init - 初始化项目

doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown

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+Shi-Tomasi Corner Detector & Good Features to Track {#tutorial_py_shi_tomasi}
+===================================================
+
+Goal
+----
+
+In this chapter,
+
+-   We will learn about the another corner detector: Shi-Tomasi Corner Detector
+-   We will see the function: **cv.goodFeaturesToTrack()**
+
+Theory
+------
+
+In last chapter, we saw Harris Corner Detector. Later in 1994, J. Shi and C. Tomasi made a small
+modification to it in their paper **Good Features to Track** which shows better results compared to
+Harris Corner Detector. The scoring function in Harris Corner Detector was given by:
+
+\f[R = \lambda_1 \lambda_2 - k(\lambda_1+\lambda_2)^2\f]
+
+Instead of this, Shi-Tomasi proposed:
+
+\f[R = min(\lambda_1, \lambda_2)\f]
+
+If it is a greater than a threshold value, it is considered as a corner. If we plot it in
+\f$\lambda_1 - \lambda_2\f$ space as we did in Harris Corner Detector, we get an image as below:
+
+![image](images/shitomasi_space.png)
+
+From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
+\f$\lambda_{min}\f$, it is considered as a corner(green region).
+
+Code
+----
+
+OpenCV has a function, **cv.goodFeaturesToTrack()**. It finds N strongest corners in the image by
+Shi-Tomasi method (or Harris Corner Detection, if you specify it). As usual, image should be a
+grayscale image. Then you specify number of corners you want to find. Then you specify the quality
+level, which is a value between 0-1, which denotes the minimum quality of corner below which
+everyone is rejected. Then we provide the minimum euclidean distance between corners detected.
+
+With all this information, the function finds corners in the image. All corners below quality
+level are rejected. Then it sorts the remaining corners based on quality in the descending order.
+Then function takes first strongest corner, throws away all the nearby corners in the range of
+minimum distance and returns N strongest corners.
+
+In below example, we will try to find 25 best corners:
+@code{.py}
+import numpy as np
+import cv2 as cv
+from matplotlib import pyplot as plt
+
+img = cv.imread('blox.jpg')
+gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
+
+corners = cv.goodFeaturesToTrack(gray,25,0.01,10)
+corners = np.int0(corners)
+
+for i in corners:
+    x,y = i.ravel()
+    cv.circle(img,(x,y),3,255,-1)
+
+plt.imshow(img),plt.show()
+@endcode
+See the result below:
+
+![image](images/shitomasi_block1.jpg)
+
+This function is more appropriate for tracking. We will see that when its time comes.
+
+Additional Resources
+--------------------
+
+Exercises
+---------