I have been checking some jigsaw puzzle games around and I have noticed some of them use a grid image to extract pieces from an image. The image that represent the grid of pieces to extract is an image with white lines (the contour of pieces) and all the rest is just transparent. I have been struggling looking for ways to use this image to extract the pieces for my own game but haven't figured out how to do it.
Could anyone give any tip about how to use this image to extract the pieces?
An example of the image can be seen here. Take into account I have painted the white lines with a blue color to make it visible here at stack overflow:
P.D. Thinking a bit more about it, the only solution I found but I think it is too slow though is use something like a flood fill algorithm to extract the pixels and build the image set using piece center as starting points. This method could be used for any set of pieces with a regular width/height.
Cheers.
Related
I have video frames with elliptical objects in them. I'm trying to detect the main ellipse using regionprops and it works just fine.
However, since I want to speed up the process, I want regionprops to only look for those ellipses in a certain area of the image. I can crop the images each frame, to only have the relevant area left, but I would rather have regionprops look only in specified areas.
Is such an option possible?
Regioprops uses label matrix provided by bwlabel(bw_image) or just logical(grayscale_image) if you suppose that there is only object in the image. To make regionprops process only a part of image you should set irrelevant part of the label matrix to zero.
Say I have an image that looks like this:
I fail to see what approach I could take in order to modify the picture so that all the background color surrounding the image is gone. So, a potential result would be this:
As you can see the white background has been cut out and now is about 2 pixels from the actual shoes.
I don't have just shoes, but I am looking for an algorithm that would let me do that. I am using Ruby and Minimagick, but I guess that first step would be to figure it out the algorithm that I could use.
EDIT: The background is not necessary white.
If I understand you right, this sounds like a simple task that doesn't need any fancy algorithms.
Find the background color of the image. One simple way to do that would be to just take the color of the pixel in, say, the top left corner. There are fancier ways you could use, but this will work for your example image.
Find the leftmost and rightmost columns containing a pixel of some color other than the background. Those columns will be the leftmost and rightmost columns of your cropped image.
Find the topmost and bottommost rows containing a pixel of some color other than the background. Those rows will be the topmost and bottommost rows of your cropped image.
Crop the image to the dimensions found above. If you want, you can adjust the dimensions to leave a border of any size you want around the image.
Use s-t minimal graph cuts algorithm from standard image processing library
I'm trying to perform image recognition via a SIFT algorithm using solid shapes.
To improve the performance, I would like to take the image (captured from an iOS camera), make all non-light colors a dark red and make the remaining pixels transparent.
An example of a before and after of what I'm trying to achieve is attached in the images on this question.
Assumption: the images are always solid-black shapes and printed on plain white paper
Can someone please help me with this or point me in the right direction?
As provided in this code, you can simply iterate over the pixel data and compare each color of the pixel with the value you want to filter. But before you should maybe apply some filters for better results.
I got a png image like this:
The blue color is represent transparent. And all the circle is a pixel group. So, I would like to find the biggest one, and remove all the small pixel, which is not group with the biggest one. In this example, the biggest one is red colour circle, and I will retain it. But the green and yellow are to small, so I will remove them. After that, I will have something like this:
Any ideas? Thanks.
If you consider only the size of objects, use the following algorithm: labellize the connex components of the mask image of the objects (all object pixels are white, transparent ones are black). Then compute the areas of the connex components, and filter them. At this step, you have a label map and a list of authorized labels. You can read the label map and overwrite the mask image with setting every pixel to white if it has an authorized label.
OpenCV does not seem to have a labelling function, but cvFloodFill can do the same thing with several calls: for each unlabeled white pixel, call FloodFill with this pixel as marker. Then you can store the result of this step in an array (of the size of the image) by assigning each newly assigned pixel with its label. Repeat this as long as you have unlabellized pixels.
Else you can recode the connex component function for binary images, this algorithm is well known and easy to implement (maybe start with Matlab's bwlabel).
The handiest way to filter objects if you have an a priori knowledge of their size is to use morphological operators. In your case, with opencv, once you've loaded your image (OpenCV supports PNG), you have to do an "openning", that is an erosion followed by a dilation.
The small objects (smaller than the size of the structuring element you chose) will disappear with erosion, while the bigger will remain and be restored with the dilation.
(reference here, cv::morphologyEx).
The shape of the big object might be altered. If you're only doing detection, it is harmless, but if you want your object to avoid transformation you'll need to apply a "top hat" transform.
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.