用法:
cucim.skimage.feature.corner_shi_tomasi(image, sigma=1)
計算Shi-Tomasi (Kanade-Tomasi) 角測量響應圖像。
該角點檢測器使用來自自相關矩陣 A 的信息:
A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy]
其中 imx 和 imy 是一階導數,使用高斯濾波器進行平均。然後將角度量定義為 A 的較小特征值:
((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2
- image:ndarray
輸入圖像。
- sigma:浮點數,可選
用於高斯核的標準偏差,用作自相關矩陣的加權函數。
- response:ndarray
Shi-Tomasi 響應圖像。
參數:
返回:
參考:
例子:
>>> from cucim.skimage.feature import corner_shi_tomasi, corner_peaks >>> square = cp.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corner_peaks(corner_shi_tomasi(square), min_distance=1) array([[2, 2], [2, 7], [7, 2], [7, 7]])
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注:本文由純淨天空篩選整理自rapids.ai大神的英文原創作品 cucim.skimage.feature.corner_shi_tomasi。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。