在计算机视觉任务中，图像分割是一项重要的任务，而对分割结果进行评估则是验证模型性能的关键一环。本文将介绍如何使用Python和OpenCV编写一个简单的批量图像分割评估脚本，以评估分割模型的性能。

1. 问题背景

假设我们有一组GT（Ground Truth）图像和相应的KMeans算法生成的分割图像。我们想要批量评估这些分割图像与GT图像之间的相似度，以便量化模型的性能。先装包

pip install numpy opencv-python tqdm hausdorff

2. 脚本概览

首先，定义了一组评估指标，包括Dice系数、IoU（Intersection over Union）、灵敏度、PPV（Positive Predictive Value）以及Hausdorff距离的95th percentile。

def dice_coef(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict * label).sum()return (2. * intersection + epsilon) / (predict.sum() + label.sum() + epsilon)def iou_score(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict & label).sum()union = (predict | label).sum()return (intersection + epsilon) / (union + epsilon)def sensitivity(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict * label).sum()return (intersection + epsilon) / (label.sum() + epsilon)def ppv(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict * label).sum()return (intersection + epsilon) / (predict.sum() + epsilon)def hd95(predict: np.ndarray, label: np.ndarray, distance="euclidean"):predict, label = transform_image_data(predict, label)predict = predict.flatten()[..., None]label = label.flatten()[..., None]distance = hausdorff.hausdorff_distance(predict, label, distance=distance)return distance * 0.95

然后，我们编写了一个函数，该函数接受包含GT和分割图像的文件夹路径，并返回每个图像的评估指标。

def batch_evaluation(data_folder, extension='.png'):gt_files = glob.glob(data_folder + '/gt*' + extension)mask_files = glob.glob(data_folder + '/mask*' + extension)dice_scores = []iou_scores = []sensitivity_scores = []ppv_scores = []hd95_distances = []for gt_file, mask_file in tqdm(zip(gt_files, mask_files), total=len(gt_files)):gt_image = cv2.imread(gt_file, 0)mask_image = cv2.imread(mask_file, 0)dice_scores.append(dice_coef(mask_image, gt_image))iou_scores.append(iou_score(mask_image, gt_image))sensitivity_scores.append(sensitivity(mask_image, gt_image))ppv_scores.append(ppv(mask_image, gt_image))hd95_distances.append(hd95(mask_image, gt_image))return {'dice_scores': dice_scores,'iou_scores': iou_scores,'sensitivity_scores': sensitivity_scores,'ppv_scores': ppv_scores,'hd95_distances': hd95_distances}

最后，我们使用这个函数对指定文件夹中的所有图像进行评估，并输出结果。

# 执行批量评估
evaluation_results = batch_evaluation(data_folder)# 输出结果
print("Dice Scores:", evaluation_results['dice_scores'])
print("IOU Scores:", evaluation_results['iou_scores'])
print("Sensitivity Scores:", evaluation_results['sensitivity_scores'])
print("PPV Scores:", evaluation_results['ppv_scores'])
print("HD95 Distances:", evaluation_results['hd95_distances'])

完整脚本如下

import glob
import cv2
import numpy as np
from tqdm import tqdmdef transform_image_data(predict: np.ndarray, label: np.ndarray):predict = predict.astype(np.bool_).astype(np.int_)label = label.astype(np.bool_).astype(np.int_)return predict, labeldef dice_coef(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict * label).sum()return (2. * intersection + epsilon) / (predict.sum() + label.sum() + epsilon)def iou_score(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict & label).sum()union = (predict | label).sum()return (intersection + epsilon) / (union + epsilon)def sensitivity(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict * label).sum()return (intersection + epsilon) / (label.sum() + epsilon)def ppv(predict: np.ndarray, label: np.ndarray, epsilon: float = 1e-5) -> float:predict, label = transform_image_data(predict, label)intersection = (predict * label).sum()return (intersection + epsilon) / (predict.sum() + epsilon)def hd95(predict: np.ndarray, label: np.ndarray, distance="euclidean"):predict, label = transform_image_data(predict, label)predict = predict.flatten()[..., None]label = label.flatten()[..., None]distance = hausdorff.hausdorff_distance(predict, label, distance=distance)return distance * 0.95def batch_evaluation(data_folder, extension='.png'):gt_files = glob.glob(data_folder + '/gt*' + extension)mask_files = glob.glob(data_folder + '/mask*' + extension)dice_scores = []iou_scores = []sensitivity_scores = []ppv_scores = []hd95_distances = []for gt_file, mask_file in tqdm(zip(gt_files, mask_files), total=len(gt_files)):gt_image = cv2.imread(gt_file, 0)mask_image = cv2.imread(mask_file, 0)dice_scores.append(dice_coef(mask_image, gt_image))iou_scores.append(iou_score(mask_image, gt_image))sensitivity_scores.append(sensitivity(mask_image, gt_image))ppv_scores.append(ppv(mask_image, gt_image))hd95_distances.append(hd95(mask_image, gt_image))return {'dice_scores': dice_scores,'iou_scores': iou_scores,'sensitivity_scores': sensitivity_scores,'ppv_scores': ppv_scores,'hd95_distances': hd95_distances}# 指定包含图像文件的文件夹路径
data_folder = '/path/to/your/data/folder'# 执行批量评估
evaluation_results = batch_evaluation(data_folder)# 输出结果
print("Dice Scores:", evaluation_results['dice_scores'])
print("IOU Scores:", evaluation_results['iou_scores'])
print("Sensitivity Scores:", evaluation_results['sensitivity_scores'])
print("PPV Scores:", evaluation_results['ppv_scores'])
print("HD95 Distances:", evaluation_results['hd95_distances'])