191 lines
8 KiB
Python
Executable file
191 lines
8 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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#
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# This script is licensed under GNU GPL version 2.0 or above
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# (c) 2021 Antonio J. Delgado
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#
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import sys
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import os
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import logging
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import json
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import click
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import click_config_file
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from logging.handlers import SysLogHandler
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import face_recognition
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import exif
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from iptcinfo3 import IPTCInfo
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import PIL
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class CustomFormatter(logging.Formatter):
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"""Logging colored formatter, adapted from https://stackoverflow.com/a/56944256/3638629"""
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grey = '\x1b[38;21m'
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blue = '\x1b[38;5;39m'
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yellow = '\x1b[38;5;226m'
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red = '\x1b[38;5;196m'
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bold_red = '\x1b[31;1m'
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reset = '\x1b[0m'
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def __init__(self, fmt):
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super().__init__()
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self.fmt = fmt
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self.FORMATS = {
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logging.DEBUG: self.grey + self.fmt + self.reset,
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logging.INFO: self.blue + self.fmt + self.reset,
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logging.WARNING: self.yellow + self.fmt + self.reset,
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logging.ERROR: self.red + self.fmt + self.reset,
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logging.CRITICAL: self.bold_red + self.fmt + self.reset
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}
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def format(self, record):
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log_fmt = self.FORMATS.get(record.levelno)
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formatter = logging.Formatter(log_fmt)
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return formatter.format(record)
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class image_classifier:
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def __init__(self, debug_level, log_file, faces_directory, directory):
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''' Initial function called when object is created '''
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self.debug_level = debug_level
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if log_file is None:
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log_file = os.path.join(os.environ.get('HOME', os.environ.get('USERPROFILE', os.getcwd())), 'log', 'image_classifier.log')
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self.log_file = log_file
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self._init_log()
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self.faces_directory = faces_directory
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self.directory = directory
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self.known_people = self.load_known_people()
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if os.access(directory, os.R_OK):
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with os.scandir(directory) as directory_item:
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for entry in directory_item:
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if not entry.name.startswith('.') and entry.is_file():
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self.process_file(directory + os.sep + entry.name)
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def process_file(self, file):
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''' Process a file, find faces, add EXIF information and
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move it to the folder of the day'''
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self._log.debug(f"Processing file '{file}'...")
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people = self.find_faces(file)
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if people:
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self._log.debug(f"Found {len(people)} known people in the image.")
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self._log.debug(json.dumps(people, indent=2))
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iptc_info = IPTCInfo(file, force=True)
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print(iptc_info['keywords'])
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with open(file, 'rb') as image_file:
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self.exif_info = exif.Image(image_file)
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if not self.exif_info.has_exif:
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self._log.debug("No exif info in the image.")
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else:
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# for key in dir(self.exif_info):
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# if not key.startswith("_"):
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# sys.stdout.write(f"{key}: ")
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# sys.stdout.write(f"{self.exif_info[key]}")
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self.append_people_to_exif(people)
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with open(file, 'wb') as new_image_file:
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new_image_file.write(self.exif_info.get_file())
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self._log.debug(f"Updated file '{file}'.")
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# get date
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self._log.debug(f"File time stamp: {self.exif_info.get('Image timestamp')} (type: {type(self.exif_info.get('Image timestamp'))})")
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# move to destination
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else:
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self._log.debug("Doesn't seem to be an image.")
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def append_people_to_exif(self, people):
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if self.is_json(self.exif_info.get('user_comment')):
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data = json.loads(self.exif_info['user_comment'])
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if 'PeopleDetected' not in data:
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data['PeopleDetected'] = list()
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else:
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data = dict()
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if self.exif_info.get('user_comment'):
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data["previous_user_comment"]=self.exif_info.get('user_comment')
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data['PeopleDetected'] = list()
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for person in people:
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if person not in data['PeopleDetected']:
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data['PeopleDetected'].append(person)
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self._log.debug(f"New 'user_comment': {json.dumps(data, indent=2)}")
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self.exif_info.set("user_comment", json.dumps(data))
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def is_json(self, data):
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try:
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result = json.loads(data)
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except TypeError:
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return False
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return True
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def load_known_people(self):
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known_people = list()
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self._log.debug(f"Looking for known faces in directory '{self.faces_directory}'...")
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if os.access(self.faces_directory, os.R_OK):
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with os.scandir(self.faces_directory) as faces_items:
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for entry in faces_items:
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if not entry.name.startswith('.') and entry.is_file():
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self._log.debug(f"Detecting known person in file '{entry.name}'...")
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person = dict()
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person['filename'] = face_recognition.load_image_file(self.faces_directory + os.sep + entry.name)
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person['name'] = os.path.basename(os.path.splitext(self.faces_directory + os.sep + entry.name)[0])
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person['encoding'] = face_recognition.face_encodings(person['filename'])[0]
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known_people.append(person)
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return known_people
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def find_faces(self, file):
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''' Find faces in an image/video file '''
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people = list()
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try:
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image = face_recognition.load_image_file(file)
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encodings = face_recognition.face_encodings(image)
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self._log.debug(f"Found {len(encodings)} faces.")
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for known_person in self.known_people:
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for encoding in encodings:
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if face_recognition.compare_faces([known_person['encoding']], encoding)[0]:
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if known_person['name'] not in people:
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people.append(known_person['name'])
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except PIL.UnidentifiedImageError as error:
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self._log.debug(f"File '{file}' don't seem to be an image.")
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return False
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return people
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def _init_log(self):
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''' Initialize log object '''
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self._log = logging.getLogger("image_classifier")
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self._log.setLevel(logging.DEBUG)
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sysloghandler = SysLogHandler()
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sysloghandler.setLevel(logging.DEBUG)
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self._log.addHandler(sysloghandler)
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streamhandler = logging.StreamHandler(sys.stdout)
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streamhandler.setLevel(logging.getLevelName(self.debug_level))
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#formatter = '%(asctime)s | %(levelname)8s | %(message)s'
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formatter = '[%(levelname)s] %(message)s'
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streamhandler.setFormatter(CustomFormatter(formatter))
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self._log.addHandler(streamhandler)
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if not os.path.exists(os.path.dirname(self.log_file)):
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os.mkdir(os.path.dirname(self.log_file))
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filehandler = logging.handlers.RotatingFileHandler(self.log_file, maxBytes=102400000)
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# create formatter
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formatter = logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
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filehandler.setFormatter(formatter)
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filehandler.setLevel(logging.DEBUG)
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self._log.addHandler(filehandler)
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return True
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@click.command()
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@click.option("--debug-level", "-d", default="INFO",
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type=click.Choice(
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["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"],
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case_sensitive=False,
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), help='Set the debug level for the standard output.')
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@click.option('--log-file', '-l', help="File to store all debug messages.")
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@click.option("--faces-directory","-f", required=True, help="Folder containing the pictures that identify people. The filename would be used as the name for the person. Just one person per picture.")
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@click.option("--directory","-d", required=True, help="Folder containing the pictures to classify.")
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@click_config_file.configuration_option()
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def __main__(debug_level, log_file, faces_directory, directory):
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object = image_classifier(debug_level, log_file, faces_directory, directory)
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if __name__ == "__main__":
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__main__()
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