After doing some opencv operation, I initialize a new image that I'd like to use. Saving this empty image gives a weird result
The lines I use to save this image are:
Mat dst2 (Size (320, 240), CV_8UC3);
imwrite("bla.jpg", dst2);
I should get a black image, but this is what I get. Moving these two lines to the start of the program everything wordks fine
Anyone had this problem before?
I just noticed that these white lines contain portions from other images I'm processing in the same program
Regards
Because you did not initialize the image with any values, you just defined the size and type, you will get random pixels (or not so random, it is probably showing pieces of pixels in memory).
It is the same concept of using/accessing an uninitialized variable.
To paint the image black you can use Mat::setTo, docs here:
http://docs.opencv.org/modules/core/doc/basic_structures.html#mat-setto
Related
I am using Python 2.7 and I used following Python and Matlab function for removing noises and fill holes in this image
.
1. Code to remove noise and fill holes using Python and Opencv
img = cv2.imread("binar.png",0)
kernel = np.ones((5,5),np.uint8)
open = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
close = cv2.morphologyEx(open, cv2.MORPH_CLOSE, kernel)
Code used in python and scipy using ndimage.binary_closing:
im = cv2.imread("binar.png", cv2.IMREAD_GRAYSCALE)
open_img = ndimage.binary_opening(im)
close_img = ndimage.binary_closing(open_img)
clg = close_img.astype(np.int)
Code used in Matlab: I used imfill and bwareaopen.
The results I got is shown below:
First image from using nd.image.binary_closing. My problem is it doesn't fill all white blobs fully. We can see inbetween minor black portion are still present.
Second image from using cv2.morphologyEx. Same problem in this also, as it also has some minor white portion in between white blobs. Here I faced one more problem. It converts some white pixels into black which should not be otherwise. I mentioned those areas with red color in image 2. Red highlighted portions is connected with larger one blobs but even then they get converted into black pixels.
Third image I got from MATLAB processing in which imfill work perfectly without converting essential white pixels into black.
So, my question is, Is there any method for Python 2.7 with which I can remove noises below certain area and fill the white blobs accurately as in Matlab? One more thing is, I want to find out the centroids and areas of those final processed blobs in last for further used. I can find out these using cv2.connectedComponentsWithStats but I want to find area and centroids after removing noises and filling blobs.
Thanks.
(I think this is not duplicate because I want to do it in Python not in Matlab. )
From Matlab's imfill() documentation:
BW2= imfill(BW,locations) performs a flood-fill operation on background pixels of the input binary image BW, starting from the points specified in locations. (...)
BW2= imfill(BW,'holes') fills holes in the input binary image BW. In this syntax, a hole is a set of background pixels that cannot be reached by filling in the background from the edge of the image.
I2= imfill(I) fills holes in the grayscale image I. In this syntax, a hole is defined as an area of dark pixels surrounded by lighter pixels.
The duplicate that I flagged shows ways to accomplish the third variant usually. However for many images, the second variant will still work fine and is extremely easy to accomplish. From the first variant you see that it mentions a flood-fill operation, which can be implemented in OpenCV with cv2.floodFill(). The second variant gives a really easy method---just flood fill from the edges, and the pixels left over are the black holes which can't be reached from outside. Then if you invert this image, you'll get white pixels for the holes, which you can add to your mask to fill in the holes.
import cv2
import numpy as np
# read image, ensure binary
img = cv2.imread('image.png', 0)
img[img!=0] = 255
# flood fill background to find inner holes
holes = img.copy()
cv2.floodFill(holes, None, (0, 0), 255)
# invert holes mask, bitwise or with img fill in holes
holes = cv2.bitwise_not(holes)
filled_holes = cv2.bitwise_or(img, holes)
cv2.imshow('', filled_holes)
cv2.waitKey()
Note that in this case, I just set the starting pixel for the background at (0,0). However it's possible that there could be, e.g., a white line going down the center which would cut off this operation to stop filling (i.e. stop finding the background) for the other half of the image. The more robust method would be to go through all of the edge pixels on the image, and flood fill every time you come across a black pixel. You can accomplish this more easily with the mask parameter in cv2.floodFill(), which allows you to continue to update the mask each time.
To find the centroids of each blob, you could use contour detection and cv2.moments() to find the centroids of each contour, or you could also do cv2.connectedComponentsWithStats() like you mentioned.
I am writing a program to analyze pictures and crop them around an object in the picture. The program crops the images well, but it leaves a weird gap on the side.
I copied the code from the approved answer on this question:
Opencv c++ detect and crop white region on image
The image I start with looks like this on a larger canvas. I get this result, but I want to get rid of the extra white space on the left side in order to crop super close to the phone case. It can be seen better if you open the image in a new tab.
Please help. I am using opencv and c++ in Visual Studio 2015.
This picture is not correctly cropped because of salt-and-pepper noise. To get rid of it you'd use median blur. You can use blurred image to fill nonBlackList and use this list to correctly crop original image. Since it appears the image was slightly magnified after the noise appeared, you should probably try aperture size at least 5 to get rid of it completly.
cv::Mat in = cv::imread("CropWhite.jpg");
cv::Mat blurred;
cv::medianBlur(in, blurred, 5);
...
if(blurred.at<cv::Vec3b>(j,i) != cv::Vec3b(255,255,255))
{
nonBlackList.push_back(cv::Point(i,j));
}
I have a black area around my image and I want to create a mask using OpenCV C++ that selects just this black area so that I can paint it later. How can i do that without affecting the image itself?
I tried to convert the image to grayscale and then using threshold to convert it to binary, but it affects my image since the result contains black pixels from inside the image.
Another Question : if i want to crop the image instead of paint it, how can i do it??
Thanks in advance,
I would solve the problem like this:
Inverse-binarize the image with a threshold of 1 (i.e. all pixels with the value 0 are set to 1, all others to 0)
use cv::findContours to find white segments
remove segments that don't touch image borders
use cv::drawContours to draw the remaining segments to a mask.
There is probably a more efficient solution in terms of runtime efficiency, but you should be able to prototype my solution quite quickly.
Looking for a way for taking a screenshot of a particular area on the screen in C++. (So not the whole screen) Then it should save it as .png .jpg whatever to use it with another function afterwards.
Also, I am going to use it, somehow, with openCV. Thought i'd mention that, maybe it's a helpful detail.
OpenCV cannot take screenshots from your computer directly. You will need a different framework/method to do this. #Ben is correct, this link would be worth investigating.
Once you have read this image in, you will need to store it into a cv:Mat so that you are able to perform OpenCV operations on it.
In order to crop an image in OpenCV the following code snippet would help.
CVMat * imagesource;
// Transform it into the C++ cv::Mat format
cv::Mat image(imagesource);
// Setup a rectangle to define your region of interest
cv::Rect myROI(10, 10, 100, 100);
// Crop the full image to that image contained by the rectangle myROI
// Note that this doesn't copy the data
cv::Mat croppedImage = image(myROI);
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.