I think the crucial point is to take the second part of the base64 string (after the comma). I think the problem is encode and decode when using http REST API. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Is this an at-all realistic configuration for a DHC-2 Beaver? The module also provides a number of factory functions, including functions to load images from files, and to create new images. Here, I find a regular processing method from the reference blog. rev2022.12.11.43106. the following code is throwing an error Error with file: string argument expected, got 'bytes'. Thank you very much. if I test with the base64 string on back end function, the picture is saved without error, and size is 400k or so. Syntax base64.b64encode ( s[, altchars]) Parameters The function takes s as an input and is the required parameter. Image Editing The open () function takes two parameters-the file to be opened and the mode. By clicking Sign up for GitHub, you agree to our terms of service and Is it cheating if the proctor gives a student the answer key by mistake and the student doesn't report it? Convert string in base64 to image and save in file python Python save the image file to a folder Here, we can see how to save image file to folder in python. 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 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() img.save(output_buffer, format='JPEG') binary_data = output_buffer.getvalue() base64_data = base64.b64encode(binary_data) return base64_data py3: I make a API call, using flask. Connect and share knowledge within a single location that is structured and easy to search. decoded_image = base64.b64decode(encoded_image) there's an API call, sending pictures using base64 string to pillow and save it. @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] @shrivijaya does https://stackoverflow.com/a/22108380/4093019 answer your question? 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. So if you then you need to match with them, just replace the characters with equivalent one after the encoding. To convert the Image to Base64 String in Python, use the Python base64 module that provides b64encode () method. PIL stands for Python Imaging Library, and it's the original library that enabled Python to deal with images. You signed in with another tab or window. We take the binary data and store it in a variable. When applying Image.filter() we will use another object inside the parenthesis. from pil import image from io import bytesio import base64 # convert image to base64 def im_2_b64 (image): buff = bytesio () image.save (buff, format="jpeg") img_str = base64.b64encode (buff.getvalue ()) return img_str # convert base64 to image def b64_2_img (data): buff = bytesio (base64.b64decode (data)) return image.open (buff) # test Oops, You will need to install Grepper and log-in to perform this action. 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. Why would Henry want to close the breach? Should teachers encourage good students to help weaker ones? Python Library Packaging Summary What is Image Processing? privacy statement. PIL is the Python Imaging Library which provides the python interpreter with image editing capabilities. Hi @Emmanuelle, thanks for your reply. And that error message. Python Programming Foundation -Self Paced Course, Data Structures & Algorithms- Self Paced Course, Python PIL | logical_and() and logical_or() method, Python PIL | ImageChops.subtract() method, Python PIL | ImageChops.subtract() and ImageChops.subtract_modulo() method, Python PIL | ImageEnhance.Color() and ImageEnhance.Contrast() method. Python uses the RFC 3548 for the base64 conversion. Conversion between PIL.Image and Base64 String, https://stackoverflow.com/questions/16065694/is-it-possible-to-create-encodeb64-from-image-object, https://stackoverflow.com/questions/31826335/how-to-convert-pil-image-image-object-to-base64-string, https://stackoverflow.com/questions/26070547/decoding-base64-from-post-to-use-in-pil, The conversion and PDF preview between base64 and string, Conversion between string and base64-including emjo expressions converted to base64, The problem of mutual conversion between PIL.Image and numpy.array of image processing, Base64 conversion between python and java (string, binary stream), JAVA base64 image string and file conversion between each other, Image conversion between PIL.Image and numpy.array, Base64 string conversion between files and, Conversion between String and base64 in Dart, Conversion between PIL.Image and numpy.array of image processing, Conversion between PIL.Image and numpy.array in image processing, Conversion between PIL.Image and np.ndarray pictures and Tensor, Java to achieve conversion between pictures and base64 string, Concurrent non-blocking IO solutions IO, IO multiplexing and asynchronous IO, LWIP + FREERTOS fault faulty client actively initiated connection, [Path Planning] The Dynamic Window Approach To Collision Avoidance (with Python Code Instance), Foreign Google servers practice China's wireless, Analysys: Baidu and Google's share in China's wireless search market exceeds 50%, [Little white introduction first lesson] Java environment installation, zero-based learning web front-end first stage study notes (first day), Linux kernel: process data structure - 2021-06-26 Learning record, Python summary: why do I write a python summary-to be completed, Simple integer coefficient filter to remove baseline drift of ECG signal, PIL: pip(2/3) install pillow, PIL library is no longer maintained, and pillow is a branch of PIL, now surpassing PIL. I am able to display the uploaded image in dash though. 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? The following are 30 code examples of PIL.Image.frombytes(). Not the answer you're looking for? Have a question about this project? Would it be possible, given current technology, ten years, and an infinite amount of money, to construct a 7,000 foot (2200 meter) aircraft carrier? At what point in the prequels is it revealed that Palpatine is Darth Sidious? . You may also want to check out all available functions/classes of the module PIL.Image, or try the search function . You . The doll.jpg is the name of the file. 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. Why do quantum objects slow down when volume increases? the code bellow resolves the troubles for url with Percent-encoding, in my context i made a bot with esp32cam and i'am using firebase for handles realtime base64 images generatated in process and next analyse it with CNN in python. Add a new light switch in line with another switch? The Image module provides a class with the same name which is used to represent a PIL image. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. python base64 to image. At first, we opened our file in 'rb' mode. Sign in Image processing is the computational transformation of images. Sorry. To decode an image using Python, we simply use the base64.b64decode (s) function. 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 conversion and PDF preview between base64 and string util.js .vue. 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. The module also provides a number of factory functions, including functions to load images from files, and to create new images. The file object must implement read(), seek(), and tell() methods, and be opened in binary mode.mode The mode. Python Code To Convert Image to Base64. Very confusing. The Image module provides a class with the same name which is used to represent a PIL image. alpha - The interpolation alpha factor. But first of all, let's explain something that can be quite confusing for a beginner. This is a lazy operation; this function identifies the file, but the file remains open and the actual image data is not read from the file until you try to process the data (or call the load() method). How does legislative oversight work in Switzerland when there is technically no "opposition" in parliament? If you are using a framework such as plone, Django, or buildout, try to replicate the issue just using Pillow. Returns type: An image object.Raises: IOError If the file cannot be found, or the image cannot be opened and identified. 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. You can run this code and see the result. PIL (Python Image Library) is an image processing package created for Python. The Python Pillow library is a fork of an older library called PIL. Does integrating PDOS give total charge of a system? Must have the same mode and size as the first image. I am getting syntax error. We do this using the open () function in python. Powered by Discourse, best viewed with JavaScript enabled, Problem when converting uploaded image in base64 to string or PIL.Image, https://github.com/akarazniewicz/smd/blob/master/models/detectors/maskrcnn.py. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, In that case, that code works fine for me once I change, Regarding your edit, where is that error occurring?I don't believe it is occurring in the code you've shown us, You didn't say anything about JSON before! I forgot this important thing. encode it in base64 using the base64 module in Python. Find centralized, trusted content and collaborate around the technologies you use most. If the Base64 String is taken with this label, it needs to be processed. image = Image.open(bytes_image).convert('RGB'). convert base64 to image python pillow image from array pil image from numpy pillow create image python pillow convert jpg to png pillow update image base64 encode image in python convert rgb image to binary in pillow bytes to Image PIL PY convert url to base64 image py python pil bytes to image Queries related to "base64 to pillow image" 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. Ready to optimize your JavaScript with Rust? I've also added a function to do the reverse conversion. 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. https://stackoverflow.com/a/22108380/4093019. Are you sure the function function accepts a PIL object or an image string? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. the test above work for me too. I am searching for this about six hours, what should we do to encode + symbol? The only difference is that in Python 3 the Base64 output is a b string. If not, please open a new issue with more specifics. 1. This usually involves working with computer languages to work on image as a 2 dimensional signal through its pixel composition. to your account. PIL was discontinued in 2011 and only supports Python 2. Do non-Segwit nodes reject Segwit transactions with invalid signature? Here's a short but complete demo of your code using a ByteIO instead of StringIO. The other option would be to forward a string (or is it a path?). Are you able to provide one of them? protected void exporttoimage (object sender, eventargs e) { string base64 = request.form [hfimagedata.uniqueid].split (',') [1]; byte [] bytes = convert.frombase64string (base64); using (image image = image.fromstream (new memorystream (bytes))) { image.save ("output.jpg", imageformat.jpeg); // or png } //file.copy (bytes.tostring 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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