# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch

from ..utils import ext_loader

ext_module = ext_loader.load_ext('_ext', ['pixel_group'])


def pixel_group(score, mask, embedding, kernel_label, kernel_contour,
                kernel_region_num, distance_threshold):
    """Group pixels into text instances, which is widely used text detection
    methods.

    Arguments:
        score (np.array or Tensor): The foreground score with size hxw.
        mask (np.array or Tensor): The foreground mask with size hxw.
        embedding (np.array or Tensor): The embedding with size hxwxc to
            distinguish instances.
        kernel_label (np.array or Tensor): The instance kernel index with
            size hxw.
        kernel_contour (np.array or Tensor): The kernel contour with size hxw.
        kernel_region_num (int): The instance kernel region number.
        distance_threshold (float): The embedding distance threshold between
            kernel and pixel in one instance.

    Returns:
        pixel_assignment (List[List[float]]): The instance coordinate list.
            Each element consists of averaged confidence, pixel number, and
            coordinates (x_i, y_i for all pixels) in order.
    """
    assert isinstance(score, (torch.Tensor, np.ndarray))
    assert isinstance(mask, (torch.Tensor, np.ndarray))
    assert isinstance(embedding, (torch.Tensor, np.ndarray))
    assert isinstance(kernel_label, (torch.Tensor, np.ndarray))
    assert isinstance(kernel_contour, (torch.Tensor, np.ndarray))
    assert isinstance(kernel_region_num, int)
    assert isinstance(distance_threshold, float)

    if isinstance(score, np.ndarray):
        score = torch.from_numpy(score)
    if isinstance(mask, np.ndarray):
        mask = torch.from_numpy(mask)
    if isinstance(embedding, np.ndarray):
        embedding = torch.from_numpy(embedding)
    if isinstance(kernel_label, np.ndarray):
        kernel_label = torch.from_numpy(kernel_label)
    if isinstance(kernel_contour, np.ndarray):
        kernel_contour = torch.from_numpy(kernel_contour)

    pixel_assignment = ext_module.pixel_group(score, mask, embedding,
                                              kernel_label, kernel_contour,
                                              kernel_region_num,
                                              distance_threshold)
    return pixel_assignment
