# 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}' print(">>>>>>>>>>>>>>>>>>> label: {} C: {}".format(label, c)) if fp_flag: annotator.box_label(xyxy, label, color=(125, 0, 125)) ##fp iou匹配上，类别错误 紫色框 else: 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_gift_cls4_' + 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 = True #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 []