I have a bunch of values that seem to be 12-bit numbers. If I put them in a matrix and scale each one to a value 0-255 and then show them as an image, I get something that looks like a photo, but it's quite bland.
I think that they might be direct reading off of a camera sensor. They have a sort of stippled pattern, kind of like plaid, that makes me think that they might be a sort of Bayer filter. https://en.wikipedia.org/wiki/Bayer_filter
I want to convert these number into RGB values. What do I need to do? For each 2x2 in the Bayer pattern, do I convert the red to R, blue to B, and then average the green values? Do I need a gamma correction?
I noticed that the max value is much lower than the full 0xfff. Do I need to scale the values?
The procedure is well-described here: https://www.strollswithmydog.com/raw-file-conversion-steps/
Looks like I was getting it mostly right by the problem was grey balance. There is a transformation that needs to be made on the sensor values to map it to the 0-255 RGB component and the transform that needs to be made depends on the color. The best way is to take a photo of a perfect grey and calibrate.
Related
This is a histogram of a grayscaled image I have:
INPUT
I need it to be more like this:
OUTPUT
And I wonder what's the best and efficient way to reach it. In general, I need n% (I guess n is between 0-20) of the image's area with the highest luminance to be brighter. Or (100-n)% to be darker. I tried chanigng each pixel's luminance one-by-one but it is highly ineffective.
You should only change the pixels you find in the interest bin that you want to modify by assigning a smaller value
I would like to use GPUImage's Histogram Equalization filter (link to .h) (link to .m) for a camera app. I'd like to use it in real time and present it as an option to be applied on the live camera feed. I understand this may be an expensive operation and cause some latency.
I'm confused about how this filter works. When selected in GPUImage's example project (Filter Showcase) the filter shows a very dark image that is biased toward red and blue which does not seem to be the way equalization should work.
Also what is the difference between the histogram types kGPUImageHistogramLuminance and kGPUImageHistogramRGB? Filter Showcase uses kGPUImageHistogramLuminance but the default in the init is kGPUImageHistogramRGB. If I switch Filter Showcase to kGPUImageHistogramRGB, I just get a black screen. My goal is an overall contrast optimization.
Does anyone have experience using this filter? Or are there current limitations with this filter that are documented somewhere?
Histogram equalization of RGB images is done using the Luminance as equalizing the RGB channels separately would render the colour information useless.
You basically convert RGB to a colour space that separates colour from intensity information. Then equalize the intensity image and finally reconvert it to RGB.
According to the documentation: http://oss.io/p/BradLarson/GPUImage
GPUImageHistogramFilter: This analyzes the incoming image and creates
an output histogram with the frequency at which each color value
occurs. The output of this filter is a 3-pixel-high, 256-pixel-wide
image with the center (vertical) pixels containing pixels that
correspond to the frequency at which various color values occurred.
Each color value occupies one of the 256 width positions, from 0 on
the left to 255 on the right. This histogram can be generated for
individual color channels (kGPUImageHistogramRed,
kGPUImageHistogramGreen, kGPUImageHistogramBlue), the luminance of the
image (kGPUImageHistogramLuminance), or for all three color channels
at once (kGPUImageHistogramRGB).
I'm not very familiar with the programming language used so I can't tell if the implementation is correct. But in the end, colours should not change too much. Pixels should just become brighter or darker.
When given an image such as this:
And not knowing the color of the object in the image, I would like to be able to automatically find the best H, S and V ranges to threshold the object itself, in order to get a result such as this:
In this example, I manually found the values and thresholded the image using cv::inRange.The output I'm looking for, are the best H, S and V ranges (min and max value each, total of 6 integer values) to threshold the given object in the image, without knowing in advance what color the object is. I need to use these values later on in my code.
Keypoints to remember:
- All given images will be of the same size.
- All given images will have the same dark background.
- All the objects I'll put in the images will be of full color.
I can brute force over all possible permutations of the 6 HSV ranges values, threshold each one and find a clever way to figure out when the best blob was found (blob size maybe?). That seems like a very cumbersome, long and highly ineffective solution though.
What would be good way to approach this? I did some research, and found that OpenCV has some machine learning capabilities, but I need to have the actual 6 values at the end of the process, and not just a thresholded image.
You could create a small 2 layer neural network for the task of dynamic HSV masking.
steps:
create/generate ground truth annotations for image and its HSV range for the required object
design a small neural network with at least 1 conv layer and 1 fcn layer.
Input : Mask of the image after applying the HSV range from ground truth( mxn)
Output : mxn mask of the image in binary
post processing : multiply the mask with the original image to get the required object highligted
I'm attempting to convert some effects created in Photoshop into code for use with php/imagemagick. Right now I'm specifically interested in how to recreate Photoshop's RGB levels feature. I'm not really familiar with the Photoshop interface, but this is the info that I am given:
RGB Level Adjust
Input levels: Shadow 0, Midtone 0.92, Highlight 255
Output levels: Shadow 0, Highlight 255
What exaclty are the input levels vs. the output levels? How would I translate this into ImageMagick? Below you can see what I have tried, but it does not correctly render the desired effect (converting Photoshop's 0-255 scale to 0-65535):
$im->levelImage(0, 0.92, 65535);
$im->levelImage(0, 1, 65535);
This was mostly a stab in the dark since the parameter names don't line up and for output levels the number of parameters don't even match. Basically I don't understand exactly what is going on when photoshop applies the adjustment. I think that's my biggest hurdle right now. Once I get that, I'll need to find corresponding effects in ImageMagick.
Can anyone shed some light on what's going on in Photoshop and how to replicate that with ImageMagick?
Shadows, Midtones and Highlights are colors that fall within a certain range of luminosity. For example, shadows are the lower range of the luminosity histogram, midtones are colors in the middle and highlights are the ones up high. However - you can't use a hard limit on these values, which is why you will have to use curves like these that weight the histogram (a pixel may lie in multiple ranges at the same time).
To adjust shadows, midtones and highlights separately, you will need to create a weighted sum per pixel that uses the current shadow, midtone and highlight values to create a resultant value.
I don't think you can do this directly using ImageMagick API's - perhaps you could simply write it as a filter.
Hope this helps.
So I stumbled across this website: http://www.fmwconcepts.com/imagemagick/levels/index.php
Based on the information there, I was able to come up with the following php which seems pretty effective at simulating what Photoshop does with input and output and all that.
function levels($im, $inshadow, $midtone, $inhighlight, $outshadow, $outhighlight, $channel = self::CHANNEL_ALL) {
$im->levelImage($inshadow, $midtone, $inhighlight, $channel);
$im->levelImage(-$outshadow, 1.0, 255 + (255 - $outhighlight), $channel);
}
This assumes that the parameters to levelImage for blackpoint and whitepoint are on a scale of 0-255. They might actually be 0-65535 on your system. If that's they can it's easy enough to adjust it. You can also check what value your setup uses with $im->getQuantumRange(). It will return an array with a string version and a long version of the quantum. From there it should be easy enough to normalize the values or just use the new range.
See the documentation: The first value is the black point (shadow) input value, the middle is a gamma (which I'm guessing is the same as Photoshop's midpoint), and the last is the white point (highlight) input value.
The output values are fixed at the quantum values of the image type, there's no need to specify them.
Assume the image is only in black and white.
Is there a software that can generate a matrix representation for the text on the image?
You should take a look at OCR-Software.
If you're referring to an image like the one below, then it's pretty straight forward.
You just do the following:
Read the image into a 2D byte array so you can access the pixels
Loop through the array and look for every black pixel (0)
Store these in a matrix however you need to
This assumes that lettering is pure black on white, you might have to allow values upto a certain value (5 or 10) if this isn't the case.