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 To resize an image, you call the resize() method of pillows image class by giving width and height. 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. 1.9kb might not seem like much, but over hundreds/thousands of pictures, it can add up. The optimize flag will do an extra pass on the image to find a way to reduce its size as much as possible. Rather it is use to increment between frames of a. I noticed you wrote 'im.seek(im.tell() + 1) load all frames'. And, in the age of responsive designs, you want more. Next, you have to write the scripts to resize each of the extracted frame and assemble them all as a new. This allows to process about 2.5k photos per hour on a single core. # 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 Entry conditions are as follows: a photo shot with the latest iPhone takes about 1.37 sec to get resized to 2 MP via Pillow 2.6 with none of the optimizations implemented. from PIL import Imageįoo = Image.open('path/to/image.jpg') # My image is a 200x374 jpeg that is 102kb large If len(mask_y) < b: mask_y = np.A built-in parameter for saving JPEGs and PNGs is optimize. If len(mask_x) < a: mask_x = np.concatenate()
# make sure the residuals are taken into account python-resize-image takes as first argument a PIL.Image and then size argument which can be a single integer or tuple of two integers.
PIL IMAGE RESIZE INSTALL
For example, the shape of the original array is (m, n), and the target shape is (a, b). Install python-resize-image using pip: pip install python-resize-image Usage. To scale up an array is also quite straight forward if you use masks for advanced slicing. Then, the stride can be (s1, s2) = (m // a, n // b) Say, the shape of the loaded numpy array is (m, n) (one channel), and the target shape is (a, b). If you are only dealing with numpy arrays, I think slicing would be enough
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