path.join(path_save, batch['im_file'][0].split('/')[-1])
+                #     print('save_path', save_path1)
+                #     cv2.imwrite(save_path1, img)
+                ####==================================
+                if self.args.plots:
+                    ###=======修改可视化匹配框=============
+                    # self.confusion_matrix.process_batch(predn, bbox, cls)
+                    self.confusion_matrix.process_batch(predn, bbox, cls, batch['im_file'][0], self.names, Annotator, colors)
+            for k in self.stats.keys():
+                self.stats[k].append(stat[k])
+
+            # Save
+            if self.args.save_json:
+                self.pred_to_json(predn, batch["im_file"][si])
+            if self.args.save_txt:
+                file = self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt'
+                self.save_one_txt(predn, self.args.save_conf, pbatch["ori_shape"], file)
+
+    def finalize_metrics(self, *args, **kwargs):
+        """Set final values for metrics speed and confusion matrix."""
+        self.metrics.speed = self.speed
+        self.metrics.confusion_matrix = self.confusion_matrix
+
+    def get_stats(self):
+        """Returns metrics statistics and results dictionary."""
+        stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()}  # to numpy
+        if len(stats) and stats["tp"].any():
+            self.metrics.process(**stats)
+        self.nt_per_class = np.bincount(
+            stats["target_cls"].astype(int), minlength=self.nc
+        )  # number of targets per class
+        return self.metrics.results_dict
+
+    def print_results(self):
+        """Prints training/validation set metrics per class."""
+        pf = "%22s" + "%11i" * 2 + "%11.3g" * len(self.metrics.keys)  # print format
+        LOGGER.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
+        if self.nt_per_class.sum() == 0:
+            LOGGER.warning(f"WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels")
+
+        # Print results per class
+        if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
+            for i, c in enumerate(self.metrics.ap_class_index):
+                LOGGER.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
+
+        if self.args.plots:
+            for normalize in True, False:
+                self.confusion_matrix.plot(
+                    save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
+                )
+
+    def _process_batch(self, detections, gt_bboxes, gt_cls):
+        """
+        Return correct prediction matrix.
+
+        Args:
+            detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
+                Each detection is of the format: x1, y1, x2, y2, conf, class.
+            labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
+                Each label is of the format: class, x1, y1, x2, y2.
+
+        Returns:
+            (torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
+        """
+        iou = box_iou(gt_bboxes, detections[:, :4])
+        return self.match_predictions(detections[:, 5], gt_cls, iou)
+
+    def build_dataset(self, img_path, mode="val", batch=None):
+        """
+        Build YOLO Dataset.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
+        """
+        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)
+
+    def get_dataloader(self, dataset_path, batch_size):
+        """Construct and return dataloader."""
+        dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
+        return build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1)  # return dataloader
+
+    def plot_val_samples(self, batch, ni):
+        """Plot validation image samples."""
+        plot_images(
+            batch["img"],
+            batch["batch_idx"],
+            batch["cls"].squeeze(-1),
+            batch["bboxes"],
+            paths=batch["im_file"],
+            fname=self.save_dir / f"val_batch{ni}_labels.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )
+
+    def plot_predictions(self, batch, preds, ni):
+        """Plots predicted bounding boxes on input images and saves the result."""
+        plot_images(
+            batch["img"],
+            *output_to_target(preds, max_det=self.args.max_det),
+            paths=batch["im_file"],
+            fname=self.save_dir / f"val_batch{ni}_pred.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )  # pred
+
+    def save_one_txt(self, predn, save_conf, shape, file):
+        """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
+        gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
+        for *xyxy, conf, cls in predn.tolist():
+            xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
+            with open(file, "a") as f:
+                f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+    def pred_to_json(self, predn, filename):
+        """Serialize YOLO predictions to COCO json format."""
+        stem = Path(filename).stem
+        image_id = int(stem) if stem.isnumeric() else stem
+        box = ops.xyxy2xywh(predn[:, :4])  # xywh
+        box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+        for p, b in zip(predn.tolist(), box.tolist()):
+            self.jdict.append(
+                {
+                    "image_id": image_id,
+                    "category_id": self.class_map[int(p[5])],
+                    "bbox": [round(x, 3) for x in b],
+                    "score": round(p[4], 5),
+                }
+            )
+
+    def eval_json(self, stats):
+        """Evaluates YOLO output in JSON format and returns performance statistics."""
+        if self.args.save_json and self.is_coco and len(self.jdict):
+            anno_json = self.data["path"] / "annotations/instances_val2017.json"  # annotations
+            pred_json = self.save_dir / "predictions.json"  # predictions
+            LOGGER.info(f"\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...")
+            try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
+                check_requirements("pycocotools>=2.0.6")
+                from pycocotools.coco import COCO  # noqa
+                from pycocotools.cocoeval import COCOeval  # noqa
+
+                for x in anno_json, pred_json:
+                    assert x.is_file(), f"{x} file not found"
+                anno = COCO(str(anno_json))  # init annotations api
+                pred = anno.loadRes(str(pred_json))  # init predictions api (must pass string, not Path)
+                eval = COCOeval(anno, pred, "bbox")
+                if self.is_coco:
+                    eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # images to eval
+                eval.evaluate()
+                eval.accumulate()
+                eval.summarize()
+                stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = eval.stats[:2]  # update mAP50-95 and mAP50
+            except Exception as e:
+                LOGGER.warning(f"pycocotools unable to run: {e}")
+        return stats

val.py

@@ -0,0 +1,4 @@
+from ultralytics import YOLOv10
+
+model = YOLOv10('runs/detect/train/weights/last.pt')
+metrics = model.val(batch=8, data='gift.yaml')