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# 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)
if torch.__version__ == 'parrots':
label = ext_module.pixel_group(
score,
mask,
embedding,
kernel_label,
kernel_contour,
kernel_region_num=kernel_region_num,
distance_threshold=distance_threshold)
label = label.tolist()
label = label[0]
list_index = kernel_region_num
pixel_assignment = []
for x in range(kernel_region_num):
pixel_assignment.append(
np.array(
label[list_index:list_index + int(label[x])],
dtype=np.float))
list_index = list_index + int(label[x])
else:
pixel_assignment = ext_module.pixel_group(score, mask, embedding,
kernel_label, kernel_contour,
kernel_region_num,
distance_threshold)
return pixel_assignment
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