# Harris角點(diǎn)檢測(cè)

import cv2
import numpy as np

img = cv2.imread('images/polygon.jpg')
img = cv2.imread('images/opencv_logo.jpg')
print(img.shape)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("origin", img)
cv2.waitKey(0)

gray = np.float32(gray)

# res = cv2.cornerHarris(gray, 2, 3, 0.04)
# - img： 輸入圖像，灰度圖像，float32
# - blockSize: 用于角點(diǎn)檢測(cè)的鄰域的大小
# - ksize: Sobel導(dǎo)數(shù)的孔徑參數(shù)
# - k: 方程中的k-Harris檢測(cè)器自由參數(shù)
# - res：返回值，灰度圖像
res = cv2.cornerHarris(gray, 2, 3, 0.04)

# 擴(kuò)大標(biāo)記的內(nèi)容
res = cv2.dilate(res, None)

# 最佳閾值因圖而異
img[res > 0.01 * res.max()] = [0, 0, 255]

cv2.imshow('Harris res', img)
if cv2.waitKey(0) & 0xff == 27:
    cv2.destroyAllWindows()

# 具有亞像素精度的角點(diǎn)
# 有時(shí)可能需要以最大的精度找到角點(diǎn)。OpenCV附帶了一個(gè)函數(shù)cv2.cornerSubPix（），它可以進(jìn)一步細(xì)化以亞像素精度檢測(cè)到的角點(diǎn)。
import cv2
import imutils
import numpy as np

filename = 'images/polygon.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 尋找Harris角點(diǎn)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)
dst = cv2.dilate(dst, None)
ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0)
dst = np.uint8(dst)

# 尋找中心點(diǎn)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)

# 定義停止和細(xì)化角點(diǎn)的條件
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv2.cornerSubPix(gray, np.float32(centroids), (5, 5), (-1, -1), criteria)

# 繪制角點(diǎn)和細(xì)化的亞像素點(diǎn)
res = np.hstack((centroids, corners))
res = np.int0(res)
# Harris角點(diǎn)用紅色像素標(biāo)記，精細(xì)角點(diǎn)用綠色像素標(biāo)記
img[res[:, 1], res[:, 0]] = [0, 0, 255]
img[res[:, 3], res[:, 2]] = [0, 255, 0]

gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
cv2.imshow("gray", img)
gray[res[:, 1], res[:, 0]] = [0, 0, 255]
gray[res[:, 3], res[:, 2]] = [0, 255, 0]

cv2.imshow('cornerSubPix res', imutils.resize(img, width=600))
cv2.waitKey(0)