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I have a few images of one object taken from different perspectives, so some part of the object
may be in the shadow. I hope to stitch the images to get one big image. I find the color in the
resultant image doesn't appear correct. Maybe I should average the images in HSV color space.
Can the color can be averaged in RGB color space? For my case, some part may be in shadow, and the images can be averaged i RGB color space?
If you are familiar with the color theory, please give me some information. Thanks.
Regards
Jogging
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I have curved rectangular object based images. There is a reference object on which the images have to be compared and the differences need to be identified.
Reference Image:
New Images:
I want to identify the difference between these images and highlight the difference.
Key Pointers:
I cannot do pixel by pixel comparison as the objects are not exactly in the same pixel
Approximate shape as to that of reference image also is acceptable
I have tried identifying the contours but as the lines are continuous it is difficult identify the defective part only
Since gif uses 8 bit color dept and png uses 24, I can notice the difference between the two picture.
I want to find the way in which i can compare the colors of two images not by looking but with calculated datas.
What I have done till now is that I calculated the BPP of both gif and png image assuming that would be the best option to compare these two format.
I'm not sure if finding the bpp will give me the absolute color difference or if it is even the correct way.
Although a gif has maximum of 256 colors, it would still have high quality as long as the original "raw" image has less than 256 colors (Imaging a cartoon picture that only used 8 colors, no shading/blending etc). In your question you mentioned you could notice the difference between the two picture, then I assume you are talking about some "raw" image that has more than 256 colors, such as a natural photograph with the sky color gradually changing.
In this case, I think you might check the histogram of the images. For the gif image it will have at most 256 entries in the histogram; for the higher quality png image, it should have a histogram that has a matching shape (peaks and valleys), but more than 256 non-zero entries. If this is true you can almost be certain the png has a higher quality (assuming they are indeed the same picture).
You may even be able to further find out how the gif reduced the number of entries by combining several neighboring entries in the png's histogram into one entry.
Is there a dataset that maps each of the ~16M RGB or hex color values to a general color family/category - e.g. red, purple, orange, beige, brown, etc. - that I could access programmatically or load into a database or JSON document to cross-refence the color codes against? The use case is to classify the results of PIL color detection of swatch files into a small set of color pickers for a shopping site. It would also work if the mapping is a bit more granular, say 100-200 categories, since it would be easy enough to map those to my target 10-15 myself. I have some knowledge of kNN classification and will work with that if I have to, but it would be so much easier to use a static mapping if one already exists.
You can use a table such as the one in X11
http://www.astrouw.edu.pl/~jskowron/colors-x11/rgb.html
In order to find color proximity, it's best to transform the colors to Lab color space first, so that euclidean distances have more meaning, and then nearest neighbor would give good results.
You could convert from RGB to CIE Lab color space wherein Euclidian distance between two color selections is perceptually more meaningful. Here is the link to all relevant color space transformation formulae used in OpenCV's color conversion method (cvtColor): http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html
Since your use case is to compare two swatches, I would advise you to use texture descriptors (http://www.robots.ox.ac.uk/~vgg/research/texclass/with.html) in addition to color information for better results.
I have an image and I am picking colors by RGB (data sampling). I select N points from a specific region in the image which has the "same" color. By "same" I mean, that part of the image belongs to an object, (let's say a yellow object). Each picked point in the RGB case has three values [R,G,B]. For example: [120,150,225]. And the maximum and minimum for each field are 255 and 0 respectively.
Let's assume that I picked N points from the region of the object in the image. The points obviously have different RGB values but from the same family (a gradient of the specific color).
Question:
I want to find a range for each RGB field that when I apply a color filter on the image the pixels related to that specific object remain (to be considered as inliers). Is it correct to find the maximum and minimum from the sampled points and consider them as the filter range? For example if the max and min of the field R are 120 ,170 respectively, can it be used as a the range that should be kept.
In my opinion, the idea is not true. Because when choosing the max and min of a set of sampled data some points will be out of that range and also there will be some point on the object that doesn't fit in this range.
What is a better solution to include more points as inliers?
If anybody needs to see collected data samples, please let me know.
I am not sure I fully grasp what you are asking for, but in my opinion filtering in RGB is not the way to go. You should use a different color space than RGB if you want to compare pixels of similar color. RGB is good for representing colors on a screen, but you actually want to look at the hue, saturation and intensity (lightness, or luminance) for analysing visible similarities in colors.
For example, you should convert your pixels to HSI or HSL color space first, then compare the different parameters you get. At that point, it is more natural to compare the resulting hue in a hue range, saturation in a saturation range, and so on.
Go here for further information on how to convert to and from RGB.
What happens here is that you implicitly try to reinvent either color indexing or histogram back-projection. You call it color filter but it is better to focus on probabilities than on colors and color spaces. Colors of course not super reliable and change with lighting (though hue tends to stay the same given non-colored illumination) that's why some color spaces are better than others. You can handle this separately but it seems that you are more interested in the principles of calculating "filtering operation" that will do segmentation of the foreground object from background. Hopefully.
In short, a histogram back-projection works by first creating a histogram for R, G, B within object area and then back-projecting them into the image in the following way. For each pixel in the image find its bin in the histogram, calculate its relative weight (probability) given overall sum of the bins and put this probability into the image. In such a way each pixel would have probability that it belongs to the object. You can improve it by dividing with probability of background if you want to model background too.
The result will be messy but somewhat resemble an object segment plus some background noise. It has to be cleaned and then reconnected into object using separate methods such as connected components, grab cut, morphological operation, blur, etc.
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.