如何始终使用 minAreaRect，c++ 将图像旋转到 180 度

Question

我的图像；

要求：

我无法理解轴是如何决定使图像始终水平的。

算法：

• 阅读图片
• 查找外部轮廓
• 绘制轮廓
• 使用外部轮廓检测 minArearect （边界框对我没有帮助）
• 获取旋转矩阵并旋转图像
• 从旋转图像中提取所需的补丁

我的代码：

//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

这个想法是使用minAreaRect计算旋转的边界框角度，然后使用getRotationMatrix2D和warpAffine对图像进行校正。 如果我们使用垂直图像，最后一步是旋转 90 度。 这是之前（左）和之后（右）以及旋转角度的结果：

-39.999351501464844

38.52387619018555

1.6167902946472168

1.9749339818954468

我在 Python 中实现了它，但您可以将相同的方法应用于 C++

代码

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()