Develop Reference

Detect black ink blob on paper - Opencv Android

I'm new to openCV, I've been getting into the samples provided for Android.
My goals is to detect color-blobs so I started with color-blob-detection sample.
I'm converting color image to grayscale and then thresholding using a binary threshold.
The background is white, blobs are black. I want to detect those black blobs. Also, I would like to draw their contour in color but I'm not able to do it because image is black and white.
I've managed to accomplish this in grayscale but I don't prefer how the contours are drawn, it's like color tolerance is too high and the contour is bigger than the actual blob (maybe blobs are too small?). I guess this 'tolerance' I talk about has something to do with setHsvColor but I don't quite understand that method.
Thanks in advance! Best Regards
UPDATE MORE INFO
The image I want to track is of ink splits. Imagine a white piece of paper with black ink splits. Right now I'm doing it in real-time (camera view). The actual app would take a picture and analyse that picture.
As I said above, I took color-blob-detection sample (android) from openCV GitHub repo. And I add this code in the onCameraFrame method (in order to convert it to black and white in real-time) The convertion is made so I don't mind if ink is black, blue, red:
mRgba = inputFrame.rgba();
/**************************************************************************/
/** BLACK AND WHITE **/
// Convert to Grey
Imgproc.cvtColor(inputFrame.gray(), mRgba, Imgproc.COLOR_GRAY2RGBA, 4);
Mat blackAndWhiteMat = new Mat ( H, W, CvType.CV_8U, new Scalar(1));
double umbral = 100.0;
Imgproc.threshold(mRgba, blackAndWhiteMat , umbral, 255, Imgproc.THRESH_BINARY);
// convert back to bitmap for displaying
Bitmap resultBitmap = Bitmap.createBitmap(mRgba.cols(), mRgba.rows(), Bitmap.Config.ARGB_8888);
blackAndWhiteMat.convertTo(blackAndWhiteMat, CvType.CV_8UC1);
Utils.matToBitmap(blackAndWhiteMat, resultBitmap);
/**************************************************************************/
This may not be the best way but it works.
Now I want to detect black blobs (ink splits). I guess they are detected because the Logcat (log entry of sample app) throws the number of contours detected, but I'm not able to see them because the image is black and white and I want the contour to be red, for example.
Here's an example image:-
And here is what I get using RGB (color-blob-detection as is, not black and white image). Notice how small blobs are not detected. (Is it possible to detect them? or are they too small?)
Thanks for your help! If you need more info I would gladly update this question
UPDATE: GitHub repo of color-blob-detection sample (second image)
GitHub Repo of openCV sample for Android

The solution is based on a combination of adaptive Image thresholding and use of the connected-component algorithm.
Assumption - The paper is the most lit area of the image whereas the ink spots on the paper are darkest regions.
from random import Random
import numpy as np
import cv2
def random_color(random):
"""
Return a random color
"""
icolor = random.randint(0, 0xFFFFFF)
return [icolor & 0xff, (icolor >> 8) & 0xff, (icolor >> 16) & 0xff]
#Read as Grayscale
img = cv2.imread('1-input.jpg', 0)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
# Gaussian to remove noisy region, comment to see its affect.
img = cv2.medianBlur(img,5)
#Find average intensity to distinguish paper region
avgPixelIntensity = cv2.mean( img )
print "Average intensity of image: ", avgPixelIntensity[0]
# Generate mask to distinguish paper region
#0.8 - used to ignore ill-illuminated region of paper
mask = cv2.inRange(img, avgPixelIntensity[0]*0.8, 255)
mask = 255 - mask
cv2.imwrite('2-maskedImg.jpg', mask)
#Approach 1
# You need to choose 4 or 8 for connectivity type(border pixels)
connectivity = 8
# Perform the operation
output = cv2.connectedComponentsWithStats(mask, connectivity, cv2.CV_8U)
# The first cell is the number of labels
num_labels = output[0]
# The second cell is the label matrix
labels = output[1]
# The third cell is the stat matrix
stats = output[2]
# The fourth cell is the centroid matrix
centroids = output[3]
cv2.imwrite("3-connectedcomponent.jpg", labels)
print "Number of labels", num_labels, labels
# create the random number
random = Random()
for i in range(1, num_labels):
print stats[i, cv2.CC_STAT_LEFT], stats[i, cv2.CC_STAT_TOP], stats[i, cv2.CC_STAT_WIDTH], stats[i, cv2.CC_STAT_HEIGHT]
cv2.rectangle(cimg, (stats[i, cv2.CC_STAT_LEFT], stats[i, cv2.CC_STAT_TOP]),
(stats[i, cv2.CC_STAT_LEFT] + stats[i, cv2.CC_STAT_WIDTH], stats[i, cv2.CC_STAT_TOP] + stats[i, cv2.CC_STAT_HEIGHT]), random_color(random), 2)
cv2.imwrite("4-OutputImage.jpg", cimg)
The Input Image
Masked Image from thresholding and invert operation.
Use of connected component.
Overlaying output of connected component on input image.

Opencv increasing accuracy of threshold

I am working on an app that is expected to remove image backgrounds using opencv, at first I tried using grabcut but it was too slow and the results were not always accurate, then I tried using threshold, although the results are not yet close th grabcut, its very fast and looks like a better, So my code is first looking at the image hue and analying which portion of it appears more, that portion is taken in as the background, the issue is at times its getting the foreground as background below is my code:
private Bitmap backGrndErase()
{
Bitmap bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.skirt);
Log.d(TAG, "bitmap: " + bitmap.getWidth() + "x" + bitmap.getHeight());
bitmap = ResizeImage.getResizedBitmap(bitmap, calculatePercentage(40, bitmap.getWidth()), calculatePercentage(40, bitmap.getHeight()));
Mat frame = new Mat();
Utils.bitmapToMat(bitmap, frame);
Mat hsvImg = new Mat();
List<Mat> hsvPlanes = new ArrayList<>();
Mat thresholdImg = new Mat();
// int thresh_type = Imgproc.THRESH_BINARY_INV;
//if (this.inverse.isSelected())
int thresh_type = Imgproc.THRESH_BINARY;
// threshold the image with the average hue value
hsvImg.create(frame.size(), CvType.CV_8U);
Imgproc.cvtColor(frame, hsvImg, Imgproc.COLOR_BGR2HSV);
Core.split(hsvImg, hsvPlanes);
// get the average hue value of the image
double threshValue = this.getHistAverage(hsvImg, hsvPlanes.get(0));
Imgproc.threshold(hsvPlanes.get(0), thresholdImg, threshValue, mThresholdValue, thresh_type);
// Imgproc.adaptiveThreshold(hsvPlanes.get(0), thresholdImg, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 11, 2);
Imgproc.blur(thresholdImg, thresholdImg, new Size(5, 5));
// dilate to fill gaps, erode to smooth edges
Imgproc.dilate(thresholdImg, thresholdImg, new Mat(), new Point(-1, -1), 1);
Imgproc.erode(thresholdImg, thresholdImg, new Mat(), new Point(-1, -1), 3);
Imgproc.threshold(thresholdImg, thresholdImg, threshValue, mThresholdValue, Imgproc.THRESH_BINARY);
//Imgproc.adaptiveThreshold(thresholdImg, thresholdImg, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 11, 2);
// create the new image
Mat foreground = new Mat(frame.size(), CvType.CV_8UC3, new Scalar(255, 255, 255));
frame.copyTo(foreground, thresholdImg);
Utils.matToBitmap(foreground,bitmap);
//return foreground;
alreadyRun = true;
return bitmap;
}
the method responsible for Hue:
private double getHistAverage(Mat hsvImg, Mat hueValues)
{
// init
double average = 0.0;
Mat hist_hue = new Mat();
// 0-180: range of Hue values
MatOfInt histSize = new MatOfInt(180);
List<Mat> hue = new ArrayList<>();
hue.add(hueValues);
// compute the histogram
Imgproc.calcHist(hue, new MatOfInt(0), new Mat(), hist_hue, histSize, new MatOfFloat(0, 179));
// get the average Hue value of the image
// (sum(bin(h)*h))/(image-height*image-width)
// -----------------
// equivalent to get the hue of each pixel in the image, add them, and
// divide for the image size (height and width)
for (int h = 0; h < 180; h++)
{
// for each bin, get its value and multiply it for the corresponding
// hue
average += (hist_hue.get(h, 0)[0] * h);
}
// return the average hue of the image
average = average / hsvImg.size().height / hsvImg.size().width;
return average;
}
A sample of the input and output:[
Input Image 2 and Output:
Input Image 3 and Output:

Indeed, as others have said you are unlikely to get good results just with a threshold on hue. You can use something similar to GrabCut, but faster.
Under the hood, GrabCut calculates foreground and background histograms, then calculates the probability of each pixel being FG/BG based on these histograms, and then optimizes the resulting probability map using graph cut to obtain a segmentation.
Last step is most expensive, and it may be ignored depending on the application. Instead, you may apply the threshold to the probability map to obtain a segmentation. It may (and will) be worse than GrabCut, but will be better than your current approach.
There are some points to consider for this approach. The choice of histogram model would be very important here. You can either consider 2 channels in some space like YUV or HSV, consider 3 channels of RGB, or consider 2 channels of normalized RGB. You also have to select an appropriate bin size for those histograms. Too small bins would lead to 'overtraining', while too large will reduce the precision. The tradeoffs between those are a topic for a separate discussion, in brief - I would advice using RGB with 64 bins per channel for start and then see what changes are better for your data.
Also, you can get better results for coarse binning if you use interpolation to get values between bins. In past I have used trilinear interpolation and it was kind of good, compared to no interpolation at all.
But remember that there are no guarantees that your segmentation will be correct without prior knowledge on object shape, either with GrabCut, thresholding or this approach.

I would try again Grabcut, it is one of the best segmentation methods available. This is the result I get
cv::Mat bgModel,fgModel; // the models (internally used)
cv::grabCut(image,// input image
object_mask,// segmentation result
rectang,// rectangle containing foreground
bgModel,fgModel, // models
5,// number of iterations
cv::GC_INIT_WITH_RECT); // use rectangle
// Get the pixels marked as likely foreground
cv::compare(object_mask,cv::GC_PR_FGD,object_mask,cv::CMP_EQ);
cv::threshold(object_mask, object_mask, 0,255, CV_THRESH_BINARY); //ensure the mask is binary
The only problem of Grabcut is that you have to give as an input a rectangle containing the object you want to extract. Apart from that it works pretty well.

Your method of finding average hue is WRONG! As you most probably know, hue is expressed as angle and takes value in [0,360] range. Therefore, a pixel with hue 360 essentially has same colour as a pixel with hue 0 (both are pure red). In the same way, a pixel with hue 350 is actually closer to a pixel with hue 10 than a pixel with hue, say for example, 300.
As for opencv, cvtColor function actually divides calculated hue value by 2 to fit it in 8 bit integer. Thus, in opencv, hue values wrap after 180. Now, consider we have two red(ish) pixels with hues 10 and 170. If we take their average, we will get 90 — hue of pure cyan, the exact opposite of red — which is not our desired value.
Therefore, to correctly find the average hue, you need to first find average pixel value in RGB colour space, then calculate the hue from this RGB value. You can create 1x1 matrix with average RGB pixel and convert it to HSV/HSL.
Following the same reasoning, applying threshold to hue image doesn't work flawlessly. It does not consider wrapping of hue values.
If I understand correctly, you want to find pixels with similar hue as the background. Assuming we know the colour of background, I would do this segmentation in RGB space. I would introduce some tolerance variable. I would use the background pixel value as centre and this tolerance as radius and thus define a sphere in RGB colour space. Now, rest is inspecting each pixel value, if it falls inside this sphere, then classify as background; otherwise, regard it as foreground pixel.

why detected object jumps using OpenCV

Ok I have a strange problem. I'll try to describe it as best as I can.
I've learned my app to detect a car when looking on it from the side
Imgproc.cvtColor(aInputFrame, grayscaleImage, Imgproc.COLOR_RGBA2RGB);
MatOfRect objects = new MatOfRect();
// Use the classifier to detect cars
if (cascadeClassifier != null) {
cascadeClassifier.detectMultiScale(grayscaleImage, objects, 1.1, 1,
2, new Size(absoluteObjectSize, absoluteObjectSize),
new Size());
}
for (int i = 0; i < dataArray.length; i++) {
Core.rectangle(aInputFrame, dataArray[i].tl(), dataArray[i].br(),
new Scalar(0, 255, 0, 255), 3);
mRenderer.setCameraPosition(-5, 5, 60f);
}
Now, this code works nice. I mean that i detects cars and it marks them with green rectangle. The problem is that the marked rectangle jumps like hell. I mean even when the phone is hold still the rectangle jumps from left to right to middle. There is never one still rectangle. I hope I've described the problem properly. I would like to stabilizy the marking cause I want to draw an overlay based on it and I can't make it to jump like this

See(1) the parameters for detectMultiScale and it expects
image of type CV_8U. You will need to convert to gray scale image
with COLOR_RGBA2GRAY instead of COLOR_RGBA2RGB
In detectMultiScale, increase the number of neighbours parameter to avoid false positives.
Suggestion: If input is a video stream, don't run
detectMultiScale on every frame. It is slow even if you use LBP
cascades. Try detection in one frame, followed by tracking techniques.

digit detection from the image in android

I am searching for how to extract a digital number from the image in android. I have to take a picture then i need to get numbers from image. OpenCV is a option . can we convert opencv into android ? Kindly suggest me any proper way. I will be grateful to you.

There are many OCR for Android
check there links
https://github.com/rmtheis/android-ocr
https://github.com/GautamGupta/Simple-Android-OCR
http://www.abbyy.com/mobileocr/android/
best OCR (Optical character recognition) example in android

OpenCV supports Android platform. You have to set up OpenCV4Android, it's instructions step by step here.
http://docs.opencv.org/doc/tutorials/introduction/android_binary_package/O4A_SDK.html
However OpenCV is not an option but only a step. Then you have to use a character recognition engine. Most popular one is Tesseract-ocr. But it is really not easy task.
Also, they often recognize all characters. If you could achieve it, extracting the digits will be the easiest part in Java.

this works for me you just need to specify the number size` ArrayList output=new ArrayList<>();
cvtColor(input,input,COLOR_BGRA2GRAY);
Mat img_threshold = new Mat();
threshold(input, img_threshold, 60, 255,THRESH_BINARY_INV);
Mat img_contours =copy(img_threshold);
//Find contours of possibles characters
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
findContours(img_contours, contours,new Mat(), Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_NONE); // all pixels of each contours
contours=sort(contours);
// Draw blue contours on a white image
Mat result=copy(img_threshold);
cvtColor(result, result, COLOR_GRAY2BGR);
drawContours(result,contours,
-1, // draw all contours
new Scalar(0,0,255), // in blue
1); // with a thickness of 1
//Start to iterate to each contour founded
ListIterator<MatOfPoint> itc = contours.listIterator();
//Remove patch that are no inside limits of aspect ratio and area.
while (itc.hasNext())
{
//Create bounding rect of object
MatOfPoint mp = new MatOfPoint(itc.next().toArray());
Rect mr = boundingRect(mp);
rectangle(result,new Point(mr.x,mr.y),new Point(mr.x+mr.width,mr.y+mr.height),new Scalar(0,255,0));
Mat auxRoi=new Mat(img_threshold,mr);
if (OCR_verifySizes(auxRoi))
{
output.add(preprocessChar(auxRoi));
}
}
return output;`

Weird issue with OpenCV drawContours on android

I stumbled upon a weird problem with OpenCV drawContours on android.
Sometimes, (without apparent pattern) function drawContours produces this:
drawContours http://img17.imageshack.us/img17/9031/screenshotgps.png
while it should obviously produce just the white part.
To put it in context:
I detect edges using canny algorithm and then I find contours with
Imgproc.findContours(dil, contours, dummy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
Then i select several contours that fit some requirements and I add them to a list:
List<MatOfPoint> goodContours = new ArrayList<MatOfPoint>();
After that I randomly select one contour and I draw it (filled with white) on mat and convert it to android Bitmap:
Mat oneContour = new Mat(orig.rows(), orig.cols(), CvType.CV_8UC1);
int index = (int) (Math.random() * goodContours.size());
Imgproc.drawContours(oneContour, goodContours, index, new Scalar(255, 255, 255), -1);
Bitmap oneContourBitmap = Bitmap.createBitmap(oneContour.cols(), oneContour.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(oneContour, oneContourBitmap);
Most of the times I get what I expect: white patch on a pure black background, but sometimes I get the above. I'm totally at a loss here. I suspect there could be some memory leakage but I try hard to release all Mat's immediately after they are of no use anymore (I also tried to release them at the end of a function where it all happens but without effect) but I'm unable to pinpoint the source of the problem.
Has anyone had similar issues?
I first discovered this on OpenCV 2.4.0 but it stays the same on 2.4.3.
Any suggestion is appreciated.