图像分割是计算机视觉中的重要任务,用于将图像中的不同区域分割成具有语义意义的区域。以下是几种常用的图像分割评价指标以及它们的代码实现示例(使用Python和常见的计算机视觉库):
与目标检测中的IoU类似,用于衡量预测分割区域与真实分割区域之间的重叠程度。
def calculate_iou(mask_true, mask_pred):
intersection = np.logical_and(mask_true, mask_pred)
union = np.logical_or(mask_true, mask_pred)
iou = np.sum(intersection) / np.sum(union)
return iou
#e.g.
import cv2
import numpy as np
mask_true=cv2.imread("round_meter_421.png",0)
mask_pred=cv2.imread("round_meter_423.png",0)
mask_true=cv2.resize(mask_true,(512,512),interpolation = cv2.INTER_LINEAR)
mask_pred=cv2.resize(mask_pred,(512,512),interpolation = cv2.INTER_LINEAR)
def calculate_iou(mask_true, mask_pred):
intersection = np.logical_and(mask_true, mask_pred)
union = np.logical_or(mask_true, mask_pred)
iou = np.sum(intersection) / np.sum(union)
return iou
print(calculate_iou(mask_true,mask_pred))
#结果0.6660
用于衡量预测分割区域与真实分割区域的重叠程度。
def calculate_dice_coefficient(mask_true, mask_pred):
intersection = np.logical_and(mask_true, mask_pred)
dice_coeff = (2.0 * np.sum(intersection)) / (np.sum(mask_true) + np.sum(mask_pred))
return dice_coeff
#e.g.
import cv2
import numpy as np
mask_true=cv2.imread("round_meter_421.png",0)
mask_pred=cv2.imread("round_meter_423.png",0)
mask_true=cv2.resize(mask_true,(512,512),interpolation = cv2.INTER_LINEAR)
mask_true = np.where(mask_true != 0, 1, mask_true)
mask_pred=cv2.resize(mask_pred,(512,512),interpolation = cv2.INTER_LINEAR)
mask_pred = np.where(mask_pred != 0, 1, mask_pred)
def calculate_dice_coefficient(mask_true, mask_pred):
intersection = np.logical_and(mask_true, mask_pred)
dice_coeff = (2.0 * np.sum(intersection)) / (np.sum(mask_true) + np.sum(mask_pred))
return dice_coeff
print(calculate_dice_coefficient(mask_true,mask_pred))
#结果是 0.7995
计算正确预测的像素数量占总像素数量的比例。
def calculate_pixel_accuracy(mask_true, mask_pred):
correct_pixels = np.sum(mask_true == mask_pred)
total_pixels = mask_true.size
pixel_accuracy = correct_pixels / total_pixels
return pixel_accuracy
#e.g.
import cv2
import numpy as np
mask_true=cv2.imread("round_meter_421.png",0)
mask_pred=cv2.imread("round_meter_423.png",0)
mask_true=cv2.resize(mask_true,(512,512),interpolation = cv2.INTER_LINEAR)
mask_true = np.where(mask_true != 0, 1, mask_true)
mask_pred=cv2.resize(mask_pred,(512,512),interpolation = cv2.INTER_LINEAR)
mask_pred = np.where(mask_pred != 0, 1, mask_pred)
def calculate_pixel_accuracy(mask_true, mask_pred):
correct_pixels = np.sum(mask_true == mask_pred)
total_pixels = mask_true.size
pixel_accuracy = correct_pixels / total_pixels
return pixel_accuracy
print(calculate_pixel_accuracy(mask_true,mask_pred))
#结果是 0.9914
计算在不同类别上的平均IoU值。
def calculate_miou(class_iou_list):
return np.mean(class_iou_list)
用于衡量分割区域的边界的预测质量。
def calculate_boundary_f1(mask_true, mask_pred):
# Calculate true positive, false positive, and false negative boundary pixels
true_positive = np.sum(np.logical_and(mask_true, mask_pred))
false_positive = np.sum(np.logical_and(np.logical_not(mask_true), mask_pred))
false_negative = np.sum(np.logical_and(mask_true, np.logical_not(mask_pred)))
precision = true_positive / (true_positive + false_positive)
recall = true_positive / (true_positive + false_negative)
f1_score = 2 * (precision * recall) / (precision + recall)
return f1_score
#e.g.
import cv2
import numpy as np
mask_true=cv2.imread("round_meter_421.png",0)
mask_pred=cv2.imread("round_meter_423.png",0)
mask_true=cv2.resize(mask_true,(512,512),interpolation = cv2.INTER_LINEAR)
mask_true = np.where(mask_true != 0, 1, mask_true)
mask_pred=cv2.resize(mask_pred,(512,512),interpolation = cv2.INTER_LINEAR)
mask_pred = np.where(mask_pred != 0, 1, mask_pred)
def calculate_boundary_f1(mask_true, mask_pred):
# Calculate true positive, false positive, and false negative boundary pixels
true_positive = np.sum(np.logical_and(mask_true, mask_pred))
false_positive = np.sum(np.logical_and(np.logical_not(mask_true), mask_pred))
false_negative = np.sum(np.logical_and(mask_true, np.logical_not(mask_pred)))
precision = true_positive / (true_positive + false_positive)
recall = true_positive / (true_positive + false_negative)
f1_score = 2 * (precision * recall) / (precision + recall)
return f1_score
print(calculate_boundary_f1(mask_true,mask_pred))
#结果是 0.7995
这些代码示例提供了基本的评价指标计算方法,实际应用中可能会涉及更多的细节和优化。使用深度学习框架(如TensorFlow、PyTorch)和计算机视觉库(如OpenCV、Scikit-image)可以更方便地计算这些评价指标,因为它们提供了丰富的内置函数和工具来处理图像分割任务。