import numpy as np
from skimage.segmentation import find_boundaries
from skimage.segmentation import felzenszwalb
from scipy.ndimage import find_objects
import measure


class HierarchicalGrouping(object):
    def __init__(self, img, img_seg, sim_strategy):
        self.img = img
        self.sim_strategy = sim_strategy
        self.img_seg = img_seg.copy()
        self.labels = np.unique(self.img_seg).tolist()

    def build_regions(self):
        self.regions = {}
        lbp_img = measure.generate_lbp_image(self.img)
        for label in self.labels:
            size = (self.img_seg == 1).sum()
            region_slice = find_objects(self.img_seg==label)[0]
            box = tuple([region_slice[i].start for i in (1,0)] +
                         [region_slice[i].stop for i in (1,0)])

            mask = self.img_seg == label
            color_hist = measure.calculate_color_hist(mask, self.img)
            texture_hist = measure.calculate_texture_hist(mask, lbp_img)

            self.regions[label] = {
                'size': size,
                'box': box,
                'color_hist': color_hist,
                'texture_hist': texture_hist
            }


    def build_region_pairs(self):
        self.s = {}
        for i in self.labels:
            neighbors = self._find_neighbors(i)
            for j in neighbors:
                if i < j:
                    self.s[(i,j)] = measure.calculate_sim(self.regions[i],
                                             self.regions[j],
                                             self.img.size,
                                             self.sim_strategy)


    def _find_neighbors(self, label):
        """
            Parameters
        ----------
            label : int
                label of the region
        Returns
        -------
            neighbors : list
                list of labels of neighbors
        """

        boundary = find_boundaries(self.img_seg == label,
                                   mode='outer')
        neighbors = np.unique(self.img_seg[boundary]).tolist()

        return neighbors

    def get_highest_similarity(self):
        return sorted(self.s.items(), key=lambda i: i[1])[-1][0]

    def merge_region(self, i, j):

        # generate a unique label and put in the label list
        new_label = max(self.labels) + 1
        self.labels.append(new_label)

        # merge blobs and update blob set
        ri, rj = self.regions[i], self.regions[j]

        new_size = ri['size'] + rj['size']
        new_box = (min(ri['box'][0], rj['box'][0]),
                  min(ri['box'][1], rj['box'][1]),
                  max(ri['box'][2], rj['box'][2]),
                  max(ri['box'][3], rj['box'][3]))
        value = {
            'box': new_box,
            'size': new_size,
            'color_hist':
                (ri['color_hist'] * ri['size']
                + rj['color_hist'] * rj['size']) / new_size,
            'texture_hist':
                (ri['texture_hist'] * ri['size']
                + rj['texture_hist'] * rj['size']) / new_size,
        }

        self.regions[new_label] = value

        # update segmentation mask
        self.img_seg[self.img_seg == i] = new_label
        self.img_seg[self.img_seg == j] = new_label

    def remove_similarities(self, i, j):

        # mark keys for region pairs to be removed
        key_to_delete = []
        for key in self.s.keys():
            if (i in key) or (j in key):
                key_to_delete.append(key)

        for key in key_to_delete:
            del self.s[key]

        # remove old labels in label list
        self.labels.remove(i)
        self.labels.remove(j)

    def calculate_similarity_for_new_region(self):
        i = max(self.labels)
        neighbors = self._find_neighbors(i)

        for j in neighbors:
            # i is larger than j, so use (j,i) instead
            self.s[(j,i)] = measure.calculate_sim(self.regions[i],
                                          self.regions[j],
                                          self.img.size,
                                          self.sim_strategy)

    def is_empty(self):
        return True if not self.s.keys() else False
    
    
    def num_regions(self):
        return len(self.s.keys())

import numpy as np
from skimage.segmentation import find_boundaries
from skimage.segmentation import felzenszwalb
from scipy.ndimage import find_objects
import measure


class HierarchicalGrouping(object):
    def __init__(self, img, img_seg, sim_strategy):
        self.img = img
        self.sim_strategy = sim_strategy
        self.img_seg = img_seg.copy()
        self.labels = np.unique(self.img_seg).tolist()

    def build_regions(self):
        self.regions = {}
        lbp_img = measure.generate_lbp_image(self.img)
        for label in self.labels:
            size = (self.img_seg == 1).sum()
            region_slice = find_objects(self.img_seg==label)[0]
            box = tuple([region_slice[i].start for i in (1,0)] +
                         [region_slice[i].stop for i in (1,0)])

            mask = self.img_seg == label
            color_hist = measure.calculate_color_hist(mask, self.img)
            texture_hist = measure.calculate_texture_hist(mask, lbp_img)

            self.regions[label] = {
                'size': size,
                'box': box,
                'color_hist': color_hist,
                'texture_hist': texture_hist
            }


    def build_region_pairs(self):
        self.s = {}
        for i in self.labels:
            neighbors = self._find_neighbors(i)
            for j in neighbors:
                if i < j:
                    self.s[(i,j)] = measure.calculate_sim(self.regions[i],
                                             self.regions[j],
                                             self.img.size,
                                             self.sim_strategy)


    def _find_neighbors(self, label):
        """
            Parameters
        ----------
            label : int
                label of the region
        Returns
        -------
            neighbors : list
                list of labels of neighbors
        """

        boundary = find_boundaries(self.img_seg == label,
                                   mode='outer')
        neighbors = np.unique(self.img_seg[boundary]).tolist()

        return neighbors

    def get_highest_similarity(self):
        return sorted(self.s.items(), key=lambda i: i[1])[-1][0]

    def merge_region(self, i, j):

        # generate a unique label and put in the label list
        new_label = max(self.labels) + 1
        self.labels.append(new_label)

        # merge blobs and update blob set
        ri, rj = self.regions[i], self.regions[j]

        new_size = ri['size'] + rj['size']
        new_box = (min(ri['box'][0], rj['box'][0]),
                  min(ri['box'][1], rj['box'][1]),
                  max(ri['box'][2], rj['box'][2]),
                  max(ri['box'][3], rj['box'][3]))
        value = {
            'box': new_box,
            'size': new_size,
            'color_hist':
                (ri['color_hist'] * ri['size']
                + rj['color_hist'] * rj['size']) / new_size,
            'texture_hist':
                (ri['texture_hist'] * ri['size']
                + rj['texture_hist'] * rj['size']) / new_size,
        }

        self.regions[new_label] = value

        # update segmentation mask
        self.img_seg[self.img_seg == i] = new_label
        self.img_seg[self.img_seg == j] = new_label

    def remove_similarities(self, i, j):

        # mark keys for region pairs to be removed
        key_to_delete = []
        for key in self.s.keys():
            if (i in key) or (j in key):
                key_to_delete.append(key)

        for key in key_to_delete:
            del self.s[key]

        # remove old labels in label list
        self.labels.remove(i)
        self.labels.remove(j)

    def calculate_similarity_for_new_region(self):
        i = max(self.labels)
        neighbors = self._find_neighbors(i)

        for j in neighbors:
            # i is larger than j, so use (j,i) instead
            self.s[(j,i)] = measure.calculate_sim(self.regions[i],
                                          self.regions[j],
                                          self.img.size,
                                          self.sim_strategy)

    def is_empty(self):
        return True if not self.s.keys() else False
    
    
    def num_regions(self):
        return len(self.s.keys())

Parameters         ----------             label : int                 label of the region         Returns         -------             neighbors : list                 list of labels of neighbors # generate a unique label and put in the label list # merge blobs and update blob set # update segmentation mask # mark keys for region pairs to be removed # remove old labels in label list # i is larger than j, so use (j,i) instead