683 lines
26 KiB
Python
683 lines
26 KiB
Python
"""A class to automatically identify a comic archive"""
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#
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# Copyright 2012-2014 ComicTagger Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import io
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import logging
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import sys
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from typing import Any, Callable
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from typing_extensions import NotRequired, TypedDict
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from comicapi import utils
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from comicapi.comicarchive import ComicArchive
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from comicapi.genericmetadata import GenericMetadata
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from comicapi.issuestring import IssueString
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from comictaggerlib.ctsettings import ct_ns
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from comictaggerlib.imagefetcher import ImageFetcher, ImageFetcherException
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from comictaggerlib.imagehasher import ImageHasher
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from comictaggerlib.resulttypes import IssueResult
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from comictalker.comictalker import ComicTalker, TalkerError
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logger = logging.getLogger(__name__)
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try:
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from PIL import Image, ImageChops
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pil_available = True
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except ImportError:
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pil_available = False
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class SearchKeys(TypedDict):
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series: str | None
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issue_number: str | None
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month: int | None
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year: int | None
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issue_count: int | None
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class Score(TypedDict):
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score: NotRequired[int]
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url: str
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hash: int
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class IssueIdentifierNetworkError(Exception):
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...
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class IssueIdentifierCancelled(Exception):
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...
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class IssueIdentifier:
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result_no_matches = 0
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result_found_match_but_bad_cover_score = 1
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result_found_match_but_not_first_page = 2
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result_multiple_matches_with_bad_image_scores = 3
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result_one_good_match = 4
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result_multiple_good_matches = 5
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def __init__(self, comic_archive: ComicArchive, config: ct_ns, talker: ComicTalker) -> None:
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self.config = config
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self.talker = talker
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self.comic_archive: ComicArchive = comic_archive
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self.image_hasher = 1
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self.only_use_additional_meta_data = False
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# a decent hamming score, good enough to call it a match
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self.min_score_thresh: int = 16
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# for alternate covers, be more stringent, since we're a bit more
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# scattershot in comparisons
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self.min_alternate_score_thresh = 12
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# the min distance a hamming score must be to separate itself from
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# closest neighbor
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self.min_score_distance = 4
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# a very strong hamming score, almost certainly the same image
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self.strong_score_thresh = 8
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# used to eliminate series names that are too long based on our search
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# string
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self.series_match_thresh = config.identifier_series_match_identify_thresh
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# used to eliminate unlikely publishers
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self.publisher_filter = [s.strip().casefold() for s in config.identifier_publisher_filter]
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self.additional_metadata = GenericMetadata()
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self.output_function: Callable[[str], None] = IssueIdentifier.default_write_output
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self.callback: Callable[[int, int], None] | None = None
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self.cover_url_callback: Callable[[bytes], None] | None = None
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self.search_result = self.result_no_matches
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self.cover_page_index = 0
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self.cancel = False
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self.match_list: list[IssueResult] = []
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def set_score_min_threshold(self, thresh: int) -> None:
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self.min_score_thresh = thresh
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def set_score_min_distance(self, distance: int) -> None:
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self.min_score_distance = distance
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def set_additional_metadata(self, md: GenericMetadata) -> None:
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self.additional_metadata = md
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def set_name_series_match_threshold(self, delta: int) -> None:
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self.series_match_thresh = delta
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def set_publisher_filter(self, flt: list[str]) -> None:
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self.publisher_filter = flt
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def set_hasher_algorithm(self, algo: int) -> None:
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self.image_hasher = algo
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def set_output_function(self, func: Callable[[str], None]) -> None:
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self.output_function = func
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def calculate_hash(self, image_data: bytes) -> int:
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if self.image_hasher == 3:
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return ImageHasher(data=image_data).p_hash()
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if self.image_hasher == 2:
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return -1 # ImageHasher(data=image_data).average_hash2()
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return ImageHasher(data=image_data).average_hash()
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def get_aspect_ratio(self, image_data: bytes) -> float:
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try:
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im = Image.open(io.BytesIO(image_data))
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w, h = im.size
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return float(h) / float(w)
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except Exception:
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return 1.5
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def crop_cover(self, image_data: bytes) -> bytes:
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im = Image.open(io.BytesIO(image_data))
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w, h = im.size
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try:
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cropped_im = im.crop((int(w / 2), 0, w, h))
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except Exception:
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logger.exception("cropCover() error")
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return b""
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output = io.BytesIO()
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cropped_im.convert("RGB").save(output, format="PNG")
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cropped_image_data = output.getvalue()
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output.close()
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return cropped_image_data
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# Adapted from https://stackoverflow.com/a/10616717/20629671
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def crop_border(self, image_data: bytes, ratio: int) -> bytes | None:
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im = Image.open(io.BytesIO(image_data))
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# RGBA doesn't work????
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tmp = im.convert("RGB")
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bg = Image.new("RGB", tmp.size, "black")
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diff = ImageChops.difference(tmp, bg)
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diff = ImageChops.add(diff, diff, 2.0, -100)
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bbox = diff.getbbox()
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width_percent = 0
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height_percent = 0
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# If bbox is None that should mean it's solid black
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if bbox:
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width = bbox[2] - bbox[0]
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height = bbox[3] - bbox[1]
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# Convert to percent
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width_percent = int(100 - ((width / im.width) * 100))
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height_percent = int(100 - ((height / im.height) * 100))
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logger.debug(
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"Width: %s Height: %s, ratio: %s %s ratio met: %s",
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im.width,
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im.height,
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width_percent,
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height_percent,
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width_percent > ratio or height_percent > ratio,
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)
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# If there is a difference return the image otherwise return None
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if width_percent > ratio or height_percent > ratio:
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output = io.BytesIO()
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im.crop(bbox).save(output, format="PNG")
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cropped_image_data = output.getvalue()
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output.close()
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return cropped_image_data
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return None
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def set_progress_callback(self, cb_func: Callable[[int, int], None]) -> None:
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self.callback = cb_func
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def set_cover_url_callback(self, cb_func: Callable[[bytes], None]) -> None:
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self.cover_url_callback = cb_func
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def get_search_keys(self) -> SearchKeys:
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ca = self.comic_archive
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search_keys: SearchKeys
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if self.only_use_additional_meta_data:
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search_keys = SearchKeys(
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series=self.additional_metadata.series,
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issue_number=self.additional_metadata.issue,
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year=self.additional_metadata.year,
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month=self.additional_metadata.month,
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issue_count=self.additional_metadata.issue_count,
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)
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return search_keys
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# see if the archive has any useful meta data for searching with
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try:
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if ca.has_cix():
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internal_metadata = ca.read_cix()
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else:
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internal_metadata = ca.read_cbi()
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except Exception as e:
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internal_metadata = GenericMetadata()
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logger.error("Failed to load metadata for %s: %s", ca.path, e)
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# try to get some metadata from filename
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md_from_filename = ca.metadata_from_filename(
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self.config.filename_complicated_parser,
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self.config.filename_remove_c2c,
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self.config.filename_remove_fcbd,
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self.config.filename_remove_publisher,
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)
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working_md = md_from_filename.copy()
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working_md.overlay(internal_metadata)
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working_md.overlay(self.additional_metadata)
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# preference order:
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# 1. Additional metadata
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# 1. Internal metadata
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# 1. Filename metadata
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search_keys = SearchKeys(
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series=working_md.series,
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issue_number=working_md.issue,
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year=working_md.year,
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month=working_md.month,
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issue_count=working_md.issue_count,
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)
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return search_keys
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@staticmethod
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def default_write_output(text: str) -> None:
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sys.stdout.write(text)
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sys.stdout.flush()
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def log_msg(self, msg: Any, newline: bool = True) -> None:
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msg = str(msg)
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if newline:
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msg += "\n"
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self.output_function(msg)
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def get_issue_cover_match_score(
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self,
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issue_id: str,
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primary_img_url: str,
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alt_urls: list[str],
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local_cover_hash_list: list[int],
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use_remote_alternates: bool = False,
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use_log: bool = True,
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) -> Score:
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# local_cover_hash_list is a list of pre-calculated hashes.
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# use_remote_alternates - indicates to use alternate covers from CV
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# If there is no URL return 0
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if not primary_img_url:
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return Score(score=0, url="", hash=0)
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try:
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url_image_data = ImageFetcher(self.config.runtime_config.user_cache_dir).fetch(
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primary_img_url, blocking=True
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)
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except ImageFetcherException as e:
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self.log_msg(f"Network issue while fetching cover image from {self.talker.name}. Aborting...")
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raise IssueIdentifierNetworkError from e
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if self.cancel:
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raise IssueIdentifierCancelled
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# alert the GUI, if needed
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if self.cover_url_callback is not None:
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self.cover_url_callback(url_image_data)
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remote_cover_list = [Score(url=primary_img_url, hash=self.calculate_hash(url_image_data))]
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if self.cancel:
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raise IssueIdentifierCancelled
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if use_remote_alternates:
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for alt_url in alt_urls:
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try:
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alt_url_image_data = ImageFetcher(self.config.runtime_config.user_cache_dir).fetch(
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alt_url, blocking=True
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)
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except ImageFetcherException as e:
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self.log_msg(f"Network issue while fetching alt. cover image from {self.talker.name}. Aborting...")
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raise IssueIdentifierNetworkError from e
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if self.cancel:
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raise IssueIdentifierCancelled
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# alert the GUI, if needed
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if self.cover_url_callback is not None:
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self.cover_url_callback(alt_url_image_data)
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remote_cover_list.append(Score(url=alt_url, hash=self.calculate_hash(alt_url_image_data)))
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if self.cancel:
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raise IssueIdentifierCancelled
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if use_log and use_remote_alternates:
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self.log_msg(f"[{len(remote_cover_list) - 1} alt. covers]", False)
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if use_log:
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self.log_msg("[ ", False)
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score_list = []
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done = False
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for local_cover_hash in local_cover_hash_list:
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for remote_cover_item in remote_cover_list:
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score = ImageHasher.hamming_distance(local_cover_hash, remote_cover_item["hash"])
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score_list.append(Score(score=score, url=remote_cover_item["url"], hash=remote_cover_item["hash"]))
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if use_log:
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self.log_msg(score, False)
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if score <= self.strong_score_thresh:
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# such a good score, we can quit now, since for sure we have a winner
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done = True
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break
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if done:
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break
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if use_log:
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self.log_msg(" ]", False)
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best_score_item = min(score_list, key=lambda x: x["score"])
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return best_score_item
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def search(self) -> list[IssueResult]:
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ca = self.comic_archive
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self.match_list = []
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self.cancel = False
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self.search_result = self.result_no_matches
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if not pil_available:
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self.log_msg("Python Imaging Library (PIL) is not available and is needed for issue identification.")
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return self.match_list
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if not ca.seems_to_be_a_comic_archive():
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self.log_msg(f"Sorry, but {ca.path} is not a comic archive!")
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return self.match_list
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cover_image_data = ca.get_page(self.cover_page_index)
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cover_hash = self.calculate_hash(cover_image_data)
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# check the aspect ratio
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# if it's wider than it is high, it's probably a two page spread
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# if so, crop it and calculate a second hash
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narrow_cover_hash = None
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aspect_ratio = self.get_aspect_ratio(cover_image_data)
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if aspect_ratio < 1.0:
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right_side_image_data = self.crop_cover(cover_image_data)
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if right_side_image_data is not None:
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narrow_cover_hash = self.calculate_hash(right_side_image_data)
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keys = self.get_search_keys()
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# normalize the issue number, None will return as ""
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keys["issue_number"] = IssueString(keys["issue_number"]).as_string()
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# we need, at minimum, a series and issue number
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if not (keys["series"] and keys["issue_number"]):
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self.log_msg("Not enough info for a search!")
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return []
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self.log_msg(f"Using {self.talker.name} to search for:")
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self.log_msg("\tSeries: " + keys["series"])
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self.log_msg("\tIssue: " + keys["issue_number"])
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if keys["issue_count"] is not None:
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self.log_msg("\tCount: " + str(keys["issue_count"]))
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if keys["year"] is not None:
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self.log_msg("\tYear: " + str(keys["year"]))
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if keys["month"] is not None:
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self.log_msg("\tMonth: " + str(keys["month"]))
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self.log_msg(f"Searching for {keys['series']} #{keys['issue_number']} ...")
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try:
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ct_search_results = self.talker.search_for_series(keys["series"])
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except TalkerError as e:
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self.log_msg(f"Error searching for series.\n{e}")
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return []
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if self.cancel:
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return []
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if ct_search_results is None:
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return []
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series_second_round_list = []
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for item in ct_search_results:
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length_approved = False
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publisher_approved = True
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date_approved = True
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# remove any series that starts after the issue year
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if keys["year"] is not None and item.start_year is not None:
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if keys["year"] < item.start_year:
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date_approved = False
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for name in [item.name, *item.aliases]:
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if utils.titles_match(keys["series"], name, self.series_match_thresh):
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length_approved = True
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break
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# remove any series from publishers on the filter
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if item.publisher is not None:
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publisher = item.publisher
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if publisher is not None and publisher.casefold() in self.publisher_filter:
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publisher_approved = False
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if length_approved and publisher_approved and date_approved:
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series_second_round_list.append(item)
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self.log_msg("Searching in " + str(len(series_second_round_list)) + " series")
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if self.callback is not None:
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self.callback(0, len(series_second_round_list))
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# now sort the list by name length
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series_second_round_list.sort(key=lambda x: len(x.name), reverse=False)
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series_by_id = {series.id: series for series in series_second_round_list}
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issue_list = None
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try:
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if len(series_by_id) > 0:
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issue_list = self.talker.fetch_issues_by_series_issue_num_and_year(
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list(series_by_id.keys()), keys["issue_number"], keys["year"]
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)
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except TalkerError as e:
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self.log_msg(f"Issue with while searching for series details. Aborting...\n{e}")
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return []
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if issue_list is None:
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return []
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shortlist = []
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# now re-associate the issues and series
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# is this really needed?
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for issue in issue_list:
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if issue.series.id in series_by_id:
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shortlist.append((series_by_id[issue.series.id], issue))
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if keys["year"] is None:
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self.log_msg(f"Found {len(shortlist)} series that have an issue #{keys['issue_number']}")
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else:
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self.log_msg(
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f"Found {len(shortlist)} series that have an issue #{keys['issue_number']} from {keys['year']}"
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)
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# now we have a shortlist of series with the desired issue number
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# Do first round of cover matching
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counter = len(shortlist)
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for series, issue in shortlist:
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if self.callback is not None:
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self.callback(counter, len(shortlist) * 3)
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counter += 1
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self.log_msg(
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f"Examining covers for ID: {series.id} {series.name} ({series.start_year}) ...",
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newline=False,
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)
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# parse out the cover date
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_, month, year = utils.parse_date_str(issue.cover_date)
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# Now check the cover match against the primary image
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hash_list = [cover_hash]
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if narrow_cover_hash is not None:
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hash_list.append(narrow_cover_hash)
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cropped_border = self.crop_border(cover_image_data, self.config.identifier_border_crop_percent)
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if cropped_border is not None:
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hash_list.append(self.calculate_hash(cropped_border))
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logger.info("Adding cropped cover to the hashlist")
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try:
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image_url = issue.image_url
|
|
alt_urls = issue.alt_image_urls
|
|
|
|
score_item = self.get_issue_cover_match_score(
|
|
issue.id, image_url, alt_urls, hash_list, use_remote_alternates=False
|
|
)
|
|
except Exception:
|
|
logger.exception("Scoring series failed")
|
|
self.match_list = []
|
|
return self.match_list
|
|
|
|
match: IssueResult = {
|
|
"series": f"{series.name} ({series.start_year})",
|
|
"distance": score_item["score"],
|
|
"issue_number": keys["issue_number"],
|
|
"cv_issue_count": series.count_of_issues,
|
|
"url_image_hash": score_item["hash"],
|
|
"issue_title": issue.name,
|
|
"issue_id": issue.id,
|
|
"series_id": series.id,
|
|
"month": month,
|
|
"year": year,
|
|
"publisher": None,
|
|
"image_url": image_url,
|
|
"alt_image_urls": alt_urls,
|
|
"description": issue.description,
|
|
}
|
|
if series.publisher is not None:
|
|
match["publisher"] = series.publisher
|
|
|
|
self.match_list.append(match)
|
|
|
|
self.log_msg(f" --> {match['distance']}", newline=False)
|
|
|
|
self.log_msg("")
|
|
|
|
if len(self.match_list) == 0:
|
|
self.log_msg(":-( no matches!")
|
|
self.search_result = self.result_no_matches
|
|
return self.match_list
|
|
|
|
# sort list by image match scores
|
|
self.match_list.sort(key=lambda k: k["distance"])
|
|
|
|
lst = []
|
|
for i in self.match_list:
|
|
lst.append(i["distance"])
|
|
|
|
self.log_msg(f"Compared to covers in {len(self.match_list)} issue(s):", newline=False)
|
|
self.log_msg(str(lst))
|
|
|
|
def print_match(item: IssueResult) -> None:
|
|
self.log_msg(
|
|
"-----> {} #{} {} ({}/{}) -- score: {}".format(
|
|
item["series"],
|
|
item["issue_number"],
|
|
item["issue_title"],
|
|
item["month"],
|
|
item["year"],
|
|
item["distance"],
|
|
)
|
|
)
|
|
|
|
best_score: int = self.match_list[0]["distance"]
|
|
|
|
if best_score >= self.min_score_thresh:
|
|
# we have 1 or more low-confidence matches (all bad cover scores)
|
|
# look at a few more pages in the archive, and also alternate covers online
|
|
self.log_msg("Very weak scores for the cover. Analyzing alternate pages and covers...")
|
|
hash_list = [cover_hash]
|
|
if narrow_cover_hash is not None:
|
|
hash_list.append(narrow_cover_hash)
|
|
for page_index in range(1, min(3, ca.get_number_of_pages())):
|
|
image_data = ca.get_page(page_index)
|
|
page_hash = self.calculate_hash(image_data)
|
|
hash_list.append(page_hash)
|
|
|
|
second_match_list = []
|
|
counter = 2 * len(self.match_list)
|
|
for m in self.match_list:
|
|
if self.callback is not None:
|
|
self.callback(counter, len(self.match_list) * 3)
|
|
counter += 1
|
|
self.log_msg(f"Examining alternate covers for ID: {m['series_id']} {m['series']} ...", newline=False)
|
|
try:
|
|
score_item = self.get_issue_cover_match_score(
|
|
m["issue_id"],
|
|
m["image_url"],
|
|
m["alt_image_urls"],
|
|
hash_list,
|
|
use_remote_alternates=True,
|
|
)
|
|
except Exception:
|
|
logger.exception("failed examining alt covers")
|
|
self.match_list = []
|
|
return self.match_list
|
|
self.log_msg(f"--->{score_item['score']}")
|
|
self.log_msg("")
|
|
|
|
if score_item["score"] < self.min_alternate_score_thresh:
|
|
second_match_list.append(m)
|
|
m["distance"] = score_item["score"]
|
|
|
|
if len(second_match_list) == 0:
|
|
if len(self.match_list) == 1:
|
|
self.log_msg("No matching pages in the issue.")
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
print_match(self.match_list[0])
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
self.search_result = self.result_found_match_but_bad_cover_score
|
|
else:
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
self.log_msg("Multiple bad cover matches! Need to use other info...")
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
self.search_result = self.result_multiple_matches_with_bad_image_scores
|
|
return self.match_list
|
|
|
|
# We did good, found something!
|
|
self.log_msg("Success in secondary/alternate cover matching!")
|
|
|
|
self.match_list = second_match_list
|
|
# sort new list by image match scores
|
|
self.match_list.sort(key=lambda k: k["distance"])
|
|
best_score = self.match_list[0]["distance"]
|
|
self.log_msg("[Second round cover matching: best score = {best_score}]")
|
|
# now drop down into the rest of the processing
|
|
|
|
if self.callback is not None:
|
|
self.callback(99, 100)
|
|
|
|
# now pare down list, remove any item more than specified distant from the top scores
|
|
for match_item in reversed(self.match_list):
|
|
if match_item["distance"] > best_score + self.min_score_distance:
|
|
self.match_list.remove(match_item)
|
|
|
|
# One more test for the case choosing limited series first issue vs a trade with the same cover:
|
|
# if we have a given issue count > 1 and the series from CV has count==1, remove it from match list
|
|
if len(self.match_list) >= 2 and keys["issue_count"] is not None and keys["issue_count"] != 1:
|
|
new_list = []
|
|
for match in self.match_list:
|
|
if match["cv_issue_count"] != 1:
|
|
new_list.append(match)
|
|
else:
|
|
self.log_msg(
|
|
f"Removing series {match['series']} [{match['series_id']}] from consideration (only 1 issue)"
|
|
)
|
|
|
|
if len(new_list) > 0:
|
|
self.match_list = new_list
|
|
|
|
if len(self.match_list) == 1:
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
print_match(self.match_list[0])
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
self.search_result = self.result_one_good_match
|
|
|
|
elif len(self.match_list) == 0:
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
self.log_msg("No matches found :(")
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
self.search_result = self.result_no_matches
|
|
else:
|
|
# we've got multiple good matches:
|
|
self.log_msg("More than one likely candidate.")
|
|
self.search_result = self.result_multiple_good_matches
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
for match_item in self.match_list:
|
|
print_match(match_item)
|
|
self.log_msg("--------------------------------------------------------------------------")
|
|
|
|
return self.match_list
|