The file until you try to process the data (or call the The file remains open and the actual image data is not read from This is a lazy operation this function identifies the file, but Opens and identifies the given image file. open ( fp, mode = 'r', formats = None ) В¶ save ( file + '.thumbnail', 'JPEG' ) Functions В¶ PIL.Image.

PIL Package (autodoc of remaining modules)ДЇrom PIL import Image import glob, os size = 128, 128 for infile in glob.

ImageChops (вЂњChannel OperationsвЂќ) Module.

Open, rotate, and display an image (using the default viewer).

I think the best approach is to have a backend option that allows the user to specify pil, cv2, or skimage when performing affine transforms. The only border mode is constant (I think there is a PR to let you set the fill value right now, but by default it can't do that), and it can't only will accept multichannel images in uint8 format (although you can use anything for grayscale). PIL is definately the most restricted in terms of its api. Likewise opencv gives you lanczos resampling, which pil and skimage do not have. skimage gives you access to several options that doesn't exist in opencv or pillow. However, each method does have its merits. skimage performs the worst by a significant margin. Pillow does pretty well, note that this benchmark includes conversion to and from numpy. The results show that opencv is a clear winner in terms of speed. Note the y-axis is on a log scale, so a minor change in slop actually means quite a lot. Here is a time comparison I did between the three warp affine methods: scikit-image, Pillow, and opencv. sum() = 118 # note when converting from pillow we need to re-set the shape data = np. AFFINE, pil_aff_params, resample = Image. ''' # note: pil uses the inverse transform aff = np. sum() = 116, 'transforming the new ndarray does not change the old' ''' However, affine transforms in pillow are not in place, so a copy would be required either way.

Perhaps there is a different way of doing this where numpy just views the pillow memory? ''' new_np_img = np. ''' Conversion from Pillow.Image to a np.ndarray does seem to force a copy. Increase np_img by 1 everywhere will increase pil_img by 1 everywhere. ''' PROOF: Modifying the np_img also changes the pil_img. 'np_img and pil_img should start off as the same')

''' # Note: pil_img.getdata() is the pil imaging core that contains the actual data pil_img = Image. We will show that `pil_img` shares memory with `np_img`. Np_img = 1 ''' CLAIM: Conversion of an np.ndarray to a Pillow.Image shares memory. My output on an Intel Core i7-6800K CPU 3.40GHz, with skimage 0.13.0 and the scientific Python stack installed from Intel's conda channel and OpenCV 3.1.0 installed from the menpo conda channel:ДЇrom PIL import Image import numpy as np # Make an zero image with a square of ones in the center np_img = np. Print( ' factor sk/cv:', time_sk / time_cv) partial( skimage_fn, interp_sk), repeat = 10, number = 50)) / 50 print( ' skimage:', time_sk * 1000, 'ms/warp') Print( 'Interpolation order: ', interp_sk) uint8)ДЇor interp_cv, interp_sk in (( cv2. inverse, order = order, preserve_range = True). data import numpy as np import timeit import functools transform = skimage.

Pil getdata