I am doing a project where i need to find a red laser dot. After changing to HSV color space model and thresholding individual H,S,V components and merging it , i found a laser dot with several noise as well , now i need to subtract all other image components except for the laser dot and the noise with their respective color so that i can process those frame for further processing like template matching to get only the laser dot reducing the noises. Hope you understand the question and Thank You, any similar help is appreciated.
What you're looking to do is apply a mask to an image. A mask is an image where any positive non-zero value acts as an indicator. What you want to do is use the mask to indicate which pixels in the original image you want.
The easiest way to apply a mask is to use the cv2.bitwise_and() function, with your thresholded image as the mask:
masked_img = cv2.bitwise_and(img, img, mask=thresholded_img)
As an example, if this is my image and this is my mask, then this would be the masked image.
I have an image here with a table.. In the column on the right the background is filled with noise
How to detect the areas with noise? I only want to apply some kind of filter on the parts with noise because I need to do OCR on it and any kind of filter will reduce the overall recognition
And what kind of filter is the best to remove the background noise in the image?
As said I need to do OCR on the image
I tried some filters/operations in OpenCV and it seems to work pretty well.
Step 1: Dilate the image -
kernel = np.ones((5, 5), np.uint8)
cv2.dilate(img, kernel, iterations = 1)
As you see, the noise is gone but the characters are very light, so I eroded the image.
Step 2: Erode the image -
kernel = np.ones((5, 5), np.uint8)
cv2.erode(img, kernel, iterations = 1)
As you can see, the noise is gone however some characters on the other columns are broken. I would recommend running these operations on the noisy column only. You might want to use HoughLines to find the last column. Then you can extract that column only, run dilation + erosion and replace this with the corresponding column in the original image.
Additionally, dilation + erosion is actually an operation called closing. This you could call directly using -
cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
As #Ermlg suggested, medianBlur with a kernel of 3 also works wonderfully.
cv2.medianBlur(img, 3)
Alternative Step
As you can see all these filters work but it is better if you implement these filters only in the part where the noise is. To do that, use the following:
edges = cv2.Canny(img, 50, 150, apertureSize = 3) // img is gray here
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, 1000, 50) // last two arguments are minimum line length and max gap between two lines respectively.
for line in lines:
for x1, y1, x2, y2 in line:
print x1, y1
// This gives the start coordinates for all the lines. You should take the x value which is between (0.75 * w, w) where w is the width of the entire image. This will give you essentially **(x1, y1) = (1896, 766)**
Then, you can extract this part only like :
extract = img[y1:h, x1:w] // w, h are width and height of the image
Then, implement the filter (median or closing) in this image. After removing the noise, you need to put this filtered image in place of the blurred part in the original image.
image[y1:h, x1:w] = median
This is straightforward in C++ :
extract.copyTo(img, new Rect(x1, y1, w - x1, h - y1))
Final Result with alternate method
Hope it helps!
My solution is based on thresholding to get the resulted image in 4 steps.
Read image by OpenCV 3.2.0.
Apply GaussianBlur() to smooth image especially the region in gray color.
Mask the image to change text to white and the rest to black.
Invert the masked image to black text in white.
The code is in Python 2.7. It can be changed to C++ easily.
import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# read Danish doc image
img = cv2.imread('./imagesStackoverflow/danish_invoice.png')
# apply GaussianBlur to smooth image
blur = cv2.GaussianBlur(img,(5,3), 1)
# threshhold gray region to white (255,255, 255) and sets the rest to black(0,0,0)
mask=cv2.inRange(blur,(0,0,0),(150,150,150))
# invert the image to have text black-in-white
res = 255 - mask
plt.figure(1)
plt.subplot(121), plt.imshow(img[:,:,::-1]), plt.title('original')
plt.subplot(122), plt.imshow(blur, cmap='gray'), plt.title('blurred')
plt.figure(2)
plt.subplot(121), plt.imshow(mask, cmap='gray'), plt.title('masked')
plt.subplot(122), plt.imshow(res, cmap='gray'), plt.title('result')
plt.show()
The following is the plotted images by the code for reference.
Here is the result image at 2197 x 3218 pixels.
As I know the median filter is the best solution to reduce noise. I would recommend to use median filter with 3x3 window. See function cv::medianBlur().
But be careful when use any noise filtration simultaneously with OCR. Its can lead to decreasing of recognition accuracy.
Also I would recommend to try using pair of functions (cv::erode() and cv::dilate()). But I'm not shure that it will best solution then cv::medianBlur() with window 3x3.
I would go with median blur (probably 5*5 kernel).
if you are planning to apply OCR the image. I would advise you to the following:
Filter the image using Median Filter.
Find contours in the filtered image, you will get only text contours (Call them F).
Find contours in the original image (Call them O).
isolate all contours in O that have intersection with any contour in F.
Faster solution:
Find contours in the original image.
Filter them based on size.
Blur (3x3 box)
Threshold at 127
Result:
If you are very worried of removing pixels that could hurt your OCR detection. Without adding artefacts ea be as pure to the original as possible. Then you should create a blob filter. And delete any blobs that are smaller then n pixels or so.
Not going to write code, but i know this works great as i use this myself, though i dont use openCV (i wrote my own multithreaded blobfilter out of speed reasons). And sorry but i cannot share my code here. Just describing how to do it.
If processing time is not an issue, a very effective method in this case would be to compute all black connected components, and remove those smaller than a few pixels. It would remove all the noisy dots (apart those touching a valid component), but preserve all characters and the document structure (lines and so on).
The function to use would be connectedComponentWithStats (before you probably need to produce the negative image, the threshold function with THRESH_BINARY_INV would work in this case), drawing white rectangles where small connected components where found.
In fact, this method could be used to find characters, defined as connected components of a given minimum and maximum size, and with aspect ratio in a given range.
I had already faced the same issue and got the best solution.
Convert source image to grayscale image and apply fastNlMeanDenoising function and then apply threshold.
Like this -
fastNlMeansDenoising(gray,dst,3.0,21,7);
threshold(dst,finaldst,150,255,THRESH_BINARY);
ALSO use can adjust threshold accorsing to your background noise image.
eg- threshold(dst,finaldst,200,255,THRESH_BINARY);
NOTE - If your column lines got removed...You can take a mask of column lines from source image and can apply to the denoised resulted image using BITWISE operations like AND,OR,XOR.
Try thresholding the image like this. Make sure your src is in grayscale. This method will only retain the pixels which are between 150 and 255 intensity.
threshold(src, output, 150, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
You might want to invert the image as you are trying to negate the gray pixels. After the operation, invert it again to get your desired result.
I'm trying to thin an image by making the border pixels of size 16x24 becoming 0. I'm not trying to get the skeletal image, I'm just trying to reduce the size of the white area. Any methods that I could use? Enlighten me please.
This is the sample image that i'm trying to thin. It is made of 16x24 white blocks
EDIT
I tried to use this
cv::Mat img=cv::imread("image.bmp", CV_LOAD_IMAGE_GRAYSCALE);//image is in binary
cv::Mat mask = img > 0;
Mat kernel = Mat::ones( 16, 24, CV_8U );
erode(mask,mask,kernel);
But the result i got was this
which is not exactly what i wanted. I want to maintain the exact same shape with just 16x24 pixels of white shaved off from the border. Any idea what went wrong?
You want to Erode your image.
Another Description
Late answer, but you should erode your image using a kernel which is twice the size you want to get rid of plus one, like:
Mat kernel = Mat::ones( 24*2+1, 16*2+1, CV_8U );
Notice I changed the places of the height and width of the block, I only know opencv from Python, but I am pretty sure the order is the same as in Python.
I would like to know if the image I read is a Black and white or colored image.
I use Opencv for all my process.
In order to detect it, I currently read my image, convert it from BGR2GRAY and I compare Histogram of the original(read as BGR) to the histogram of the second (known as B&W).
In pseudo code this looks like that:
cv::Mat img = read("img.png", -1);
cv::Mat bw = cvtColor(img.clone(), bw, CV_BGR2GRAY);
if (computeHistogram(img) == computeHistogram(bw))
cout << "Black And White !"<< endl;
Is there a better way to do it ? I am searching for the lightest algo I can Implement and best practices.
Thanks for the help.
Edit: I forgot to say that I convert my images in HSL in order to compare Luminance Histograms.
Storing grayscale images in RGB format causes all three fields to be equal. It means for every pixel in a grayscale image saved in RGB format we have R = G = B. So you can easily check this for your image.
I have a little problem. I need to create the voronoi diagram of a BW image by using openCV and C++. I should have something like the output of the Matlab function voronoin.
The goal is to create a mask for each region of the diagram.
This is an example I made in Matlab:
matlab voronoi diagram
So, for each region I should create a mask or to have a different color.
I tried the openCV function distanceTransform in order to get the voronoi labels.
Mat bwCoresGoodInv = 255 - bwCoresGood;
distanceTransform(bwCoresGoodInv, distTr,voronoiLabels, CV_DIST_L2, CV_DIST_MASK_PRECISE, DIST_LABEL_PIXEL);
namedWindow( "voronoiDistLab", CV_WINDOW_AUTOSIZE );
voronoiLabels = voronoiLabels*5;
imshow( "voronoiDistLab", voronoiLabels );
the results is the following image:
voronoi labels openCV
as you can see in each region there are differents colors(in particular there is something in correspondence to the cell), is there a way to have just a color?
thank you in advance
If you are asking how to get different colors than the gray scale values provided by displaying the labels, one approach (probably not the most efficient) is to run cv::findContours on an edge-detected image of the label image, and then iterate through each contour found and draw it onto a new image, it can be filled or outlined. It's not super exact and can leave gaps, some dilation on the edge image may be required.
It would be very nice if distanceTransform returned a data-structure that mapped the range of intensity values in the label image to every pixel that has that value, maybe with a vector of binary images where the nth image in the vector is a binary mask with an isolated nth label region- but I think as it is now this would have to be done by the user.