Commit 1a22758

committed

update ch29-30

1 parent 6107d70 commit 1a22758Copy full SHA for 1a22758

File tree

+54

-26

lines changed

+54

-26

lines changed

Lines changed: 9 additions & 0 deletions

Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,9 @@`
	`1`	`+`
	`2`	`+找到图像特征的技术被称为特征检测。`
	`3`	`+`
	`4`	`+描述特征`
	`5`	`+`
	`6`	`+计算机也要对特征周围的区域进行描述,这样它才能在其他图像中找到相同的特征。`
	`7`	`+我们把这种描述称为特征描述`
	`8`	`+`
	`9`	`+角点的一个特性:向任何方向移动变化都很大`

Lines changed: 34 additions & 0 deletions

Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,34 @@`
	`1`	`+# --coding:utf8--#`
	`2`	`+__author__ = 'play4fun'`
	`3`	`+"""`
	`4`	`+create time:15-10-28 下午7:27`
	`5`	`+`
	`6`	`+cv2.cornerHarris() 参数如下`
	`7`	`+• img - 数据类型为 float32 的入图像。`
	`8`	`+• blockSize - 点检测中考的域大小。`
	`9`	`+• ksize - Sobel 求导中使用的窗口大小`
	`10`	`+• k - Harris 点检测方程中的自由参数取值参数为 [0,04 0.06].`
	`11`	`+"""`
	`12`	`+`
	`13`	`+import cv2`
	`14`	`+import numpy as np`
	`15`	`+`
	`16`	`+filename = '../data/chessboard.jpeg'`
	`17`	`+# filename = '../data/chessboard-3.png'`
	`18`	`+# filename = '../data/corner-detection.jpg'`
	`19`	`+`
	`20`	`+img = cv2.imread(filename)`
	`21`	`+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)`
	`22`	`+gray = np.float32(gray)`
	`23`	`+`
	`24`	`+# 输入图像必是 float32 最后一个参数在 0.04 到 0.05 之间`
	`25`	`+dst = cv2.cornerHarris(gray, 2, 3, 0.04)`
	`26`	`+# result is dilated for marking the corners, not important`
	`27`	`+dst = cv2.dilate(dst, None)`
	`28`	`+# Threshold for an optimal value, it may vary depending on the image.`
	`29`	`+img[dst > 0.01 * dst.max()] = [0, 0, 255]`
	`30`	`+`
	`31`	`+cv2.imshow('dst', img)`
	`32`	`+`
	`33`	`+cv2.waitKey(0)`
	`34`	`+cv2.destroyAllWindows()`

Lines changed: 11 additions & 1 deletion

Original file line number	Diff line number	Diff line change
`@@ -2,6 +2,16 @@`
`2`	`2`	`__author__ = 'play4fun'`
`3`	`3`	`"""`
`4`	`4`	`create time:15-10-29 上午7:47`
	`5`	`+`
	`6`	`+最大精度的角点检测`
	`7`	`+`
	`8`	`+首先我们找到 Harris 角点`
	`9`	`+ 然后将角点的重心传给这个函数进行修正。`
	`10`	`+ Harris 角点用红色像素标出`
	`11`	`+ 绿色像素是修正后的像素。`
	`12`	`+ 在使用个函数是我们定义一个爹代停止条件。`
	`13`	`+ 当代次数到或者精度条件满后代就会停止。`
	`14`	`+ 我们同样需要定义进行角点搜索的邻域大小。`
`5`	`15`	`"""`
`6`	`16`
`7`	`17`	`import cv2`
`@@ -33,7 +43,7 @@`
`33`	`43`	`corners = cv2.cornerSubPix(gray, np.float32(centroids), (5, 5), (-1, -1), criteria)`
`34`	`44`	`# Now draw them`
`35`	`45`	`res = np.hstack((centroids, corners))`
`36`		`-# np.int0 可以用来省略小数点后的数字,非四舍五入`
	`46`	`+# np.int0 可以用来省略小数点后的数字,非四舍五入`
`37`	`47`	`res = np.int0(res)`
`38`	`48`	`img[res[:, 1], res[:, 0]] = [0, 0, 255]`
`39`	`49`	`img[res[:, 3], res[:, 2]] = [0, 255, 0]`

Lines changed: 0 additions & 25 deletions

This file was deleted.

80.5 KB

Comments

(0)