python pil image to base64

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PIL is the Python Imaging Library which provides the python interpreter with image editing capabilities. But the image file cant be corrupted as I can clearly display it in the browser? The Image module provides a class with the same name which is used to represent a PIL image. The conversion and PDF preview between base64 and string util.js .vue. Not the answer you're looking for? Syntax base64.b64encode ( s[, altchars]) Parameters The function takes s as an input and is the required parameter. If necessary, add the image to a zip or tar archive. PIL stands for Python Imaging Library, and it's the original library that enabled Python to deal with images. I think the crucial point is to take the second part of the base64 string (after the comma). If its confirmed that a PIL object is accepted, I would suggest to put a ipdb breakpoint inside the callback taking as input the upload component, so that you can try inside the debugger to create a valid PIL object, inspect its content etc. open image from base64 string python pil convert pil.image to base64 cannot identify image file when converting from base64 to pil open b64 image using python pil pil image from base 64 string pil image to base64 python pil image object to base64 pil create image from base64 PIL image base64 encode pil export as base64 python This usually involves working with computer languages to work on image as a 2 dimensional signal through its pixel composition. decoded_image = base64.b64decode(encoded_image) I am searching for this about six hours. privacy statement. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Using ImageFilter you can apply some awesome filters to your images -with and within Python! The Image module provides a class with the same name which is used to represent a PIL image. I try to put the code together, and let you know once ready, I found out that base64 shoud be encoded again because symbol as '+' will not be sent. PIL (Python Image Library) is an image processing package created for Python. How do I access environment variables in Python? Does aliquot matter for final concentration? thanks radarhere. The following are 30 code examples of PIL.Image.frombytes(). Maybe you know a way to forward a path string from an object that is saved only in RAM of the browser? The module also provides a number of factory functions, including functions to load images from files, and to create new images. Manually raising (throwing) an exception in Python, Iterating over dictionaries using 'for' loops. By clicking Sign up for GitHub, you agree to our terms of service and The only difference is that in Python 3 the Base64 output is a b string. In this Python tutorial, we're going to show you how to open, show and save an image using PIL (pillow) library in Python. At what point in the prequels is it revealed that Palpatine is Darth Sidious? We do this using the open () function in python. The doll.jpg is the name of the file. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. PIL.Image.blend(im1, im2, alpha) [source] # Creates a new image by interpolating between two input images, using a constant alpha: out = image1 * (1.0 - alpha) + image2 * alpha Parameters: im1 - The first image. Python Code To Convert Image to Base64. Where does the idea of selling dragon parts come from? base64 to PIL Image import base64 from io import BytesIO from PIL import Image with open("test.jpg", "rb") as f: im_b64 = base64.b64encode(f.read()) im_bytes = base64.b64decode(im_b64) # im_bytes is a binary image im_file = BytesIO(im_bytes) # convert image to file-like object img = Image.open(im_file) # img is now PIL Image object First, we import the base64 module Then open the file which contains base64 string data for an image. Ready to optimize your JavaScript with Rust? the following code is throwing an error Error with file: string argument expected, got 'bytes'. image = Image.open(bytes_image).convert('RGB'). the url generated by my bot is like this : for some reason the .b64decode() method doesn't understand Percent-encoding, acknowledge that you have read and understood our, Data Structure & Algorithm Classes (Live), Full Stack Development with React & Node JS (Live), Fundamentals of Java Collection Framework, Full Stack Development with React & Node JS(Live), GATE CS Original Papers and Official Keys, ISRO CS Original Papers and Official Keys, ISRO CS Syllabus for Scientist/Engineer Exam, Face Detection using Python and OpenCV with webcam, Perspective Transformation Python OpenCV, Top 40 Python Interview Questions & Answers, Adding new column to existing DataFrame in Pandas, How to get column names in Pandas dataframe. @shrivijaya does https://stackoverflow.com/a/22108380/4093019 answer your question? Must have the same mode and size as the first image. If you are using a framework such as plone, Django, or buildout, try to replicate the issue just using Pillow. Well occasionally send you account related emails. Image processing is the computational transformation of images. PIL is the Python Imaging Library which provides the python interpreter with image editing capabilities. How can I convert an image to base64 using python 2.7? It should be noted that the Base64 String resulting from the conversion of the image content is without the header/html tag (data:image/jpeg;base64,), this is used to declare the data type when h5 is used. Python PIL (pillow) library can be used for advanced image processing needs but you will still need to cover the basics about handling images. Print the string. to your account. I am getting syntax error. Python 3 codes to encode an image in Base64 After you had decided to encode your image in Base64, you can proceed with coding a utility function with Python 3: 1 2 3 4 5 import base64 def get_base64_encoded_image (image_path): with open(image_path, "rb") as img_file: return base64.b64encode (img_file.read ()).decode ('utf-8') Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content, Sending image to data buffer, but I get a key error. Have a question about this project? Hi @fabmeyer, welcome to the forum! Then we close the file. The Image module provides a class with the same name which is used to represent a PIL image. Keep in mind that base64 encoded images do take up slightly more base than the binary equivalent, but they are safe to transfer over text-only mediums that do not support binary. I also tried using BytesIO but it threw an error about needed a format of string and not bytes. PIL.Image.open() Opens and identifies the given image file. I tried the following: (uploaded image is called upload, function for further use called function). // Upload PDF-Get upload PDF path and preview Preview componentPDF preview component https://github.com/adou2236/virtual-scroll-p Two methods: Note: I used it in the WeChat applet. Please include code that reproduces the issue and whenever possible, an image that demonstrates the issue. https://stackoverflow.com/questions/16065694/is-it-possible-to-create-encodeb64-from-image-objecthttps://stackoverflow.com/questions/31826335/how-to-convert-pil-image-image-object-to-base64-stringhttps://stackoverflow.com/questions/26070547/decoding-base64-from-post-to-use-in-pil. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. I am able to display the uploaded image in dash though. Convert string "Jun 1 2005 1:33PM" into datetime. By using our site, you alpha - The interpolation alpha factor. #sequences ('+' becomes '%2B', '/' becomes '%2F' and '=' becomes '%3D'), which makes the string unnecessarily longer. I've also added a function to do the reverse conversion. Fuck. Very confusing. https://stackoverflow.com/a/22108380/4093019. The text was updated successfully, but these errors were encountered: The following code works for me without problems. Image Editing Code #1: Python3 import PIL im = PIL.Image.new (mode="RGB", size=(200, 200)) im.show () Output: Code #2: Python3 import PIL im = PIL.Image.new (mode = "RGB", size = (200, 200), color = (153, 153, 255)) im.show () Output: One can alter the value of color tuple to get different colors or we can simply use color name (for single band images). python pil bytes to image save image to database using pillow django save pillow image to database django Queries related to "pillow image to base64" pil image to base64 pillow image to base64 pil load image from base64 python pillow image to base64 Pillow base64 image pil from base64 pil open image from base64 python pil base64 to image Does Python have a string 'contains' substring method? We take the binary data and store it in a variable. to resolve this i modified the code and voila: Convert base64 string to png and jpg failed, 'data:image/png;base64,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