
'''
Project: Predicting movie genres from movie posters
Course: COMPSCI 682 Neural Networks: A Modern Introduction

File: data_manage.py
Description: Manages each movie as an object with its distinct features.
Author: <NAME>
Adapted from: Vleminckx, Benoit. dnn-movie-posters. (2018). Github repository. https://github.com/benckx/dnn-movie-posters.
'''

import io
import os.path
import numpy as np
import pandas as pd
from PIL import Image
import urllib.request

images_dir = 'data/images/'
parsed_movies = []

class Movie:
	'''
	The object movie stores all information about a particular movie that is provided
	by the dataset. Along with various functions that make this object ML ready.
	'''

	movie_id = 0
	title = ''
	year = 0
	genres = []
	poster_url = ''

	def poster_exists(self):
	    return os.path.isfile(self.poster_file_path())

	def download_poster(self):
		try:
			response = urllib.request.urlopen(self.poster_url)
			data = response.read()
			file = open(self.poster_file_path(), 'wb')
			file.write(bytearray(data))
			file.close()
			return data
		except:
			print('error downloading poster')

	def poster_file_path(self, size=100):
		return images_dir + str(size) + '/' + self.poster_file_name()

	def poster_file_name(self):
		return str(self.movie_id) + '.jpg'

	def img_to_rgb(self, poster_size):
		'''
		Takes an image of 3 channels (RGB) can converts it to a row of pixels.
		'''
		path = images_dir + str(poster_size) + '/' + str(self.movie_id) + '.jpg'
		data = open(path, 'rb').read()
		img = Image.open(io.BytesIO(data))
		rgb_img = img.convert('RGB')
		pixels = []
		for i in range(img.size[0]):
			row =[]
			for j in range(img.size[1]):
				r, g, b = rgb_img.getpixel((i, j))
				pixel = [r/255, g/255, b/255]
				row.append(pixel)
			pixels.append(row)
		return pixels

	def genres_to_vector(self, genres):
		'''
		Takes all the genres and converts them into multi-hot encoded vector.
		'''
		if len(genres) == 1:
			has_genre = self.has_genre(genres[0])
			return [int(has_genre), int(not has_genre)]
		else:
			vector = []
			if self.has_any_genre(genres):
				for genre in genres:
					vector.append(int(self.has_genre(genre)))
			return vector

	def has_genre(self, genre):
		return genre in self.genres

	def has_any_genre(self, genres):
		return len(set(self.genres).intersection(genres)) > 0

	def is_valid(self) -> bool:
		'''
		Checks if movie exists in a dataset.
		'''
		return self.poster_url.startswith('https://') \
			and 1900 <= self.year <= 2018 \
			and len(self.title) > 1 \
			and len(self.genres) > 1

	def __str__(self):
		return str(self.title) + '(' + str(self.year) + ')'

def download_posters(min_year=0):
	'''
	Downloads movie posters for a given min_year, if already downloaded
	it is ignored.
	'''
	for movie in list_movies():
		print(str(movie))
		if movie.year >= min_year:
			if movie.poster_exists() == False:
				movie.download_poster()
				if movie.poster_exists() == True:
					print('downloaded')
				else: 
					print('cannot download')
			else:
				print('already downloaded')
		else:
			print('movie is too old to download')

def list_movies(year=None, genres=None):
	'''
	Lists all movies within a given year and genre resulting a list of
	Movie objects.
	'''
	if len(parsed_movies) == 0:
		data = pd.read_csv('data/MovieGenre.csv', encoding='ISO-8859-1')
		for i, r in data.iterrows():
			movie = parse_row(r)
			if movie.is_valid():
				parsed_movies.append(movie)

		parsed_movies.sort(key=lambda m: m.movie_id)

	result = parsed_movies

	if year != None:
		result = [movie for movie in result if movie.year == year]

	if genres != None:
		result = [movie for movie in result if movie.has_any_genre(genres)]

	return result

def parse_row(row):
	'''
	Parses a row in the dataset CSV file and converts that into a Movie object.
	'''
	movie = Movie()
	movie.movie_id = int(row['imdbId'])
	movie.title = row['Title'][:-7]
	year = row['Title'][-5:-1]
	if year.isdigit() and len(year)==4:
		movie.year = int(year)

	url = str(row['Poster'])
	if len(url) > 0:
		movie.poster_url = url.replace('"', '')

	genre_str = str(row['Genre'])
	if len(genre_str) > 0:
		movie.genres = genre_str.split('|')

	return movie

def search_movie(movie_id=None, title=None):
	'''
	Search a movie by its movie id or by title.
	'''
	movies = list_movies()
	for movie in movies:
		if movie_id is not None and movie.movie_id == movie_id:
			return movie 
		if title is not None and movie.title == title:
			return movie 

def list_movies_external(year=None, genres=None):
	'''
	Using a different datasheet for external testing. Hence a different function to
	avoid double name errors.
	'''
	if len(parsed_movies) == 0:
		data = pd.read_csv('data/MovieGenre_external.csv', encoding='ISO-8859-1')
		for i, r in data.iterrows():
			movie = parse_row(r)
			# if movie.is_valid():
			parsed_movies.append(movie)

		parsed_movies.sort(key=lambda m: m.movie_id)

	result = parsed_movies

	if year != None:
		result = [movie for movie in result if movie.year == year]

	if genres != None:
		result = [movie for movie in result if movie.has_any_genre(genres)]

	return result

def search_movie_external(movie_id=None, title=None):
	'''
	Searches movie by movie id or title from the external dataset.
	'''
	movies = list_movies_external()
	for movie in movies:
		if movie_id is not None and movie.movie_id == movie_id:
			return movie 
		if title is not None and movie.title == title:
			return movie 















'''
Project: Predicting movie genres from movie posters
Course: COMPSCI 682 Neural Networks: A Modern Introduction

File: data_manage.py
Description: Manages each movie as an object with its distinct features.
Author: <NAME>
Adapted from: Vleminckx, Benoit. dnn-movie-posters. (2018). Github repository. https://github.com/benckx/dnn-movie-posters.
'''

import io
import os.path
import numpy as np
import pandas as pd
from PIL import Image
import urllib.request

images_dir = 'data/images/'
parsed_movies = []

class Movie:
	'''
	The object movie stores all information about a particular movie that is provided
	by the dataset. Along with various functions that make this object ML ready.
	'''

	movie_id = 0
	title = ''
	year = 0
	genres = []
	poster_url = ''

	def poster_exists(self):
	    return os.path.isfile(self.poster_file_path())

	def download_poster(self):
		try:
			response = urllib.request.urlopen(self.poster_url)
			data = response.read()
			file = open(self.poster_file_path(), 'wb')
			file.write(bytearray(data))
			file.close()
			return data
		except:
			print('error downloading poster')

	def poster_file_path(self, size=100):
		return images_dir + str(size) + '/' + self.poster_file_name()

	def poster_file_name(self):
		return str(self.movie_id) + '.jpg'

	def img_to_rgb(self, poster_size):
		'''
		Takes an image of 3 channels (RGB) can converts it to a row of pixels.
		'''
		path = images_dir + str(poster_size) + '/' + str(self.movie_id) + '.jpg'
		data = open(path, 'rb').read()
		img = Image.open(io.BytesIO(data))
		rgb_img = img.convert('RGB')
		pixels = []
		for i in range(img.size[0]):
			row =[]
			for j in range(img.size[1]):
				r, g, b = rgb_img.getpixel((i, j))
				pixel = [r/255, g/255, b/255]
				row.append(pixel)
			pixels.append(row)
		return pixels

	def genres_to_vector(self, genres):
		'''
		Takes all the genres and converts them into multi-hot encoded vector.
		'''
		if len(genres) == 1:
			has_genre = self.has_genre(genres[0])
			return [int(has_genre), int(not has_genre)]
		else:
			vector = []
			if self.has_any_genre(genres):
				for genre in genres:
					vector.append(int(self.has_genre(genre)))
			return vector

	def has_genre(self, genre):
		return genre in self.genres

	def has_any_genre(self, genres):
		return len(set(self.genres).intersection(genres)) > 0

	def is_valid(self) -> bool:
		'''
		Checks if movie exists in a dataset.
		'''
		return self.poster_url.startswith('https://') \
			and 1900 <= self.year <= 2018 \
			and len(self.title) > 1 \
			and len(self.genres) > 1

	def __str__(self):
		return str(self.title) + '(' + str(self.year) + ')'

def download_posters(min_year=0):
	'''
	Downloads movie posters for a given min_year, if already downloaded
	it is ignored.
	'''
	for movie in list_movies():
		print(str(movie))
		if movie.year >= min_year:
			if movie.poster_exists() == False:
				movie.download_poster()
				if movie.poster_exists() == True:
					print('downloaded')
				else: 
					print('cannot download')
			else:
				print('already downloaded')
		else:
			print('movie is too old to download')

def list_movies(year=None, genres=None):
	'''
	Lists all movies within a given year and genre resulting a list of
	Movie objects.
	'''
	if len(parsed_movies) == 0:
		data = pd.read_csv('data/MovieGenre.csv', encoding='ISO-8859-1')
		for i, r in data.iterrows():
			movie = parse_row(r)
			if movie.is_valid():
				parsed_movies.append(movie)

		parsed_movies.sort(key=lambda m: m.movie_id)

	result = parsed_movies

	if year != None:
		result = [movie for movie in result if movie.year == year]

	if genres != None:
		result = [movie for movie in result if movie.has_any_genre(genres)]

	return result

def parse_row(row):
	'''
	Parses a row in the dataset CSV file and converts that into a Movie object.
	'''
	movie = Movie()
	movie.movie_id = int(row['imdbId'])
	movie.title = row['Title'][:-7]
	year = row['Title'][-5:-1]
	if year.isdigit() and len(year)==4:
		movie.year = int(year)

	url = str(row['Poster'])
	if len(url) > 0:
		movie.poster_url = url.replace('"', '')

	genre_str = str(row['Genre'])
	if len(genre_str) > 0:
		movie.genres = genre_str.split('|')

	return movie

def search_movie(movie_id=None, title=None):
	'''
	Search a movie by its movie id or by title.
	'''
	movies = list_movies()
	for movie in movies:
		if movie_id is not None and movie.movie_id == movie_id:
			return movie 
		if title is not None and movie.title == title:
			return movie 

def list_movies_external(year=None, genres=None):
	'''
	Using a different datasheet for external testing. Hence a different function to
	avoid double name errors.
	'''
	if len(parsed_movies) == 0:
		data = pd.read_csv('data/MovieGenre_external.csv', encoding='ISO-8859-1')
		for i, r in data.iterrows():
			movie = parse_row(r)
			# if movie.is_valid():
			parsed_movies.append(movie)

		parsed_movies.sort(key=lambda m: m.movie_id)

	result = parsed_movies

	if year != None:
		result = [movie for movie in result if movie.year == year]

	if genres != None:
		result = [movie for movie in result if movie.has_any_genre(genres)]

	return result

def search_movie_external(movie_id=None, title=None):
	'''
	Searches movie by movie id or title from the external dataset.
	'''
	movies = list_movies_external()
	for movie in movies:
		if movie_id is not None and movie.movie_id == movie_id:
			return movie 
		if title is not None and movie.title == title:
			return movie 














Project: Predicting movie genres from movie posters Course: COMPSCI 682 Neural Networks: A Modern Introduction  File: data_manage.py Description: Manages each movie as an object with its distinct features. Author: <NAME> Adapted from: Vleminckx, Benoit. dnn-movie-posters. (2018). Github repository. https://github.com/benckx/dnn-movie-posters. The object movie stores all information about a particular movie that is provided 	by the dataset. Along with various functions that make this object ML ready. Takes an image of 3 channels (RGB) can converts it to a row of pixels. Takes all the genres and converts them into multi-hot encoded vector. Checks if movie exists in a dataset. Downloads movie posters for a given min_year, if already downloaded 	it is ignored. Lists all movies within a given year and genre resulting a list of 	Movie objects. Parses a row in the dataset CSV file and converts that into a Movie object. Search a movie by its movie id or by title. Using a different datasheet for external testing. Hence a different function to 	avoid double name errors. # if movie.is_valid(): Searches movie by movie id or title from the external dataset.