Image.save(output_image, "JPEG", quality=quality)Īs you can see, we keep storing the image in a temp buffer and reading the size of the buffer to know the file size. If file_size <= target or quality <= minimum_quality: Image.save(output_buffer, "JPEG", quality=quality) In that case, we need to keep decreasing the quality of the image until we get to the right filesize: minimum_quality = 50 # recommended, but optional (set to 0 if you don't want it) However, let's say you HAVE to bring the image size <= 100 kb, no matter what. Image.save("output_file.jpg", "JPEG", quality=95) Image = image.resize((WIDTH, HEIGHT)) #smaller width and height than the original Resizing an image, storing it as a JPEG and reducing the quality to 95 saves up a lot of bytes on the final output: image = Image.open("input_file.png") This_image.save("path\where\to_save\your_image.jpg",quality=50) This_image = Image.open("path\to\your_image.jpg") Without django foo.py: from PIL import Image This_image = this_image.resize((int(TARGET_WIDTH),int(new_height)),Image.ANTIALIAS) Image = models.ImageField(upload_to='theme_image/') In django models.py after image saved, it will be proccessed again from PIL import Image You can change TARGET_WIDTH for your required width There are two options, they are doing the same logic, first one is how i did in django project, second is on pure python This script will reduce your image's width and height, with saving it's proportion, and reducing size also I prefer quality 85 with optimize because the quality isn't affected much, and the file size is much smaller. Using a quality of 75 (default if argument is left out) would yield: Using a quality of 85 instead of 95 in this case would yield: Now to try and get it down to 5kb to 10 kb, you can change the quality value in the save options. Many operations act on each band separately, e.g., histograms. For example, a PNG image might have ‘R’, ‘G’, ‘B’, and ‘A’ bands for the red, green, blue, and alpha transparency values. 1.9kb might not seem like much, but over hundreds/thousands of pictures, it can add up. The Python Imaging Library allows you to store several bands in a single image, provided they all have the same dimensions and depth. The optimize flag will do an extra pass on the image to find a way to reduce its size as much as possible. # downsize the image with an ANTIALIAS filter (gives the highest quality)įoo = foo.resize((160,300),Image.ANTIALIAS)įoo.save('path/to/save/image_scaled.jpg', quality=95) # The saved downsized image size is 24.8kbįoo.save('path/to/save/image_scaled_opt.jpg', optimize=True, quality=95) # The saved downsized image size is 22.9kb from PIL import Imageįoo = Image.open('path/to/image.jpg') # My image is a 200x374 jpeg that is 102kb large ) which further might blur your image if you choose a lossy image format.A built-in parameter for saving JPEGs and PNGs is optimize. Upon storing the image, certain formats do "lossy" storage to minimize file size (JPG) others are lossless (PNG, TIFF, JPG2000. You can try to change the algorithm or you can apply further postprocessing ("sharpening") to enrich the contrasts again. You can use max (sizes) - size value of the image in a while loop to add the padding to each image. Some kind of algorithm ( interpolation=cv2.INTER_CUBIC, others here) tweaks the pixel values to merge/average them so you do not loose too much of information. You can use: image cv2.copyMakeBorder (src, top, bottom, left, right, borderType) Where src is your source image and top, bottom, left, right are the padding around the image. That is exactly what happens when resizing images. blending neighbouring pixels into some kind of weighted average and replace say 476 pixels with slightly altered 439 pixels.discarding single values or by cropping an image which is not what you want to do) Putting the same amount of information (stored as pixels in your source image) into a smaller pixelarea only works by How can you pack 2000 pixels into a box that only holds 1800? You can't.
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