I'm trying to add a monochromatic noise to an image similar to Photoshop version using command line however I can't see any option to achieve it.
I've created a code in JS that does it very well and the logic here is very simple:
Foreach pixel:
Generate random noise pixel
Add or subtract (random) noise pixel to/from original pixel
The create a monochromatic noise add/subtract are on a pixel not channel basis e.g.
Pi - original pixel
Pr - noise pixel
MonoPixel = Pi+Pr or Pi-Pr
Is there any way I can randomly add or subtract pixels via command line ?
Thanks
You can use the ImageMagick +noise command to add noise. To get monochromatic noise, you'll have to do something more complex where you create a separate noise image combined with a base color and composite that with your source image.
This link may be helpful: http://brunogirin.blogspot.com/2009/09/making-noise-with-imagemagick.html
You could try and build your own little shell function. Use $RANDOM (Bash environment variable which returns a random integer in the range 0..32767) and see if it is an odd or an even number. Make odd to mean + and even to mean -.
echo $(($RANDOM % 2))
should return 1 ($RANDOM was odd) or 0 ($RANDOM was even) in random order...
Related
I want to predict disease and I want to try to make the image have some noise or disruption in specific spot or randomly spot is there any method or solution for it??
is there any way to add noise (random value) to image with tensorflow
I read the image and convert it to array and make a copy of it and then add to it some number is that right??
and i have noticed that when convert it the array became values of zeros and ones even it in rgb form.
i expect the some value in the array or the value in the image change to another values so when imshow (the image) notice some noise (different from guassian noise) so when the input to the model become different from the original image
I have trying this but operand didn't match between(224,224,3) and (224,224)
but when set colormode to grayscal the operand work but i didnt see that much of change in image.
,when trying replace img.size with img.height did'nt work either
img = tf.keras.preprocessing.image.load_img("/content/person1_bacteria_2.jpeg",color_mode="rgb",target_size=(256, 256))
nois_factor = 0.3
n = nois_factor * np.random.randn(*img.size)
noise_image = img + n
plt.imshow(noise_image)
I have the task to simulate a camera with a full well capacity of 10.000 Photons per sensor element
in numpy. My first Idea was to do it like that:
camera = np.random.normal(0.0,1/10000,np.shape(img))
Imgwithnoise= img+camera
but it hardly shows an effect.
Has someone an idea how to do it?
From what I interpret from your question, if each physical pixel of the sensor has a 10,000 photon limit, this points to the brightest a digital pixel can be on your image. Similarly, 0 incident photons make the darkest pixels of the image.
You have to create a map from the physical sensor to the digital image. For the sake of simplicity, let's say we work with a grayscale image.
Your first task is to fix the colour bit-depth of the image. That is to say, is your image an 8-bit colour image? (Which usually is the case) If so, the brightest pixel has a brightness value = 255 (= 28 - 1, for 8 bits.) The darkest pixel is always chosen to have a value 0.
So you'd have to map from the range 0 --> 10,000 (sensor) to 0 --> 255 (image). The most natural idea would be to do a linear map (i.e. every pixel of the image is obtained by the same multiplicative factor from every pixel of the sensor), but to correctly interpret (according to the human eye) the brightness produced by n incident photons, often different transfer functions are used.
A transfer function in a simplified version is just a mathematical function doing this map - logarithmic TFs are quite common.
Also, since it seems like you're generating noise, it is unwise and conceptually wrong to add camera itself to the image img. What you should do, is fix a noise threshold first - this can correspond to the maximum number of photons that can affect a pixel reading as the maximum noise value. Then you generate random numbers (according to some distribution, if so required) in the range 0 --> noise_threshold. Finally, you use the map created earlier to add this noise to the image array.
Hope this helps and is in tune with what you wish to do. Cheers!
I use vkGetPhysicalDeviceSurfaceFormatsKHR to get supported image formats for the swapchain, and (on Linux+Nvidia, using SDL) I get VK_FORMAT_B8G8R8A8_UNORM as the first option and I go ahead and create the swapchain with that format:
VkSwapchainCreateInfoKHR swapchain_info = {
...
.imageFormat = format, /* taken from vkGetPhysicalDeviceSurfaceFormatsKHR */
...
};
So far, it all makes sense. The image format used to draw on the screen is the usual 8-bits-per-channel BGRA.
As part of my learning process, I have so far arrived at setting up a lot of stuff but not yet the graphics pipeline1. So I am trying the only command I can use that doesn't need a pipeline: vkCmdClearColorImage2.
The VkClearColorValue used to define the clear color can take the color as float, uint32_t or int32_t, depending on the format of the image. I would have expected, based on the image format given to the swapchain, that I should give it uint32_t values, but that doesn't seem to be correct. I know because the screen color didn't change. I tried giving it floats and it works.
My question is, why does the clear color need to be specified in floats when the image format is VK_FORMAT_B8G8R8A8_UNORM?
1 Actually I have, but thought I would try out the simpler case of no pipeline first. I'm trying to incrementally use Vulkan (given its verbosity) particularly because I'm also writing tutorials on it as I learn.
2 Actually, it technically doesn't need a render pass, but I figured hey, I'm not using any pipeline stuff here, so let's try it without a pipeline and it worked.
My rendering loop is essentially the following:
acquire image from swapchain
create a command buffer with the following:
transition from VK_IMAGE_LAYOUT_UNDEFINED to VK_IMAGE_LAYOUT_GENERAL (because I'm clearing the image outside a render pass)
clear the image
transition from VK_IMAGE_LAYOUT_GENERAL to VK_IMAGE_LAYOUT_PRESENT_SRC_KHR
submit command buffer to queue (taking care of synchronization with swapchain with semaphores)
submit for presentation
My question is, why does the clear color need to be specified in floats when the image format is VK_FORMAT_B8G8R8A8_UNORM?
Because the normalized, scaled, or sRGB image formats are really just various forms of floating-point compression. A normalized integer is a way of storing floating-point values on the range [0, 1] or [-1, 1], but using a much smaller amount of data than even a 16-bit float. A scaled integer is a way of storing floating point values on the range [0, MAX] or [-MIN, MAX]. And sRGB is just a compressed way of storing linear color values on the range [0, 1], but in a gamma-corrected color space that puts precision in different places than the linear color values would suggest.
You see the same things with inputs to the vertex shader. A vec4 input type can be fed by normalized formats just as well as by floating-point formats.
How can I accomplish this? A programmatic solution (Objective-c) is great, but even a non-progarmmatic one is good.
I have pixelmator -> But that doesn't give you the option. I can't seem to do it with Preview either.
I have tried googling, but haven't been able to find a solution so far. The only tool I have been able to use to do this is TexturePacker, but that creates a sprite sheet.
You can use libpng to convert the PNG image to three-byte (8:8:8) RGB. Then you can downsample to the 5:6:5 16-bit color values of RGB565. If r, g, and b are the respective 8-bit colors (stored in an unsigned char type), then the 16-bit RGB565 value is:
((r >> 3) << 11) | ((g >> 2) << 5) | (b >> 3)
You can improve a tad on this by rounding instead of chopping, being careful to not overflow the values. You can also force the green value to be equal to the blue and red values when they are all equal in the original 8-bit values. Otherwise it is possible to have colors that were originally gray inadvertently take on color after conversion.
Create Bitmap Context with color RGB565 using Quartz, paint your PNG on this context, save this bitmap context to file.
PNG does not support a RGB565 packing. You can always apply a posterize to the image (programatically or with ImageMagick or with any image editor), which amounts to discard the lower significant bits in each channel. When saving to PNG, you will still be saving 8 bits per channel (unless you use a palette), but even then you will get an appreciable reduction in size, because of the PNG compression.
A quick example: original:
after a simple posterize with 32 levels (equivalent to a RGB555) applied with XnView
The size goes from 89KB to 47KB, with a small quality loss.
In case of synthetic images with gradients, the quality loss could be much more noticiable (banding).
I received this answer from the creator of texture packer:
you can do it from command line - see
http://www.texturepacker.com/uncategorized/batch-converting-images-to-pvr-or-pvr-ccz/
Just adjust the opt and set output to .png instead of pvr.ccz
Make sure that you do not overwrite your source images.
According to Wikipedia, which is always right, the only 16-bit PNG is a greyscale PNG. http://en.wikipedia.org/wiki/Portable_Network_Graphics
If you just add your 32-bit (alpha) or 24-bit (no alpha) PNG to your project as normal, and then set the texture format in Cocos2D, all should be fine. The code for that is:
[CCTexture2D setDefaultAlphaPixelFormat:kCCTexture2DPixelFormat_RGB565];
I am working on a program that needs to fit numerous cosine waves in order to determine one of the parameters for the function. The equation that I am using is y = y_0 + Acos((4*pi*L)/x + pi) where L is the value that I am trying to obtain from the best fit line.
I know that it is possible to do this correctly by hand for each set of data, but what is the best way to automate this process? I am currently reading in the data from text files, and running a loop with the initial paramiters changing until I have an array of paramater values that have an amplitude similar to the data, then I check the percent difference between points on the center peak and two end peaks to try to pick the best one. It in consistently picking lower values than what I get when fitting by hand (almost exactly one phase off). So is there a way to improve this method, or another method that works better?
Edit: My LabVIEW version has a cos fitting VI which is what I am using, the problem is when I try to automate the fitting by changing the initial parameters using a loop, I cant figure out how to get the program to pick the same best fit line as a human would pick.
Why not just use a Fast Fourier Transform? This should be way faster than fitting a cosine. In the result vector of complex numbers look for the largest peak of in the totals. You're given frequency (position in the FFT result vector), amplitude and phase.
You can evaluate the goodness of the fit by computing the difference between fitting curve and your data. A VI does this in the "Advanced curve fitting" palette. Then all you have to do is pick up the best fit.