Car*_*son 3 python opencv numpy scikit-learn
我正在尝试进行一些图像比较,首先从查找杰卡德索引开始。我正在使用 Jaccard Index 的 sklearn.metrics 实现,使用下面的示例以及一小部分数字,它的工作原理与预期一致。
import numpy as np
from sklearn.metrics import jaccard_similarity_score
#The y_pred represents the values that the program has found
y_pred = [0,0,1,0,0,0,1,1,1,1,0,1,0,1,0,0,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,1,0,1,1,1]
#The y_true represents the values that are actually correct
y_true = [1,0,0,1,0,1,1,0,1,1,1,0,1,0,1,1,0,1,1,0,0,1,0,1,0,1,0,1,0,1,1,1,1,1,0,1,1,0,0,0,0,1,1,1,0,1,0,1,1,1]
iou = jaccard_similarity_score(y_true, y_pred)
Run Code Online (Sandbox Code Playgroud)
虽然它给出了一个错误......
ValueError: unknown is not supported
Run Code Online (Sandbox Code Playgroud)
当我给它喂两个图像时,例如......
iou = jaccard_similarity_score(img_true, img_pred)
Run Code Online (Sandbox Code Playgroud)
我不确定该怎么做,我尝试使用 OpenCV 将图像转换为灰度并将这两个图像设置为类型(浮动),但在任何一种情况下都没有运气。
| 归档时间: |
|
| 查看次数: |
7856 次 |
| 最近记录: |