#!/usr/bin/env python
# -*- coding: utf-8 -*-


# TODO import as cv
import cv2
import numpy as np
from .helpers import luminance
from copy import deepcopy

# subclassing numpy ndarray
# Vorgehen: https://docs.scipy.org/doc/numpy/user/basics.subclassing.html
# resize Probleme https://sourceforge.net/p/numpy/mailman/message/12594801/
# andere ownership Probleme könne angeblich mit out= gelöst werden
# "Use __new__ when you need to control the creation of a new instance.
# Use __init__ when you need to control initialization of a new instance."
class Contrast(np.ndarray):
    """Core class for a color contrast in a movie

       subclasses a numpy array"""
    def __new__(cls, frames, input_array=None):
        obj = input_array
        if type(obj) == np.ndarray:
            obj = np.asarray(input_array, dtype=np.uint8).view(cls).copy()
        else:
            input_array = np.zeros((0), dtype=np.uint8)
            obj = np.asarray(input_array).view(cls).copy()
        obj._frames = frames
        obj._channel = 2
        obj._frm_step = 50
        obj._save = False
        return obj

    def __array_finalize__(self, obj):
        if obj is None: return
        self._frames = getattr(obj, '_frames', None)
        self._channel = getattr(obj, '_channel', None)
        self._frm_step = getattr(obj, '_frame_step', None)
        self._bins = getattr(obj, '_bins', None)
        self._threshold = getattr(obj, '_threshold', None)
        self._save = getattr(obj, '_save', None)

    def __array_wrap__(self, out_arr, context=None):
        return np.ndarray.__array_wrap__(self, out_arr, context)


# subclassing subclass of numpy http://stackoverflow.com/questions/7342637/how-to-subclass-a-subclass-of-numpy-ndarray
# TODO es gibt noch das Problem, dass numpy nach mehreren Berechnungen von drive eine max recursion Warnung ausgiebt, warum? Brauche ich __del__
class VHistStack(Contrast):
    def __new__(cls, frames, input_array=None):
        obj = Contrast.__new__(cls, frames, input_array)
        obj._bins = 16
        obj._threshold = 60000
        return obj

    def __array_finalize__(self, obj):
        if obj is None: return
        Contrast.__array_finalize__(self, obj)
        self._bins = getattr(obj, '_bins', None)
        self._threshold = getattr(obj, '_threshold', None)


    # TODO jetzt ausschließlich mit self numpy rechnen statt mit contrast_points liste
    def derive(self):
        contrast_points = []
        # pwd list sollte in Frames sein und hier nur durchlaufen werden
        for frm_nr in range(self._frames.start, self._frames.end, self._frm_step):
            pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'

            img = cv2.imread(pwd)

            if self._channel == 2:
                _img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                luminances = luminance(_img)
                hist_value, _ = np.histogram(luminances, bins=self._bins, range=(0, 255))

            else:

                img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL)
                hist_value = cv2.calcHist([img_hsv], [self._channel], None, [16], [0, 256])

            for bin_index, point in enumerate(hist_value):
                if point > self._threshold:
                    contrast_points.append((frm_nr, bin_index, int(point)))

        contrast_points = np.asarray(contrast_points, np.uint8)
        shape = contrast_points.shape
        self.resize(shape, refcheck=False)
        self[:, :] = contrast_points

        return deepcopy(self)  # TODO does not create a new object