图像处理 - 我需要找到任何quadrilater的4个角

Question

我需要在我的图像中应用cvWarpPerspective进行转换

• 这个:ROI原创
• 进入:图像变形

但要做到这一点,我需要通过ROI的角落作为参数,我不能手动,因为我有306个图像,它将花费很多时间.

我已经尝试过Harris Corner探测器,Susan和几个imagej插件,但我尝试过的每个算法不仅可以检测角落,还可以检测更多点,而我只需要角落.(如果我将更多点作为参数传递,最终图像会变形)

有什么建议？

Answer 1

使用Harris角点探测器的想法很好,但为了只获得角落,你需要做一些预处理.

1. 阈值图像并仅获取大于0的值.opencv阈值函数.

2. 找到两个连接的组件.opencv findContours

3. 找到每个组件的凸包.opencv convexhull
4. 填充凸壳.opencv fillPoly

5. 运行Harris角点探测器.opencv特征探测器哈里斯

6. 做最大抑制.对于每个角落,看看他的邻居,如果他是新角落插入一个在新的角落图像,否则放零.

7. 拿八个最突出的角落.4 x corrdinate左边一个是左qaudrilater而右边4个是右四边形

不是Java代码,而是python实现希望它足够有用:

import cv2
import numpy as np

# Params
neigbrhoodSize = 5

# Read img
img = cv2.imread('AIL6s.png')
img = img[:,:,0]

# Threshold
Ithresh = img
Ithresh[img > 0] = 1

# Find contour
fillImage = np.zeros(Ithresh.shape)
im, contours, hierarchy = cv2.findContours(Ithresh.astype(np.uint8),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE )
hulls = []
for contour in contours:

    # Find convex hull for each area
    hull = cv2.convexHull(contour)
    hulls.append(hull)

# Fill the convex area
cv2.fillPoly(fillImage, hulls, 255)

# Run corner detection
corners = cv2.cornerHarris(fillImage.astype(np.uint8), 3, 3, 0.05)

# Run max suppression
maxCorners = corners.flatten().argsort()[-50:][::-1]
maxCornersYX = np.unravel_index(maxCorners,corners.shape)
cornersMaxSupression = np.zeros_like(corners)
for i in range(len(maxCorners)):

    # Look at the neighborhood of the point. If it is the max value insert 1 to cornersMaxSupression else insert 0
    pointX = maxCornersYX[1][i]
    pointY = maxCornersYX[0][i]
    maxArea = np.max(corners[pointY-neigbrhoodSize:pointY+neigbrhoodSize,pointX-neigbrhoodSize:pointX+neigbrhoodSize])
    currentMax = np.max(cornersMaxSupression[pointY-neigbrhoodSize:pointY+neigbrhoodSize,pointX-neigbrhoodSize:pointX+neigbrhoodSize])
    if maxArea==corners[pointY, pointX] and currentMax==0:
        cornersMaxSupression[pointY, pointX] = 1

# Take the eight max values of cornersMaxSupression
eightMaxCorners = corners.flatten().argsort()[-8:][::-1]
eightMaxCornersYX = np.unravel_index(eightMaxCorners,corners.shape)

# Divide the image to the two small images
fillImage[:,1:fillImage.shape[1]/2] = fillImage[:,1:fillImage.shape[1]/2]*2