"""
The photo module contains the :class:`Photo` class, which is used to track
image objects (JPG, DNG, etc.).

.. moduleauthor:: Jaisen Mathai <jaisen@jmathai.com>
"""

import imagehash
import imghdr
import logging
import numpy as np
import os
from PIL import Image, UnidentifiedImageError
import time

from .media import Media


class Photo(Media):

    """A photo object.

    :param str source: The fully qualified path to the photo file
    """

    __name__ = 'Photo'

    #: Valid extensions for photo files.
    extensions = ('arw', 'cr2', 'dng', 'gif', 'heic', 'jpeg', 'jpg', 'nef', 'png', 'rw2')

    def __init__(self, source=None, hash_size=8, ignore_tags=set(),
            logger=logging.getLogger()):
        super().__init__(source, ignore_tags)

        self.hash_size = hash_size
        self.logger = logger
        logger.setLevel(logging.INFO)

        # HEIC extension support (experimental, not tested)
        self.pyheif = False
        try:
            from pyheif_pillow_opener import register_heif_opener
            self.pyheif = True
            # Allow to open HEIF/HEIC images from pillow
            register_heif_opener()
        except ImportError as e:
            self.logger.info(e)

    def is_image(self, img_path):
        """Check whether the file is an image.
        :returns: bool
        """
        # gh-4 This checks if the source file is an image.
        # It doesn't validate against the list of supported types.
        # We check with imghdr and pillow.
        if imghdr.what(img_path) is None:
            # Pillow is used as a fallback
            # imghdr won't detect all variants of images (https://bugs.python.org/issue28591)
            # see https://github.com/jmathai/elodie/issues/281
            # before giving up, we use `pillow` imaging library to detect file type
            #
            # It is important to note that the library doesn't decode or load the
            # raster data unless it really has to. When you open a file,
            # the file header is read to determine the file format and extract
            # things like mode, size, and other properties required to decode the file,
            # but the rest of the file is not processed until later.
            try:
                im = Image.open(img_path)
            except (IOError, UnidentifiedImageError):
                return False

            if(im.format is None):
                return False

        return True

    def get_images(self, file_paths):
        '''
        :returns: img_path generator
        '''
        for img_path in file_paths:
            if self.is_image(img_path):
                yield img_path

    def get_images_hashes(self, file_paths):
        """Get image hashes"""
        hashes = {}
        duplicates = []
        # Searching for duplicates.
        for img_path in self.get_images(file_paths):
            with Image.open(img_path) as img:
                yield imagehash.average_hash(img, self.hash_size)

    def find_duplicates(self, file_paths):
        """Find duplicates"""
        for temp_hash in get_images_hashes(file_paths):
            if temp_hash in hashes:
                self.logger.info("Duplicate {} \nfound for image {}\n".format(img_path, hashes[temp_hash]))
                duplicates.append(img_path)
            else:
                hashes[temp_hash] = img_path

        return duplicates

    def remove_duplicates(self, duplicates):
        for duplicate in duplicates:
            try:
                os.remove(duplicate)
            except OSError as error:
                self.logger.error(error)

    def remove_duplicates_interactive(self, duplicates):
        if len(duplicates) != 0:
            answer = input(f"Do you want to delete these {duplicates} images? Y/n: ")
            if(answer.strip().lower() == 'y'):
                self.remove_duplicates(duplicates)
                self.logger.info(f'{duplicate} deleted successfully!')
        else:
            self.logger.info("No duplicates found")

    def get_hash(self, img_path):
        with Image.open(img_path) as img:
            return imagehash.average_hash(img, self.hash_size).hash

    def diff(self, hash1, hash2):
        return np.count_nonzero(hash1 != hash2)

    def similarity(self, img_diff):
        threshold_img = img_diff / (self.hash_size**2)
        similarity_img = round((1 - threshold_img) * 100)

        return similarity_img

    def find_similar(self, image, file_paths, similarity=80):
        '''
        Find similar images
        :returns: img_path generator
        '''
        hash1 = ''
        if self.is_image(image):
            hash1 = self.get_hash(image)

        self.logger.info(f'Finding similar images to {image}')

        threshold = 1 - similarity/100
        diff_limit = int(threshold*(self.hash_size**2))

        for img_path in self.get_images(file_paths):
            if img_path == image:
                continue
            hash2 = self.get_hash(img_path)
            img_diff = self.diff(hash1, hash2)
            if img_diff <= diff_limit:
                similarity_img = self.similarity(img_diff)
                self.logger.info(f'{img_path} image found {similarity_img}% similar to {image}')
                yield img_path