I have a image with size of 5760 x 3840px with size of 1.14mb. But I would like to reduce its size to 200 kb by not changing the dimensions of the image. How Could I do this using photoshop? Please help me
This answer is based on a large format file size that has a resolution of 300 dpi. If the image is 5760 x 3840, your canvas size will be 19.2 x 12.8. in order to not "reduce" the dimension (printable area) you are going o have to reduce the image size [ALT + CTRL/CMD + I] and then reduce the resolution from there.
At 300 DPI:
At 72 DPI:
This reduction in resolution can decrease the file size dramatically, There is a chance of artifacts, but as you are starting at a high resolution the compression algorithms will smooth it out to almost non-existent.
NOW... If you are starting at 72dpi and you are looking for a good way to generate a lower file size for the web, your best bet may be to do a Safe for web option [ALT + CTRL/CMD + SHIFT + S] and generat a .jpg, .gif or a .png based on the final needs of the file. If it is an photograph with a lot of colors, I would go .jpg. If you have a lot of areas of solid color (logo perhaps) I would go with .png or .gif.
The Save for Web option allows you to see, side by side, the results of the export BEFORE going through the save process. it also allows you to alter the settings of the save process to dial in your results. best of all, it gives you a pretty good preview of the expected file size.
Either way this should help you save that larger file size for future use.
Related
We can see examples of how to make a hexagon sticker.
The one with the cat on it is a good example of a hex sticker with an image.
i.e.
imgurl <- system.file("figures/cat.png", package="hexSticker")
sticker(imgurl, package="hexSticker", p_size=20, s_x=1, s_y=.75, s_width=.6,
filename="inst/figures/imgfile.png")
I have a high resolution png, which looks very nice on its own, but when I put it into the hexsticker, it looses resolution (badly).
I am not so experienced with hexsticker (nor with its graphics/ggplot internals), so am not sure how to force the resulting graphic to be of a higher resolution (or if it's possible to generate it in vector?)
Turns out it's easy, just use the dpi argument, like so
sticker(imgurl, package="hexSticker", p_size=20, s_x=1, s_y=.75, s_width=.6,
filename="inst/figures/imgfile.png",
dpi = 1000 # higher dpi means higher resolution
)
A-Frame sky default radius= 5,000
diameter=10,000
So if I was to create a equirectangular image in photoshop or other program, which size should I use? 10,000 x 5,000 pixels or 10,000 x 5,000 meters?
It doesn't matter much. The exported size of the image only counts for resolution. You can balance between having a higher-resolution image or saving on file size over the network. Keep it at a minimum resolution that you are satisfied with.
I am new to the field of medical imaging - and trying to solve this (potentially basic problem). For a machine learning purpose, I am trying to standardize and normalize a library of DICOM images, to ensure that all images have the same rotation and are at the same scale (e.g. in mm). I have been playing around with the Mango viewer, and understand that one can create transformation matrices that might be helpful in this regard. I have however the following basic questions:
I would have thought that a scaling of the image would have changed the pixel spacing in the image header. Does this tag not provide the distance between pixels, and should this not change as a result of scaling?
What is the easiest way to standardize a library of images (ideally in python)? Is it possible and should one extract a mean pixel spacing across all images, and then scaling all images to match that mean? or is there a smarter way to ensure consistency in scaling and rotation?
Many thanks in advance, W
Does this tag not provide the distance between pixels, and should this
not change as a result of scaling?
Think of the image voxels as fixed units of space, which are sampling your image. When you apply your transform, you are translating/rotating/scaling your image around within these fixed units of space. That is, the size and shape of the voxels doesn't change. They just sample different parts of your image.
You can resample your image by making your voxels bigger or smaller or changing their shape (pixel spacing), but this can be independent of the transform you are applying to the image.
What is the easiest way to standardize a library of images (ideally in
python)?
One option is FSL-FLIRT, although it only accepts data in NIFTI format, so you'd have to convert your DICOMs to NIFTI. There is also this Python interface to FSL.
Is it possible and should one extract a mean pixel spacing across all
images, and then scaling all images to match that mean? or is there a
smarter way to ensure consistency in scaling and rotation?
I think you'd just to have pick a reference image to register all your other images too. There's no right answer: picking the highest resolution image/voxel dimensions or an average or some resampling into some other set of dimensions all sound reasonable.
I create a big image stitched out of many single microscope images.
Suddenly, (after several month of working properly) the stitched overview images became blurry and they are containing strange structural artefacts like askew lines (not the rectangulars, they are because of not perfect stitching)
If I open any particular tile in full size, they are not blurry and the artefacts are hardly observable. (Consider, the image below is already 4x scaled)
The overview image is created manually by scaling each tile using QImage::scaled and copying all of them to the corresponding region in the big image. I'm not using opencv's stitching.
I assume, this happens because of image contents, because most if the overview images are ok.
The question is, how can I avoid such hardly observable artefacts to become very clearly visible after scaling? Is there some means in OpenCV or QImage?
Is there any algorithms to find out, if image content could lead to such effect for defined scale-factor?
Many thanks in advance!
Are you sure the camera is calibrated properly? That the lightning is uniform? Is the lens clear? Do you have electrical components that interfere with the camera connection?
If you add image frames of photos on a uniform material (or non-uniform material, moved randomly for significant time), the resultant integrated image should be completely uniform.
If your produced image is not uniform, especially if you get systematic noise (like the apparent sinusoidal noise in the provided pictures), write a calibration function that transforms image -> calibrated image.
Filtering in Fourier space is another way to filter out the noise but considering that the image is rotated you will lose precision, and you'll be cutting off components of the real signal, too. The following empiric method will reduce the noise in your particular case significantly:
ground_output: composite image with per-pixel sum of >10 frames (more is better) over uniform material (e.g. excited slab of phosphorus)
ground_input: the average(or sqrt(sum of px^2)) in ground_output
calib_image: ground_input /(per px) ground_output. Saved for the session, or persistent in a file (important: ensure no lossy compression! (jpeg)).
work_input: the images to work on
work_output = work_input *(per px) calib_image: images calibrated for systematic noise.
If you can't create a perfect ground_input target such as having a uniform material on hand, do not worry too much. If you move any material uniformly (or randomly) for enough time, it will act as a uniform material in this case (think of a blurred photo).
This method has the added advantage of calibrating solitary faulty pixels that ccd cameras have (eg NormalPixel.value(signal)).
If you want to have more fun you can always fit the calibration function to something more complex than a zero-intercept line (steps 3. and 5.).
I suggest scaling the image with some other software to verify if the artifacts are in fact caused by Qt or are inherent in the image you've captured.
The askew lines look a lot like analog tv interference, or CCTV noise induced by 50 or 60 Hz power lines running alongside the signal cable or some other electrical interference on the signal.
If the image distortion is caused by signal interference then you can try to mitigate it by moving the signal lines away from whatever could be the source of the problem, or fit something to try to filter the noise (baluns for example).
My project is a large map that can be panned around containing "info spots" that can be clicked. For now I use four large images, each spans 5000x5000 pixels (so total map size is 20'000x20'000 pixel). On my AMD Phenom 9950 Quad-Core with 8GB RAM and an NVIDIA GeForce 610 this takes a certain while to load while it's quite fast afterwards when panning the image. I tried tiling it up but there's no visible enhancement in loading speed as the image still has to be loaded completely before it's separated into tiles.
The only way to have some real improvement on speed and memory usage would be, to only load those parts of the map image that are actually shown.
Does PyGame offer any way of doing so? I'm thinking of a "theoretical" tile map which contains the needed x and y values of each tile (I group them a little, less to compute each frame) and a theoretical image information (like: which image and which position therein). Only when a tile comes near the visible part of the screen, its image information is loaded, otherwise it remains a number and string value.
Would this make any sense? Is there any way to achieve this?
The only way to accomplish this with Pygame would be to break the images themselves into smaller squares (say 250x250), and then, as the user pans, just get the current topleft x,y coordinates, as well as the screen size, and load any tiles that fit into that screen or around the buffer edge into memory, and clear out any others that are outside that range. The math will be fairly straightforward unless you add support for rotation and/or zooming. I would name the tiles after their location as a multiple of the square size (for example the tile at 500, 500 would be named 2-2.png). This will make it very trivial to generate the tile name that you need to load at each location - take the current x/y coordinates, integer divide by 250, subtract the buffer tile amount, and then loop by your screen width integer divided by 250 plus 1 plus the buffer tile amount for each row. Do that loop for each column.
After reading #lukevp 's reply, I was interested and tried this:
http://imgur.com/Q1N2UtU
Go get this image and create a folder named 'test_data'. Now place this image, code outside of test_data folder and run. The output would be cropped figures named as per their order (It's a bit off on the edges as the image is 1920 * 1080). You can try it with your custom size tho. Also note that I am on ubuntu so take care to appropriate paths.
OUTPUT: http://imgur.com/2v4ucGI Final link: http://imgur.com/a/GHc9l
import pygame, os
pygame.init()
original_image = pygame.image.load('test_pic.jpg')
x_max = 1920
y_max = 1080
current_x = 0
current_y = 0
count = 1
begin_surf = pygame.Surface((x_max,y_max), flags = pygame.SRCALPHA)
begin_surf.blit(original_image,(0,0))
cropped_surf = pygame.Surface((100,100),flags=pygame.SRCALPHA)
while current_y + 100 < y_max:
while current_x + 100 < x_max:
cropped_surf.blit(begin_surf, (0,0), (current_x, current_y,100,100))
pygame.image.save(cropped_surf, os.path.join("test_data", str(count) + '.jpg'))
current_x += 100
count += 1
current_x = 0
current_y += 100
Would now actually be working to load those images and span them as he said.