How to rotate the image to 180 degree always using minAreaRect, c++ - c++

Question

My Images;
Requirement:
I am not able to understand how axis is decided to make the image always horizontal.
Algorithm:
Read the image
Find external contour
Draw the contours
Use the external contour to detect minArearect (bounding box will not help for me)
get the rotation matrix and rotate the image
Extract the required patch from the rotated image
My code:
//read the image
img = imread("90.jpeg")
cv::Mat contourOutput = img.clone();
// detect external contour(images will have noise, although example images doesn't have)
std::vector<std::vector<cv::Point> > contours;
cv::findContours(contourOutput, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
int largest_area = 0;
int largest_contour_index = 0;
for (size_t i = 0; i < contours.size(); i++) {
double area = contourArea(contours[i]);
// copy the largest contour index
if (area > largest_area) {
largest_area = area;
largest_contour_index = i;
}
}
//draw contours
drawContours(img, contours, largest_contour_index, Scalar(255, 0, 0),
2);
// detect minimum area rect to get the angle and centre
cv::RotatedRect box = cv::minAreaRect(cv::Mat(contours[largest_contour_index]));
// take the box angle
double angle = box.angle;
if (angle < -45) {
box.angle += 90;
}
angle = box.angle;
// create rotation matrix
cv::Mat rot_mat = cv::getRotationMatrix2D(box.center, angle, 1);
// Apply the transformation
cv::Mat rotated;
cv::warpAffine(img, rotated, rot_mat, img.size(), cv::INTER_CUBIC);
cv::Size box_size = box.size;
if (box.angle < -45.)
std::swap(box_size.width, box_size.height);
// get the cropped image
cv::Mat cropped;
cv::getRectSubPix(rotated, box_size, box.center, cropped);
// Display the image
namedWindow("image2", WINDOW_NORMAL);
imshow("image2", cropped);
waitKey(0);

Answer 1

The idea is to compute the rotated bounding box angle using minAreaRect then deskew the image with getRotationMatrix2D and warpAffine. One final step is to rotate by 90 degrees if we are working with a vertical image. Here's the results with before (left) and after (right) and the angle of rotation:
-39.999351501464844
38.52387619018555
1.6167902946472168
1.9749339818954468
I implemented it in Python but you can adapt the same approach into C++
Code
import cv2
import numpy as np
# Load image, grayscale, and Otsu's threshold
image = cv2.imread('4.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Compute rotated bounding box
coords = np.column_stack(np.where(thresh > 0))
angle = cv2.minAreaRect(coords)[-1]
# Determine rotation angle
if angle < -45:
angle = -(90 + angle)
else:
angle = -angle
print(angle)
# Rotate image to deskew
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
# Vertical image so rotate to horizontal
h, w, _ = rotated.shape
if h > w:
rotated = cv2.rotate(rotated, cv2.ROTATE_90_CLOCKWISE)
cv2.imshow('rotated', rotated)
cv2.imwrite('rotated.png', rotated)
cv2.waitKey()