import cv2
import numpy as np
import pyautogui as pag
#getting a small region as a screenshot
region = (460, 400, 600, 400)
pag.screenshot('da2.png', region = region)
#using that screenshot to detect where a template might be and saving the image
img_rgb = cv2.imread('da2.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('player2.png',0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
threshold = 0.6335
loc = np.where( res >= threshold)
for pt in zip(*loc[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 25)
cv2.imshow('Detected',img_rgb)
cv2.waitKey(0)
cv2.imwrite('01.png',img_rgb)
cv2.destroyAllWindows()
What I'm trying to do is to detect other players in a game using screenshots and a player template. The problem is that I don't know how to get the coordinates of the place where the templates where detected.
I also came across to a program that does a clone out of my screen for a certain region in real-time
import numpy as np
from PIL import ImageGrab
import cv2
def process_img(image):
processed_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
processed_img = cv2.Canny(processed_img, threshold1 = 200, threshold2=300)
return processed_img
def main():
while True:
screen = np.array(ImageGrab.grab(bbox=(460,400,1140,920)))
new_screen = process_img(screen)
cv2.imshow('window', new_screen)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
main()
I don't know how to use this code to detect the templates that might appear on the screen ...
Studying your original code...specifically this line:
for pt in zip(*loc[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 25)
If you see a rectangle drawn in the image that you've read, then pt is the top left vertex of this rectangle and (pt[0] + w, pt[1] + h) is the bottom right vertex of this rectangle.
The convention is that (0,0) is located on the top-left corner of the image, and (img.cols,img.rows) is located on the bottom right corner.
To get the integral coordinates of a point pt, you can use the pt.x and pt.y objects
I want to read text on the object. But OCR program can't recognize it. When I give the small part, it can recognize. I have to transform circle text to linear text. How can I do this? Thanks.
you can transform the image from Cartesian coordinate system to Polar coordinate system to prepare circle path text image for OCR program. This function logPolar() can help.
Here are some steps to prepare circle path text image:
Find the circles' centers using HoughCircles().
Get the mean and do some offset, so get the center.
(Optinal) Crop a square of the image from the center.
Do logPolar(), then rotate it if necessary.
After detect circles and get the mean of centers and do offset.
The croped image:
After logPolar() and rotate()
My Python3-OpenCV3.3 code is presented here, maybe it helps.
#!/usr/bin/python3
# 2017.10.10 12:44:37 CST
# 2017.10.10 14:08:57 CST
import cv2
import numpy as np
##(1) Read and resize the original image(too big)
img = cv2.imread("circle.png")
img = cv2.resize(img, (W//4, H//4))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
## (2) Detect circles
circles = cv2.HoughCircles(gray, method=cv2.HOUGH_GRADIENT, dp=1, minDist=3, circles=None, param1=200, param2=100, minRadius = 200, maxRadius=0 )
## make canvas
canvas = img.copy()
## (3) Get the mean of centers and do offset
circles = np.int0(np.array(circles))
x,y,r = 0,0,0
for ptx,pty, radius in circles[0]:
cv2.circle(canvas, (ptx,pty), radius, (0,255,0), 1, 16)
x += ptx
y += pty
r += radius
cnt = len(circles[0])
x = x//cnt
y = y//cnt
r = r//cnt
x+=5
y-=7
## (4) Draw the labels in red
for r in range(100, r, 20):
cv2.circle(canvas, (x,y), r, (0, 0, 255), 3, cv2.LINE_AA)
cv2.circle(canvas, (x,y), 3, (0,0,255), -1)
## (5) Crop the image
dr = r + 20
croped = img[y-dr:y+dr+1, x-dr:x+dr+1].copy()
## (6) logPolar and rotate
polar = cv2.logPolar(croped, (dr,dr),80, cv2.WARP_FILL_OUTLIERS )
rotated = cv2.rotate(polar, cv2.ROTATE_90_COUNTERCLOCKWISE)
## (7) Display the result
cv2.imshow("Canvas", canvas)
cv2.imshow("croped", croped)
cv2.imshow("polar", polar)
cv2.imshow("rotated", rotated)
cv2.waitKey();cv2.destroyAllWindows()
I'm detecting markers on images captured by my iPad. Because of that I want to calculate translations and rotations between them, I want to change change perspective on images these image, so it would look like I'm capturing them directly above markers.
Right now I'm using
points2D.push_back(cv::Point2f(0, 0));
points2D.push_back(cv::Point2f(50, 0));
points2D.push_back(cv::Point2f(50, 50));
points2D.push_back(cv::Point2f(0, 50));
Mat perspectiveMat = cv::getPerspectiveTransform(points2D, imagePoints);
cv::warpPerspective(*_image, *_undistortedImage, M, cv::Size(_image->cols, _image->rows));
Which gives my these results (look at the right-bottom corner for result of warpPerspective):
As you probably see result image contains recognized marker in left-top corner of the result image. My problem is that I want to capture whole image (without cropping) so I could detect other markers on that image later.
How can I do that? Maybe I should use rotation/translation vectors from solvePnP function?
EDIT:
Unfortunatelly changing size of warped image don't help much, because image is still translated so left-top corner of marker is in top-left corner of image.
For example when I've doubled size using:
cv::warpPerspective(*_image, *_undistortedImage, M, cv::Size(2*_image->cols, 2*_image->rows));
I've recieved these images:
Your code doesn't seem to be complete, so it is difficult to say what the problem is.
In any case the warped image might have completely different dimensions compared to the input image so you will have to adjust the size paramter you are using for warpPerspective.
For example try to double the size:
cv::warpPerspective(*_image, *_undistortedImage, M, 2*cv::Size(_image->cols, _image->rows));
Edit:
To make sure the whole image is inside this image, all corners of your original image must be warped to be inside the resulting image. So simply calculate the warped destination for each of the corner points and adjust the destination points accordingly.
To make it more clear some sample code:
// calculate transformation
cv::Matx33f M = cv::getPerspectiveTransform(points2D, imagePoints);
// calculate warped position of all corners
cv::Point3f a = M.inv() * cv::Point3f(0, 0, 1);
a = a * (1.0/a.z);
cv::Point3f b = M.inv() * cv::Point3f(0, _image->rows, 1);
b = b * (1.0/b.z);
cv::Point3f c = M.inv() * cv::Point3f(_image->cols, _image->rows, 1);
c = c * (1.0/c.z);
cv::Point3f d = M.inv() * cv::Point3f(_image->cols, 0, 1);
d = d * (1.0/d.z);
// to make sure all corners are in the image, every position must be > (0, 0)
float x = ceil(abs(min(min(a.x, b.x), min(c.x, d.x))));
float y = ceil(abs(min(min(a.y, b.y), min(c.y, d.y))));
// and also < (width, height)
float width = ceil(abs(max(max(a.x, b.x), max(c.x, d.x)))) + x;
float height = ceil(abs(max(max(a.y, b.y), max(c.y, d.y)))) + y;
// adjust target points accordingly
for (int i=0; i<4; i++) {
points2D[i] += cv::Point2f(x,y);
}
// recalculate transformation
M = cv::getPerspectiveTransform(points2D, imagePoints);
// get result
cv::Mat result;
cv::warpPerspective(*_image, result, M, cv::Size(width, height), cv::WARP_INVERSE_MAP);
I implemented littleimp's answer in python in case anyone needs it. It should be noted that this will not work properly if the vanishing points of the polygons are falling within the image.
import cv2
import numpy as np
from PIL import Image, ImageDraw
import math
def get_transformed_image(src, dst, img):
# calculate the tranformation
mat = cv2.getPerspectiveTransform(src.astype("float32"), dst.astype("float32"))
# new source: image corners
corners = np.array([
[0, img.size[0]],
[0, 0],
[img.size[1], 0],
[img.size[1], img.size[0]]
])
# Transform the corners of the image
corners_tranformed = cv2.perspectiveTransform(
np.array([corners.astype("float32")]), mat)
# These tranformed corners seems completely wrong/inverted x-axis
print(corners_tranformed)
x_mn = math.ceil(min(corners_tranformed[0].T[0]))
y_mn = math.ceil(min(corners_tranformed[0].T[1]))
x_mx = math.ceil(max(corners_tranformed[0].T[0]))
y_mx = math.ceil(max(corners_tranformed[0].T[1]))
width = x_mx - x_mn
height = y_mx - y_mn
analogy = height/1000
n_height = height/analogy
n_width = width/analogy
dst2 = corners_tranformed
dst2 -= np.array([x_mn, y_mn])
dst2 = dst2/analogy
mat2 = cv2.getPerspectiveTransform(corners.astype("float32"),
dst2.astype("float32"))
img_warp = Image.fromarray((
cv2.warpPerspective(np.array(image),
mat2,
(int(n_width),
int(n_height)))))
return img_warp
# image coordingates
src= np.array([[ 789.72, 1187.35],
[ 789.72, 752.75],
[1277.35, 730.66],
[1277.35,1200.65]])
# known coordinates
dst=np.array([[0, 1000],
[0, 0],
[1092, 0],
[1092, 1000]])
# Create the image
image = Image.new('RGB', (img_width, img_height))
image.paste( (200,200,200), [0,0,image.size[0],image.size[1]])
draw = ImageDraw.Draw(image)
draw.line(((src[0][0],src[0][1]),(src[1][0],src[1][1]), (src[2][0],src[2][1]),(src[3][0],src[3][1]), (src[0][0],src[0][1])), width=4, fill="blue")
#image.show()
warped = get_transformed_image(src, dst, image)
warped.show()
There are two things you need to do:
Increase the size of the output of cv2.warpPerspective
Translate the warped source image such that the center of the warped source image matches with the center of cv2.warpPerspective output image
Here is how code will look:
# center of source image
si_c = [x//2 for x in image.shape] + [1]
# find where center of source image will be after warping without comepensating for any offset
wsi_c = np.dot(H, si_c)
wsi_c = [x/wsi_c[2] for x in wsi_c]
# warping output image size
stitched_frame_size = tuple(2*x for x in image.shape)
# center of warping output image
wf_c = image.shape
# calculate offset for translation of warped image
x_offset = wf_c[0] - wsi_c[0]
y_offset = wf_c[1] - wsi_c[1]
# translation matrix
T = np.array([[1, 0, x_offset], [0, 1, y_offset], [0, 0, 1]])
# translate tomography matrix
translated_H = np.dot(T.H)
# warp
stitched = cv2.warpPerspective(image, translated_H, stitched_frame_size)
I´m trying to find the corners on a image, I don´t need the contours, only the 4 corners. I will change the perspective using 4 corners.
I´m using Opencv, but I need to know the steps to find the corners and what function I will use.
My images will be like this:(without red points, I will paint the points after)
EDITED:
After suggested steps, I writed the code: (Note: I´m not using pure OpenCv, I´m using javaCV, but the logic it´s the same).
// Load two images and allocate other structures (I´m using other image)
IplImage colored = cvLoadImage(
"res/scanteste.jpg",
CV_LOAD_IMAGE_UNCHANGED);
IplImage gray = cvCreateImage(cvGetSize(colored), IPL_DEPTH_8U, 1);
IplImage smooth = cvCreateImage(cvGetSize(colored), IPL_DEPTH_8U, 1);
//Step 1 - Convert from RGB to grayscale (cvCvtColor)
cvCvtColor(colored, gray, CV_RGB2GRAY);
//2 Smooth (cvSmooth)
cvSmooth( gray, smooth, CV_BLUR, 9, 9, 2, 2);
//3 - cvThreshold - What values?
cvThreshold(gray,gray, 155, 255, CV_THRESH_BINARY);
//4 - Detect edges (cvCanny) -What values?
int N = 7;
int aperature_size = N;
double lowThresh = 20;
double highThresh = 40;
cvCanny( gray, gray, lowThresh*N*N, highThresh*N*N, aperature_size );
//5 - Find contours (cvFindContours)
int total = 0;
CvSeq contour2 = new CvSeq(null);
CvMemStorage storage2 = cvCreateMemStorage(0);
CvMemStorage storageHull = cvCreateMemStorage(0);
total = cvFindContours(gray, storage2, contour2, Loader.sizeof(CvContour.class), CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
if(total > 1){
while (contour2 != null && !contour2.isNull()) {
if (contour2.elem_size() > 0) {
//6 - Approximate contours with linear features (cvApproxPoly)
CvSeq points = cvApproxPoly(contour2,Loader.sizeof(CvContour.class), storage2, CV_POLY_APPROX_DP,cvContourPerimeter(contour2)*0.005, 0);
cvDrawContours(gray, points,CvScalar.BLUE, CvScalar.BLUE, -1, 1, CV_AA);
}
contour2 = contour2.h_next();
}
}
So, I want to find the cornes, but I don´t know how to use corners function like cvCornerHarris and others.
First, check out /samples/c/squares.c in your OpenCV distribution. This example provides a square detector, and it should be a pretty good start on how to detect corner-like features. Then, take a look at OpenCV's feature-oriented functions like cvCornerHarris() and cvGoodFeaturesToTrack().
The above methods can return many corner-like features - most will not be the "true corners" you are looking for. In my application, I had to detect squares that had been rotated or skewed (due to perspective). My detection pipeline consisted of:
Convert from RGB to grayscale (cvCvtColor)
Smooth (cvSmooth)
Threshold (cvThreshold)
Detect edges (cvCanny)
Find contours (cvFindContours)
Approximate contours with linear features (cvApproxPoly)
Find "rectangles" which were structures that: had polygonalized contours possessing 4 points, were of sufficient area, had adjacent edges were ~90 degrees, had distance between "opposite" vertices was of sufficient size, etc.
Step 7 was necessary because a slightly noisy image can yield many structures that appear rectangular after polygonalization. In my application, I also had to deal with square-like structures that appeared within, or overlapped the desired square. I found the contour's area property and center of gravity to be helpful in discerning the proper rectangle.
At a first glance, for a human eye there are 4 corners. But in computer vision, a corner is considered to be a point that has large gradient change in intensity across its neighborhood. The neighborhood can be a 4 pixel neighborhood or an 8 pixel neighborhood.
In the equation provided to find the gradient of intensity, it has been considered for 4-pixel neighborhood SEE DOCUMENTATION.
Here is my approach for the image in question. I have the code in python as well:
path = r'C:\Users\selwyn77\Desktop\Stack\corner'
filename = 'env.jpg'
img = cv2.imread(os.path.join(path, filename))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #--- convert to grayscale
It is a good choice to always blur the image to remove less possible gradient changes and preserve the more intense ones. I opted to choose the bilateral filter which unlike the Gaussian filter doesn't blur all the pixels in the neighborhood. It rather blurs pixels which has similar pixel intensity to that of the central pixel. In short it preserves edges/corners of high gradient change but blurs regions that have minimal gradient changes.
bi = cv2.bilateralFilter(gray, 5, 75, 75)
cv2.imshow('bi',bi)
To a human it is not so much of a difference compared to the original image. But it does matter. Now finding possible corners:
dst = cv2.cornerHarris(bi, 2, 3, 0.04)
dst returns an array (the same 2D shape of the image) with eigen values obtained from the final equation mentioned HERE.
Now a threshold has to be applied to select those corners beyond a certain value. I will use the one in the documentation:
#--- create a black image to see where those corners occur ---
mask = np.zeros_like(gray)
#--- applying a threshold and turning those pixels above the threshold to white ---
mask[dst>0.01*dst.max()] = 255
cv2.imshow('mask', mask)
The white pixels are regions of possible corners. You can find many corners neighboring each other.
To draw the selected corners on the image:
img[dst > 0.01 * dst.max()] = [0, 0, 255] #--- [0, 0, 255] --> Red ---
cv2.imshow('dst', img)
(Red colored pixels are the corners, not so visible)
In order to get an array of all pixels with corners:
coordinates = np.argwhere(mask)
UPDATE
Variable coor is an array of arrays. Converting it to list of lists
coor_list = [l.tolist() for l in list(coor)]
Converting the above to list of tuples
coor_tuples = [tuple(l) for l in coor_list]
I have an easy and rather naive way to find the 4 corners. I simply calculated the distance of each corner to every other corner. I preserved those corners whose distance exceeded a certain threshold.
Here is the code:
thresh = 50
def distance(pt1, pt2):
(x1, y1), (x2, y2) = pt1, pt2
dist = math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 )
return dist
coor_tuples_copy = coor_tuples
i = 1
for pt1 in coor_tuples:
print(' I :', i)
for pt2 in coor_tuples[i::1]:
print(pt1, pt2)
print('Distance :', distance(pt1, pt2))
if(distance(pt1, pt2) < thresh):
coor_tuples_copy.remove(pt2)
i+=1
Prior to running the snippet above coor_tuples had all corner points:
[(4, 42),
(4, 43),
(5, 43),
(5, 44),
(6, 44),
(7, 219),
(133, 36),
(133, 37),
(133, 38),
(134, 37),
(135, 224),
(135, 225),
(136, 225),
(136, 226),
(137, 225),
(137, 226),
(137, 227),
(138, 226)]
After running the snippet I was left with 4 corners:
[(4, 42), (7, 219), (133, 36), (135, 224)]
UPDATE 2
Now all you have to do is just mark these 4 points on a copy of the original image.
img2 = img.copy()
for pt in coor_tuples:
cv2.circle(img2, tuple(reversed(pt)), 3, (0, 0, 255), -1)
cv2.imshow('Image with 4 corners', img2)
Here's an implementation using cv2.goodFeaturesToTrack() to detect corners. The approach is
Convert image to grayscale
Perform canny edge detection
Detect corners
Optionally perform 4-point perspective transform to get top-down view of image
Using this starting image,
After converting to grayscale, we perform canny edge detection
Now that we have a decent binary image, we can use cv2.goodFeaturesToTrack()
corners = cv2.goodFeaturesToTrack(canny, 4, 0.5, 50)
For the parameters, we give it the canny image, set the maximum number of corners to 4 (maxCorners), use a minimum accepted quality of 0.5 (qualityLevel), and set the minimum possible Euclidean distance between the returned corners to 50 (minDistance). Here's the result
Now that we have identified the corners, we can perform a 4-point perspective transform to obtain a top-down view of the object. We first order the points clockwise then draw the result onto a mask.
Note: We could have just found contours on the Canny image instead of doing this step to create the mask, but pretend we only had the 4 corner points to work with
Next we find contours on this mask and filter using cv2.arcLength() and cv2.approxPolyDP(). The idea is that if the contour has 4 points, then it must be our object. Once we have this contour, we perform a perspective transform
Finally we rotate the image depending on the desired orientation. Here's the result
Code for only detecting corners
import cv2
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 120, 255, 1)
corners = cv2.goodFeaturesToTrack(canny,4,0.5,50)
for corner in corners:
x,y = corner.ravel()
cv2.circle(image,(x,y),5,(36,255,12),-1)
cv2.imshow('canny', canny)
cv2.imshow('image', image)
cv2.waitKey()
Code for detecting corners and performing perspective transform
import cv2
import numpy as np
def rotate_image(image, angle):
# Grab the dimensions of the image and then determine the center
(h, w) = image.shape[:2]
(cX, cY) = (w / 2, h / 2)
# grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# Compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# Adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# Perform the actual rotation and return the image
return cv2.warpAffine(image, M, (nW, nH))
def order_points_clockwise(pts):
# sort the points based on their x-coordinates
xSorted = pts[np.argsort(pts[:, 0]), :]
# grab the left-most and right-most points from the sorted
# x-roodinate points
leftMost = xSorted[:2, :]
rightMost = xSorted[2:, :]
# now, sort the left-most coordinates according to their
# y-coordinates so we can grab the top-left and bottom-left
# points, respectively
leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
(tl, bl) = leftMost
# now, sort the right-most coordinates according to their
# y-coordinates so we can grab the top-right and bottom-right
# points, respectively
rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
(tr, br) = rightMost
# return the coordinates in top-left, top-right,
# bottom-right, and bottom-left order
return np.array([tl, tr, br, bl], dtype="int32")
def perspective_transform(image, corners):
def order_corner_points(corners):
# Separate corners into individual points
# Index 0 - top-right
# 1 - top-left
# 2 - bottom-left
# 3 - bottom-right
corners = [(corner[0][0], corner[0][1]) for corner in corners]
top_r, top_l, bottom_l, bottom_r = corners[0], corners[1], corners[2], corners[3]
return (top_l, top_r, bottom_r, bottom_l)
# Order points in clockwise order
ordered_corners = order_corner_points(corners)
top_l, top_r, bottom_r, bottom_l = ordered_corners
# Determine width of new image which is the max distance between
# (bottom right and bottom left) or (top right and top left) x-coordinates
width_A = np.sqrt(((bottom_r[0] - bottom_l[0]) ** 2) + ((bottom_r[1] - bottom_l[1]) ** 2))
width_B = np.sqrt(((top_r[0] - top_l[0]) ** 2) + ((top_r[1] - top_l[1]) ** 2))
width = max(int(width_A), int(width_B))
# Determine height of new image which is the max distance between
# (top right and bottom right) or (top left and bottom left) y-coordinates
height_A = np.sqrt(((top_r[0] - bottom_r[0]) ** 2) + ((top_r[1] - bottom_r[1]) ** 2))
height_B = np.sqrt(((top_l[0] - bottom_l[0]) ** 2) + ((top_l[1] - bottom_l[1]) ** 2))
height = max(int(height_A), int(height_B))
# Construct new points to obtain top-down view of image in
# top_r, top_l, bottom_l, bottom_r order
dimensions = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1],
[0, height - 1]], dtype = "float32")
# Convert to Numpy format
ordered_corners = np.array(ordered_corners, dtype="float32")
# Find perspective transform matrix
matrix = cv2.getPerspectiveTransform(ordered_corners, dimensions)
# Return the transformed image
return cv2.warpPerspective(image, matrix, (width, height))
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 120, 255, 1)
corners = cv2.goodFeaturesToTrack(canny,4,0.5,50)
c_list = []
for corner in corners:
x,y = corner.ravel()
c_list.append([int(x), int(y)])
cv2.circle(image,(x,y),5,(36,255,12),-1)
corner_points = np.array([c_list[0], c_list[1], c_list[2], c_list[3]])
ordered_corner_points = order_points_clockwise(corner_points)
mask = np.zeros(image.shape, dtype=np.uint8)
cv2.fillPoly(mask, [ordered_corner_points], (255,255,255))
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.015 * peri, True)
if len(approx) == 4:
transformed = perspective_transform(original, approx)
result = rotate_image(transformed, -90)
cv2.imshow('canny', canny)
cv2.imshow('image', image)
cv2.imshow('mask', mask)
cv2.imshow('transformed', transformed)
cv2.imshow('result', result)
cv2.waitKey()
find contours with RETR_EXTERNAL option.(gray -> gaussian filter -> canny edge -> find contour)
find the largest size contour -> this will be the edge of the rectangle
find corners with little calculation
Mat m;//image file
findContours(m, contours_, hierachy_, RETR_EXTERNAL);
auto it = max_element(contours_.begin(), contours_.end(),
[](const vector<Point> &a, const vector<Point> &b) {
return a.size() < b.size(); });
Point2f xy[4] = {{9000,9000}, {0, 1000}, {1000, 0}, {0,0}};
for(auto &[x, y] : *it) {
if(x + y < xy[0].x + xy[0].y) xy[0] = {x, y};
if(x - y > xy[1].x - xy[1].y) xy[1] = {x, y};
if(y - x > xy[2].y - xy[2].x) xy[2] = {x, y};
if(x + y > xy[3].x + xy[3].y) xy[3] = {x, y};
}
xy[4] will be the four corners.
I was able to extract four corners this way.
Apply houghlines to the canny image - you will get a list of points
apply convex hull to this set of points