182 lines
7.4 KiB
Python
182 lines
7.4 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from logging import getLogger
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from re import sub
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from string import punctuation
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from ..downloadutils import DownloadUtils as DU
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from .. import utils, variables as v
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LOG = getLogger('PLEX.api.fanartlookup')
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API_KEY = utils.settings('themoviedbAPIKey')
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# How far apart can a video's airing date be (in years)
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YEARS_APART = 1
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# levenshtein_distance_ratio() returns a value between 0 (no match) and 1 (full
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# match). What's the threshold?
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LEVENSHTEIN_RATIO_THRESHOLD = 0.95
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# Which character should we ignore when matching video titles?
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EXCLUDE_CHARS = set(punctuation)
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def external_item_id(title, year, plex_type, collection):
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LOG.debug('Start identifying %s (%s, %s)', title, year, plex_type)
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year = int(year) if year else None
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media_type = 'tv' if plex_type == v.PLEX_TYPE_SHOW else plex_type
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# if the title has the year in remove it as tmdb cannot deal with it...
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# replace e.g. 'The Americans (2015)' with 'The Americans'
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title = sub(r'\s*\(\d{4}\)$', '', title, count=1)
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url = 'https://api.themoviedb.org/3/search/%s' % media_type
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parameters = {
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'api_key': API_KEY,
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'language': v.KODILANGUAGE,
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'query': title.encode('utf-8')
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}
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data = DU().downloadUrl(url,
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authenticate=False,
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parameters=parameters,
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timeout=7)
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try:
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data = data['results']
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except (AttributeError, KeyError, TypeError):
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LOG.debug('No match found on themoviedb for %s (%s, %s)',
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title, year, media_type)
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return
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LOG.debug('themoviedb returned results: %s', data)
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# Some entries don't contain a title or id - get rid of them
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data = [x for x in data if 'title' in x and 'id' in x]
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# Get rid of all results that do NOT have a matching release year
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if year:
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data = [x for x in data if __year_almost_matches(year, x)]
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if not data:
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LOG.debug('Empty results returned by themoviedb for %s (%s, %s)',
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title, year, media_type)
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return
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# Calculate how similar the titles are
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title = sanitize_string(title)
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for entry in data:
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entry['match_score'] = levenshtein_distance_ratio(
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sanitize_string(entry['title']), title)
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# (one of the possibly many) best match using levenshtein distance ratio
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entry = max(data, key=lambda x: x['match_score'])
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if entry['match_score'] < LEVENSHTEIN_RATIO_THRESHOLD:
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LOG.debug('Best themoviedb match not good enough: %s', entry)
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return
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# Check if we got several matches. If so, take the most popular one
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best_matches = [x for x in data if
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x['match_score'] == entry['match_score']
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and 'popularity' in x]
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entry = max(best_matches, key=lambda x: x['popularity'])
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LOG.debug('Found themoviedb match: %s', entry)
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# lookup external tmdb_id and perform artwork lookup on fanart.tv
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tmdb_id = entry.get('id')
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parameters = {'api_key': API_KEY}
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if media_type == 'movie':
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url = 'https://api.themoviedb.org/3/movie/%s' % tmdb_id
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parameters['append_to_response'] = 'videos'
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elif media_type == 'tv':
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url = 'https://api.themoviedb.org/3/tv/%s' % tmdb_id
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parameters['append_to_response'] = 'external_ids,videos'
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media_id, poster, background = None, None, None
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for language in (v.KODILANGUAGE, 'en'):
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parameters['language'] = language
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data = DU().downloadUrl(url,
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authenticate=False,
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parameters=parameters,
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timeout=7)
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try:
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data.get('test')
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except AttributeError:
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LOG.warning('Could not download %s with parameters %s',
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url, parameters)
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continue
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if collection is False:
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if data.get('imdb_id'):
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media_id = str(data.get('imdb_id'))
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break
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if (data.get('external_ids') and
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data['external_ids'].get('tvdb_id')):
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media_id = str(data['external_ids']['tvdb_id'])
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break
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else:
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if not data.get('belongs_to_collection'):
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continue
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media_id = data.get('belongs_to_collection').get('id')
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if not media_id:
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continue
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media_id = str(media_id)
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LOG.debug('Retrieved collections tmdb id %s for %s',
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media_id, title)
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url = 'https://api.themoviedb.org/3/collection/%s' % media_id
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data = DU().downloadUrl(url,
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authenticate=False,
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parameters=parameters,
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timeout=7)
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try:
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data.get('poster_path')
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except AttributeError:
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LOG.debug('Could not find TheMovieDB poster paths for %s'
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' in the language %s', title, language)
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continue
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if not poster and data.get('poster_path'):
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poster = ('https://image.tmdb.org/t/p/original%s' %
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data.get('poster_path'))
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if not background and data.get('backdrop_path'):
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background = ('https://image.tmdb.org/t/p/original%s' %
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data.get('backdrop_path'))
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return media_id, poster, background
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def __year_almost_matches(year, entry):
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try:
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entry_year = int(entry['release_date'][0:4])
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except (KeyError, ValueError):
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return True
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return abs(year - entry_year) <= YEARS_APART
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def sanitize_string(s):
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s = s.lower().strip()
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# Get rid of chars in EXCLUDE_CHARS
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s = ''.join(character for character in s if character not in EXCLUDE_CHARS)
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# Get rid of multiple spaces
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s = ' '.join(s.split())
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return s
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def levenshtein_distance_ratio(s, t):
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"""
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Calculates levenshtein distance ratio between two strings.
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The more similar the strings, the closer the result will be to 1.
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The farther disjunct the string, the closer the result to 0
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https://www.datacamp.com/community/tutorials/fuzzy-string-python
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"""
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# Initialize matrix of zeros
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rows = len(s) + 1
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cols = len(t) + 1
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distance = [[0 for x in range(cols)] for y in range(rows)]
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# Populate matrix of zeros with the indeces of each character of both strings
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for i in range(1, rows):
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for k in range(1,cols):
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distance[i][0] = i
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distance[0][k] = k
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# Iterate over the matrix to compute the cost of deletions,insertions and/or substitutions
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for col in range(1, cols):
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for row in range(1, rows):
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if s[row-1] == t[col-1]:
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cost = 0 # If the characters are the same in the two strings in a given position [i,j] then the cost is 0
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else:
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# In order to align the results with those of the Python Levenshtein package, if we choose to calculate the ratio
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# the cost of a substitution is 2. If we calculate just distance, then the cost of a substitution is 1.
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cost = 2
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distance[row][col] = min(distance[row-1][col] + 1, # Cost of deletions
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distance[row][col-1] + 1, # Cost of insertions
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distance[row-1][col-1] + cost) # Cost of substitutions
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return ((len(s)+len(t)) - distance[row][col]) / (len(s)+len(t))
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