Images processor with cli and json flask server.

processor.py

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+import glob
+import os
+from PIL import Image
+import concurrent.futures
+import argparse
+import fileinput
+import uuid
+import os.path
+
+IMAGE_TYPES = ['.png', '.jpg', '.jpeg', '.JPG', '.PNG']
+OUTPUT_EXTENSION = 'jpg'
+OUTPUT_FALLBACK = os.path.dirname(__file__)
+OUTPUT_SIZES = [
+    { 'dimensions': (250, 250), 'name': 'thumb', 'crop': True },
+    { 'dimensions': (650, 650), 'name': 'sm', 'crop': False },
+    { 'dimensions': (1200, 1200), 'name': 'md', 'crop': False },
+    { 'dimensions': (1800, 1800), 'name': 'lg', 'crop': False }]
+
+
+
+def processImage(file, outputPath=None):
+    outputPath = args.output if 'args.output' in globals() else os.path.join(OUTPUT_FALLBACK, 'output')
+    print('outputpath', outputPath)
+    image = Image.open(file)
+    fileID = uuid.uuid4().hex
+
+    for size in OUTPUT_SIZES:
+        temp = image.copy()
+        
+        if size['crop']:
+            temp = temp.crop(squareDimensions(temp.size))
+
+        temp.thumbnail(size['dimensions'], Image.LANCZOS)
+        
+        filename = generateFilename(fileID, size['name'], outputPath)
+        temp.save(filename)
+    
+    return '.'.join([fileID, OUTPUT_EXTENSION])
+
+def generateFilename(fileID, modifier, outputPath):
+    filename = "{}_{}.{}".format(fileID, modifier, OUTPUT_EXTENSION)
+    return os.path.join(outputPath, filename)
+
+def squareDimensions(dimensions):
+    (width, height) = dimensions
+
+    if width > height:
+       delta = width - height
+       left = int(delta/2)
+       upper = 0
+       right = height + left
+       lower = height
+    else:
+       delta = height - width
+       left = 0
+       upper = int(delta/2)
+       right = width
+       lower = width + upper
+
+    return (left, upper, right, lower)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Process some images')
+    parser.add_argument('files', metavar="files", type=str, help='Directory of images to process')
+    parser.add_argument('--output', metavar="DIR", help="Output directory")
+
+    class Args:
+        pass
+
+    args = Args()
+    args = parser.parse_args()
+
+    # Create a pool of processes. By default, one is created for each CPU in your machine.
+    with concurrent.futures.ProcessPoolExecutor() as executor:
+        # Get a list of files to process
+        image_files = glob.glob('{}/*'.format(args.files))
+
+        print('Processing and generating images in following sizes: {}'.format(OUTPUT_SIZES))
+        # Process the list of files, but split the work across the process pool to use all CPUs!
+        for image_file, output_file in zip(image_files, executor.map(processImage, image_files)):
+            print(f"Processed image {image_file} and save as {output_file}")