Image processing is the computational transformation of images. The module also provides a number of factory functions, including functions to load images from files, and to create new images. The module also provides a number of factory functions, including functions to load images from files, and to create new images. By clicking Sign up for GitHub, you agree to our terms of service and Thank you very much. So you may find few are using / and few are using + on their encoded string. Presuming that it works for you, it would then be a problem with the specific images that you are using. But first of all, let's explain something that can be quite confusing for a beginner. Python - Convert Image to String and vice-versa Difficulty Level : Easy Last Updated : 23 Dec, 2020 Read Discuss Practice Video Courses To store or transfer an Image to some we need to convert it into a string such that the string should portray the image which we give as input. 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). Several error messages complain about an invalid file name, maybe the function is expecting a path instead? PIL is the Python Imaging Library which provides the python interpreter with image editing capabilities. At what point in the prequels is it revealed that Palpatine is Darth Sidious? PIL (Python Image Library) is an image processing package created for Python. Manually raising (throwing) an exception in Python, Iterating over dictionaries using 'for' loops. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. The other option would be to forward a string (or is it a path?). The open () function takes two parameters-the file to be opened and the mode. Pillow: 5.3.0. codec, image_path="c:" (, ) ( ) time () img. The only difference is that in Python 3 the Base64 output is a b string. python base64 to image. If given, this argument must be r. Making statements based on opinion; back them up with references or personal experience. Oops, You will need to install Grepper and log-in to perform this action. . You can run this code and see the result. Should teachers encourage good students to help weaker ones? https://stackoverflow.com/a/22108380/4093019. # see more at https://en.wikipedia.org/wiki/Percent-encoding, 'data:image/jpeg;base64,%2F9j%2F4AAQSkZJRgABAQEAAAAAAAD%2F2wBDAAoHCAkIBgoJCAkLCwoMDxkQDw4ODx8WFxIZJCAmJiQgIyIoLToxKCs2KyIjMkQzNjs9QEFAJzBHTEY%2FSzo%2FQD7%2F2wBDAQsLCw8NDx0QEB0%2BKSMpPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj4%2BPj7%2FxAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv%2FxAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5%2Bjp6vHy8%2FT19vf4%2Bfr%2FxAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv%2FxAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4%2BTl5ufo6ery8%2FT19vf4%2Bfr%2FwAARCAB4AKADASEAAhEBAxEB%2F9oADAMBAAIRAxEAPwCrDCn9xfyq0sKf3F%2F75pWRzJEghT%2B4v5U7yU%2FuL%2F3zSKsJ5K%2F3F%2F75pPJT%2Fnmv%2FfNLQob5S%2F8APNP%2B%2BaaYU%2F55p%2F3zTAaYk%2FuJ%2FwB81GYU%2FwCeaf8AfNAWIzEn%2FPNP%2B%2BaieJf7if8AfNAWIWiT%2Fnmn%2FfNQPGn9xP8AvmmBXeNf7i%2FlUDRr%2FdX8qBELIv8AdX8qo3c8cIxtGfpVDM17h2PAxUJds5zQVY9WgWriR0jIlCUuykUGym7KRQ3ZTClIBhSoytMZGVqJloEQOtV2WmBA61WdaYileSCGIk1z75dtzUDREx9Kjqij1%2B2WtGNOKgyJhHR5dIoTZTSlIY3bTGWgZGVqNloAhZaiZaYEDrUDrQIrOtVpBTAwNXO64Vf7ozWU7ZqxkZFMoKPY7UVrQrUGJYCUvl0ihmymFKQxhSmFKBkRWo2WkBCy1A60AQutV3FUIrSCqsgpgcxqzbrpvbis0etWNEbGmUFHs9n2rZtxUGRcCU7ZUjIylMKUhjClRMlAyJlqJloAhZagdaYFZxUDigCrIKqSCmI5bVF2yvxyWrN2%2FKa0KI8YplAHstlW%2FaiszM0FWn7aQxhSoylIZGVqJkpDImWoWWgCBlqu4pgVZBVdxTAqyVUkFMRg6xECUasaRcVYyoeaYeKZR7FZH5q6Cz7VDMjVSpcVBQwimFaQEbLUL8UDIiKgegCu4qu4pgVZRVV6AKsgqpLTAx9VTMOR2rAl6VaApngVCaoo9fgOJBW%2FYvUMzNuHpU%2FaoGJTSKkZE%2FFQMuetAETrVdqYED1XegCrJVR6YFWSqklMCjMMgg9DXNXcXlSFe1WgKEgqEiqGesKea17KXpUEHQWsmVq8DxUDCmtUjK3lATGTvQ1AEL1VegZA9VpKYFWSqslMCrJVOWmBTkrK1CLzIie681QjCkFV3qij1EGr1pJzUmaN6zm4rVik4qGUS76XNSBG1RNQMgeqz0AQPVWSmMrSVUkoEVZDVSQ0wKctU5eaoRl3NoCMx8VmSxEHkYqkUei7uasQSc0jM17SateGbipKLSS1MHqRiFqiY0AQsarvQMruarSGgRWkNVJDQMqy1UkqhFOQ1UkpiK70zFUM6HzPmqxCaTINCB8VqQS1JRfjlqyslIY%2FfTC1IZC7VA7UAV3NV3NAFSQ1Xc0xFSQ1UkNNAVJKqvTEQGk7E0wN1Vq1EtIkuxcVfhbikUXYzVhWoGSeZQXqRkTNULtTAruaryNQBVc1WkNAirIaqSmmBUc1XamIiNNf0pgdMi1ZRaRBZjWrUYpFFlDip1koKJN1JmkBGzVCzUDIXaqrtQBA7VWkamBUkNVZGoEVXNV3NUIgaVV%2B8QKrSXMfPzCgDt1qzHSM0WEqdaCyUGnZpDF30vmUhiF6iZqYyBzVVzSAgdqrOaYFVzVWQ0ElZzVG4uFTr1qgMidy5yajXmqGf%2F%2FZAAAAAAAAAABRZSrCUhk6mn7qQxN1N30DG%2BbUTNTAgkNU5GoEU5WqnI1MRj39zjKLWS5qgITShqYz%2F9kAAAAAAAAAAAAAAAAAJKgyHoxarkS0Fl2MVYWkMsR1ZWkUSZozQMYTTDJSAiZ6qyGqEVJTVKVqBGBq1zhNgPWudkPNWhEJqM0xnWWwMabSauR81LMy7EtXI6QyylTpSKLCtUoakMfvpN9MYwvULPSAhZ6iaSmBXkas29kEcTMelMRyN5MZHJzVA1QDDTKYH%2F%2FZAAAAAAAAAAAAAAAAKKvQipMjQgWtCIVIyZasJQUThqfuoGLvo8ygYwyVG0tAFcz1Ez0gIHeqsklMRzGvXOZ9ufuiudc5NaCRGabQM%2F%2FZAAAAAAAAAAAAAAAAAABVlNcXrs268fngcCqRJguaiqijbhieQ8fnWvaosSjAGe9KRBoRc1oQLUCL8QxVkCgomSpgaQx26l8ygY0yUwyUARNJVdphnFMCB3qvI1AilcziOJnboBXB3sxllZj1Y5qkIpGm1RR%2F%2F9kAAAAAAAAAEjVyvieXHlp3xmtEScm5yaZQM%2F%2FZAAAAAAAAAAAAAAAZ%2FWrRPy0wK7mqz0wPQKSgzFpaChc0ZpDEzSbqQxpamFqQyJmqFmoAhZqiZqYDQ3zCq8z4Dt6ZNAjAleprW53DY3UdKYx7mq7GmSeh0hoIEooKDNGaQxM0zdSGIWqItQMiZqiZqBkLNURagQin5qqzviCQ%2FwCyaYjAkNV2cqdynkUxl%2BOYTRBvzprGgk9EpuaCBM0FqChM0maBjS1MLUhjC1Rs1AzB1HXfJlMcChiP4jVOPxC%2B799ECP8AZquQDXWdZYw8bZU9DTS1SA5T%2FKqF%2Bf8AQn%2FD%2BdAjEc1WkPBpjEtpvKk%2F2T1rQzQI%2F9kA'. Python 3 import base64 from io import BytesIO buffered = BytesIO () image.save (buffered, format="JPEG") img_str = base64.b64encode (buffered.getvalue ()) Python 2 import base64 import cStringIO buffer = cStringIO.StringIO () image.save (buffer, format="JPEG") img_str = base64.b64encode (buffer.getvalue ()) Share Improve this answer Follow It provides various classes and methods that aid in the creation, editing, and exportation of image documents. If he had met some scary fish, he would immediately return to the surface. to your account. 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. The best reproductions are self-contained scripts with minimal dependencies. And that error message. Connect and share knowledge within a single location that is structured and easy to search. Python: python 2.7.11. I've also added a function to do the reverse conversion. the saved pictures are empty, size is 2K; I found out that base64 shoud be encoded again because symbol as '+' will not be sent. Python PIL resize image 26 Aug 2020 programming toggle width In [1]: from PIL import Image import base64 import io In [ ]: In [ ]: In [2]: with open("img.png", "rb") as f: b64str = base64.b64encode(f.read()) In [3]: data = f"data:image/png;base64, {b64str.decode ()}" data2 = b64str.decode() In [ ]: In [4]: fp A filename (string), pathlib.Path object or a file object. And getting a string (or path) out of it is also not a good idea I think as the image will be uploaded from the user? 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. How does legislative oversight work in Switzerland when there is technically no "opposition" in parliament? 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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. Does Python have a ternary conditional operator? @radarhere Does integrating PDOS give total charge of a system? But the image file cant be corrupted as I can clearly display it in the browser? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Thank you so much, 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 Very confusing. Then we read the image file and encoded it with the following line: base64.b64encode (img_file.read ()) - b64encode () is a method to encode the data into base64 You must read the image file before you encode it. In this Python tutorial, we're going to show you how to open, show and save an image using PIL (pillow) library in Python. 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') rev2022.12.11.43106. [ Gift : Animated Search Engine : https://www.hows.tech/p/recommended.html ] PYTHON : Convert PIL Image to byte ar. Here, I find a regular processing method from the reference blog. Using ImageFilter you can apply some awesome filters to your images -with and within Python! @Emmanuelle I was able to solve the problem: If anyone is interested to know how to convert an uploaded image from base64 to a PIL Image object: encoded_image = upload.split (",") [1] decoded_image = base64.b64decode (encoded_image) bytes_image = BytesIO (decoded_image) image = Image.open (bytes_image).convert ('RGB') Sign in Now that we know some of the fundamentals of PIL, let's try to do some tricks. To convert the Image to Base64 String in Python, use the Python base64 module that provides b64encode () method. I tried the following: (uploaded image is called upload, function for further use called function). If not, please open a new issue with more specifics. So if you then you need to match with them, just replace the characters with equivalent one after the encoding. I also tried using BytesIO but it threw an error about needed a format of string and not bytes. PIL is the Python Imaging Library which provides the python interpreter with image editing capabilities. The module also provides a number of factory functions, including functions to load images from files, and to create new images. The Python Pillow library is a fork of an older library called PIL. Do non-Segwit nodes reject Segwit transactions with invalid signature? We take the binary data and store it in a variable. 1.Convert PIL.Image to Base64 String py2 First convert the image content to a binary stream using CStringIO.StringIO, then base64 encoding # -*- coding: utf-8 -*- import base64 from cStringIO import StringIO # pip2 install pillow from PIL import Image def image_to_base64(image_path): img = Image.open (image_path) output_buffer = StringIO () I am searching for this about six hours. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Please include code that reproduces the issue and whenever possible, an image that demonstrates the issue. 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 If necessary, add the image to a zip or tar archive. 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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