I have the following code to generate GIFs from images, the code works fine and the gif is saved locally, what I want to do rather the saving the GIF locally, I want for example data URI that I could return to my project using a request. How can I generate the GIF and return it without saving it?
my code to generate the GIF
import os
import imageio as iio
import imageio
png_dir='./'
images=[]
for file_name in url:
images.append(file_name)
imageio.imwrite('movie.gif', images, format='gif')
I found I can save it as bytes with the following code
gif_encoded = iio.mimsave("<bytes>", images, format='gif')
it will save the GIF as bytes then you can encode it.
encoded_string = base64.b64encode(gif_encoded)
encoded_string = b'data:image/gif;base64,'+encoded_string
decoded_string = encoded_string.decode()
for more examples check this out
https://imageio.readthedocs.io/en/stable/examples.html#read-from-fancy-sources
Related
I am using tinytags module in python to get the cover art of a mp3 file and want to display or store it. The return type of the variable is showing to be bytes. I have tried fumbling around with PIL using frombytes but to no avail. Is there any method to convert the bytes to image?
from tinytag import TinyTag
tag = TinyTag.get("03. Me, Myself & I.mp3", image=True)
img = tag.get_image()
I actually got a PNG image when I called tag.get_image() but I guess you might get a JPEG. Either way, you can wrap it in a BytesIO and open it with PIL/Pillow or display it. Carrying on from your code:
from PIL import Image
import io
...
im = tag.get_image()
# Make a PIL Image
pi = Image.open(io.BytesIO(im))
# Save as PNG, or JPEG
pi.save('cover.png')
# Display
pi.show()
Note that you don't have to use PIL/Pillow. You could look at the first few bytes and if they are a PNG signature (\x89PNG) save data as binary with PNG extension. If the signature is JPEG (\xff \xd8) save data as binary with JPEG extension.
In order to remove sensitive content from a PDF, I am converting it to image and back to PDF again.
I am able to do this while saving the jpeg image, however I would eventually like to adapt my code so that the file is in memory the whole time. PDF in memory -> JPEG in memory -> PDF in memory. I'm having trouble with the intermediary step.
from pdf2image import convert_from_path, convert_from_bytes
import img2pdf
images = convert_from_path('testing.pdf', fmt='jpeg')
image = images[0]
# opening from filename
with open("output/output.pdf","wb") as f:
f.write(img2pdf.convert(image.tobytes()))
On the last line, I am getting the error:
ImageOpenError: cannot read input image (not jpeg2000). PIL: error reading image: cannot identify image file <_io.BytesIO object at 0x1040cc8f0>
I'm not sure how to be converting this image to the string that img2pdf is looking for.
The pdf2image module will extract the images as Pillow images. And according the Pillow tobytes() documention: "This method returns the raw image data from the internal storage." Which is some bitmap representation.
To get your code working use BytesIO module like so:
# opening from filename
import io
with open("output/output.pdf","wb") as f, io.BytesIO() as output:
image.save(output, format='jpg')
f.write(img2pdf.convert(output.getvalue()))
I am working with web services using requests to get an image based on parameters passed. The first response I get is a XML schema with file reference URL.
<?xml version="1.0"?>
<Coverages schemaLocation="http://localhost/server/schemas/wcs/1.1.1/wcsCoverages.xsd">
<Coverage>
<Title>Filename</Title>
<Abstract/>
<Identifier>Filename</Identifier>
<Reference href="http://localhost/server/temp/filename.tif"/>
</Coverage>
</Coverages>
Next using xml.etree.ElementTree I extracted the URL. Next what I need is to dsiplay that tiff image (or any other image) on the Jupyter Notebook without downloading (as one image can be more than 50 or 100 MB sometimes)
Currently I am downloading and plotting the file after reading and converting file data into array ( as pyplot plot image array/matrix ).
import requests as req # request wcs urls
import xml.etree.ElementTree as ET # used for xml parsing
import matplotlib.pyplot as plt # display image
import gdal
# Download the File to local directory using Chunks
chunk_size=1024
local_filename = url.split('/')[-1] # Filename from url
r = req.get(url, stream=True)
with open(local_filename, 'wb') as f:
for chunk in r.iter_content(chunk_size):
if chunk:
f.write(chunk)
# Read File Raster Data as Array using Gdal
gtif = gdal.Open(local_filename)
georaster = gtif.ReadAsArray()
# Plot image using matplotlib
plt.imshow(georaster)
plt.title(local_filename)
plt.show()
So is there any way to convert the raw response from requests API for file directly into image array (in chunks or whole) and display it on notebook (without downloading and taking space on local directory)
The raw response from get request for tiff file is below
resp2 = req.get('tiffFielurl')
rawdata = resp2.content
rawdata[0:10]
Output: b'MM\x00*\x00\x00\x00\x08\x00\x10'
I tried searching for this question but not found any good answer on it so if there is any related question or duplicate provide me the link.
You can try plotting tiff images using ipyplot package
import ipyplot
ipyplot.plot_images(
['https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff',
'https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff',
'https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff'],
img_width=250)
After doing so much research and trying different solutions it seems to me the only way to do the above procedure i.e displaying Tiff file for now is downloading and reading the data using gdal and converting into array and display using matplotlib .
As the solution mentioned in following link is only accepting "PNG" files.
How to plot remote image (from http url)
whcih comes to conclusion we need PIL library that I also tried and fails.
from PIL import Image
resp2 = req.get('tiffFielurl')
resp2.raw.decode_content = True
im = Image.open(resp2.raw)
im
Gives Output:
<PIL.TiffImagePlugin.TiffImageFile image mode=I;16BS size=4800x4800 at 0x11CB7C50>
and converting the PIL object to numpy array or even getting data or pixels from PIL object gives error of unrecognized mode error.
im.getdata()
im.getpixels((0,0))
numpy.array(im)
All gives same error
257 if not self.im or\
258 self.im.mode != self.mode or self.im.size != self.size:
--> 259 self.im = Image.core.new(self.mode, self.size)
260 # create palette (optional)
261 if self.mode == "P":
ValueError: unrecognized mode
It comes out that PIL even don't support 16bit Signed integer pixel that is defined in Tiff object of PIL above.
https://pillow.readthedocs.io/en/4.0.x/handbook/concepts.html#concept-modes
I have a feature with my application where a user can upload a photo. I would like to convert the photo into a JPG file and then upload it to my servers.
The photo is received in base64. I've seen other answers which suggest using PIL however, it saves the image to a local directory. Instead I would like to convert the received image into a base64 JPG image.
How would I do this? Thanks.
try this:
import base64
from PIL import Image
from io import BytesIO
im = Image.open(BytesIO(base64.b64decode(data)))
output = BytesIO()
im.save(output, 'JPEG')
jpg_img = output.read()
it will save the data to a stream ( the same way tou enter it) and then you can read from that stream
I want to serve matplotlib generated images with django.
If the image is a static png file, the following code works great:
from django.http import HttpResponse
def static_image_view(request):
response = HttpResponse(mimetype='image/png')
with open('test.png', 'rb') as f:
response.write(f.read())
return response
However, if the image is dynamically generated:
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
def dynamic_image_view(request):
response = HttpResponse(mimetype='image/png')
fig = plt.figure()
plt.plot(np.random.rand(100))
plt.savefig(response, format='png')
plt.close(fig)
return response
When accessing the url in Chrome (v36.0), the image will show up for a few seconds, then disappear and turn to the alt text. It seems that the browser doesn't know the image has already finished loading and waits until timeout. Checking with Chrome > Tools > Developer tools > Network supports this hypothesis: although the image appears after only about 1 sec, the status of the corresponding http request becomes "failed" after about 5 sec.
Note again, this strange phenomenon occurs only with the dynamically generated image, so it shouldn't be Chrome's problem (though it doesn't happen with IE or FireFox, presumably due to different rules in dealing with timeout requests).
To make it more tricky (i.e., hard to reproduce), it seems to be network speed dependent. It happens if I access the url from an IP in China, but not if via a proxy in the US (which seems to be faster visiting the host on which django is running)...
According to #HSquirrel, I tested writing the png into temporary disk file. Strangely, saving file with matplotlib didn't work,
plt.savefig('MPL.png', format='png')
with open('MPL.png', 'rb') as f:
response.write(f.read())
while saving file with PIL worked:
import io
from PIL import Image
f = io.BytesIO()
plt.savefig(f, format='png')
f.seek(0)
im = Image.open(f)
im.save('PIL.png', 'PNG')
Attempt of getting rid of temp file failed:
im.save(response, 'PNG')
However, if I generate the image data stream with PIL rather than matplotlib, temporary disk file would be unnecessary. The following code works:
from PIL import Image, ImageDraw
im = Image.new('RGBA', (256,256), (0,255,0,255))
draw = ImageDraw.Draw(im)
draw.line((100,100, 150,200), fill=128, width=3)
im.save(response, 'PNG')
Finally, savefig(response, 'jepg') has no problem at all.
Have you tried saving the image to disk and then returning that? (you can periodically clear your disk of such generated images based on their time of creation)
If that gives the same problem, it might be a problem with the way the png is generated. Than you could use some kind of image library (like PIL) to make sure all your png's are (re)generated in a way that works with all browsers.
EDIT:
I've checked the png you've linked and I've played around with it a bit, opening and saving it with different programs and with PIL. I get different binary data every time. It seems each program decides which chunks to keep and which to remove. They all encode the png image data differently as well (as far as I can see, I am by no means a specialist in this, I just looked at the binary data based on the specs).
There are a few different paths you can take:
1.The quick and dirty one:
import io
from PIL import Image
f = io.BytesIO()
plt.savefig(f, format='png')
f.seek(0)
im = Image.open(f)
tempfilename = generatetempfilename()
im.save(tempfilename, 'PNG')
with open(tempfilename, 'rb') as f:
response.write(f.read())
2.Adapt how matplotlib makes PNG files (possibly by just using PIL for
it as well). See
http://matplotlib.org/users/customizing.html#customizing-matplotlib
3.If it's an option for you, use jpeg.
4.Figure out what's wrong with the PNG generated by matplotlib and fix
it binary (I don't recommend this). You can use xxd (linux command: xxd test.png) to figure out how the files look in binary and then see how things go using the png spec: overview chunk spec