I am using pixel colour inspection to detect collisions. I know there are other ways to achieve this but this is my use case.
I draw a shape cloned from the main canvas on to a second canvas switching the fill and stroke colours to pur black. I then use getImageData() to get an array of pixel colours and inspect them - if I see black I have a collision with something.
However, some pixels are shades of grey because the second canvas is applying antialiasing to the shape. I want only black or transparent pixels.
How can I get the second canvas to be composed of either transparent or black only?
I have achieved this long in the past with Windows GDI via compositing/xor combinations etc. However, GDI did not always apply antialiasing. I guess the answer lies in globalCompositeOperation or filter but I cannot see what settings/filters or sequence to apply.
I appreciate I have not provided sample code but I am hoping that someone can throw me a bone and I'll work up a snippet here which might become a standard cut & paste for posterity from that.
I need to convert an image to greyscale except for a single color. For example, if there is some red in the image (like a red bus), this will remain in color, but the rest of the image will remain in black & white.
I think I should be able to do a rudimentary job of this by going over each pixel individually, such as here: http://brandontreb.com/image-manipulation-retrieving-and-updating-pixel-values-for-a-uiimage . I am assuming I would just leave certain pixels alone if their red component was above a certain amount, and green/blue was below a certain amount. Otherwise, set the pixel to grayscale. Is this a good approach?
I'm more interested in whether or not it is possible to do to the live camera input, such as with a Core Image filter, or using GPUImage, but I haven't been able to find any suitable filters. Any suggestions?
Update:
This seems to be possible using GPUImage with a GPUImageLookupFilter, as per: https://stackoverflow.com/a/19340583/334982
I've created a lookup.png file in Photoshop, by dropping the Saturation for all colours except red to 0. This works ok, but it doesn't seem to grey out all colours. For example, my skin still looks fairly skin coloured, and my brown table is still fairly brown.
I am designing a more powerful color picker for Qt and looking for some advice. How would one go about generating fast real-time color rectangles such as the ones found in Photoshop (for HSB and RGB). I was originally thinking of using QImage and scanline to calculate all the pixels individually but this would probably be too slow.
I was thinking it would be better to write an OpenGL shader. As I can recall you can assign colors to vertices and it would interpolate the changes for you. I just have no idea how this would be done in Qt or if this is even worth the effort.
I am using QGraphicsView to display the rectangle. Any advice would be appreciated.
Ok so looking into QGradients a bit more could you not use multiple QGradient to create the effect you need?
For the last of the 3 examples you could create a single gradient with multiple stops for the colours themselves then overlay this with a QGradient of black (alpha 0) to black (alpha 255) with apropriate stops to get the gradient to come in at the right point.
I have an image of an object taken in a studio. The image is well lighten from multiple sources and stands on a mate white background. the background is also lighten.
Most of the shadows that fall on the background are eliminated by the lights but there are still very little light shadows that I would like to remove.
Until now, the only solutions I found involved in manual intervention. I would like to know if there are known methods for this or if anybody has an idea how to approach such a problem.
The object can also contain white elements and at this point I can't change the background color (to green or blue).
Thanks.
If you have strong contrast between foreground and background you could use a simple floodfill algorithm that stops on hitting a large contrast difference to classify pixels as background and foreground. Then just adjust levels of background to saturate shadows to white while retaining somewhat reasonable edge quality. It helps if your input data is significantly higher resolution than the output. If you have soft edges or just need really good edge quality you'll need to employ an algorithm that for each edge pixel estimates background color, foreground color and transparency. A good approach is the Soft Scissors paper from SIGGRAPH 2007.
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.