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Cropping an image with an outline

I have a captured image, the image consists of a table. I want to crop the table out of that image.
This is a sample image.
Can someone suggest what can be done?
I have to use it in android.

Use a hough transform to find the lines in the image.
OpenCV can easily do this and has java bindings. See the tutorial on this page on how to do something very similar.
https://docs.opencv.org/3.4.1/d9/db0/tutorial_hough_lines.html
Here is the java code provided in the tutorial:
import org.opencv.core.*;
import org.opencv.core.Point;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
class HoughLinesRun {
public void run(String[] args) {
// Declare the output variables
Mat dst = new Mat(), cdst = new Mat(), cdstP;
String default_file = "../../../../data/sudoku.png";
String filename = ((args.length > 0) ? args[0] : default_file);
// Load an image
Mat src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
// Check if image is loaded fine
if( src.empty() ) {
System.out.println("Error opening image!");
System.out.println("Program Arguments: [image_name -- default "
+ default_file +"] \n");
System.exit(-1);
}
// Edge detection
Imgproc.Canny(src, dst, 50, 200, 3, false);
// Copy edges to the images that will display the results in BGR
Imgproc.cvtColor(dst, cdst, Imgproc.COLOR_GRAY2BGR);
cdstP = cdst.clone();
// Standard Hough Line Transform
Mat lines = new Mat(); // will hold the results of the detection
Imgproc.HoughLines(dst, lines, 1, Math.PI/180, 150); // runs the actual detection
// Draw the lines
for (int x = 0; x < lines.rows(); x++) {
double rho = lines.get(x, 0)[0],
theta = lines.get(x, 0)[1];
double a = Math.cos(theta), b = Math.sin(theta);
double x0 = a*rho, y0 = b*rho;
Point pt1 = new Point(Math.round(x0 + 1000*(-b)), Math.round(y0 + 1000*(a)));
Point pt2 = new Point(Math.round(x0 - 1000*(-b)), Math.round(y0 - 1000*(a)));
Imgproc.line(cdst, pt1, pt2, new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
}
// Probabilistic Line Transform
Mat linesP = new Mat(); // will hold the results of the detection
Imgproc.HoughLinesP(dst, linesP, 1, Math.PI/180, 50, 50, 10); // runs the actual detection
// Draw the lines
for (int x = 0; x < linesP.rows(); x++) {
double[] l = linesP.get(x, 0);
Imgproc.line(cdstP, new Point(l[0], l[1]), new Point(l[2], l[3]), new Scalar(0, 0, 255), 3, Imgproc.LINE_AA, 0);
}
// Show results
HighGui.imshow("Source", src);
HighGui.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst);
HighGui.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP);
// Wait and Exit
HighGui.waitKey();
System.exit(0);
}
}
public class HoughLines {
public static void main(String[] args) {
// Load the native library.
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
new HoughLinesRun().run(args);
}
}
Lines or LinesP will contain the found lines. Instead of drawing them (as in the example) you will want to manipulate them a little further.
Sort the found lines by slope.
The two largest clusters will be horizontal lines and then vertical lines.
For the horizontal lines calculate and sort by the y intercept.
The largest y intercept describes the top of the table.
The smallest y intercept is the bottom of the table.
For the vertical lines calculate and sort by the x intercept.
The largest x intercept is the right side of the table.
The smallest x intercept is the left side of the table.
You'll now have the coordinates of the four table corners and can do standard image manipulation to crop/rotate etc. OpenCV can help you will this step too.

Convert your image to grayscale.
Threshold your image to drop noise.
Find the minimum area rect of the non-blank pixels.
In python the code would look like:
import cv2
import numpy as np
img = cv2.imread('table.jpg')
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 222, 255, cv2.THRESH_BINARY )
# write out the thresholded image to debug the 222 value
cv2.imwrite("thresh.png", thresh)
indices = np.where(thresh != 255)
coords = np.array([(b,a) for a, b in zip(*(indices[0], indices[1]))])
# coords = cv2.convexHull(coords)
rect = cv2.minAreaRect(coords)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(img, [box], 0, (0, 0, 255), 2)
cv2.imwrite("box.png", img)
For me this produces the following image.
If your image didn't have the red squares it would be a tighter fit.

findContours in opencv Java

if (DETECT_RED_OBJECTS_ONLY)
{
Imgproc.cvtColor(inputFrame, gray,
Imgproc.COLOR_BGR2RGB);
}
else
{
Imgproc.cvtColor(inputFrame, gray, Imgproc.COLOR_BGR2GRAY);
}
Imgproc.cvtColor(inputFrame, dst, Imgproc.COLOR_BGR2RGB);
// down-scale and upscale the image to filter out the noise
Imgproc.pyrDown(gray, downscaled, new Size(gray.cols() / 2, gray.rows() 2));
Imgproc.pyrUp(downscaled, upscaled, gray.size());
if (DETECT_RED_OBJECTS_ONLY) {
// convert the image from RGBA to HSV
Imgproc.cvtColor(upscaled, hsv, Imgproc.COLOR_RGB2HSV);
// threshold the image for the lower and upper HSV red range
Core.inRange(hsv, HSV_LOW_RED1, HSV_LOW_RED2, lowerRedRange);
Core.inRange(hsv, HSV_HIGH_RED1, HSV_HIGH_RED2, upperRedRange);
// put the two thresholded images together
Core.addWeighted(lowerRedRange, 1.0, upperRedRange, 1.0, 0.0, bw);
// apply canny to get edges only
System.out.println(bw);
Imgproc.Canny(bw, bw, 0, 255);
} else {
// Use Canny instead of threshold to catch squares with gradient shading
Imgproc.Canny(upscaled, bw, 0, 255);
}
// dilate canny output to remove potential
// holes between edge segments
Imgproc.dilate(bw, bw, new Mat(), new Point(-1, 1), 1);
// find contours and store them all as a list
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
contourImage = bw.clone();
Imgproc.findContours(
contourImage,
contours,
hierarchyOutputVector,
Imgproc.RETR_EXTERNAL,
Imgproc.CHAIN_APPROX_SIMPLE
);
System.out.println("contours" + contours);
// loop over all found contours
for (MatOfPoint cnt : contours) {
MatOfPoint2f curve = new MatOfPoint2f(cnt.toArray());
// approximates a polygonal curve with the specified precision
Imgproc.approxPolyDP(
curve,
approxCurve,
0.02 * Imgproc.arcLength(curve, true),
true
);
int numberVertices = (int) approxCurve.total();
When I use triangle as an input image , this one works and detect triangle[counters become 1 and numberVertices becomes 3]. But when i input a rectangle image , contours become 2 and numberVertices becomes 2. It must be 4 . Isn't it. Can anyone help me to find the bug.

If you are using this in latest visual studio, I suggest you to use :
JavaList List<MatOfPoint> contours = new JavaList<MatOfPoint>();
this might change the behavior of your contours. Please let me know if this fixed your bug. Otherwise lets try with something else

Android OpenCV: color detection giving weird result

I've just learnt how to detect a color from OpenCV Java, Getting region of interest from image.
Ultimately, I want to know how can I detect the AA battery(with and or without black tape)
I'm now trying to detect the battery in the picture, but the battery is not fully black, and in turn giving me weird result:
I covered the battery with black tape and tried again the result seems better but it is detecting the battery in two separate sections:
Code:
private Bitmap findRoiBlack(Bitmap sourceBitmap) {
Bitmap roiBitmap = null;
Scalar green = new Scalar(0, 255, 0, 255);
Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(sourceBitmap, sourceMat);
Mat roiTmp = sourceMat.clone();
final Mat hsvMat = new Mat();
sourceMat.copyTo(hsvMat);
// convert mat to HSV format for Core.inRange()
Imgproc.cvtColor(hsvMat, hsvMat, Imgproc.COLOR_RGB2HSV);
Scalar lowerb = new Scalar(0, 0, 0); // lower color border for BLACK
Scalar upperb = new Scalar(180, 255, 30); // upper color border for BLACK
//Scalar lowerb = new Scalar(0, 0, 200); // lower color border for WHITE
//Scalar upperb = new Scalar(180, 255, 255); // upper color border for WHITE
Core.inRange(hsvMat, lowerb, upperb, roiTmp); // select only blue pixels
// find contours
List<MatOfPoint> contours = new ArrayList<>();
List<RotatedRect> boundingRects = new ArrayList<>();
Imgproc.findContours(roiTmp, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
// find appropriate bounding rectangles
for (MatOfPoint contour : contours) {
MatOfPoint2f areaPoints = new MatOfPoint2f(contour.toArray());
RotatedRect boundingRect = Imgproc.minAreaRect(areaPoints);
double rectangleArea = boundingRect.size.area();
// test min ROI area in pixels
if (rectangleArea > 400) {
Point rotated_rect_points[] = new Point[4];
boundingRect.points(rotated_rect_points);
Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points));
// test vertical ROI orientation
if (rect.height > rect.width) {
Imgproc.rectangle(sourceMat, rect.tl(), rect.br(), green, 3);
}
}
}
roiBitmap = Bitmap.createBitmap(sourceMat.cols(), sourceMat.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(sourceMat, roiBitmap);
return roiBitmap;
}

Easiest way - is add color marker to battery. Other way is set solid, well distinguishable background for vertical channel of Your installation (may be even backlight - in this case You should find just black/low brightness object on white/high brightness background). If it's not possible and You have solid background - try to "invert" approach: don't try find battery (because it has many colors) - find background (because it has one solid color) - object with "non background" colors probably battery (and You have additional hints: battery is "vertical" rectangle with 1/4 proportions (AAA battery has 10.5 mm diameter and 44.6 mm length), it is approximately on the vertical center of image and has chromium-plated high brightness elements on top and bottom, etc.).

Best parameters for pupil detection using hough? java opencv

--------------read edit below---------------
I am trying to detect the edge of the pupils and iris within various images. I am altering parameters and such but I can only manage to ever get one iris/pupil outline correct, or get unnecessary outlines in the background, or none at all. Is the some specific parameters that I should try to try and get the correct outlines. Or is there a way that I can crop the image just to the eyes, so the system can focus on that part?
This is my UPDATED method:
private void findPupilIris() throws IOException {
//converts and saves image in grayscale
Mat newimg = Imgcodecs.imread("/Users/.../pic.jpg");
Mat des = new Mat(newimg.rows(), newimg.cols(), newimg.type());
Mat norm = new Mat();
Imgproc.cvtColor(newimg, des, Imgproc.COLOR_BGR2HSV);
List<Mat> hsv = new ArrayList<Mat>();
Core.split(des, hsv);
Mat v = hsv.get(2); //gets the grey scale version
Imgcodecs.imwrite("/Users/Lisa-Maria/Documents/CapturedImages/B&Wpic.jpg", v); //only writes mats
CLAHE clahe = Imgproc.createCLAHE(2.0, new Size(8,8) ); //2.0, new Size(8,8)
clahe.apply(v,v);
// Imgproc.GaussianBlur(v, v, new Size(9,9), 3); //adds left pupil boundary and random circle on 'a'
// Imgproc.GaussianBlur(v, v, new Size(9,9), 13); //adds right outer iris boundary and random circle on 'a'
Imgproc.GaussianBlur(v, v, new Size(9,9), 7); //adds left outer iris boundary and random circle on left by hair
// Imgproc.GaussianBlur(v, v, new Size(7,7), 15);
Core.addWeighted(v, 1.5, v, -0.5, 0, v);
Imgcodecs.imwrite("/Users/.../after.jpg", v); //only writes mats
if (v != null) {
Mat circles = new Mat();
Imgproc.HoughCircles( v, circles, Imgproc.CV_HOUGH_GRADIENT, 2, v.rows(), 100, 20, 20, 200 );
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
System.out.println("circles.cols() " + circles.cols());
if(circles.cols() > 0) {
System.out.println("1");
for (int x = 0; x < circles.cols(); x++) {
System.out.println("2");
double vCircle[] = circles.get(0, x);
if(vCircle == null) {
break;
}
Point pt = new Point(Math.round(vCircle[0]), Math.round(vCircle[1]));
int radius = (int) Math.round(vCircle[2]);
//draw the found circle
Imgproc.circle(v, pt, radius, new Scalar(255,0,0),2); //newimg
//Imgproc.circle(des, pt, radius/3, new Scalar(225,0,0),2); //pupil
Imgcodecs.imwrite("/Users/.../Houghpic.jpg", v); //newimg
//draw the mask: white circle on black background
// Mat mask = new Mat( new Size( des.cols(), des.rows() ), CvType.CV_8UC1 );
// Imgproc.circle(mask, pt, radius, new Scalar(255,0,0),2);
// des.copyTo(des,mask);
// Imgcodecs.imwrite("/Users/..../mask.jpg", des); //newimg
Imgproc.logPolar(des, norm, pt, radius, Imgproc.WARP_FILL_OUTLIERS);
Imgcodecs.imwrite("/Users/..../Normalised.jpg",norm);
}
}
}
}
Result: hough pic

Following discussion in comments, I am posting a general answer with some results I got on the worst case image uploaded by the OP.
Note : The code I am posting is in Python, since it is the fastest for me to write
Step 1. As you ask for a way to crop the image, so as to focus on the eyes only, you might want to look at Face Detection. Since, the image essentially requires to find eyes only, I did the following:
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
eyes = eye_cascade.detectMultiScale(v) // v is the value channel of the HSV image
// The results "eyes" gives you the dimensions of the rectangle where the eyes are detected as [x, y, w, h]
// Just for drawing
cv2.rectangle(v, (x1, y1), (x1+w1, y1+h1), (0, 255, 0), 2)
cv2.rectangle(v, (x2, y2), (x2+w2, y2+h2), (0, 255, 0), 2)
Now, once you have the bounding rectangles, you can crop the rectangles from the image like:
crop_eye1 = v[y1:y1+h1, x1:x1+w1]
crop_eye2 = v[y2:y2+h2, x2:x2+w2]
After you obtain the rectangles, I would suggest looking into different color spaces instead of RGB/BGR, HSV/Lab/Luv in particular.
Because the R, G, and B components of an object’s color in a digital image are all correlated with the amount of light hitting the object, and therefore with each other, image descriptions in terms of those components make object discrimination difficult. Descriptions in terms of hue/lightness/chroma or hue/lightness/saturation are often more relevant
Then, once, you have the eyes, its time to equalize the contrast of the image, however, I suggest using CLAHE and play with the parameters for clipLimit and tileGridSize. Here is a code which I implemented a while back in Java:
private static Mat clahe(Mat image, int ClipLimit, Size size){
CLAHE clahe = Imgproc.createCLAHE();
clahe.setClipLimit(ClipLimit);
clahe.setTilesGridSize(size);
Mat dest_image = new Mat();
clahe.apply(image, dest_image);
return dest_image;
}
Once you are satisfied, you should sharpen the image so that HoughCircle is robust. You should look at unsharpMask. Here is the code in Java for UnsharpMask I implemented in Java:
private static Mat unsharpMask(Mat input_image, Size size, double sigma){
// Make sure the {input_image} is gray.
Mat sharpend_image = new Mat(input_image.rows(), input_image.cols(), input_image.type());
Mat Blurred_image = new Mat(input_image.rows(), input_image.cols(), input_image.type());
Imgproc.GaussianBlur(input_image, Blurred_image, size, sigma);
Core.addWeighted(input_image, 2.0D, Blurred_image, -1.0D, 0.0D, sharpened_image);
return sharpened_image;
}
Alternatively, you could use bilateral filter, which is edge preserving smoothing, or read through this for defining a custom kernel for sharpening image.
Hope it helps and best of luck!

Crop out part from images (findContours?) {opencv, java}

I have this image with boxes containing letters, like this:
I have been able to crop out each box., like this:
Now to my question. How can i crop out the letters only from each box? The desired result looks like this
I would like to use findContours but I am not really sure of how to achieve this since it will detect the noise and everything around as well.

Approach
I suggest the following approach according to this fact that you can extract the box. If you are give the box follow the steps, I think that would work:
Find the center of image
Find the contours in the image - those can be candidates
Find the bounding rectangle of each contour
Find the center of each bounding rectangle
Find the distance of each bounding rectangle from the center of image
Find the minimum distance - your answer
Note: There is a var named pad which control the padding of the result figure!
Do this for all your boxes. I hope that will help!
Good Luck :)
Python Code
# reading image in grayscale
image = cv2.imread('testing2.jpg',cv2.CV_LOAD_IMAGE_GRAYSCALE)
# thresholding to get a binary one
ret, image = cv2.threshold(image, 100,255,cv2.THRESH_BINARY_INV)
# finding the center of image
image_center = (image.shape[0]/2, image.shape[1]/2)
if image is None:
print 'can not read the image data'
# finding image contours
contours, hier = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# finding distance of each contour from the center of image
d_min = 1000
for contour in contours:
# finding bounding rect
rect = cv2.boundingRect(contour)
# skipping the outliers
if rect[3] > image.shape[1]/2 and rect[2] > image.shape[0]/2:
continue
pt1 = (rect[0], rect[1])
# finding the center of bounding rect-digit
c = (rect[0]+rect[2]*1/2, rect[1]+rect[3]*1/2)
d = np.sqrt((c[0] - image_center[0])**2 + (c[1]-image_center[1])**2)
# finding the minimum distance from the center
if d < d_min:
d_min = d
rect_min = [pt1, (rect[2],rect[3])]
# fetching the image with desired padding
pad = 5
result = image[rect_min[0][1]-pad:rect_min[0][1]+rect_min[1][1]+pad, rect_min[0][0]-pad:rect_min[0][0]+rect_min[1][0]+pad]
plt.imshow(result*255, 'gray')
plt.show()
Java Code
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
// reading image
Mat image = Highgui.imread(".\\testing2.jpg", Highgui.CV_LOAD_IMAGE_GRAYSCALE);
// clone the image
Mat original = image.clone();
// thresholding the image to make a binary image
Imgproc.threshold(image, image, 100, 128, Imgproc.THRESH_BINARY_INV);
// find the center of the image
double[] centers = {(double)image.width()/2, (double)image.height()/2};
Point image_center = new Point(centers);
// finding the contours
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(image, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
// finding best bounding rectangle for a contour whose distance is closer to the image center that other ones
double d_min = Double.MAX_VALUE;
Rect rect_min = new Rect();
for (MatOfPoint contour : contours) {
Rect rec = Imgproc.boundingRect(contour);
// find the best candidates
if (rec.height > image.height()/2 & rec.width > image.width()/2)
continue;
Point pt1 = new Point((double)rec.x, (double)rec.y);
Point center = new Point(rec.x+(double)(rec.width)/2, rec.y + (double)(rec.height)/2);
double d = Math.sqrt(Math.pow((double)(pt1.x-image_center.x),2) + Math.pow((double)(pt1.y -image_center.y), 2));
if (d < d_min)
{
d_min = d;
rect_min = rec;
}
}
// slicing the image for result region
int pad = 5;
rect_min.x = rect_min.x - pad;
rect_min.y = rect_min.y - pad;
rect_min.width = rect_min.width + 2*pad;
rect_min.height = rect_min.height + 2*pad;
Mat result = original.submat(rect_min);
Highgui.imwrite("result.png", result);
EDIT:
Java code added!
Result