如何摆脱扫描形式的偏斜?
How to get rid of skewness in scanned form?
我必须构建一个程序,该程序采用倾斜形式(已扫描的图像)进行图像处理。第一步是摆脱偏斜。我成功地获得了图像的轮廓,并且我正在尝试执行此 post 中显示的 four_point_transform
。但是,由于以下原因,我的代码失败了:
错误
java.lang.RuntimeException: OpenCV(4.4.0) C:\projects\javacpp-presets\opencv\cppbuild\windows-x86_64\opencv-4.4.0\modules\imgproc\src\imgwarp.cpp:3391: error: (-215:Assertion failed) src.checkVector(2, CV_32F) == 4 && dst.checkVector(2, CV_32F) == 4 in function 'cv::getPerspectiveTransform
代码
protected static void fixSkeweness(Mat mat){
Mat mask = new Mat();
Mat gray = new Mat();
Mat denoised = new Mat();
Mat bin = new Mat();
Mat hierarchy = new Mat();
MatVector contours = new MatVector();
cvtColor(mat, gray, COLOR_BGR2GRAY);
//Normalize
GaussianBlur(gray, denoised, new Size(5, 5), 0);
threshold(denoised, mask, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
normalize(gray, gray, 0, 255, NORM_MINMAX, -1, mask);
// Convert image to binary
threshold(gray, bin, 150, 255, THRESH_BINARY);
// Find contours
findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE);
long contourCount = contours.size();
System.out.println("Countour count " + contourCount);
double maxArea = 0;
int maxAreaId = 0;
for (int i = 0; i < contourCount; ++i) {
// Calculate the area of each contour
Mat contour = contours.get(i);
double area = contourArea(contour);
if(area > maxArea){
maxAreaId = i;
maxArea = area;
}
}
Double peri = arcLength(contours.get(maxAreaId), true);
Mat newcontour = new Mat();
approxPolyDP(contours.get(maxAreaId), newcontour,0.02 * peri, true);
Mat result = new Mat();
getPerspectiveTransform(newcontour.reshape(4,2), result);
imwrite("src/test/resources/isDataPage/fourPointTransform.jpg", result);
}
失败的代码行是:
getPerspectiveTransform(newcontour.reshape(4,2), result);
请问我能得到一些帮助吗?
示例图片:
按照建议的答案工作代码
protected static Mat findBiggestContour(Mat mat){
Mat mask = new Mat();
Mat gray = new Mat();
Mat denoised = new Mat();
Mat bin = new Mat();
Mat hierarchy = new Mat();
MatVector contours = new MatVector();
//Pre-process image
cvtColor(mat, gray, COLOR_BGR2GRAY);
threshold(gray, bin, 0, 255, THRESH_BINARY_INV + THRESH_OTSU);
findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
double maxArea = 0;
int maxAreaId = 0;
for (int i = 0; i < contours.size(); ++i) {
// Calculate the area of each contour
Mat contour = contours.get(i);
double area = contourArea(contour);
if(area > 5000 && i!=0){
maxAreaId = i;
maxArea = area;
}
}
//Get Min Area Rect and inverse it
RotatedRect rect = minAreaRect(contours.get(maxAreaId));
float newAngle = rect.angle();
if (rect.angle() < 45){
newAngle = newAngle + 90;
}
RotatedRect angle =rect.angle( newAngle);
int h = mat.size().height();
int w = mat.size().width();
int centerW = w/2;
int centerH = h/2;
//find rotation matrix and apply it woohoo
Point2f center = new Point2f(centerW, centerH);
Mat m = getRotationMatrix2D(center, angle.angle(), 1.0);
Mat rotated = new Mat();
warpAffine(mat,rotated,m, new Size(w, h),INTER_CUBIC,BORDER_REPLICATE,new Scalar(10,10));
imwrite("src/test/resources/tmp2/rotrated.png",rotated);
return rotated;
}
getPerspectiveTransform()
正在以其他方式工作(请参阅我的评论)。但是,我发现 minAreaRect()
是更合适的方法。我没有准备好 java 环境,所以这里是 python 代码。希望大家在转换的过程中没有困难。
img = cv2.imread('images/form.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# some preprocessing as you did
# your src image is pretty clean though, and if they all are like that,
# I wouldn't use blur as it makes form borders less obvious
# gray = cv2.blur(gray, (5, 5))
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# find the largest contour assuming it will be some nice rectangle
ctrs, hier = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
largest_ctr_idx = max(range(len(ctrs)), key=lambda i: cv2.contourArea(ctrs[i]))
# get the contour's rotation angle
angle = cv2.minAreaRect(ctrs[largest_ctr_idx])[-1]
if angle < -45:
angle += 90
# find rotation matrix and apply it woohoo
h, w = img.shape[:2]
center = (w // 2, h // 2)
m = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, m, (w, h), flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
找到轮廓:
校正图像:
我必须构建一个程序,该程序采用倾斜形式(已扫描的图像)进行图像处理。第一步是摆脱偏斜。我成功地获得了图像的轮廓,并且我正在尝试执行此 post four_point_transform
。但是,由于以下原因,我的代码失败了:
错误
java.lang.RuntimeException: OpenCV(4.4.0) C:\projects\javacpp-presets\opencv\cppbuild\windows-x86_64\opencv-4.4.0\modules\imgproc\src\imgwarp.cpp:3391: error: (-215:Assertion failed) src.checkVector(2, CV_32F) == 4 && dst.checkVector(2, CV_32F) == 4 in function 'cv::getPerspectiveTransform
代码
protected static void fixSkeweness(Mat mat){
Mat mask = new Mat();
Mat gray = new Mat();
Mat denoised = new Mat();
Mat bin = new Mat();
Mat hierarchy = new Mat();
MatVector contours = new MatVector();
cvtColor(mat, gray, COLOR_BGR2GRAY);
//Normalize
GaussianBlur(gray, denoised, new Size(5, 5), 0);
threshold(denoised, mask, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
normalize(gray, gray, 0, 255, NORM_MINMAX, -1, mask);
// Convert image to binary
threshold(gray, bin, 150, 255, THRESH_BINARY);
// Find contours
findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE);
long contourCount = contours.size();
System.out.println("Countour count " + contourCount);
double maxArea = 0;
int maxAreaId = 0;
for (int i = 0; i < contourCount; ++i) {
// Calculate the area of each contour
Mat contour = contours.get(i);
double area = contourArea(contour);
if(area > maxArea){
maxAreaId = i;
maxArea = area;
}
}
Double peri = arcLength(contours.get(maxAreaId), true);
Mat newcontour = new Mat();
approxPolyDP(contours.get(maxAreaId), newcontour,0.02 * peri, true);
Mat result = new Mat();
getPerspectiveTransform(newcontour.reshape(4,2), result);
imwrite("src/test/resources/isDataPage/fourPointTransform.jpg", result);
}
失败的代码行是:
getPerspectiveTransform(newcontour.reshape(4,2), result);
请问我能得到一些帮助吗?
示例图片:
按照建议的答案工作代码
protected static Mat findBiggestContour(Mat mat){
Mat mask = new Mat();
Mat gray = new Mat();
Mat denoised = new Mat();
Mat bin = new Mat();
Mat hierarchy = new Mat();
MatVector contours = new MatVector();
//Pre-process image
cvtColor(mat, gray, COLOR_BGR2GRAY);
threshold(gray, bin, 0, 255, THRESH_BINARY_INV + THRESH_OTSU);
findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
double maxArea = 0;
int maxAreaId = 0;
for (int i = 0; i < contours.size(); ++i) {
// Calculate the area of each contour
Mat contour = contours.get(i);
double area = contourArea(contour);
if(area > 5000 && i!=0){
maxAreaId = i;
maxArea = area;
}
}
//Get Min Area Rect and inverse it
RotatedRect rect = minAreaRect(contours.get(maxAreaId));
float newAngle = rect.angle();
if (rect.angle() < 45){
newAngle = newAngle + 90;
}
RotatedRect angle =rect.angle( newAngle);
int h = mat.size().height();
int w = mat.size().width();
int centerW = w/2;
int centerH = h/2;
//find rotation matrix and apply it woohoo
Point2f center = new Point2f(centerW, centerH);
Mat m = getRotationMatrix2D(center, angle.angle(), 1.0);
Mat rotated = new Mat();
warpAffine(mat,rotated,m, new Size(w, h),INTER_CUBIC,BORDER_REPLICATE,new Scalar(10,10));
imwrite("src/test/resources/tmp2/rotrated.png",rotated);
return rotated;
}
getPerspectiveTransform()
正在以其他方式工作(请参阅我的评论)。但是,我发现 minAreaRect()
是更合适的方法。我没有准备好 java 环境,所以这里是 python 代码。希望大家在转换的过程中没有困难。
img = cv2.imread('images/form.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# some preprocessing as you did
# your src image is pretty clean though, and if they all are like that,
# I wouldn't use blur as it makes form borders less obvious
# gray = cv2.blur(gray, (5, 5))
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# find the largest contour assuming it will be some nice rectangle
ctrs, hier = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
largest_ctr_idx = max(range(len(ctrs)), key=lambda i: cv2.contourArea(ctrs[i]))
# get the contour's rotation angle
angle = cv2.minAreaRect(ctrs[largest_ctr_idx])[-1]
if angle < -45:
angle += 90
# find rotation matrix and apply it woohoo
h, w = img.shape[:2]
center = (w // 2, h // 2)
m = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, m, (w, h), flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
找到轮廓:
校正图像: