How can I select a mask of irregular shape in an image in Matlab without using the imfreehand but automatically? The background of the image is black and I would like to select the entire image as mask just without the black background (everything without the black background).
try this:
threshold=0; % or a different value if needed
mask=image>threshold;
given that the background is truly black, i.e. pixels values are 0, the set threshold to zero. Otherwise, select a value that captures the background (there are ways to do that automatically)
Related
Using GIMP 2, I have an image of a grey chair on a white background, as below:
I now want to set the background to transparent. Therefore, I decided to use GIMP's "Color To Alpha" tool. So, I told it to set all pixels which are white (255, 255, 255) to transparent, as below:
This did set all white pixels to transparent. However, it also set the grey pixels on the chair to be partially transparent, as below:
So when I export this image and place it in front of a background, there is no white box around the chair -- but the background partially shows through the chair.
What am I doing wrong?
First, this question is offtopic here, and should be on https://graphicdesign.stackexchange.com .
Second, it is trivial enough just to answer: the color to alpha plug-in is not there to turn a single color, as seem on the image, to transparency: it is a sophisticated plug-in that will remove one color of your image in a way that, if you lace the new image over a background of the same color the color you removed, you get the original image back.
Thus, in your case, it removed the "whiteness" of your chair, transforming all pixels to different opaque shades of black - so that when placed over white, you get the original image.
To simply remove the white, you have to cick on the Select By Color tool (by default th 5th icon on the toolbox), click on the white background to have it selected, and then just edit->cut. (It won't work if your image layer does not have transparency to start with - if that is the case, prior to edit->cut do Layer->Transparency->Add Alpha Channel).
If you get aliased borders, then, after edit>cut, but prior to dismissing your selection, you can do Select->Border... by 1 or 2px, and then use the color to alpha filter with White on this selection.
For more information on Color to Alpha, I have this other answer its use and comparison with edit-cut here: https://graphicdesign.stackexchange.com/questions/28058/gimp-color-to-alpha-is-not-selectable/28097#28097
Just use a selection to restrict the action of Color to alpha where it matters: backgroundand edge pixels:
Select background with fuzzy select
Select>Grow by one pixel so that the selection ofverlaps the edge pixels
Color>Color to alpha
I have a problem with a binarization method.
I have images with text, which I want to binarize.
I want the text ends up being white, but there are images with the text darker than the background and there are images with text less dark than the background.
I want to binarize images like these, but I want the text in white color in the binarized images.
By the way, I am binarizing images with this code. This code is good for images with text darker than the background but it isn't good for text less darker than the background. I think I need a method to know if the text is more or less dark than the background for to invert or no invert the binarization.
umb = graythresh(originalImage);
binaryImage =(~im2bw(originalImage,umb));
How can I do it?
Thanks a lot for the help
there are 2 possible solutions which I had in mind:
solution1:
generate a grayscale image using rgb2gray function.
generate a histogram from the grayscale image, and ignore the transparent pixels. you can use imhist function.
check what is the histogram maximal value. if the value is high - the background is probably light and the text should be darker than the background. in this case - take the negative image (for example, by using imcomplement), and then binarize it. otherwise - you can binarize it as is.
solution 2:
the solution asserts that the image is simple enough, i.e. doesn't have a lot of connected components other than the letters.
binarize the input image.
divide the image into connected components using bwconncomp function.
for each connected component find it's representative value, it can either be 0 or 1
check what is the most common represantative value. if it is 1 - the letters are dark. in this case take the negative image and than binarize. otherwise - binarize the input image as is.
good luck!
I have an indexed image (2-D, not rgb), and i use imagesc to display the image. This function gives a range from blue to red, which can be set by colormap, and can be viewed by colorbar.
Now, I want to change the background, that is usually 0's or NaN's, to white or black, but that it will not affect or change the range of the colormap/colorbar. I've tried converting all the image to 3-D rgb, but this prevent the option of changing the contrast, or the clims, like in imagesc.
So, there is other way to do that?
EDIT:
#Shai's solution was good, but it caused other problem-
When I have an image with a range of values and the background is NaN's, and I display the image in a specific contrast (by imagesc(img, [-1,1]) for example), I get range of colors between -1 to 1, and i get white in the background (because i put [1 1 1] in the first entry of the colormap), but also all the values under the minimum of contrast (i.e., <-1 in the example) also get the white color instead of the bottom dark blue in the colorbar scale.
Any idea for that...?
Many Thanks.
Consider putting [1, 1, 1] as the first entry of your colormap to get the background as white.
I have more then 1 week reading about selective color change of an image. It meand selcting a color from a color picker and then select a part of image in which I want to change the color and apply the changing of color form original color to color of color picker.
E.g. if I select a blue color in color picker and I also select a red part in the image I should be able to change red color to blue color in all the image.
Another example. If I have an image with red apples and oranges and if I select an apple on the image and a blue color in the color picket, then all apples should be changing the color from red to blue.
I have some ideas but of course I need something more concrete on how to do this
Thank you for reading
As a starting point, consider clustering the colors of your image. If you don't know how many clusters you want, then you will need methods to determine whether to merge or not two given clusters. For the moment, let us suppose that we know that number. For example, given the following image at left, I mapped its colors to 3 clusters, which have the mean colors as shown in the middle, and representing each cluster by its mean color gives the figure at right.
With the output at right, now what you need is a method to replace colors. Suppose the user clicks (a single point) somewhere in your image, then you know the positions in the original image that you will need to modify. For the next image, the user (me) clicked on a point that is contained by the "orange" cluster. Then he clicked on some blue hue. From that, you make a mask representing the points in the "orange" cluster and play with that. I considered a simple gaussian filter followed by a flat dilation 3x5. Then you replace the hues in the original image according to the produced mask (after the low pass filtering, the values on it are also considered as a alpha value for compositing the images).
Not perfect at all, but you could have a better clustering than me and also a much-less-primitive color replacement method. I intentionally skipped the details about clustering method, color space, and others, because I used only basic k-means on RGB without any pre-processing of the input. So you can consider the results above as a baseline for anything else you can do.
Given the image, a selected color, and a target new color - you can't do much that isn't ugly. You also need a range, some amount of variation in color, so you can say one pixel's color is "close enough" while another is clearly "different".
First step of processing: You create a mask image, which is grayscale and varying from 0.0 to 1.0 (or from zero to some maximum value we'll treat as 1.0), and the same size as the input image. For each input pixel, test if its color is sufficiently near the selected color. If it's "the same" or "close enough" put 1.0 in the mask. If it's different, put 0.0. If is sorta borderline, put an in-between value. Exactly how to do this depends on the details of the image.
This might work best in LAB space, and testing for sameness according to the angle of the A,B coordinates relative to their origin.
Once you have the mask, put it aside. Now color-transform the whole image. This might be best done in HSV space. Don't touch the V channel. Add a constant to S, modulo 360deg (or mod 256, if S is stored as bytes) and multiply S by a constant chosen so that the coordinates in HSV corresponding to the selected color is moved to the HSV coordinates for the target color. Convert the transformed S and H, with the unchanged L, back to RGB.
Finally, use the mask to blend the original image with the color-transformed one. Apply this to each channel - red, green, blue:
output = (1-mask)*original + mask*transformed
If you're doing it all in byte arrays, 0 is 0.0 and 255 is 1.0, and be careful of overflow and signed/unsigned problems.
Using images of articles of clothing taken against a consistent background, I would like to make all pixels in the image transparent except for the clothing. What is the best way to go about this? I have researched the algorithms that are common for this and the open source library opencv. Aside from rolling my own or using opencv is there an easy way to do this? I am open to any language or platform.
Thanks
If your background is consistend in an image but inconsistent across images it could get tricky, but here is what I would do:
Separate the image into some intensity/colour form such as YUV or Lab.
Make a histogram over the colour part. Find the most occuring colour, this is (most likely) your background (update) maybe a better trick here would be to find the most occuring colour of all pixels within one or two pixels from the edge of the image.
Starting from the eddges of the image, set all pixels that have that colour and are connected to the edge through pixels of that colour to transparent.
The edge of the piece of clothing is now going to look a bit ugly because it consist of pixels that gain their colour from both the background and the piece of clothing. To combat this you need to do a bit more work:
Find the edge of the piece of clothing through some edge detection mechanism.
Replace the colour of the edge pixels with a blend of the colour just "inside" the edge pixel (i.e. the colour of the clothing in that region) and transparent (if your output image format supports that).
If you want to get really fancy, you increase the transparency depending on how much "like" the background colour the colour of that pixel is.
Basically, find the color of the background and subtract it, but I guess you knew this. It's a little tricky to do this all automatically, but it seems possible.
First, take a look at blob detection with OpenCV and see if this is basically done for you.
To do it yourself:
find the background: There are several options. Probably easiest is to histogram the image, and the large number of pixels with similar values are the background, and if there are two large collections, the background will be the one with a big hole in the middle. Another approach is to take a band around the perimeter as the background color, but this seems inferior as, for example, reflection from a flash could dramatically brighten more centrally located background pixels.
remove the background: a first take at this would be to threshold the image based on the background color, and then run the "open" or "close" algorithms on this, and then use this as a mask to select your clothing article. (The point of open/close is to not remove small background colored items on the clothing, like black buttons on a white blouse, or, say, bright reflections on black clothing.)
OpenCV is a good tool for this.
The trickiest part of this will probably be at the shadow around the object (e.g. a black jacket on a white background will have a continuous gray shadow at some of the edges and where to make this cut?), but if you get this far, post another question.
if you know the exact color intensity of the background and it will never change and the articles of clothing will never coincide with this color, then this is a simple application of background subtraction, that is everything that is not a particular color intensity is considered an "on" pixel, one of interest. You can then use connected component labeling (http://en.wikipedia.org/wiki/Connected_Component_Labeling) to figure out seperate groupings of objects.
for a color image, with the same background on every pictures:
convert your image to HSV or HSL
determine the Hue value of the background (+/-10): do this step once, using photoshop for example, then use the same value on all your pictures.
perform a color threshold: on the hue channel exclude the hue of the background ([0,hue[ + ]hue, 255] typically), for all other channels include the whole value range (0 to 255 typically). this will select pixels which are NOT the background.
perform a "fill holes" operation (normally found along blob analysis or labelling functions) to complete the part of the clothes which may have been of the same color than the background.
now you have an image which is a "mask" of the clothes: non-zero pixels represents the clothes, 0 pixels represents the background.
this step of the processing depends on how you want to make pixels transparent: typically, if you save your image as PNG with an alpha (transparency) channel, use a logical AND (also called "masking") operation between the alpha channel of the original image and the mask build in the previous step.
voilà, the background disappeared, save the resulting image.