带有哈里斯角检测的2张图像的Python OpenCV搜索对应关系

Question

我的老师给了我们以下练习：

目前我所做的唯一过程是使用 cv2.cornerHarris() 获取两个图像的哈里斯角并将图片彼此相邻放置。

现在我不知道如何获取角本身和它们周围的区域来生成可用于模板匹配的模板。

我希望如果我掌握了这个技巧，我可能能够解决剩下的练习。 也许你们中的一些人可以帮助我？关于它是如何工作的一个简短的解释会非常友好，这样我就可以学到更多:)

这是我当前的代码：

import cv2
import numpy as np

churchLeft = cv2.imread("./Church/church_left.png")
churchRight = cv2.imread("./Church/church_right.png")


def doHarris(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = np.float32(gray)

    dst = cv2.cornerHarris(gray, 2, 3, 0.01)


    # result is dilated for marking the corners, not important
    dst = cv2.dilate(dst, None)

    # Threshold for an optimal value, it may vary depending on the image.
    img[dst > 0.01 * dst.max()] = [0, 0, 255]


    return img


churchLeftHarris = doHarris(churchLeft)
churchRightHarris = doHarris(churchRight)

hor = np.hstack((churchLeftHarris, churchRightHarris))

cv2.imshow('test', hor)
while (1):
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break

Answer 1

你可以试试我的代码如下：

import numpy as np
import cv2
from matplotlib import pyplot as plt

"""
Difference between goodFeaturesToTrack and Harrisdetector:
The main difference with the Harris algorithm is that you should
specify the minimum distance between each point, the quality level
and the number of corners to detect.

"""
#You can use this Method to detect the Harriscorners instead of goodFeaturesToTrack :

#dst1 = cv2.cornerHarris(gray1, 5, 7, 0.04)
#ret1, dst1 = cv2.threshold(dst1, 0.1 * dst1.max(), 255, 0)
#dst1 = np.uint8(dst1)
#ret1, labels1, stats1, centroids1 = cv2.connectedComponentsWithStats(dst1)
#criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
#corners1 = cv2.cornerSubPix(gray1, np.float32(centroids1), (5, 5), (-1, -1), 
#criteria)
#corners1 = np.int0(corners1)


def correlation_coefficient(window1, window2):
    product = np.mean((window1 - window1.mean()) * (window2 - window2.mean()))
    stds = window1.std() * window2.std()
    if stds == 0:
        return 0
    else:
        product /= stds
        return product


window_size_width = 7
window_size_height = 7
lineThickness = 2

img1 = cv2.imread('church_left.png')
img2 = cv2.imread('church_right.png')
width, height, ch = img1.shape[::]
img2_copy = img2.copy()
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

corners1 = cv2.goodFeaturesToTrack(gray1, 30, 0.01, 5)
corners1 = np.int0(corners1)

corners2 = cv2.goodFeaturesToTrack(gray2, 30, 0.01, 5)
corners2 = np.int0(corners2)

corners_windows1 = []

for i in corners1:
    x, y = i.ravel()
    cv2.circle(img1, (x, y), 3, 255, -1)

corners_windows2 = []
for i in corners2:
    x, y = i.ravel()
    cv2.circle(img2, (x, y), 3, 255, -1)

plt.imshow(img1), plt.show()

methods = ['SSD', 'NCC']
for method in methods:
    matches = []
    for id1, i in enumerate(corners1):
        x1, y1 = i.ravel()
        if y1 - window_size_height < 0 or y1 + window_size_height > height or x1 - window_size_width < 0 or x1 + window_size_width > width:
            continue
        pt1 = (x1, y1)
        print("pt1: ", pt1)
        template = img1[y1 - window_size_height:y1 + window_size_height, x1 - window_size_width:x1 + window_size_width]
        max_val = 0
        Threshold = 1000000
        id_max = 0
        for id2, i in enumerate(corners2):
            x2, y2 = i.ravel()

            if y2 - window_size_height < 0 or y2 + window_size_height > height or x2 - window_size_width < 0 or x2 + window_size_width > width:
                continue
            window2 = img2[y2 - window_size_height:y2 + window_size_height,
                      x2 - window_size_width:x2 + window_size_width]
            if method == 'SSD':
                temp_min_val = np.sum((template - window2) ** 2)
            elif method == 'NCC':
                temp_min_val = correlation_coefficient(template, window2)
            if temp_min_val < Threshold:
                Threshold = temp_min_val
                pt2 = (x2 + 663, y2)
        matches.append((pt1, pt2))
    stacked_img = np.hstack((img1, img2))
    #show the first 15 matches
    for match in matches[:15]:
        cv2.line(stacked_img, match[0], match[1], (0, 255, 0), lineThickness)
    matches = []
    plt.imshow(stacked_img), plt.show()