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“1”, “L”, or “RGBA”, and must have the same size as the composite ( image1, image2, mask ) #Ĭreate composite image by blending images using a transparency mask. If necessary, the result is clipped to fit intoĪn Image object. There are no restrictions on theĪlpha value. If alpha is 0.0, aĬopy of the first image is returned. Must have the same mode and size asĪlpha – The interpolation alpha factor. Out = image1 * ( 1.0 - alpha ) + image2 * alpha Parameters :
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blend ( im1, im2, alpha ) #Ĭreates a new image by interpolating between two input images, using Must have mode RGBA, and the same size asĪn Image object. alpha_composite ( im1, im2 ) #Īlpha composite im2 over im1. If the number of pixels is greater than twice _IMAGE_PIXELS, then aĭecompressionBombError will be raised instead. The logging documentation to have warnings output to the logging facility instead of stderr. Warnings.simplefilter('ignore', Image.DecompressionBombWarning). Warnings.simplefilter('error', Image.DecompressionBombWarning) or suppressed entirely with If desired, the warning can be turned into an error with It can be disabledīy setting Image.MAX_IMAGE_PIXELS = None. This threshold can be changed by setting _IMAGE_PIXELS. Image is over a certain limit, _IMAGE_PIXELS. Which decompress into a huge amount of data and are designed to crash or cause disruption by using upĪ lot of memory), Pillow will issue a DecompressionBombWarning if the number of pixels in an To protect against potential DOS attacks caused by “ decompression bombs” (i.e. TypeError – If formats is not None, a list or a tuple. ValueError – If the mode is not “r”, or if a StringIO PIL.UnidentifiedImageError – If the image cannot be opened and You can print the set ofĪvailable formats by running python3 -m PIL or usingįileNotFoundError – If the file cannot be found. This can be used to restrict the set of formats checked. If given, this argument must be “r”.įormats – A list or tuple of formats to attempt to load the file in. The file object must implement file.read, Parameters :įp – A filename (string), pathlib.Path object or a file object. 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
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Opens and identifies the given image file. open ( fp, mode = 'r', formats = None ) # save ( file + ".thumbnail", "JPEG" ) Functions # PIL.Image. Scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.From PIL import Image import glob, os size = 128, 128 for infile in glob. Scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp) If len(img.shape) is 3 and not isinstance(padColor, (list, tuple, np.ndarray)): # color image but only one color provided New_h = np.round(float(new_w) / aspect).astype(int) If (saspect > aspect) or ((saspect = 1) and (aspect = 1)): # new vertical image Pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int)Įlif aspect sh or w > sw: # shrinking image New_h = np.round(new_w/aspect).astype(int) So, after resizing we'll end up with a 1000xN or Nx1000 image (where N sh or w > sw: # shrinking imageĪspect = w/h # if on Python 2, you might need to cast as a float: float(w)/h To shrink an image, it will generally look best with CV_INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with CV_INTER_CUBIC (slow) or CV_INTER_LINEAR (faster but still looks OK). Note that if aspect is greater than 1, then the image is oriented horizontally, while if it's less than 1, the image is oriented vertically (and is square if aspect = 1).ĭifferent interpolation methods will look better depending on whether you're stretching the image to a larger resolution, or scaling it down to a lower resolution. So the most robust way to do this is to find the aspect ratio and calculate what the smaller dimension would be when the bigger one is stretched to 1000. However, resize() requires that you put in either the destination size (in both dimensions) or the scaling (in both dimensions), so you can't just put one or the other in for 1000 and let it calculate the other for you. You can use resize() in OpenCV to resize the image up/down to the size you need.