#
#
#      0=================================0
#      |    Kernel Point Convolutions    |
#      0=================================0
#
#
# ----------------------------------------------------------------------------------------------------------------------
#
#      Class handling datasets
#
# ----------------------------------------------------------------------------------------------------------------------
#
#      Hugues THOMAS - 11/06/2018
#

# ----------------------------------------------------------------------------------------------------------------------
#
#           Imports and global variables
#       \**********************************/
#

# Common libs
import time
import os
import numpy as np
import sys
import torch
from torch.utils.data import DataLoader, Dataset
from utils.config import Config
from utils.mayavi_visu import *
from kernels.kernel_points import create_3D_rotations

# Subsampling extension
import cpp_wrappers.cpp_subsampling.grid_subsampling as cpp_subsampling
import cpp_wrappers.cpp_neighbors.radius_neighbors as cpp_neighbors

# ----------------------------------------------------------------------------------------------------------------------
#
#           Utility functions
#       \***********************/
#

def grid_subsampling(points, features=None, labels=None, sampleDl=0.1, verbose=0):
    """
    CPP wrapper for a grid subsampling (method = barycenter for points and features)
    :param points: (N, 3) matrix of input points
    :param features: optional (N, d) matrix of features (floating number)
    :param labels: optional (N,) matrix of integer labels
    :param sampleDl: parameter defining the size of grid voxels
    :param verbose: 1 to display
    :return: subsampled points, with features and/or labels depending of the input
    """

    if (features is None) and (labels is None):
        return cpp_subsampling.subsample(points,
                                         sampleDl=sampleDl,
                                         verbose=verbose)
    elif (labels is None):
        return cpp_subsampling.subsample(points,
                                         features=features,
                                         sampleDl=sampleDl,
                                         verbose=verbose)
    elif (features is None):
        return cpp_subsampling.subsample(points,
                                         classes=labels,
                                         sampleDl=sampleDl,
                                         verbose=verbose)
    else:
        return cpp_subsampling.subsample(points,
                                         features=features,
                                         classes=labels,
                                         sampleDl=sampleDl,
                                         verbose=verbose)


def batch_grid_subsampling(points, batches_len, features=None, labels=None, sampleDl=0.1, max_p=0, verbose=0):
    """
    CPP wrapper for a grid subsampling (method = barycenter for points and features)
    :param points: (N, 3) matrix of input points
    :param features: optional (N, d) matrix of features (floating number)
    :param labels: optional (N,) matrix of integer labels
    :param sampleDl: parameter defining the size of grid voxels
    :param verbose: 1 to display
    :return: subsampled points, with features and/or labels depending of the input
    """

    if (features is None) and (labels is None):
        return cpp_subsampling.subsample_batch(points,
                                               batches_len,
                                               sampleDl=sampleDl,
                                               max_p=max_p,
                                               verbose=verbose)
    elif (labels is None):
        return cpp_subsampling.subsample_batch(points,
                                               batches_len,
                                               features=features,
                                               sampleDl=sampleDl,
                                               max_p=max_p,
                                               verbose=verbose)
    elif (features is None):
        return cpp_subsampling.subsample_batch(points,
                                               batches_len,
                                               classes=labels,
                                               sampleDl=sampleDl,
                                               max_p=max_p,
                                               verbose=verbose)
    else:
        return cpp_subsampling.subsample_batch(points,
                                               batches_len,
                                               features=features,
                                               classes=labels,
                                               sampleDl=sampleDl,
                                               max_p=max_p,
                                               verbose=verbose)


def batch_neighbors(queries, supports, q_batches, s_batches, radius):
    """
    Computes neighbors for a batch of queries and supports
    :param queries: (N1, 3) the query points
    :param supports: (N2, 3) the support points
    :param q_batches: (B) the list of lengths of batch elements in queries
    :param s_batches: (B)the list of lengths of batch elements in supports
    :param radius: float32
    :return: neighbors indices
    """

    return cpp_neighbors.batch_query(queries, supports, q_batches, s_batches, radius=radius)


# ----------------------------------------------------------------------------------------------------------------------
#
#           Class definition
#       \**********************/


class PointCloudDataset(Dataset):
    """Parent class for Point Cloud Datasets."""

    def __init__(self, name):
        """
        Initialize parameters of the dataset here.
        """

        self.name = name
        self.path = ''
        self.label_to_names = {}
        self.num_classes = 0
        self.label_values = np.zeros((0,), dtype=np.int32)
        self.label_names = []
        self.label_to_idx = {}
        self.name_to_label = {}
        self.config = Config()
        self.neighborhood_limits = []

        return

    def __len__(self):
        """
        Return the length of data here
        """
        return 0

    def __getitem__(self, idx):
        """
        Return the item at the given index
        """

        return 0

    def init_labels(self):

        # Initialize all label parameters given the label_to_names dict
        self.num_classes = len(self.label_to_names)
        self.label_values = np.sort([k for k, v in self.label_to_names.items()])
        self.label_names = [self.label_to_names[k] for k in self.label_values]
        self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
        self.name_to_label = {v: k for k, v in self.label_to_names.items()}

    def augmentation_transform(self, points, normals=None, verbose=False):
        """Implementation of an augmentation transform for point clouds."""

        ##########
        # Rotation
        ##########

        # Initialize rotation matrix
        R = np.eye(points.shape[1])

        if points.shape[1] == 3:
            if self.config.augment_rotation == 'vertical':

                # Create random rotations
                theta = np.random.rand() * 2 * np.pi
                c, s = np.cos(theta), np.sin(theta)
                R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)

            elif self.config.augment_rotation == 'all':

                # Choose two random angles for the first vector in polar coordinates
                theta = np.random.rand() * 2 * np.pi
                phi = (np.random.rand() - 0.5) * np.pi

                # Create the first vector in carthesian coordinates
                u = np.array([np.cos(theta) * np.cos(phi), np.sin(theta) * np.cos(phi), np.sin(phi)])

                # Choose a random rotation angle
                alpha = np.random.rand() * 2 * np.pi

                # Create the rotation matrix with this vector and angle
                R = create_3D_rotations(np.reshape(u, (1, -1)), np.reshape(alpha, (1, -1)))[0]

        R = R.astype(np.float32)

        #######
        # Scale
        #######

        # Choose random scales for each example
        min_s = self.config.augment_scale_min
        max_s = self.config.augment_scale_max
        if self.config.augment_scale_anisotropic:
            scale = np.random.rand(points.shape[1]) * (max_s - min_s) + min_s
        else:
            scale = np.random.rand() * (max_s - min_s) - min_s

        # Add random symmetries to the scale factor
        symmetries = np.array(self.config.augment_symmetries).astype(np.int32)
        symmetries *= np.random.randint(2, size=points.shape[1])
        scale = (scale * symmetries * 2 - 1).astype(np.float32)

        #######
        # Noise
        #######

        noise = (np.random.randn(points.shape[0], points.shape[1]) * self.config.augment_noise).astype(np.float32)

        ##################
        # Apply transforms
        ##################

        # Do not use np.dot because it is multi-threaded
        #augmented_points = np.dot(points, R) * scale + noise
        augmented_points = np.sum(np.expand_dims(points, 2) * R, axis=1) * scale + noise


        if normals is None:
            return augmented_points, scale, R
        else:
            # Anisotropic scale of the normals thanks to cross product formula
            normal_scale = scale[[1, 2, 0]] * scale[[2, 0, 1]]
            augmented_normals = np.dot(normals, R) * normal_scale
            # Renormalise
            augmented_normals *= 1 / (np.linalg.norm(augmented_normals, axis=1, keepdims=True) + 1e-6)

            if verbose:
                test_p = [np.vstack([points, augmented_points])]
                test_n = [np.vstack([normals, augmented_normals])]
                test_l = [np.hstack([points[:, 2]*0, augmented_points[:, 2]*0+1])]
                show_ModelNet_examples(test_p, test_n, test_l)

            return augmented_points, augmented_normals, scale, R

    def big_neighborhood_filter(self, neighbors, layer):
        """
        Filter neighborhoods with max number of neighbors. Limit is set to keep XX% of the neighborhoods untouched.
        Limit is computed at initialization
        """

        # crop neighbors matrix
        if len(self.neighborhood_limits) > 0:
            return neighbors[:, :self.neighborhood_limits[layer]]
        else:
            return neighbors

    def classification_inputs(self,
                              stacked_points,
                              stacked_features,
                              labels,
                              stack_lengths):

        # Starting radius of convolutions
        r_normal = self.config.first_subsampling_dl * self.config.conv_radius

        # Starting layer
        layer_blocks = []

        # Lists of inputs
        input_points = []
        input_neighbors = []
        input_pools = []
        input_stack_lengths = []
        deform_layers = []

        ######################
        # Loop over the blocks
        ######################

        arch = self.config.architecture

        for block_i, block in enumerate(arch):

            # Get all blocks of the layer
            if not ('pool' in block or 'strided' in block or 'global' in block or 'upsample' in block):
                layer_blocks += [block]
                continue

            # Convolution neighbors indices
            # *****************************

            deform_layer = False
            if layer_blocks:
                # Convolutions are done in this layer, compute the neighbors with the good radius
                if np.any(['deformable' in blck for blck in layer_blocks]):
                    r = r_normal * self.config.deform_radius / self.config.conv_radius
                    deform_layer = True
                else:
                    r = r_normal
                conv_i = batch_neighbors(stacked_points, stacked_points, stack_lengths, stack_lengths, r)

            else:
                # This layer only perform pooling, no neighbors required
                conv_i = np.zeros((0, 1), dtype=np.int32)

            # Pooling neighbors indices
            # *************************

            # If end of layer is a pooling operation
            if 'pool' in block or 'strided' in block:

                # New subsampling length
                dl = 2 * r_normal / self.config.conv_radius

                # Subsampled points
                pool_p, pool_b = batch_grid_subsampling(stacked_points, stack_lengths, sampleDl=dl)

                # Radius of pooled neighbors
                if 'deformable' in block:
                    r = r_normal * self.config.deform_radius / self.config.conv_radius
                    deform_layer = True
                else:
                    r = r_normal

                # Subsample indices
                pool_i = batch_neighbors(pool_p, stacked_points, pool_b, stack_lengths, r)

            else:
                # No pooling in the end of this layer, no pooling indices required
                pool_i = np.zeros((0, 1), dtype=np.int32)
                pool_p = np.zeros((0, 1), dtype=np.float32)
                pool_b = np.zeros((0,), dtype=np.int32)

            # Reduce size of neighbors matrices by eliminating furthest point
            conv_i = self.big_neighborhood_filter(conv_i, len(input_points))
            pool_i = self.big_neighborhood_filter(pool_i, len(input_points))

            # Updating input lists
            input_points += [stacked_points]
            input_neighbors += [conv_i.astype(np.int64)]
            input_pools += [pool_i.astype(np.int64)]
            input_stack_lengths += [stack_lengths]
            deform_layers += [deform_layer]

            # New points for next layer
            stacked_points = pool_p
            stack_lengths = pool_b

            # Update radius and reset blocks
            r_normal *= 2
            layer_blocks = []

            # Stop when meeting a global pooling or upsampling
            if 'global' in block or 'upsample' in block:
                break

        ###############
        # Return inputs
        ###############

        # Save deform layers

        # list of network inputs
        li = input_points + input_neighbors + input_pools + input_stack_lengths
        li += [stacked_features, labels]

        return li


    def segmentation_inputs(self,
                            stacked_points,
                            stacked_features,
                            labels,
                            stack_lengths):

        # Starting radius of convolutions
        r_normal = self.config.first_subsampling_dl * self.config.conv_radius

        # Starting layer
        layer_blocks = []

        # Lists of inputs
        input_points = []
        input_neighbors = []
        input_pools = []
        input_upsamples = []
        input_stack_lengths = []
        deform_layers = []

        ######################
        # Loop over the blocks
        ######################

        arch = self.config.architecture

        for block_i, block in enumerate(arch):

            # Get all blocks of the layer
            if not ('pool' in block or 'strided' in block or 'global' in block or 'upsample' in block):
                layer_blocks += [block]
                continue

            # Convolution neighbors indices
            # *****************************

            deform_layer = False
            if layer_blocks:
                # Convolutions are done in this layer, compute the neighbors with the good radius
                if np.any(['deformable' in blck for blck in layer_blocks]):
                    r = r_normal * self.config.deform_radius / self.config.conv_radius
                    deform_layer = True
                else:
                    r = r_normal
                conv_i = batch_neighbors(stacked_points, stacked_points, stack_lengths, stack_lengths, r)

            else:
                # This layer only perform pooling, no neighbors required
                conv_i = np.zeros((0, 1), dtype=np.int32)

            # Pooling neighbors indices
            # *************************

            # If end of layer is a pooling operation
            if 'pool' in block or 'strided' in block:

                # New subsampling length
                dl = 2 * r_normal / self.config.conv_radius

                # Subsampled points
                pool_p, pool_b = batch_grid_subsampling(stacked_points, stack_lengths, sampleDl=dl)

                # Radius of pooled neighbors
                if 'deformable' in block:
                    r = r_normal * self.config.deform_radius / self.config.conv_radius
                    deform_layer = True
                else:
                    r = r_normal

                # Subsample indices
                pool_i = batch_neighbors(pool_p, stacked_points, pool_b, stack_lengths, r)

                # Upsample indices (with the radius of the next layer to keep wanted density)
                up_i = batch_neighbors(stacked_points, pool_p, stack_lengths, pool_b, 2 * r)

            else:
                # No pooling in the end of this layer, no pooling indices required
                pool_i = np.zeros((0, 1), dtype=np.int32)
                pool_p = np.zeros((0, 3), dtype=np.float32)
                pool_b = np.zeros((0,), dtype=np.int32)
                up_i = np.zeros((0, 1), dtype=np.int32)

            # Reduce size of neighbors matrices by eliminating furthest point
            conv_i = self.big_neighborhood_filter(conv_i, len(input_points))
            pool_i = self.big_neighborhood_filter(pool_i, len(input_points))
            if up_i.shape[0] > 0:
                up_i = self.big_neighborhood_filter(up_i, len(input_points)+1)

            # Updating input lists
            input_points += [stacked_points]
            input_neighbors += [conv_i.astype(np.int64)]
            input_pools += [pool_i.astype(np.int64)]
            input_upsamples += [up_i.astype(np.int64)]
            input_stack_lengths += [stack_lengths]
            deform_layers += [deform_layer]

            # New points for next layer
            stacked_points = pool_p
            stack_lengths = pool_b

            # Update radius and reset blocks
            r_normal *= 2
            layer_blocks = []

            # Stop when meeting a global pooling or upsampling
            if 'global' in block or 'upsample' in block:
                break

        ###############
        # Return inputs
        ###############

        # Save deform layers

        # list of network inputs
        li = input_points + input_neighbors + input_pools + input_upsamples + input_stack_lengths
        li += [stacked_features, labels]

        return li