From aa7ea8ecf800102011acafdf609254fa87c0f875 Mon Sep 17 00:00:00 2001 From: lee <770918727@qq.com> Date: Sat, 21 Jun 2025 13:37:38 +0800 Subject: [PATCH 1/2] =?UTF-8?q?=E8=B5=A0=E5=93=81=E6=B5=8B=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- datasets_preprocess/split_train_val_.py | 3 +- datasets_preprocess/voc_label_.py | 13 +- ultralytics/cfg/models/v10/yolov10s.yaml | 5 +- .../val_confusion_confusion_visual_0621.py | 337 +++ ultralytics/utils/metrics_confusion_visual.py | 1825 +++++++++++++++++ 5 files changed, 2174 insertions(+), 9 deletions(-) create mode 100644 ultralytics/models/yolo/detect/val_confusion_confusion_visual_0621.py create mode 100644 ultralytics/utils/metrics_confusion_visual.py diff --git a/datasets_preprocess/split_train_val_.py b/datasets_preprocess/split_train_val_.py index 4d38e21..9704ac8 100644 --- a/datasets_preprocess/split_train_val_.py +++ b/datasets_preprocess/split_train_val_.py @@ -4,9 +4,10 @@ import random import argparse parser = argparse.ArgumentParser() -parser.add_argument('--img_path', default='/home/lc/data_center/gift/ori_image/images', type=str, +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() diff --git a/datasets_preprocess/voc_label_.py b/datasets_preprocess/voc_label_.py index 65a149d..b17cb35 100644 --- a/datasets_preprocess/voc_label_.py +++ b/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 @@ -76,9 +77,9 @@ def convert_annotation(image_id, imgname_list, label_path, Annotation_path, imag # abs_path = os.getcwd() -image_path = '/home/lc/data_center/gift/ori_image/images/' # img实际存放地址 -Annotation_path = '/home/lc/data_center/gift/ori_image/xmls/' # xml实际存放地址 -label_path = '/home/lc/data_center/gift/ori_image/labels/' # 保存路径 +image_path = '/home/lc/data_center/gift/v2/images/' # img实际存放地址 +Annotation_path = '/home/lc/data_center/gift/v2/xml/' # xml实际存放地址 +label_path = '/home/lc/data_center/gift/v2/labels/' # 保存路径 # wd = getcwd() imgname_list = [] diff --git a/ultralytics/cfg/models/v10/yolov10s.yaml b/ultralytics/cfg/models/v10/yolov10s.yaml index c61e08c..93a3745 100644 --- a/ultralytics/cfg/models/v10/yolov10s.yaml +++ b/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 diff --git a/ultralytics/models/yolo/detect/val_confusion_confusion_visual_0621.py b/ultralytics/models/yolo/detect/val_confusion_confusion_visual_0621.py new file mode 100644 index 0000000..f784cac --- /dev/null +++ b/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 diff --git a/ultralytics/utils/metrics_confusion_visual.py b/ultralytics/utils/metrics_confusion_visual.py new file mode 100644 index 0000000..2b1b5f6 --- /dev/null +++ b/ultralytics/utils/metrics_confusion_visual.py @@ -0,0 +1,1825 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +"""Model validation metrics.""" + +import math +import os +import warnings +from pathlib import Path + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import torch + +from ultralytics.utils import LOGGER, SimpleClass, TryExcept, plt_settings + +OKS_SIGMA = ( + np.array([0.26, 0.25, 0.25, 0.35, 0.35, 0.79, 0.79, 0.72, 0.72, 0.62, 0.62, 1.07, 1.07, 0.87, 0.87, 0.89, 0.89]) + / 10.0 +) + + +def bbox_ioa(box1, box2, iou=False, eps=1e-7): + """ + Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format. + + Args: + box1 (np.ndarray): A numpy array of shape (n, 4) representing n bounding boxes. + box2 (np.ndarray): A numpy array of shape (m, 4) representing m bounding boxes. + iou (bool): Calculate the standard IoU if True else return inter_area/box2_area. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (np.ndarray): A numpy array of shape (n, m) representing the intersection over box2 area. + """ + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1.T + b2_x1, b2_y1, b2_x2, b2_y2 = box2.T + + # Intersection area + inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * ( + np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1) + ).clip(0) + + # Box2 area + area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + if iou: + box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1) + area = area + box1_area[:, None] - inter_area + + # Intersection over box2 area + return inter_area / (area + eps) + + +def box_iou(box1, box2, eps=1e-7): + """ + Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + + Args: + box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes. + box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2. + """ + + # NOTE: need float32 to get accurate iou values + box1 = torch.as_tensor(box1, dtype=torch.float32) + box2 = torch.as_tensor(box2, dtype=torch.float32) + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + + +def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + """ + Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4). + + Args: + box1 (torch.Tensor): A tensor representing a single bounding box with shape (1, 4). + box2 (torch.Tensor): A tensor representing n bounding boxes with shape (n, 4). + xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in + (x1, y1, x2, y2) format. Defaults to True. + GIoU (bool, optional): If True, calculate Generalized IoU. Defaults to False. + DIoU (bool, optional): If True, calculate Distance IoU. Defaults to False. + CIoU (bool, optional): If True, calculate Complete IoU. Defaults to False. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags. + """ + + # Get the coordinates of bounding boxes + if xywh: # transform from xywh to xyxy + (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) + w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 + b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ + b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ + else: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) + b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + + # Intersection area + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * ( + b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) + ).clamp_(0) + + # Union Area + union = w1 * h1 + w2 * h2 - inter + eps + + # IoU + iou = inter / union + if CIoU or DIoU or GIoU: + cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width + ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw.pow(2) + ch.pow(2) + eps # convex diagonal squared + rho2 = ( + (b2_x1 + b2_x2 - b1_x1 - b1_x2).pow(2) + (b2_y1 + b2_y2 - b1_y1 - b1_y2).pow(2) + ) / 4 # center dist**2 + if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi**2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + return iou - rho2 / c2 # DIoU + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf + return iou # IoU + + +def mask_iou(mask1, mask2, eps=1e-7): + """ + Calculate masks IoU. + + Args: + mask1 (torch.Tensor): A tensor of shape (N, n) where N is the number of ground truth objects and n is the + product of image width and height. + mask2 (torch.Tensor): A tensor of shape (M, n) where M is the number of predicted objects and n is the + product of image width and height. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (torch.Tensor): A tensor of shape (N, M) representing masks IoU. + """ + intersection = torch.matmul(mask1, mask2.T).clamp_(0) + union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def kpt_iou(kpt1, kpt2, area, sigma, eps=1e-7): + """ + Calculate Object Keypoint Similarity (OKS). + + Args: + kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints. + kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints. + area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth. + sigma (list): A list containing 17 values representing keypoint scales. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (torch.Tensor): A tensor of shape (N, M) representing keypoint similarities. + """ + d = (kpt1[:, None, :, 0] - kpt2[..., 0]).pow(2) + (kpt1[:, None, :, 1] - kpt2[..., 1]).pow(2) # (N, M, 17) + sigma = torch.tensor(sigma, device=kpt1.device, dtype=kpt1.dtype) # (17, ) + kpt_mask = kpt1[..., 2] != 0 # (N, 17) + e = d / (2 * sigma).pow(2) / (area[:, None, None] + eps) / 2 # from cocoeval + # e = d / ((area[None, :, None] + eps) * sigma) ** 2 / 2 # from formula + return ((-e).exp() * kpt_mask[:, None]).sum(-1) / (kpt_mask.sum(-1)[:, None] + eps) + + +def _get_covariance_matrix(boxes): + """ + Generating covariance matrix from obbs. + + Args: + boxes (torch.Tensor): A tensor of shape (N, 5) representing rotated bounding boxes, with xywhr format. + + Returns: + (torch.Tensor): Covariance metrixs corresponding to original rotated bounding boxes. + """ + # Gaussian bounding boxes, ignore the center points (the first two columns) because they are not needed here. + gbbs = torch.cat((boxes[:, 2:4].pow(2) / 12, boxes[:, 4:]), dim=-1) + a, b, c = gbbs.split(1, dim=-1) + cos = c.cos() + sin = c.sin() + cos2 = cos.pow(2) + sin2 = sin.pow(2) + return a * cos2 + b * sin2, a * sin2 + b * cos2, (a - b) * cos * sin + + +def probiou(obb1, obb2, CIoU=False, eps=1e-7): + """ + Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf. + + Args: + obb1 (torch.Tensor): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format. + obb2 (torch.Tensor): A tensor of shape (N, 5) representing predicted obbs, with xywhr format. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (torch.Tensor): A tensor of shape (N, ) representing obb similarities. + """ + x1, y1 = obb1[..., :2].split(1, dim=-1) + x2, y2 = obb2[..., :2].split(1, dim=-1) + a1, b1, c1 = _get_covariance_matrix(obb1) + a2, b2, c2 = _get_covariance_matrix(obb2) + + t1 = ( + ((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps) + ) * 0.25 + t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)) * 0.5 + t3 = ( + ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2)) + / (4 * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + eps) + + eps + ).log() * 0.5 + bd = (t1 + t2 + t3).clamp(eps, 100.0) + hd = (1.0 - (-bd).exp() + eps).sqrt() + iou = 1 - hd + if CIoU: # only include the wh aspect ratio part + w1, h1 = obb1[..., 2:4].split(1, dim=-1) + w2, h2 = obb2[..., 2:4].split(1, dim=-1) + v = (4 / math.pi**2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - v * alpha # CIoU + return iou + + +def batch_probiou(obb1, obb2, eps=1e-7): + """ + Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf. + + Args: + obb1 (torch.Tensor | np.ndarray): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format. + obb2 (torch.Tensor | np.ndarray): A tensor of shape (M, 5) representing predicted obbs, with xywhr format. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. + + Returns: + (torch.Tensor): A tensor of shape (N, M) representing obb similarities. + """ + obb1 = torch.from_numpy(obb1) if isinstance(obb1, np.ndarray) else obb1 + obb2 = torch.from_numpy(obb2) if isinstance(obb2, np.ndarray) else obb2 + + x1, y1 = obb1[..., :2].split(1, dim=-1) + x2, y2 = (x.squeeze(-1)[None] for x in obb2[..., :2].split(1, dim=-1)) + a1, b1, c1 = _get_covariance_matrix(obb1) + a2, b2, c2 = (x.squeeze(-1)[None] for x in _get_covariance_matrix(obb2)) + + t1 = ( + ((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps) + ) * 0.25 + t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)) * 0.5 + t3 = ( + ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2)) + / (4 * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + eps) + + eps + ).log() * 0.5 + bd = (t1 + t2 + t3).clamp(eps, 100.0) + hd = (1.0 - (-bd).exp() + eps).sqrt() + return 1 - hd + + +def smooth_BCE(eps=0.1): + """ + Computes smoothed positive and negative Binary Cross-Entropy targets. + + This function calculates positive and negative label smoothing BCE targets based on a given epsilon value. + For implementation details, refer to https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441. + + Args: + eps (float, optional): The epsilon value for label smoothing. Defaults to 0.1. + + Returns: + (tuple): A tuple containing the positive and negative label smoothing BCE targets. + """ + return 1.0 - 0.5 * eps, 0.5 * eps + + +class ConfusionMatrix: + """ + A class for calculating and updating a confusion matrix for object detection and classification tasks. + + Attributes: + task (str): The type of task, either 'detect' or 'classify'. + matrix (np.ndarray): The confusion matrix, with dimensions depending on the task. + nc (int): The number of classes. + conf (float): The confidence threshold for detections. + iou_thres (float): The Intersection over Union threshold. + """ + + def __init__(self, nc, conf=0.25, iou_thres=0.45, task="detect"): + """Initialize attributes for the YOLO model.""" + self.task = task + self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == "detect" else np.zeros((nc, nc)) + self.nc = nc # number of classes + self.conf = 0.25 if conf in (None, 0.001) else conf # apply 0.25 if default val conf is passed + self.iou_thres = iou_thres + + def process_cls_preds(self, preds, targets): + """ + Update confusion matrix for classification task. + + Args: + preds (Array[N, min(nc,5)]): Predicted class labels. + targets (Array[N, 1]): Ground truth class labels. + """ + preds, targets = torch.cat(preds)[:, 0], torch.cat(targets) + for p, t in zip(preds.cpu().numpy(), targets.cpu().numpy()): + self.matrix[p][t] += 1 + ### ======== 修改可视化匹配框============== + def copy_imgs(self, img): + images = [] + for _ in range(self.nc): + images.append(img.copy()) + return images + def create_annotator(self, images, Annotator, names): + annotators = [] + for img in images: + annotator = Annotator(img, line_width=3, example=str(names)) + annotators.append(annotator) + return annotators + def makdirs_file(self, date_path, imgName, length, str_c=''): + paths = [] + for i in range(length): + path = os.path.join(date_path, str_c, f'{str_c}_{i+1}') + os.makedirs(path, exist_ok=True) + img_path = os.path.join(path, imgName) + paths.append(img_path) + return paths + + def plot_box_fixGtCls(self, detect_cur, gt_box, gt_cls, names, colors, cls_flag_list, annotator, fp_flag=False): + ###根据pred框类别分别存储文件夹 + if len(detect_cur) > 0: + detect_cur = detect_cur.squeeze() ## 部分detect_cur是二维向量 + detect_cur_cpu = detect_cur.cpu().tolist() + xyxy = detect_cur_cpu[:4] + conf = detect_cur_cpu[4] + cls = detect_cur_cpu[5] + c = int(cls) + label = f'{names[c]} {conf:.2f}' + if len(cls_flag_list) > 1: + cls_flag_list[c] = True + if fp_flag: + annotator.box_label(xyxy, label, color=(0, 0, 0)) ##fp 背景误检 黑色框 + else: + annotator.box_label(xyxy, label, color=colors(names[c], True)) + # annotator.box_label(xyxy, label, color=(255,0,0)) + ######=======取相应的gt box ============ + if len(gt_box) > 0: + # labs = labels_cur[0] + labs = gt_box.squeeze() + xyxy = [m.cpu().item() for m in labs] + gt_cls = int(gt_cls.cpu().item()) + label = names[gt_cls] + annotator.box_label(xyxy, label, color=(255, 0, 0)) + return cls_flag_list + def plot_box_fixPredCls(self, detect_cur, gt_box, gt_cls, iou, names, colors, cls_flag_list, annotator, fn_flag=False, fp_flag=False): + ##根据gt框类别分别存储文件 + ######=======取相应的gt box 根据gt类别分类============ + if len(gt_box) > 0: + # labs = labels_cur[0] + labs = gt_box.squeeze() + xyxy = [m.cpu().item() for m in labs] + gt_cls = int(gt_cls.cpu().item()) + label = names[gt_cls] + if fn_flag: + annotator.box_label(xyxy, label, color=(0, 0, 255)) ## fn 漏检 红色框 + else: + annotator.box_label(xyxy, label, color=(255, 0, 0)) ##tp 蓝色 + if len(cls_flag_list) > 1: + cls_flag_list[gt_cls] = True + # annotator.box_label(xyxy, label, color=colors(names[cls_gt], True)) + ######=======取相应的pred box ============ + if len(detect_cur) > 0: + detect_cur = detect_cur.squeeze() ## 部分detect_cur是二维向量 + detect_cur_cpu = detect_cur.cpu().tolist() + xyxy = detect_cur_cpu[:4] + conf = detect_cur_cpu[4] + cls = detect_cur_cpu[5] + c = int(cls) + label = f'{names[c]} {conf:.2f} iou:{float(iou):.2f}' + 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: + annotator.box_label(xyxy, label, color=colors(names[c], True)) + return cls_flag_list + def write_imgs(self, annotators, cls_flag_list, paths): + if len(cls_flag_list) > 1: + true_indices = [i for i, val in enumerate(cls_flag_list) if val] ##fix pred class 5 + for cls_index in true_indices: + annotator = annotators[cls_index] + img_pred5 = annotator.result() + save_path = paths[cls_index] + cv2.imwrite(save_path, img_pred5) + else: + img = annotators.result() + cv2.imwrite(paths, img) + ####=======可视化匹配增加================= + def process_batch(self, detections, gt_bboxes, gt_cls, img_path, names, Annotator, colors): + """ + Update confusion matrix for object detection task. + + Args: + detections (Array[N, 6] | Array[N, 7]): Detected bounding boxes and their associated information. + Each row should contain (x1, y1, x2, y2, conf, class) + or with an additional element `angle` when it's obb. + gt_bboxes (Array[M, 4]| Array[N, 5]): Ground truth bounding boxes with xyxy/xyxyr format. + gt_cls (Array[M]): The class labels. + """ + if gt_cls.shape[0] == 0: # Check if labels is empty + if detections is not None: + detections = detections[detections[:, 4] > self.conf] + detection_classes = detections[:, 5].int() + for dc in detection_classes: + self.matrix[dc, self.nc] += 1 # false positives + + return + if detections is None: + gt_classes = gt_cls.int() + for gc in gt_classes: + self.matrix[self.nc, gc] += 1 # background FN + return + + detections = detections[detections[:, 4] > self.conf] + gt_classes = gt_cls.int() + detection_classes = detections[:, 5].int() + is_obb = detections.shape[1] == 7 and gt_bboxes.shape[1] == 5 # with additional `angle` dimension + iou = ( + batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1)) + if is_obb + else box_iou(gt_bboxes, detections[:, :4]) + ) + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + ###============================================ + ### 按类别保存图片，根据类别数量新建多少张图片和annotators + im = cv2.imread(img_path) + img_name = img_path.split('/')[-1] + images_fp_bg = self.copy_imgs(im) + images_fp = self.copy_imgs(im) + images_fn = self.copy_imgs(im) + images_tp = self.copy_imgs(im) + images_all = self.copy_imgs(im) + annotators_fp_bg = self.create_annotator(images_fp_bg, Annotator, names) + annotators_fp = self.create_annotator(images_fp, Annotator, names) + annotators_fn = self.create_annotator(images_fn, Annotator, names) + annotators_tp = self.create_annotator(images_tp, Annotator, names) + annotators_all = self.create_annotator(images_all, Annotator, names) + ### 新建不同检测类别保存的文件夹，文件夹名称为类别索引 + date_path = 'confusion_0717_cls10_' + str(self.iou_thres) + paths_fn = self.makdirs_file(date_path, img_name, self.nc, str_c='FN') + paths_fp = self.makdirs_file(date_path, img_name, self.nc, str_c='FP') + paths_fp_bg = self.makdirs_file(date_path, img_name, self.nc, str_c='FP_bg') + paths_tp = self.makdirs_file(date_path, img_name, self.nc, str_c='TP') + paths_all = self.makdirs_file(date_path, img_name, length=1, str_c='allBox') + ###=========================================== + + n = matches.shape[0] > 0 + # m0, m1, _ = matches.transpose().astype(int) + ###===================== + ### 设置每个类别、fp,fn的flag + m0, m1, _ = matches.transpose().astype(int)##m0为matches中gt box的索引，m1为pred box的索引 + _,_ , iou_match = matches.transpose().astype(float) + cls_flag_list_fn = [False for _ in range(self.nc)] ## fn 按照gt的类别分配存储文件夹 + cls_flag_list_fp = [False for _ in range(self.nc)] ## 两种情况的fp：1、gt与pred框匹配上但类别不同的fp，按照gt的类别分配存储文件夹 + cls_flag_list_fp_bg = [False for _ in range(self.nc)] ## 2、gt为背景时的fp，按照pred类别分配存储文件夹 + cls_flag_list_tp = [False for _ in range(self.nc)] ###混淆矩阵斜对角线上类别flag + + ## 混淆矩阵可视化 flag 设置 + save_oneImg = False ### 将所有pred_box与gt_box匹配结果画在一张图片上 + save_byClass = True ### 将所有pred_box与gt_box匹配结果按box类别分类保存，其中tp、fn、fp按gt_box类别划分，fp_bg按pred_box划分 + cls_flag_list = [] + ###========================= + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: # （真实框和预测框匹配数不为0） 且 （当前遍历真实框与matches记录的m0一致） + self.matrix[detection_classes[m1[j]], gc] += 1 # correct + ###========================= + detect_cur = detections[m1[j]] + detect_cls_cur = detection_classes[m1[j]].cpu().item() + gt_box_cur = gt_bboxes[m0[j]] + gt_cls_cur = gt_cls[m0[j]] + true_ind = [i for i, n in enumerate(j) if n] ## 找出j中ture的索引 + iou_box = iou_match[true_ind] + ####============================================= + + ### TP + if (gc == detect_cls_cur): + if save_byClass: + annotator_tp = annotators_tp[int(gc)] + cls_flag_list_tp = self.plot_box_fixPredCls(detect_cur, gt_box_cur, gt_cls_cur, iou_box, names, colors, cls_flag_list_tp, + annotator_tp) + if save_oneImg: + self.plot_box_fixPredCls(detect_cur, gt_box_cur, gt_cls_cur, iou_box, names, colors, cls_flag_list, + annotators_all[0]) + ### iou匹配上但类别不相同的框 FP + else: + for gc in range(self.nc): + if save_byClass: + anno_fp = annotators_fp[int(gc)] + cls_flag_list_fp = self.plot_box_fixPredCls(detect_cur, gt_box_cur, gt_cls_cur, iou_box, names, colors, + cls_flag_list_fp, anno_fp) ##根据gt的类别分配存储文件夹 + if save_oneImg: + self.plot_box_fixPredCls(detect_cur, gt_box_cur, gt_cls_cur, iou_box, names, colors, cls_flag_list, + annotators_all[0], fp_flag=True) ### 紫色 + ###=================================== + + else: # 预测为背景，真实为gt_cls + self.matrix[self.nc, gc] += 1 # true background + # ####============取gt box FN====================== + # if (gc == fix_class): + # fn_flag = True + gt_box_cur = gt_bboxes[i] + gt_cls_cur = gt_cls[i] + detect_cur = [] + iou_box = 0 + ###=======将gt_box画在一张图上========== + if save_oneImg: + self.plot_box_fixPredCls(detect_cur, gt_box_cur, gt_cls_cur, iou_box, names, colors, cls_flag_list, + annotators_all[0], fn_flag=True) + if save_byClass: + anno_fn = annotators_fn[int(gc)] + cls_flag_list_fn = self.plot_box_fixPredCls(detect_cur, gt_box_cur, gt_cls_cur, iou_box, names, colors, cls_flag_list_fn, + anno_fn, fn_flag=True) ##根据gt的类别分配存储文件夹 + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # predicted background + # ####============取pred box FP====================== + # if (dc == fix_class): + gt_box = [] + gt_cls = None + detect_cur = detections[i] + if save_oneImg: + self.plot_box_fixGtCls(detect_cur, gt_box, gt_cls, names, colors, cls_flag_list, + annotators_all[0], fp_flag=True) ## 黑色框 + if save_byClass: + anno_fp_bg = annotators_fp_bg[int(dc)] + cls_flag_list_fp_bg = self.plot_box_fixGtCls(detect_cur, gt_box, gt_cls, names, colors, cls_flag_list_fp_bg, + anno_fp_bg) ##根据pred的类别分配存储文件夹 + if save_byClass: + self.write_imgs(annotators_fn, cls_flag_list_fn, paths_fn) + self.write_imgs(annotators_fp, cls_flag_list_fp, paths_fp) + self.write_imgs(annotators_fp_bg, cls_flag_list_fp_bg, paths_fp_bg) + self.write_imgs(annotators_tp, cls_flag_list_tp, paths_tp) + if save_oneImg: + self.write_imgs(annotators_all[0], cls_flag_list, paths_all[0]) + + def matrix(self): + """Returns the confusion matrix.""" + return self.matrix + + def tp_fp(self): + """Returns true positives and false positives.""" + tp = self.matrix.diagonal() # true positives + fp = self.matrix.sum(1) - tp # false positives + # fn = self.matrix.sum(0) - tp # false negatives (missed detections) + return (tp[:-1], fp[:-1]) if self.task == "detect" else (tp, fp) # remove background class if task=detect + + @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") + @plt_settings() + def plot(self, normalize=True, save_dir="", names=(), on_plot=None): + """ + Plot the confusion matrix using seaborn and save it to a file. + + Args: + normalize (bool): Whether to normalize the confusion matrix. + save_dir (str): Directory where the plot will be saved. + names (tuple): Names of classes, used as labels on the plot. + on_plot (func): An optional callback to pass plots path and data when they are rendered. + """ + import seaborn as sn + + array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) + nc, nn = self.nc, len(names) # number of classes, names + sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size + labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels + ticklabels = (list(names) + ["background"]) if labels else "auto" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered + sn.heatmap( + array, + ax=ax, + annot=nc < 30, + annot_kws={"size": 8}, + cmap="Blues", + fmt=".2f" if normalize else ".0f", + square=True, + vmin=0.0, + xticklabels=ticklabels, + yticklabels=ticklabels, + ).set_facecolor((1, 1, 1)) + title = "Confusion Matrix" + " Normalized" * normalize + ax.set_xlabel("True") + ax.set_ylabel("Predicted") + ax.set_title(title) + plot_fname = Path(save_dir) / f'{title.lower().replace(" ", "_")}.png' + fig.savefig(plot_fname, dpi=250) + plt.close(fig) + if on_plot: + on_plot(plot_fname) + + def print(self): + """Print the confusion matrix to the console.""" + for i in range(self.nc + 1): + LOGGER.info(" ".join(map(str, self.matrix[i]))) + + +def smooth(y, f=0.05): + """Box filter of fraction f.""" + nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) + p = np.ones(nf // 2) # ones padding + yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded + return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed + + +@plt_settings() +def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=(), on_plot=None): + """Plots a precision-recall curve.""" + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean()) + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title("Precision-Recall Curve") + fig.savefig(save_dir, dpi=250) + plt.close(fig) + if on_plot: + on_plot(save_dir) + + +@plt_settings() +def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric", on_plot=None): + """Plots a metric-confidence curve.""" + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) + + y = smooth(py.mean(0), 0.05) + ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}") + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title(f"{ylabel}-Confidence Curve") + fig.savefig(save_dir, dpi=250) + plt.close(fig) + if on_plot: + on_plot(save_dir) + + +def compute_ap(recall, precision): + """ + Compute the average precision (AP) given the recall and precision curves. + + Args: + recall (list): The recall curve. + precision (list): The precision curve. + + Returns: + (float): Average precision. + (np.ndarray): Precision envelope curve. + (np.ndarray): Modified recall curve with sentinel values added at the beginning and end. + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.0], recall, [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = "interp" # methods: 'continuous', 'interp' + if method == "interp": + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x-axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +# def ap_per_class( +# tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names=(), eps=1e-16, prefix="" +# ): +# """ +# Computes the average precision per class for object detection evaluation. +# +# Args: +# tp (np.ndarray): Binary array indicating whether the detection is correct (True) or not (False). +# conf (np.ndarray): Array of confidence scores of the detections. +# pred_cls (np.ndarray): Array of predicted classes of the detections. +# target_cls (np.ndarray): Array of true classes of the detections. +# plot (bool, optional): Whether to plot PR curves or not. Defaults to False. +# on_plot (func, optional): A callback to pass plots path and data when they are rendered. Defaults to None. +# save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path. +# names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple. +# eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16. +# prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string. +# +# Returns: +# (tuple): A tuple of six arrays and one array of unique classes, where: +# tp (np.ndarray): True positive counts at threshold given by max F1 metric for each class.Shape: (nc,). +# fp (np.ndarray): False positive counts at threshold given by max F1 metric for each class. Shape: (nc,). +# p (np.ndarray): Precision values at threshold given by max F1 metric for each class. Shape: (nc,). +# r (np.ndarray): Recall values at threshold given by max F1 metric for each class. Shape: (nc,). +# f1 (np.ndarray): F1-score values at threshold given by max F1 metric for each class. Shape: (nc,). +# ap (np.ndarray): Average precision for each class at different IoU thresholds. Shape: (nc, 10). +# unique_classes (np.ndarray): An array of unique classes that have data. Shape: (nc,). +# p_curve (np.ndarray): Precision curves for each class. Shape: (nc, 1000). +# r_curve (np.ndarray): Recall curves for each class. Shape: (nc, 1000). +# f1_curve (np.ndarray): F1-score curves for each class. Shape: (nc, 1000). +# x (np.ndarray): X-axis values for the curves. Shape: (1000,). +# prec_values: Precision values at mAP@0.5 for each class. Shape: (nc, 1000). +# """ +# +# # Sort by objectness +# i = np.argsort(-conf) +# tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] +# +# # Find unique classes +# unique_classes, nt = np.unique(target_cls, return_counts=True) +# nc = unique_classes.shape[0] # number of classes, number of detections +# +# # Create Precision-Recall curve and compute AP for each class +# x, prec_values = np.linspace(0, 1, 1000), [] +# +# # Average precision, precision and recall curves +# ap, p_curve, r_curve = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) +# for ci, c in enumerate(unique_classes): +# i = pred_cls == c +# n_l = nt[ci] # number of labels +# n_p = i.sum() # number of predictions +# if n_p == 0 or n_l == 0: +# continue +# +# # Accumulate FPs and TPs +# fpc = (1 - tp[i]).cumsum(0) +# tpc = tp[i].cumsum(0) +# +# # Recall +# recall = tpc / (n_l + eps) # recall curve +# r_curve[ci] = np.interp(-x, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases +# +# # Precision +# precision = tpc / (tpc + fpc) # precision curve +# p_curve[ci] = np.interp(-x, -conf[i], precision[:, 0], left=1) # p at pr_score +# +# # AP from recall-precision curve +# for j in range(tp.shape[1]): +# ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) +# if plot and j == 0: +# prec_values.append(np.interp(x, mrec, mpre)) # precision at mAP@0.5 +# +# prec_values = np.array(prec_values) # (nc, 1000) +# +# # Compute F1 (harmonic mean of precision and recall) +# f1_curve = 2 * p_curve * r_curve / (p_curve + r_curve + eps) +# names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data +# names = dict(enumerate(names)) # to dict +# if plot: +# plot_pr_curve(x, prec_values, ap, save_dir / f"{prefix}PR_curve.png", names, on_plot=on_plot) +# plot_mc_curve(x, f1_curve, save_dir / f"{prefix}F1_curve.png", names, ylabel="F1", on_plot=on_plot) +# plot_mc_curve(x, p_curve, save_dir / f"{prefix}P_curve.png", names, ylabel="Precision", on_plot=on_plot) +# plot_mc_curve(x, r_curve, save_dir / f"{prefix}R_curve.png", names, ylabel="Recall", on_plot=on_plot) +# +# i = smooth(f1_curve.mean(0), 0.1).argmax() # max F1 index +# p, r, f1 = p_curve[:, i], r_curve[:, i], f1_curve[:, i] # max-F1 precision, recall, F1 values +# tp = (r * nt).round() # true positives +# fp = (tp / (p + eps) - tp).round() # false positives +# return tp, fp, p, r, f1, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values +#####===============split mAP change====================================== +def ap_per_class( + tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names=(), eps=1e-16, prefix="" +): + """ + Computes the average precision per class for object detection evaluation. + + Args: + tp (np.ndarray): Binary array indicating whether the detection is correct (True) or not (False). + conf (np.ndarray): Array of confidence scores of the detections. + pred_cls (np.ndarray): Array of predicted classes of the detections. + target_cls (np.ndarray): Array of true classes of the detections. + plot (bool, optional): Whether to plot PR curves or not. Defaults to False. + on_plot (func, optional): A callback to pass plots path and data when they are rendered. Defaults to None. + save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path. + names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple. + eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16. + prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string. + + Returns: + (tuple): A tuple of six arrays and one array of unique classes, where: + tp (np.ndarray): True positive counts at threshold given by max F1 metric for each class.Shape: (nc,). + fp (np.ndarray): False positive counts at threshold given by max F1 metric for each class. Shape: (nc,). + p (np.ndarray): Precision values at threshold given by max F1 metric for each class. Shape: (nc,). + r (np.ndarray): Recall values at threshold given by max F1 metric for each class. Shape: (nc,). + f1 (np.ndarray): F1-score values at threshold given by max F1 metric for each class. Shape: (nc,). + ap (np.ndarray): Average precision for each class at different IoU thresholds. Shape: (nc, 10). + unique_classes (np.ndarray): An array of unique classes that have data. Shape: (nc,). + p_curve (np.ndarray): Precision curves for each class. Shape: (nc, 1000). + r_curve (np.ndarray): Recall curves for each class. Shape: (nc, 1000). + f1_curve (np.ndarray): F1-score curves for each class. Shape: (nc, 1000). + x (np.ndarray): X-axis values for the curves. Shape: (1000,). + prec_values: Precision values at mAP@0.5 for each class. Shape: (nc, 1000). + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes, nt = np.unique(target_cls, return_counts=True) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + x, prec_values = np.linspace(0, 1, 1000), [] + + # Average precision, precision and recall curves + ap, p_curve, r_curve = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = nt[ci] # number of labels + n_p = i.sum() # number of predictions + if n_p == 0 or n_l == 0: + continue + + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + eps) # recall curve + r_curve[ci] = np.interp(-x, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p_curve[ci] = np.interp(-x, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): ### tp.shape->[box_num, cls_num] j为遍历类别 + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and j == 0: + prec_values.append(np.interp(x, mrec, mpre)) # precision at mAP@0.5 + + prec_values = np.array(prec_values) # (nc, 1000) + + # Compute F1 (harmonic mean of precision and recall) + f1_curve = 2 * p_curve * r_curve / (p_curve + r_curve + eps) + # print('f1_curve', f1_curve.shape, f1_curve) + names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data + names = dict(enumerate(names)) # to dict + if plot: + plot_pr_curve(x, prec_values, ap, save_dir / f"{prefix}PR_curve.png", names, on_plot=on_plot) + plot_mc_curve(x, f1_curve, save_dir / f"{prefix}F1_curve.png", names, ylabel="F1", on_plot=on_plot) + plot_mc_curve(x, p_curve, save_dir / f"{prefix}P_curve.png", names, ylabel="Precision", on_plot=on_plot) + plot_mc_curve(x, r_curve, save_dir / f"{prefix}R_curve.png", names, ylabel="Recall", on_plot=on_plot) + #### ===============固定P取T和R的值,画出PRT表格=============== + p_dig = [f'{x / 100:.3f}' for x in range(70, 100)] ## 设置P的取值范围 + # p_dig = [f'{x/100:.3f}' for x in range(70,90)] + + # for ind in range(p.shape[0]): ### 类别 + for ind in range(p_curve.shape[0]): + t_save = [] + r_save = [] + p_save = [] + line_p = 'P ' + line_r = 'R ' + line_t = 'T ' + for p_num in p_dig: ### 长度类别数量 + for jnd in range(p_curve.shape[1]): ### 长度为1000 + p_que = f'{p_curve[ind][jnd]:.3f}' ## P + ##设置P的值和实际P列表中的值可能不存在相同的,故设置一个差值范围,保证设置的每个P值都能在实际P列表中找到相应的值 + if (p_que == p_num) or (abs(float(p_que) - float(p_num)) < 0.05): + line_r += f'{r_curve[ind][jnd]:.3f}' + ' ' ## R + line_t += f'{x[jnd]:.3f}' + ' ' ##Conf + line_p += p_num + ' ' + break + date = 'prt/' + os.makedirs(date, exist_ok=True) + # txt_name = date + str(nms_iou) + '.txt' + txt_name = date + '.txt' + with open(txt_name, 'a') as f: + f.write(str(ind) + '\n') + f.write(line_p + '\n') + f.write(line_t + '\n') + f.write(line_r + '\n') + ###===============固定P取T和R的值,画出PRT表格=============== + ####============原始输出f1_mean最大值对应的置信度下每个类别p,r,f1 + # print('noSmooth_i', f1_curve.mean(0).argmax()) + i = smooth(f1_curve.mean(0), 0.1).argmax() # max F1 index + # print('smooth_i', i) + p, r, f1 = p_curve[:, i], r_curve[:, i], f1_curve[:, i] # max-F1 precision, recall, F1 values + # print(f"p：{p}\nr:{r}\nf1:{f1}") + # tp = (r * nt).round() # true positives + # fp = (tp / (p + eps) - tp).round() # false positives + # return tp, fp, p, r, f1, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values + ####================================================================ + ###==========统计每个类别所对应的F1最大值=============== + i_list = np.argmax(f1_curve, axis=1) ###每类别最大值的索引输出 + conf_cls = [float(i / 1000) for i in i_list] + # print('conf_cls', conf_cls) + f1_c = [] + f1_all = [f'{x:.3f}' for x in f1_curve[:, i]] ### f1_mean最大值对应的置信度下每个类别的F1值 + for cls_num, index in enumerate(i_list): + f1_c.append((f'{f1_curve[cls_num, index]:.3f}')) + ###===========统计每个类别所对应的F1最大值=============== + ###============取每个类别的最大F1所对应conf下的p,r,f1============= + p_, r_, f1_ = p_curve[:, i], r_curve[:, i], f1_curve[:, i] ### f1_mean最大值对应的置信度下每个类别p,r,f1 + # print(f"p1：{p_}\nr1:{r_}\nf1_1:{f1_}") + #####取每个类别的最大F1所对应conf下的p,r,f1 + for cls_id, ind in enumerate(i_list): + p_[cls_id] = p_curve[cls_id, ind] + r_[cls_id] = r_curve[cls_id, ind] + f1_[cls_id] = f1_curve[cls_id, ind] + # print(f"p_：{p_}\nr_:{r_}\nf1_:{f1_}") + tp = (r_ * nt).round() # true positives + fp = (tp / (p_ + eps) - tp).round() # false positives + # fn = (nt - tp).round()rhttp://oneclick.lenovo.com.cn + + + return tp, fp, p_, r_, f1_, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values + ####======================================================= + + +class Metric(SimpleClass): + """ + Class for computing evaluation metrics for YOLOv8 model. + + Attributes: + p (list): Precision for each class. Shape: (nc,). + r (list): Recall for each class. Shape: (nc,). + f1 (list): F1 score for each class. Shape: (nc,). + all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10). + ap_class_index (list): Index of class for each AP score. Shape: (nc,). + nc (int): Number of classes. + + Methods: + ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or []. + ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or []. + mp(): Mean precision of all classes. Returns: Float. + mr(): Mean recall of all classes. Returns: Float. + map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float. + map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float. + map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float. + mean_results(): Mean of results, returns mp, mr, map50, map. + class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i]. + maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,). + fitness(): Model fitness as a weighted combination of metrics. Returns: Float. + update(results): Update metric attributes with new evaluation results. + """ + + def __init__(self) -> None: + """Initializes a Metric instance for computing evaluation metrics for the YOLOv8 model.""" + ####======split mAP add======= + self.tp = [] + self.fp = [] + ###======================= + self.p = [] # (nc, ) + self.r = [] # (nc, ) + self.f1 = [] # (nc, ) + self.all_ap = [] # (nc, 10) + self.ap_class_index = [] # (nc, ) + self.nc = 0 + + @property + def ap50(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 0] if len(self.all_ap) else [] + ###===split mAP add====== + @property + def ap55(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 1] if len(self.all_ap) else [] + @property + def ap60(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 2] if len(self.all_ap) else [] + @property + def ap65(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 3] if len(self.all_ap) else [] + @property + def ap70(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 4] if len(self.all_ap) else [] + @property + def ap75(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 5] if len(self.all_ap) else [] + @property + def ap80(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 6] if len(self.all_ap) else [] + @property + def ap85(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 7] if len(self.all_ap) else [] + @property + def ap90(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 8] if len(self.all_ap) else [] + ### ====================== + @property + def ap95(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. + """ + return self.all_ap[:, 9] if len(self.all_ap) else [] + @property + def ap(self): + """ + Returns the Average Precision (AP) at an IoU threshold of 0.5-0.95 for all classes. + + Returns: + (np.ndarray, list): Array of shape (nc,) with AP50-95 values per class, or an empty list if not available. + """ + return self.all_ap.mean(1) if len(self.all_ap) else [] + + @property + def mp(self): + """ + Returns the Mean Precision of all classes. + + Returns: + (float): The mean precision of all classes. + """ + return self.p.mean() if len(self.p) else 0.0 + + @property + def mr(self): + """ + Returns the Mean Recall of all classes. + + Returns: + (float): The mean recall of all classes. + """ + return self.r.mean() if len(self.r) else 0.0 + + @property + def map50(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.5. + + Returns: + (float): The mAP at an IoU threshold of 0.5. + """ + return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 + ##====split mAP add====== + @property + def map55(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.55. + + Returns: + (float): The mAP at an IoU threshold of 0.55. + """ + return self.all_ap[:, 1].mean() if len(self.all_ap) else 0.0 + @property + def map60(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.6. + + Returns: + (float): The mAP at an IoU threshold of 0.6. + """ + return self.all_ap[:, 2].mean() if len(self.all_ap) else 0.0 + @property + def map65(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.65. + + Returns: + (float): The mAP at an IoU threshold of 0.65. + """ + return self.all_ap[:, 3].mean() if len(self.all_ap) else 0.0 + @property + def map70(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.7. + + Returns: + (float): The mAP at an IoU threshold of 0.7. + """ + return self.all_ap[:, 4].mean() if len(self.all_ap) else 0.0 + @property + def map75(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.75. + + Returns: + (float): The mAP at an IoU threshold of 0.75. + """ + return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0 + @property + def map80(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.8. + + Returns: + (float): The mAP at an IoU threshold of 0.8. + """ + return self.all_ap[:, 6].mean() if len(self.all_ap) else 0.0 + @property + def map85(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.85. + + Returns: + (float): The mAP at an IoU threshold of 0.85. + """ + return self.all_ap[:, 7].mean() if len(self.all_ap) else 0.0 + @property + def map90(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.9. + + Returns: + (float): The mAP at an IoU threshold of 0.9. + """ + return self.all_ap[:, 8].mean() if len(self.all_ap) else 0.0 + @property + def map95(self): + """ + Returns the mean Average Precision (mAP) at an IoU threshold of 0.95. + + Returns: + (float): The mAP at an IoU threshold of 0.95. + """ + return self.all_ap[:, 9].mean() if len(self.all_ap) else 0.0 + ##==================== + + @property + def map(self): + """ + Returns the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05. + + Returns: + (float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05. + """ + return self.all_ap.mean() if len(self.all_ap) else 0.0 + + def mean_results(self): + """Mean of results, return mp, mr, map50, map.""" + # return [self.mp, self.mr, self.map50, self.map] + ##====split mAP add====== + return [self.mp, self.mr, self.map50, self.map55, self.map60, self.map65, self.map70, self.map75, + self.map80, self.map85, self.map90, self.map95, self.map] + + def class_result(self, i): + """Class-aware result, return p[i], r[i], ap50[i], ap[i].""" + # return self.p[i], self.r[i], self.ap50[i], self.ap[i] + ##====split mAP add====== + return ( + self.p[i], self.r[i], self.ap50[i], self.ap55[i], self.ap60[i], self.ap65[i], self.ap70[i], self.ap75[i], + self.ap80[i], self.ap85[i], self.ap90[i], self.ap95[i], self.ap[i]) + + @property + def maps(self): + """MAP of each class.""" + maps = np.zeros(self.nc) + self.map + for i, c in enumerate(self.ap_class_index): + maps[c] = self.ap[i] + return maps + + def fitness(self): + """Model fitness as a weighted combination of metrics.""" + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + # return (np.array(self.mean_results()) * w).sum() + ####====split mAP add============ + result_list = self.mean_results()[:3] + result_list.append(self.mean_results()[-1]) + return (np.array(result_list) * w).sum() + ##========================= + def update(self, results): + """ + Updates the evaluation metrics of the model with a new set of results. + + Args: + results (tuple): A tuple containing the following evaluation metrics: + - p (list): Precision for each class. Shape: (nc,). + - r (list): Recall for each class. Shape: (nc,). + - f1 (list): F1 score for each class. Shape: (nc,). + - all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10). + - ap_class_index (list): Index of class for each AP score. Shape: (nc,). + + Side Effects: + Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based + on the values provided in the `results` tuple. + """ + ( + + self.tp, + self.fp, + self.p, + self.r, + self.f1, + self.all_ap, + self.ap_class_index, + self.p_curve, + self.r_curve, + self.f1_curve, + self.px, + self.prec_values, + ) = results + + @property + def curves(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [] + + @property + def curves_results(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [ + [self.px, self.prec_values, "Recall", "Precision"], + [self.px, self.f1_curve, "Confidence", "F1"], + [self.px, self.p_curve, "Confidence", "Precision"], + [self.px, self.r_curve, "Confidence", "Recall"], + ] + + +class DetMetrics(SimpleClass): + """ + This class is a utility class for computing detection metrics such as precision, recall, and mean average precision + (mAP) of an object detection model. + + Args: + save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory. + plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False. + on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. + names (tuple of str): A tuple of strings that represents the names of the classes. Defaults to an empty tuple. + + Attributes: + save_dir (Path): A path to the directory where the output plots will be saved. + plot (bool): A flag that indicates whether to plot the precision-recall curves for each class. + on_plot (func): An optional callback to pass plots path and data when they are rendered. + names (tuple of str): A tuple of strings that represents the names of the classes. + box (Metric): An instance of the Metric class for storing the results of the detection metrics. + speed (dict): A dictionary for storing the execution time of different parts of the detection process. + + Methods: + process(tp, conf, pred_cls, target_cls): Updates the metric results with the latest batch of predictions. + keys: Returns a list of keys for accessing the computed detection metrics. + mean_results: Returns a list of mean values for the computed detection metrics. + class_result(i): Returns a list of values for the computed detection metrics for a specific class. + maps: Returns a dictionary of mean average precision (mAP) values for different IoU thresholds. + fitness: Computes the fitness score based on the computed detection metrics. + ap_class_index: Returns a list of class indices sorted by their average precision (AP) values. + results_dict: Returns a dictionary that maps detection metric keys to their computed values. + curves: TODO + curves_results: TODO + """ + + def __init__(self, save_dir=Path("../models/utils"), plot=False, on_plot=None, names=()) -> None: + """Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names.""" + self.save_dir = save_dir + self.plot = plot + self.on_plot = on_plot + self.names = names + self.box = Metric() + self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0} + self.task = "detect" + + def process(self, tp, conf, pred_cls, target_cls): + """Process predicted results for object detection and update metrics.""" + # results = ap_per_class( + # tp, + # conf, + # pred_cls, + # target_cls, + # plot=self.plot, + # save_dir=self.save_dir, + # names=self.names, + # on_plot=self.on_plot, + # )[2:] + # self.box.nc = len(self.names) + # self.box.update(results) + ###===========split mAP add===================== + results = ap_per_class( + tp, + conf, + pred_cls, + target_cls, + plot=self.plot, + save_dir=self.save_dir, + names=self.names, + on_plot=self.on_plot, + ) + self.box.nc = len(self.names) + self.box.update(results) + return results + ##============================= + + @property + def keys(self): + ####==========split mAP============== + """Returns a list of keys for accessing specific metrics.""" + # return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"] + return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP55(B)", "metrics/mAP60(B)", + "metrics/mAP65(B)", "metrics/mAP70(B)", "metrics/mAP75(B)", "metrics/mAP80(B)", "metrics/mAP85(B)", + "metrics/mAP90(B)", "metrics/mAP95(B)", "metrics/mAP50-95(B)"] + + def mean_results(self): + """Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95.""" + return self.box.mean_results() + + def class_result(self, i): + """Return the result of evaluating the performance of an object detection model on a specific class.""" + return self.box.class_result(i) + + @property + def maps(self): + """Returns mean Average Precision (mAP) scores per class.""" + return self.box.maps + + @property + def fitness(self): + """Returns the fitness of box object.""" + return self.box.fitness() + + @property + def ap_class_index(self): + """Returns the average precision index per class.""" + return self.box.ap_class_index + + @property + def results_dict(self): + """Returns dictionary of computed performance metrics and statistics.""" + return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) + + @property + def curves(self): + """Returns a list of curves for accessing specific metrics curves.""" + return ["Precision-Recall(B)", "F1-Confidence(B)", "Precision-Confidence(B)", "Recall-Confidence(B)"] + + @property + def curves_results(self): + """Returns dictionary of computed performance metrics and statistics.""" + return self.box.curves_results + + +class SegmentMetrics(SimpleClass): + """ + Calculates and aggregates detection and segmentation metrics over a given set of classes. + + Args: + save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory. + plot (bool): Whether to save the detection and segmentation plots. Default is False. + on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. + names (list): List of class names. Default is an empty list. + + Attributes: + save_dir (Path): Path to the directory where the output plots should be saved. + plot (bool): Whether to save the detection and segmentation plots. + on_plot (func): An optional callback to pass plots path and data when they are rendered. + names (list): List of class names. + box (Metric): An instance of the Metric class to calculate box detection metrics. + seg (Metric): An instance of the Metric class to calculate mask segmentation metrics. + speed (dict): Dictionary to store the time taken in different phases of inference. + + Methods: + process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions. + mean_results(): Returns the mean of the detection and segmentation metrics over all the classes. + class_result(i): Returns the detection and segmentation metrics of class `i`. + maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. + fitness: Returns the fitness scores, which are a single weighted combination of metrics. + ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP). + results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score. + """ + + def __init__(self, save_dir=Path("../models/utils"), plot=False, on_plot=None, names=()) -> None: + """Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names.""" + self.save_dir = save_dir + self.plot = plot + self.on_plot = on_plot + self.names = names + self.box = Metric() + self.seg = Metric() + self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0} + self.task = "segment" + + def process(self, tp, tp_m, conf, pred_cls, target_cls): + """ + Processes the detection and segmentation metrics over the given set of predictions. + + Args: + tp (list): List of True Positive boxes. + tp_m (list): List of True Positive masks. + conf (list): List of confidence scores. + pred_cls (list): List of predicted classes. + target_cls (list): List of target classes. + """ + + results_mask = ap_per_class( + tp_m, + conf, + pred_cls, + target_cls, + plot=self.plot, + on_plot=self.on_plot, + save_dir=self.save_dir, + names=self.names, + prefix="Mask", + )[2:] + self.seg.nc = len(self.names) + self.seg.update(results_mask) + results_box = ap_per_class( + tp, + conf, + pred_cls, + target_cls, + plot=self.plot, + on_plot=self.on_plot, + save_dir=self.save_dir, + names=self.names, + prefix="Box", + )[2:] + self.box.nc = len(self.names) + self.box.update(results_box) + + @property + def keys(self): + """Returns a list of keys for accessing metrics.""" + return [ + "metrics/precision(B)", + "metrics/recall(B)", + "metrics/mAP50(B)", + "metrics/mAP50-95(B)", + "metrics/precision(M)", + "metrics/recall(M)", + "metrics/mAP50(M)", + "metrics/mAP50-95(M)", + ] + + def mean_results(self): + """Return the mean metrics for bounding box and segmentation results.""" + return self.box.mean_results() + self.seg.mean_results() + + def class_result(self, i): + """Returns classification results for a specified class index.""" + return self.box.class_result(i) + self.seg.class_result(i) + + @property + def maps(self): + """Returns mAP scores for object detection and semantic segmentation models.""" + return self.box.maps + self.seg.maps + + @property + def fitness(self): + """Get the fitness score for both segmentation and bounding box models.""" + return self.seg.fitness() + self.box.fitness() + + @property + def ap_class_index(self): + """Boxes and masks have the same ap_class_index.""" + return self.box.ap_class_index + + @property + def results_dict(self): + """Returns results of object detection model for evaluation.""" + return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) + + @property + def curves(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [ + "Precision-Recall(B)", + "F1-Confidence(B)", + "Precision-Confidence(B)", + "Recall-Confidence(B)", + "Precision-Recall(M)", + "F1-Confidence(M)", + "Precision-Confidence(M)", + "Recall-Confidence(M)", + ] + + @property + def curves_results(self): + """Returns dictionary of computed performance metrics and statistics.""" + return self.box.curves_results + self.seg.curves_results + + +class PoseMetrics(SegmentMetrics): + """ + Calculates and aggregates detection and pose metrics over a given set of classes. + + Args: + save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory. + plot (bool): Whether to save the detection and segmentation plots. Default is False. + on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. + names (list): List of class names. Default is an empty list. + + Attributes: + save_dir (Path): Path to the directory where the output plots should be saved. + plot (bool): Whether to save the detection and segmentation plots. + on_plot (func): An optional callback to pass plots path and data when they are rendered. + names (list): List of class names. + box (Metric): An instance of the Metric class to calculate box detection metrics. + pose (Metric): An instance of the Metric class to calculate mask segmentation metrics. + speed (dict): Dictionary to store the time taken in different phases of inference. + + Methods: + process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions. + mean_results(): Returns the mean of the detection and segmentation metrics over all the classes. + class_result(i): Returns the detection and segmentation metrics of class `i`. + maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. + fitness: Returns the fitness scores, which are a single weighted combination of metrics. + ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP). + results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score. + """ + + def __init__(self, save_dir=Path("../models/utils"), plot=False, on_plot=None, names=()) -> None: + """Initialize the PoseMetrics class with directory path, class names, and plotting options.""" + super().__init__(save_dir, plot, names) + self.save_dir = save_dir + self.plot = plot + self.on_plot = on_plot + self.names = names + self.box = Metric() + self.pose = Metric() + self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0} + self.task = "pose" + + def process(self, tp, tp_p, conf, pred_cls, target_cls): + """ + Processes the detection and pose metrics over the given set of predictions. + + Args: + tp (list): List of True Positive boxes. + tp_p (list): List of True Positive keypoints. + conf (list): List of confidence scores. + pred_cls (list): List of predicted classes. + target_cls (list): List of target classes. + """ + + results_pose = ap_per_class( + tp_p, + conf, + pred_cls, + target_cls, + plot=self.plot, + on_plot=self.on_plot, + save_dir=self.save_dir, + names=self.names, + prefix="Pose", + )[2:] + self.pose.nc = len(self.names) + self.pose.update(results_pose) + results_box = ap_per_class( + tp, + conf, + pred_cls, + target_cls, + plot=self.plot, + on_plot=self.on_plot, + save_dir=self.save_dir, + names=self.names, + prefix="Box", + )[2:] + self.box.nc = len(self.names) + self.box.update(results_box) + + @property + def keys(self): + """Returns list of evaluation metric keys.""" + return [ + "metrics/precision(B)", + "metrics/recall(B)", + "metrics/mAP50(B)", + "metrics/mAP50-95(B)", + "metrics/precision(P)", + "metrics/recall(P)", + "metrics/mAP50(P)", + "metrics/mAP50-95(P)", + ] + + def mean_results(self): + """Return the mean results of box and pose.""" + return self.box.mean_results() + self.pose.mean_results() + + def class_result(self, i): + """Return the class-wise detection results for a specific class i.""" + return self.box.class_result(i) + self.pose.class_result(i) + + @property + def maps(self): + """Returns the mean average precision (mAP) per class for both box and pose detections.""" + return self.box.maps + self.pose.maps + + @property + def fitness(self): + """Computes classification metrics and speed using the `targets` and `pred` inputs.""" + return self.pose.fitness() + self.box.fitness() + + @property + def curves(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [ + "Precision-Recall(B)", + "F1-Confidence(B)", + "Precision-Confidence(B)", + "Recall-Confidence(B)", + "Precision-Recall(P)", + "F1-Confidence(P)", + "Precision-Confidence(P)", + "Recall-Confidence(P)", + ] + + @property + def curves_results(self): + """Returns dictionary of computed performance metrics and statistics.""" + return self.box.curves_results + self.pose.curves_results + + +class ClassifyMetrics(SimpleClass): + """ + Class for computing classification metrics including top-1 and top-5 accuracy. + + Attributes: + top1 (float): The top-1 accuracy. + top5 (float): The top-5 accuracy. + speed (Dict[str, float]): A dictionary containing the time taken for each step in the pipeline. + + Properties: + fitness (float): The fitness of the model, which is equal to top-5 accuracy. + results_dict (Dict[str, Union[float, str]]): A dictionary containing the classification metrics and fitness. + keys (List[str]): A list of keys for the results_dict. + + Methods: + process(targets, pred): Processes the targets and predictions to compute classification metrics. + """ + + def __init__(self) -> None: + """Initialize a ClassifyMetrics instance.""" + self.top1 = 0 + self.top5 = 0 + self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0} + self.task = "classify" + + def process(self, targets, pred): + """Target classes and predicted classes.""" + pred, targets = torch.cat(pred), torch.cat(targets) + correct = (targets[:, None] == pred).float() + acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy + self.top1, self.top5 = acc.mean(0).tolist() + + @property + def fitness(self): + """Returns mean of top-1 and top-5 accuracies as fitness score.""" + return (self.top1 + self.top5) / 2 + + @property + def results_dict(self): + """Returns a dictionary with model's performance metrics and fitness score.""" + return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness])) + + @property + def keys(self): + """Returns a list of keys for the results_dict property.""" + return ["metrics/accuracy_top1", "metrics/accuracy_top5"] + + @property + def curves(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [] + + @property + def curves_results(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [] + + +class OBBMetrics(SimpleClass): + def __init__(self, save_dir=Path("../models/utils"), plot=False, on_plot=None, names=()) -> None: + self.save_dir = save_dir + self.plot = plot + self.on_plot = on_plot + self.names = names + self.box = Metric() + self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0} + + def process(self, tp, conf, pred_cls, target_cls): + """Process predicted results for object detection and update metrics.""" + results = ap_per_class( + tp, + conf, + pred_cls, + target_cls, + plot=self.plot, + save_dir=self.save_dir, + names=self.names, + on_plot=self.on_plot, + )[2:] + self.box.nc = len(self.names) + self.box.update(results) + + @property + def keys(self): + """Returns a list of keys for accessing specific metrics.""" + return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"] + + def mean_results(self): + """Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95.""" + return self.box.mean_results() + + def class_result(self, i): + """Return the result of evaluating the performance of an object detection model on a specific class.""" + return self.box.class_result(i) + + @property + def maps(self): + """Returns mean Average Precision (mAP) scores per class.""" + return self.box.maps + + @property + def fitness(self): + """Returns the fitness of box object.""" + return self.box.fitness() + + @property + def ap_class_index(self): + """Returns the average precision index per class.""" + return self.box.ap_class_index + + @property + def results_dict(self): + """Returns dictionary of computed performance metrics and statistics.""" + return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) + + @property + def curves(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [] + + @property + def curves_results(self): + """Returns a list of curves for accessing specific metrics curves.""" + return [] From 201d0bccc03070191d1adff635bfb8ca07f0f336 Mon Sep 17 00:00:00 2001 From: lee <770918727@qq.com> Date: Sat, 21 Jun 2025 13:37:50 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E8=B5=A0=E5=93=81=E6=B5=8B=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- val.py | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 val.py diff --git a/val.py b/val.py new file mode 100644 index 0000000..0eea73c --- /dev/null +++ b/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')