173 lines
5.7 KiB
Python
173 lines
5.7 KiB
Python
"""
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The photo module contains the :class:`Photo` class, which is used to track
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image objects (JPG, DNG, etc.).
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.. moduleauthor:: Jaisen Mathai <jaisen@jmathai.com>
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"""
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import imagehash
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import imghdr
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import logging
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import numpy as np
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import os
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from PIL import Image
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import time
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from .media import Media
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class Photo(Media):
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"""A photo object.
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:param str source: The fully qualified path to the photo file
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"""
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__name__ = 'Photo'
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#: Valid extensions for photo files.
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extensions = ('arw', 'cr2', 'dng', 'gif', 'heic', 'jpeg', 'jpg', 'nef', 'png', 'rw2')
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def __init__(self, source=None, ignore_tags=set()):
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super().__init__(source, ignore_tags)
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# We only want to parse EXIF once so we store it here
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self.exif = None
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# Optionally import Pillow - see gh-325
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# https://github.com/jmathai/elodie/issues/325
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self.pillow = None
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try:
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from PIL import Image
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self.pillow = Image
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except ImportError:
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pass
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def is_valid(self):
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"""Check the file extension against valid file extensions.
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The list of valid file extensions come from self.extensions. This
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also checks whether the file is an image.
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:returns: bool
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"""
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source = self.source
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# HEIC is not well supported yet so we special case it.
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# https://github.com/python-pillow/Pillow/issues/2806
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extension = os.path.splitext(source)[1][1:].lower()
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if(extension != 'heic'):
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# gh-4 This checks if the source file is an image.
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# It doesn't validate against the list of supported types.
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# We check with imghdr and pillow.
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if(imghdr.what(source) is None):
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# Pillow is used as a fallback and if it's not available we trust
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# what imghdr returned.
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if(self.pillow is None):
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return False
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else:
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# imghdr won't detect all variants of images (https://bugs.python.org/issue28591)
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# see https://github.com/jmathai/elodie/issues/281
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# before giving up, we use `pillow` imaging library to detect file type
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#
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# It is important to note that the library doesn't decode or load the
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# raster data unless it really has to. When you open a file,
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# the file header is read to determine the file format and extract
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# things like mode, size, and other properties required to decode the file,
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# but the rest of the file is not processed until later.
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try:
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im = self.pillow.open(source)
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except IOError:
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return False
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if(im.format is None):
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return False
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return extension in self.extensions
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class CompareImages:
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def __init__(self, file_paths, hash_size=8, logger=logging.getLogger()):
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self.file_paths = file_paths
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self.hash_size = hash_size
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self.logger = logger
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logger.setLevel(logging.INFO)
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def get_images(self):
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'''
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:returns: img_path generator
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'''
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for img_path in self.file_paths:
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if imghdr.what(img_path) is not None:
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yield img_path
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def find_duplicates(self):
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"""
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Find duplicates
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"""
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hashes = {}
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duplicates = []
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# Searching for duplicates.
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for img_path in self.get_images():
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if imghdr.what(img_path) is not None:
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with Image.open(img_path) as img:
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temp_hash = imagehash.average_hash(img, self.hash_size)
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if temp_hash in hashes:
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self.logger.info("Duplicate {} \nfound for image {}\n".format(img_path, hashes[temp_hash]))
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duplicates.append(img_path)
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else:
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hashes[temp_hash] = img_path
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return duplicates
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def remove_duplicates(self, duplicates):
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for duplicate in duplicates:
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try:
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os.remove(duplicate)
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except OSError as error:
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self.logger.error(error)
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def remove_duplicates_interactive(self, duplicates):
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if len(duplicates) != 0:
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answer = input(f"Do you want to delete these {duplicates} images? Y/n: ")
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if(answer.strip().lower() == 'y'):
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self.remove_duplicates(duplicates)
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self.logger.info(f'{duplicate} deleted successfully!')
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else:
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self.logger.info("No duplicates found")
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def find_similar(self, image, similarity=80):
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'''
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Find similar images
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:returns: img_path generator
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'''
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threshold = 1 - similarity/100
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diff_limit = int(threshold*(self.hash_size**2))
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hash1 = ''
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if imghdr.what(image) is not None:
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with Image.open(image) as img:
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hash1 = imagehash.average_hash(img, self.hash_size).hash
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self.logger.info(f'Finding similar images to {image}')
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for img_path in self.get_images():
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if img_path == image:
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continue
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with Image.open(img_path) as img:
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hash2 = imagehash.average_hash(img, self.hash_size).hash
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diff_images = np.count_nonzero(hash1 != hash2)
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if diff_images <= diff_limit:
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threshold_img = diff_images / (self.hash_size**2)
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similarity_img = round((1 - threshold_img) * 100)
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self.logger.info(f'{img_path} image found {similarity_img}% similar to {image}')
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yield img_path
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