mirror of
https://github.com/KevinMidboe/leifs-image-processor.git
synced 2025-10-29 17:50:20 +00:00
91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
import glob
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import os
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from PIL import Image
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import concurrent.futures
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import argparse
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import fileinput
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import uuid
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import os.path
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IMAGE_TYPES = ['.png', '.jpg', '.jpeg', '.JPG', '.PNG']
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OUTPUT_EXTENSION = 'jpg'
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OUTPUT_FALLBACK = os.path.dirname(__file__)
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OUTPUT_SIZES = [
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{ 'dimensions': (300, 300), 'name': 'thumb', 'crop': True },
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{ 'dimensions': (650, 650), 'name': 'sm', 'crop': False },
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{ 'dimensions': (1200, 1200), 'name': 'md', 'crop': False },
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{ 'dimensions': (2500, 2500), 'name': 'lg', 'crop': False }]
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def processImage(file, outputPath=None):
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if outputPath == None:
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outputPath = args.output if 'args.output' in globals() else os.path.join(OUTPUT_FALLBACK, 'output')
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else:
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outputPath = os.path.join(OUTPUT_FALLBACK, outputPath)
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print('outputpath', outputPath)
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image = Image.open(file)
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fileID = uuid.uuid4().hex
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for size in OUTPUT_SIZES:
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temp = image.copy()
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if size['crop']:
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temp = temp.crop(squareDimensions(temp.size))
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temp.thumbnail(size['dimensions'], Image.LANCZOS)
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filename = generateFilename(fileID, size['name'], outputPath)
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temp.save(filename)
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return {
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'filename': '.'.join([fileID, OUTPUT_EXTENSION]),
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'folder': outputPath,
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'variations': list(map(lambda vairation: vairation['name'], OUTPUT_SIZES))
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}
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def generateFilename(fileID, modifier, outputPath):
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filename = "{}_{}.{}".format(fileID, modifier, OUTPUT_EXTENSION)
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return os.path.join(outputPath, filename)
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def squareDimensions(dimensions):
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(width, height) = dimensions
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if width > height:
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delta = width - height
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left = int(delta/2)
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upper = 0
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right = height + left
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lower = height
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else:
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delta = height - width
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left = 0
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upper = int(delta/2)
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right = width
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lower = width + upper
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return (left, upper, right, lower)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Process some images')
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parser.add_argument('files', metavar="files", type=str, help='Directory of images to process')
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parser.add_argument('--output', metavar="DIR", help="Output directory")
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class Args:
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pass
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args = Args()
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args = parser.parse_args()
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# Create a pool of processes. By default, one is created for each CPU in your machine.
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with concurrent.futures.ProcessPoolExecutor() as executor:
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# Get a list of files to process
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image_files = glob.glob('{}/*'.format(args.files))
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print('Processing and generating images in following sizes: {}'.format(OUTPUT_SIZES))
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# Process the list of files, but split the work across the process pool to use all CPUs!
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for image_file, output_file in zip(image_files, executor.map(processImage, image_files)):
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print(f"Processed image {image_file} and save as {output_file}")
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