Detect corners of grid
Question:
I am trying to detect the corners of a grid within various pictures I have to process. Images can be skewed and some might be relatively well oriented but we cannot guarantee that all images will be like this.
To determine the corners of the grid, I have tried using Hough lines but to no avail. Sometimes the Hough lines don’t identify the edge of the grid and it is hard to determine which of the lines drawn belong to the edge of the grid and which of them are grid lines.
I then decided to use contours to detect the edges of the grid. It however, picks up on numerous contours and it leads to the same problem of identifying which are at the corners amongst all the contours identified.
To help aid this, I’ve used bilateral filtering, Canny edge detection, morphological dilation, and Harris edge detection as seen in a question with a similar problem as mine. Even after applying all those measures, I still get tons of false corners and sometimes true corners aren’t identified.
I was wondering if anyone had a way for me to improve the results of my corner detection or if anyone had a completely different suggestion in mind that might help solve my problem. The goal is to get the corners so I can perform a homography using a 10 X 10 grid to account for skewing in the images. It will also help map grid square to pixel space which is very useful.
This is my code (a bit sloppy with the naming and all but I’ll try and fix that later). Also, yes, I went all out on the bilateral filtering, it seemed to help get rid of unnecessary contours and corners.
I also seem to get one error when I tried applying the Hough lines to my contoured image:
error: (-215) img.type() == (((0) & ((1 << 3) - 1)) + (((1)-1) << 3)) in function cv::HoughLinesStandard
from PIL import Image
import numpy as np
import cv2
import glob
#import images using opencv
images = [cv2.imread(file) for file in glob.glob("SpAMImages/*.jpg")]
for image in images:
#resizes image gotten from folder and performs bilateral filtering
img = cv2.bilateralFilter(cv2.resize(image, (715,715)), 15, 800, 800)
#applies a canny edge detection filter on the images loaded from the folder
gridEdges = cv2.Canny(img, 140, 170)
#apply image dilation
kernel = np.ones((5,5), np.uint8)
gridEdges = cv2.dilate(gridEdges, kernel, iterations=1)
gridEdges = np.float32(gridEdges)
gridEdges = cv2.blur(gridEdges,(10,10))
gridEdges = cv2.cornerHarris(gridEdges,2,3,0.04)
gridEdges = cv2.dilate(gridEdges,None)
img[gridEdges>0.01*gridEdges.max()]=[0,0,255]
#draw contours on current image
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contourImage, contours, hierarchy =
cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contour = cv2.drawContours(img, contours, -1, (0,255,0), 3)
'''
def largest_4_sided_contour(thresh, show_contours=True):
contourImage, contours, hierarchy =
cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contour = cv2.drawContours(img, contours, -1, (0,255,0), 3)
contours = sorted(contours, key = cv2.contourArea, reverse = True)
for cnt in contours[:min(5, len(contours))]:
print(len(cnt))
if len(cnt) == 4:
return cnt
return None
print(largest_4_sided_contour(thresh))
#applies a hough transformation to extract gridlines from the image
-----------THIS LINE BELOW GIVES ME THE ERROR-----------------
lines = cv2.HoughLines(img, 1, np.pi/180, 245)
#iterates through an array of lines gottne from the hough transform
#and draws them unto the image
for i in range(len(lines)):
for rho,theta in lines[i]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(img, (x1,y1),(x2,y2),(0,0,255),2)
cv2.imwrite('houghlines.jpg', img)
'''
#resize window because for some reason they are too large.
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('image', 800, 800)
#display all the images produced from above processes
cv2.imshow('image', img)
cv2.imshow('dilated edges', gridEdges)
#cv2.imshow('contour', contour)
cv2.waitKey(0)
'''
retrieve image size from imported image.
testImageWidth, testImageHeight = img.shape[:2]
print(testImageHeight, testImageWidth)'''
These are some images gotten from my attempt to obtain corners using contour detection and Harris corner detection.
Identifying corners using contours and harris corner detection:
And some examples of the images I have to work with.
Prime example grid:
Somewhat skewed grid:
Thanks for any help in advance!!!
Answers:
You’re working in Python with OpenCV, but I’m going to give you an answer using MATLAB with DIPimage. I intend this answer to be about the concepts, not about the code. I’m sure there are ways to accomplish all of these things in Python with OpenCV.
My aim here is to find the four corners of the board. The grid itself can be guessed, since it’s just an equidistant division of the board, there is no need to try to detect all lines. The four corners give all information about the perspective transformation.
The simplest way to detect the board is to recognize that it is light-colored and has a dark-colored background. Starting with a grey-value image, I apply a small closing (I used a circle with a diameter of 7 pixels, this is suitable for the down-sampled image I used as example, but you might want to increase the size appropriately for the full-size image). This gives this result:
Next, I binarize using Otsu threshold selection, and remove holes (that part is not important, the rest would work if there are holes too). The connected components that we see now correspond to the board and the neighboring boards (or whatever the other white things are around the board).
Selecting the largest connected component is a fairly common procedure. In the code below I label the image (identifies connected components), count the number of pixels per connected component, and select the one with the most pixels.
Finally, subtracting from this result its erosion leaves us only with the pixels at the edge of the board (here in blue overlaid on the input image):
The trick I’m using to find the corners is fairly simple, but fails here because one of the corners is not in the image. Using Hough on these four edges would probably be a more robust way to go about it. Use this other answer for some ideas and code on how to go about that.
In any case, I’m finding as the top-left corner of the board the edge pixel that is closest to the top-left corner of the image. Likewise for the other 3 corners. These results are the red dots in the image above.
A third option here would be to convert the outline into a polygon, simplify it with the Douglas–Peucker algorithm, discard the edges that go along the image edge (this is where corners are not in the image), and extend the two edges on either side of this to find the vertex that is outside the image.
The MATLAB (with DIPimage) code follows.
img = readim('https://i.stack.imgur.com/GYZGa.jpg');
img = colorspace(img,'gray');
% Downsample, makes display easier
img = gaussf(img,2);
img = img(0:4:end,0:4:end);
% Simplify and binarize
sim = closing(img,7);
brd = threshold(sim); % uses Otsu threshold selection
% Fill the holes
brd = fillholes(brd);
% Keep only the largest connected component
brd = label(brd);
msr = measure(brd);
[~,I] = max(msr,'size');
brd = brd == msr(I).id;
% Extract edges
brd = brd - erosion(brd,3,'rectangular');
% Find corners
pts = findcoord(brd);
[~,top_left] = min(sum(pts.^2,2));
[~,top_right] = min(sum((pts-[imsize(brd,1),0]).^2,2));
[~,bottom_left] = min(sum((pts-[0,imsize(brd,2)]).^2,2));
[~,bottom_right] = min(sum((pts-[imsize(brd,1),imsize(brd,2)]).^2,2));
% Make an image with corner pixels set
cnr = newim(brd,'bin');
cnr(pts(top_left,1),pts(top_left,2)) = 1;
cnr(pts(top_right,1),pts(top_right,2)) = 1;
cnr(pts(bottom_left,1),pts(bottom_left,2)) = 1;
cnr(pts(bottom_right,1),pts(bottom_right,2)) = 1;
cnr = dilation(cnr,3);
% Save images
writeim(sim,'so1.png')
out = overlay(img,brd,[0,0,255]);
out = overlay(out,cnr,[255,0,0]);
writeim(out,'so2.png')
I have somewhat of an answer for you, although not complete, it might just help you.
I use the Ramer–Douglas–Peucker algorithm to determine the contours and then extract rectangular boxes from the contours. I then use the percentage of the "box" area to the image area to remove smaller boxes. This removes most of the junk boxes.
Here is an example of what I did in python code:
Finding Contours:
def findcontours(self):
logging.info("Inside findcontours Contours...")
# Pre-process image
imgGray = self.imgProcess.toGrey(self.img)
logging.info("Success on converting image to greyscale")
imgThresh = self.imgProcess.toBinary(imgGray)
logging.info("Success on converting image to binary")
logging.info("Finding contours...")
image, contours, hierarchy = cv2.findContours(imgThresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
logging.info("Contours found: %d", len(contours))
return contours
Using Contours to find boxes:
def getRectangles(self, contours):
arrrect = []
imgArea = self.getArea()
logging.info("Image Area is: %d", imgArea)
for cnt in contours:
epsilon = 0.01*cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, False)
area = cv2.contourArea(approx)
rect = cv2.minAreaRect(approx)
box = cv2.boxPoints(rect)
box = np.int0(box)
percentage = (area * 100) / imgArea
if percentage > 0.3:
arrrect.append(box)
return arrrect
To combine these 2 methods:
def process(self):
logging.info("Processing image...")
self.shape_handler = ShapeHandler(self.img)
contours = self.shape_handler.findcontours()
logging.info("Finding Rectangles from contours...")
rectangles = self.shape_handler.getRectangles(contours)
img = self.imgDraw.draw(self.img, rectangles, "Green", 10)
cv2.drawContours(img, array, -1, (0,255,0), thickness)
self.display(img)
logging.info("Amount of Rectangles Found: %d", len(rectangles))
Display Image:
def display(self, img):
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow("image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
The final step would be to combine any intersecting boxes since you’re only interested in the edges/corners and then only getting the box with the largest area. Look here to check how to combine boxes.
My coding source: OpenCV 3.1 Documentation
Result on your images:
Normal:
Skew:
Hope this helps!
I am trying to detect the corners of a grid within various pictures I have to process. Images can be skewed and some might be relatively well oriented but we cannot guarantee that all images will be like this.
To determine the corners of the grid, I have tried using Hough lines but to no avail. Sometimes the Hough lines don’t identify the edge of the grid and it is hard to determine which of the lines drawn belong to the edge of the grid and which of them are grid lines.
I then decided to use contours to detect the edges of the grid. It however, picks up on numerous contours and it leads to the same problem of identifying which are at the corners amongst all the contours identified.
To help aid this, I’ve used bilateral filtering, Canny edge detection, morphological dilation, and Harris edge detection as seen in a question with a similar problem as mine. Even after applying all those measures, I still get tons of false corners and sometimes true corners aren’t identified.
I was wondering if anyone had a way for me to improve the results of my corner detection or if anyone had a completely different suggestion in mind that might help solve my problem. The goal is to get the corners so I can perform a homography using a 10 X 10 grid to account for skewing in the images. It will also help map grid square to pixel space which is very useful.
This is my code (a bit sloppy with the naming and all but I’ll try and fix that later). Also, yes, I went all out on the bilateral filtering, it seemed to help get rid of unnecessary contours and corners.
I also seem to get one error when I tried applying the Hough lines to my contoured image:
error: (-215) img.type() == (((0) & ((1 << 3) - 1)) + (((1)-1) << 3)) in function cv::HoughLinesStandard
from PIL import Image
import numpy as np
import cv2
import glob
#import images using opencv
images = [cv2.imread(file) for file in glob.glob("SpAMImages/*.jpg")]
for image in images:
#resizes image gotten from folder and performs bilateral filtering
img = cv2.bilateralFilter(cv2.resize(image, (715,715)), 15, 800, 800)
#applies a canny edge detection filter on the images loaded from the folder
gridEdges = cv2.Canny(img, 140, 170)
#apply image dilation
kernel = np.ones((5,5), np.uint8)
gridEdges = cv2.dilate(gridEdges, kernel, iterations=1)
gridEdges = np.float32(gridEdges)
gridEdges = cv2.blur(gridEdges,(10,10))
gridEdges = cv2.cornerHarris(gridEdges,2,3,0.04)
gridEdges = cv2.dilate(gridEdges,None)
img[gridEdges>0.01*gridEdges.max()]=[0,0,255]
#draw contours on current image
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contourImage, contours, hierarchy =
cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contour = cv2.drawContours(img, contours, -1, (0,255,0), 3)
'''
def largest_4_sided_contour(thresh, show_contours=True):
contourImage, contours, hierarchy =
cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contour = cv2.drawContours(img, contours, -1, (0,255,0), 3)
contours = sorted(contours, key = cv2.contourArea, reverse = True)
for cnt in contours[:min(5, len(contours))]:
print(len(cnt))
if len(cnt) == 4:
return cnt
return None
print(largest_4_sided_contour(thresh))
#applies a hough transformation to extract gridlines from the image
-----------THIS LINE BELOW GIVES ME THE ERROR-----------------
lines = cv2.HoughLines(img, 1, np.pi/180, 245)
#iterates through an array of lines gottne from the hough transform
#and draws them unto the image
for i in range(len(lines)):
for rho,theta in lines[i]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(img, (x1,y1),(x2,y2),(0,0,255),2)
cv2.imwrite('houghlines.jpg', img)
'''
#resize window because for some reason they are too large.
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('image', 800, 800)
#display all the images produced from above processes
cv2.imshow('image', img)
cv2.imshow('dilated edges', gridEdges)
#cv2.imshow('contour', contour)
cv2.waitKey(0)
'''
retrieve image size from imported image.
testImageWidth, testImageHeight = img.shape[:2]
print(testImageHeight, testImageWidth)'''
These are some images gotten from my attempt to obtain corners using contour detection and Harris corner detection.
Identifying corners using contours and harris corner detection:
And some examples of the images I have to work with.
Prime example grid:
Somewhat skewed grid:
Thanks for any help in advance!!!
You’re working in Python with OpenCV, but I’m going to give you an answer using MATLAB with DIPimage. I intend this answer to be about the concepts, not about the code. I’m sure there are ways to accomplish all of these things in Python with OpenCV.
My aim here is to find the four corners of the board. The grid itself can be guessed, since it’s just an equidistant division of the board, there is no need to try to detect all lines. The four corners give all information about the perspective transformation.
The simplest way to detect the board is to recognize that it is light-colored and has a dark-colored background. Starting with a grey-value image, I apply a small closing (I used a circle with a diameter of 7 pixels, this is suitable for the down-sampled image I used as example, but you might want to increase the size appropriately for the full-size image). This gives this result:
Next, I binarize using Otsu threshold selection, and remove holes (that part is not important, the rest would work if there are holes too). The connected components that we see now correspond to the board and the neighboring boards (or whatever the other white things are around the board).
Selecting the largest connected component is a fairly common procedure. In the code below I label the image (identifies connected components), count the number of pixels per connected component, and select the one with the most pixels.
Finally, subtracting from this result its erosion leaves us only with the pixels at the edge of the board (here in blue overlaid on the input image):
The trick I’m using to find the corners is fairly simple, but fails here because one of the corners is not in the image. Using Hough on these four edges would probably be a more robust way to go about it. Use this other answer for some ideas and code on how to go about that.
In any case, I’m finding as the top-left corner of the board the edge pixel that is closest to the top-left corner of the image. Likewise for the other 3 corners. These results are the red dots in the image above.
A third option here would be to convert the outline into a polygon, simplify it with the Douglas–Peucker algorithm, discard the edges that go along the image edge (this is where corners are not in the image), and extend the two edges on either side of this to find the vertex that is outside the image.
The MATLAB (with DIPimage) code follows.
img = readim('https://i.stack.imgur.com/GYZGa.jpg');
img = colorspace(img,'gray');
% Downsample, makes display easier
img = gaussf(img,2);
img = img(0:4:end,0:4:end);
% Simplify and binarize
sim = closing(img,7);
brd = threshold(sim); % uses Otsu threshold selection
% Fill the holes
brd = fillholes(brd);
% Keep only the largest connected component
brd = label(brd);
msr = measure(brd);
[~,I] = max(msr,'size');
brd = brd == msr(I).id;
% Extract edges
brd = brd - erosion(brd,3,'rectangular');
% Find corners
pts = findcoord(brd);
[~,top_left] = min(sum(pts.^2,2));
[~,top_right] = min(sum((pts-[imsize(brd,1),0]).^2,2));
[~,bottom_left] = min(sum((pts-[0,imsize(brd,2)]).^2,2));
[~,bottom_right] = min(sum((pts-[imsize(brd,1),imsize(brd,2)]).^2,2));
% Make an image with corner pixels set
cnr = newim(brd,'bin');
cnr(pts(top_left,1),pts(top_left,2)) = 1;
cnr(pts(top_right,1),pts(top_right,2)) = 1;
cnr(pts(bottom_left,1),pts(bottom_left,2)) = 1;
cnr(pts(bottom_right,1),pts(bottom_right,2)) = 1;
cnr = dilation(cnr,3);
% Save images
writeim(sim,'so1.png')
out = overlay(img,brd,[0,0,255]);
out = overlay(out,cnr,[255,0,0]);
writeim(out,'so2.png')
I have somewhat of an answer for you, although not complete, it might just help you.
I use the Ramer–Douglas–Peucker algorithm to determine the contours and then extract rectangular boxes from the contours. I then use the percentage of the "box" area to the image area to remove smaller boxes. This removes most of the junk boxes.
Here is an example of what I did in python code:
Finding Contours:
def findcontours(self):
logging.info("Inside findcontours Contours...")
# Pre-process image
imgGray = self.imgProcess.toGrey(self.img)
logging.info("Success on converting image to greyscale")
imgThresh = self.imgProcess.toBinary(imgGray)
logging.info("Success on converting image to binary")
logging.info("Finding contours...")
image, contours, hierarchy = cv2.findContours(imgThresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
logging.info("Contours found: %d", len(contours))
return contours
Using Contours to find boxes:
def getRectangles(self, contours):
arrrect = []
imgArea = self.getArea()
logging.info("Image Area is: %d", imgArea)
for cnt in contours:
epsilon = 0.01*cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, False)
area = cv2.contourArea(approx)
rect = cv2.minAreaRect(approx)
box = cv2.boxPoints(rect)
box = np.int0(box)
percentage = (area * 100) / imgArea
if percentage > 0.3:
arrrect.append(box)
return arrrect
To combine these 2 methods:
def process(self):
logging.info("Processing image...")
self.shape_handler = ShapeHandler(self.img)
contours = self.shape_handler.findcontours()
logging.info("Finding Rectangles from contours...")
rectangles = self.shape_handler.getRectangles(contours)
img = self.imgDraw.draw(self.img, rectangles, "Green", 10)
cv2.drawContours(img, array, -1, (0,255,0), thickness)
self.display(img)
logging.info("Amount of Rectangles Found: %d", len(rectangles))
Display Image:
def display(self, img):
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow("image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
The final step would be to combine any intersecting boxes since you’re only interested in the edges/corners and then only getting the box with the largest area. Look here to check how to combine boxes.
My coding source: OpenCV 3.1 Documentation
Result on your images:
Normal:
Skew:
Hope this helps!