I have a 21:9 image with 1920x816 resolution, and i want to add black bars on top and bottom in order to comapre it with same one with 1920x1080 resolution . I tried 2 solutions for that, one using OpenCV and second using Image from Pillow. However, both of those reduced quality of images.
Not edited images are taken from video file using VapourSynth and FFMS2.
Comparison between files: (1920x816 frame.png and 1920x1080 frame.png are not edited files)
https://diff.pics/rKVbxTRRPG35
Am i missing some important options that will prevent quality loss? Or should i use different library for that?
Code that i used for OpenCV:
import cv2
img = cv2.imread('1920x816 frame.png')
color = [0, 0, 0]
top, bottom, left, right = [132, 132, 0, 0]
img_with_border = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
cv2.imwrite("1920x1080 after OpenCV.png", img_with_border)
And for Pillow:
from PIL import Image, ImageOps
old_im = Image.open("1920x816 frame.png")
new_im = ImageOps.expand(old_im, border=(0, 132))
new_im.save("1920x1080 after Pillow.png", "PNG")
Doesn't look like there's a difference i quality. But there is a difference in Gamma value between the image files. Your original was saved with gamma 0.45455, and the processed image was saved with no gamma value.
Here's an explanation of what gamma means in png files: https://hsivonen.fi/png-gamma/
It's probably best to just strip the gamma value from both images. Pillow doesn't provide any simple way to work with the gamma metadata, and I'm not sure if openCV does either.
After some searching, i realized that i am an idiot. If i take frame with Vapoursynth, i might as well use it to add the needed border.
video = core.std.AddBorders(clip = video, top = 132, bottom = 132, color = [0,0,0])
That gets the job done without any additional compression. Maybe someone will make use of it.
Related
As I am trying to create a gif file, the file has been created successfully but it is pixelating. So if anyone can help me out with how to increase resolution.
.Here is the code:-
import PIL
from PIL import Image
import NumPy as np
image_frames = []
days = np.arange(0, 12)
for i in days:
new_frame = PIL.Image.open(
r"C:\Users\Harsh Kotecha\PycharmProjects\pythonProject1\totalprecipplot" + "//" + str(i) + ".jpg"
)
image_frames.append(new_frame)
image_frames[0].save(
"precipitation.gif",
format="GIF",
append_images=image_frames[1:],
save_all="true",
duration=800,
loop=0,
quality=100,
)
Here is the Gif file:-
Here are the original images:-
image1
image2
iamge3
Updated Answer
Now that you have provided some images I had a go at disabling the dithering:
#!/usr/bin/env python3
from PIL import Image
# User editable values
method = Image.FASTOCTREE
colors = 250
# Load images precip-01.jpg through precip-12.jpg, quantize to common palette
imgs = []
for i in range(1,12):
filename = f'precip-{i:02d}.jpg'
print(f'Loading: {filename}')
try:
im = Image.open(filename)
pImage = im.quantize(colors=colors, method=method, dither=0)
imgs.append(pImage)
except:
print(f'ERROR: Unable to open {filename}')
imgs[0].save(
"precipitation.gif",
format="GIF",
append_images=imgs[1:],
save_all="true",
duration=800,
loop=0
)
Original Answer
Your original images are JPEGs which means they likely have many thousands of colours 2. When you make an animated GIF (or even a static GIF) each frame can only have 256 colours in its palette.
This can create several problems:
each frame gets a new, distinct palette stored with it, thereby increasing the size of the GIF (each palette is 0.75kB)
colours get dithered in an attempt to make the image look as close as possible to the original colours
different colours can get chosen for frames that are nearly identical which means colours flicker between distinct shades on successive frames - can cause "twinkling" like stars
If you want to learn about GIFs, you can learn 3,872 times as much as I will ever know by reading Anthony Thyssen's excellent notes here, here and here.
Your image is suffering from the first problem because it has 12 "per frame" local colour tables as well as a global colour table3. It is also suffering from the second problem - dithering.
To avoid the dithering, you probably want to do some of the following:
load all images and append them all together into a 12x1 monster image, and find the best palette for all the colours. As all your images are very similar, I think that you'll get away with generating a palette just from the first image without needing to montage all 12 - that'll be quicker
now palettize each image, with dithering disabled and using the single common palette
save your animated sequence of the palletised images, pushing in the singe common palette from the first step above
2: You can count the number of colours in an image with ImageMagick, using:
magick YOURIMAGE -format %k info:
3: You can see the colour tables in a GIF with gifsicle using:
gifsicle -I YOURIMAGE.GIF
I am trying to crop an image of a piece of card/paper or such so that the card/paper is in focus. I tried the below code but the problem is that it works only when the object in question is alone in the picture. If it is a blank background with nothing else in it- the cropping is flawless, otherwise it does not work as expected.
I am attempting create a system which crops different kinds of images and puts them through a classifier and then extracts text from them.
import cv2
import numpy as np
filenames = "img.jpg"
img = cv2.imread(filenames)
blurred = cv2.blur(img, (3,3))
canny = cv2.Canny(blurred, 50, 200)
## find the non-zero min-max coords of canny
pts = np.argwhere(canny>0)
y1,x1 = pts.min(axis=0)
y2,x2 = pts.max(axis=0)
## crop the region
cropped = img[y1:y2, x1:x2]
filename_cropped = filenames.split('.')
filename_cropped[0] = filename_cropped[0] + '_cropped'
filename_cropped = '.'.join(filename_cropped)
cv2.imwrite(filename_cropped, cropped)
An sample image that works is
Something that does not work is
Can anyone help with this?
The first image works because the entire images besides your target is empty. Canny will also give other results when there is more in the image.
If you are looking for those specific cards I suggest you try to use some colour filtering first. You can try to filer for the blue/purple hue of the card.
Increasing the canny threshold could also work, but you will always still be finding the hand as well in this image unless you add some colour filtering.
You can also try Sobel edge detection . This will probably highlight the instant edges of the card pretty well. But then again, it will also show the hand, so you can't just take all the Sobel/Canny outputs. You need to add processing before it that isolates the card, or after it that can find the rectangular shape of the card in the sobel/canny.
I'm trying to convert EPS images to JPEG using Pillow. But the results are of low quality. I'm trying to use resize method, but it gets completely ignored. I set up the size of JPEG image as (3600, 4700), but the resulted image has (360, 470) size. My code is:
eps_image = Image.open('img.eps')
height = eps_image.height * 10
width = eps_image.width * 10
new_size = (height, width)
print(new_size) # prints (3600, 4700)
eps_image.resize(new_size, Image.ANTIALIAS)
eps_image.save(
'img.jpeg',
format='JPEG'
dpi=(9000, 9000),
quality=95)
UPD. Vasu Deo.S noticed one my error, and thanks to him the JPG image has become bigger, but quality is still low. I've tried different DPI, sizes, resample values for resize function, but the result does not change much. How can i make it better?
The problem is that PIL is a raster image processor, as opposed to a vector image processor. It "rasterises" vector images (such as your EPS file and SVG files) onto a grid when it opens them because it can only deal with rasters.
If that grid doesn't have enough resolution, you can never regain it. Normally, it rasterises at 100dpi, so if you want to make bigger images, you need to rasterise onto a larger grid before you even get started.
Compare:
from PIL import Image
eps_image = Image.open('image.eps')
eps_image.save('a.jpg')
The result is 540x720:
And this:
from PIL import Image
eps_image = Image.open('image.eps')
# Rasterise onto 4x higher resolution grid
eps_image.load(scale=4)
eps_image.save('a.jpg')
The result is 2160x2880:
You now have enough quality to resize however you like.
Note that you don't need to write any Python to do this at all - ImageMagick will do it all for you. It is included in most Linux distros and is available for macOS and Windows and you just use it in Terminal. The equivalent command is like this:
magick -density 400 input.eps -resize 800x600 -quality 95 output.jpg
It's because eps_image.resize(new_size, Image.ANTIALIAS) returns an resized copy of an image. Therefore you have to store it in a separate variable. Just change:-
eps_image.resize(new_size, Image.ANTIALIAS)
to
eps_image = eps_image.resize(new_size, Image.ANTIALIAS)
UPDATE:-
These may not solve the problem completely, but still would help.
You are trying to save your output image as a .jpeg, which is a
lossy compression format, therefore information is lost during the
compression/transformation (for the most part). Change the output
file extension to a lossless compression format like .png so that
data would not be compromised during compression. Also change
quality=95 to quality=100 in Image.save()
You are using Image.ANTIALIAS for resampling the image, which is
not that good when upscaling the image (it has been replaced by
Image.LANCZOS in newer version, the clause still exists for
backward compatibility). Try using Image.BICUBIC, which produces
quite favorable results (for the most part) when upscaling the image.
I've been having trouble trying to get PIL to nicely downsample images. The goal, in this case, is for my website to automagically downsample->cache the original image file whenever a different size is required, thus removing the pain of maintaining multiple versions of the same image. However, I have not had any luck. I've tried:
image.thumbnail((width, height), Image.ANTIALIAS)
image.save(newSource)
and
image.resize((width, height), Image.ANTIALIAS).save(newSource)
and
ImageOps.fit(image, (width, height), Image.ANTIALIAS, (0, 0)).save(newSource)
and all of them seem to perform a nearest-neighbout downsample, rather than averaging over the pixels as it should Hence it turns images like
http://www.techcreation.sg/media/projects//software/Java%20Games/images/Tanks3D%20Full.png
to
http://www.techcreation.sg/media/temp/0x5780b20fe2fd0ed/Tanks3D.png
which isn't very nice. Has anyone else bumped into this issue?
That image is an indexed-color (palette or P mode) image. There are a very limited number of colors to work with and there's not much chance that a pixel from the resized image will be in the palette, since it will need a lot of in-between colors. So it always uses nearest-neighbor mode when resizing; it's really the only way to keep the same palette.
This behavior is the same as in Adobe Photoshop.
You want to convert to RGB mode first and resize it, then go back to palette mode before saving, if desired. (Actually I would just save it in RGB mode, and then turn PNGCrush loose on the folder of resized images.)
This is over a year old, but in case anyone is still looking:
Here is a sample of code that will see if an image is in a palette mode, and make adjustments
import Image # or from PIL import Image
img = Image.open(sourceFile)
if 'P' in img.mode: # check if image is a palette type
img = img.convert("RGB") # convert it to RGB
img = img.resize((w,h),Image.ANTIALIAS) # resize it
img = img.convert("P",dither=Image.NONE, palette=Image.ADAPTIVE)
#convert back to palette
else:
img = img.resize((w,h),Image.ANTIALIAS) # regular resize
img.save(newSourceFile) # save the image to the new source
#img.save(newSourceFile, quality = 95, dpi=(72,72), optimize = True)
# set quality, dpi , and shrink size
By converting the paletted version to RGB, we can resize it with the anti alias. If you want to reconvert it back, then you have to set dithering to NONE, and use an ADAPTIVE palette. If there options aren't included your result (if reconverted to palette) will be grainy. Also you can use the quality option, in the save function, on some image formats to improve the quality even more.
I have a 2D drawing with a black background and white lines (exported from Autocad) and I want to create a thumbnail preserving lines, using Python PIL library.
But what I obtain using the 'thumbnail' method is just a black picture scattered with white dots.
Note that if I put the image into an IMG tag with fixed width, I obtain exactly what I want (but the image is entirely loaded).
After your comments, here is my sample code:
from PIL import Image
fn = 'filename.gif'
im = Image(fn)
im.convert('RGB')
im.thumbnail((300, 300), Image.ANTIALIAS)
im.save('newfilename.png', 'PNG')
How can I do?
The default resizing method used by thumbnail is NEAREST, which is a really bad choice. If you're resizing to 1/5 of the original size for example, it will output one pixel and throw out the next 4 - a one-pixel wide line has only a 1 out of 5 chance of showing up at all in the result!
The surprising thing is that BILINEAR and BICUBIC aren't much better. They take a formula and apply it to the 2 or 3 closest pixels to the source point, but there's still lots of pixels they don't look at, and the formula will deemphasize the line anyway.
The best choice is ANTIALIAS, which appears to take all of the original image into consideration without throwing away any pixels. The lines will become dimmer but they won't disappear entirely; you can do an extra step to improve the contrast if necessary.
Note that all of these methods will fall back to NEAREST if you're working with a paletted image, i.e. im.mode == 'P'. You must always convert to 'RGB'.
from PIL import Image
im = Image.open(fn)
im = im.convert('RGB')
im.thumbnail(size, Image.ANTIALIAS)
Here's an example taken from the electronics.stackexchange site https://electronics.stackexchange.com/questions/5412/easiest-and-best-poe-ethernet-chip-micro-design-for-diy-interface-with-custom-ard/5418#5418
Using the default NEAREST algorithm, which I assume is similar to the results you had:
Using the ANTIALIAS algorithm:
By default, im.resize uses the NEAREST filter, which is going to do what you're seeing -- lose information unless it happens to fall on an appropriately moduloed pixel.
Instead call
im.resize(size, Image.BILINEAR)
This should preserve your lines. If not, try Image.BICUBIC or Image.ANTIALIAS. Any of those should work better than NEAREST.