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Source code for mmocr.datasets.pipelines.ocr_seg_targets
# Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from mmdet.core import BitmapMasks
from mmdet.datasets.builder import PIPELINES
import mmocr.utils.check_argument as check_argument
from mmocr.models.builder import build_convertor
[docs]@PIPELINES.register_module()
class OCRSegTargets:
"""Generate gt shrunk kernels for segmentation based OCR framework.
Args:
label_convertor (dict): Dictionary to construct label_convertor
to convert char to index.
attn_shrink_ratio (float): The area shrunk ratio
between attention kernels and gt text masks.
seg_shrink_ratio (float): The area shrunk ratio
between segmentation kernels and gt text masks.
box_type (str): Character box type, should be either
'char_rects' or 'char_quads', with 'char_rects'
for rectangle with ``xyxy`` style and 'char_quads'
for quadrangle with ``x1y1x2y2x3y3x4y4`` style.
"""
def __init__(self,
label_convertor=None,
attn_shrink_ratio=0.5,
seg_shrink_ratio=0.25,
box_type='char_rects',
pad_val=255):
assert isinstance(attn_shrink_ratio, float)
assert isinstance(seg_shrink_ratio, float)
assert 0. < attn_shrink_ratio < 1.0
assert 0. < seg_shrink_ratio < 1.0
assert label_convertor is not None
assert box_type in ('char_rects', 'char_quads')
self.attn_shrink_ratio = attn_shrink_ratio
self.seg_shrink_ratio = seg_shrink_ratio
self.label_convertor = build_convertor(label_convertor)
self.box_type = box_type
self.pad_val = pad_val
[docs] def shrink_char_quad(self, char_quad, shrink_ratio):
"""Shrink char box in style of quadrangle.
Args:
char_quad (list[float]): Char box with format
[x1, y1, x2, y2, x3, y3, x4, y4].
shrink_ratio (float): The area shrunk ratio
between gt kernels and gt text masks.
"""
points = [[char_quad[0], char_quad[1]], [char_quad[2], char_quad[3]],
[char_quad[4], char_quad[5]], [char_quad[6], char_quad[7]]]
shrink_points = []
for p_idx, point in enumerate(points):
p1 = points[(p_idx + 3) % 4]
p2 = points[(p_idx + 1) % 4]
dist1 = self.l2_dist_two_points(p1, point)
dist2 = self.l2_dist_two_points(p2, point)
min_dist = min(dist1, dist2)
v1 = [p1[0] - point[0], p1[1] - point[1]]
v2 = [p2[0] - point[0], p2[1] - point[1]]
temp_dist1 = (shrink_ratio * min_dist /
dist1) if min_dist != 0 else 0.
temp_dist2 = (shrink_ratio * min_dist /
dist2) if min_dist != 0 else 0.
v1 = [temp * temp_dist1 for temp in v1]
v2 = [temp * temp_dist2 for temp in v2]
shrink_point = [
round(point[0] + v1[0] + v2[0]),
round(point[1] + v1[1] + v2[1])
]
shrink_points.append(shrink_point)
poly = np.array(shrink_points)
return poly
[docs] def shrink_char_rect(self, char_rect, shrink_ratio):
"""Shrink char box in style of rectangle.
Args:
char_rect (list[float]): Char box with format
[x_min, y_min, x_max, y_max].
shrink_ratio (float): The area shrunk ratio
between gt kernels and gt text masks.
"""
x_min, y_min, x_max, y_max = char_rect
w = x_max - x_min
h = y_max - y_min
x_min_s = round((x_min + x_max - w * shrink_ratio) / 2)
y_min_s = round((y_min + y_max - h * shrink_ratio) / 2)
x_max_s = round((x_min + x_max + w * shrink_ratio) / 2)
y_max_s = round((y_min + y_max + h * shrink_ratio) / 2)
poly = np.array([[x_min_s, y_min_s], [x_max_s, y_min_s],
[x_max_s, y_max_s], [x_min_s, y_max_s]])
return poly
[docs] def generate_kernels(self,
resize_shape,
pad_shape,
char_boxes,
char_inds,
shrink_ratio=0.5,
binary=True):
"""Generate char instance kernels for one shrink ratio.
Args:
resize_shape (tuple(int, int)): Image size (height, width)
after resizing.
pad_shape (tuple(int, int)): Image size (height, width)
after padding.
char_boxes (list[list[float]]): The list of char polygons.
char_inds (list[int]): List of char indexes.
shrink_ratio (float): The shrink ratio of kernel.
binary (bool): If True, return binary ndarray
containing 0 & 1 only.
Returns:
char_kernel (ndarray): The text kernel mask of (height, width).
"""
assert isinstance(resize_shape, tuple)
assert isinstance(pad_shape, tuple)
assert check_argument.is_2dlist(char_boxes)
assert check_argument.is_type_list(char_inds, int)
assert isinstance(shrink_ratio, float)
assert isinstance(binary, bool)
char_kernel = np.zeros(pad_shape, dtype=np.int32)
char_kernel[:resize_shape[0], resize_shape[1]:] = self.pad_val
for i, char_box in enumerate(char_boxes):
if self.box_type == 'char_rects':
poly = self.shrink_char_rect(char_box, shrink_ratio)
elif self.box_type == 'char_quads':
poly = self.shrink_char_quad(char_box, shrink_ratio)
fill_value = 1 if binary else char_inds[i]
cv2.fillConvexPoly(char_kernel, poly.astype(np.int32),
(fill_value))
return char_kernel
def l2_dist_two_points(self, p1, p2):
return ((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)**0.5
def __call__(self, results):
img_shape = results['img_shape']
resize_shape = results['resize_shape']
h_scale = 1.0 * resize_shape[0] / img_shape[0]
w_scale = 1.0 * resize_shape[1] / img_shape[1]
char_boxes, char_inds = [], []
char_num = len(results['ann_info'][self.box_type])
for i in range(char_num):
char_box = results['ann_info'][self.box_type][i]
num_points = 2 if self.box_type == 'char_rects' else 4
for j in range(num_points):
char_box[j * 2] = round(char_box[j * 2] * w_scale)
char_box[j * 2 + 1] = round(char_box[j * 2 + 1] * h_scale)
char_boxes.append(char_box)
char = results['ann_info']['chars'][i]
char_ind = self.label_convertor.str2idx([char])[0][0]
char_inds.append(char_ind)
resize_shape = tuple(results['resize_shape'][:2])
pad_shape = tuple(results['pad_shape'][:2])
binary_target = self.generate_kernels(
resize_shape,
pad_shape,
char_boxes,
char_inds,
shrink_ratio=self.attn_shrink_ratio,
binary=True)
seg_target = self.generate_kernels(
resize_shape,
pad_shape,
char_boxes,
char_inds,
shrink_ratio=self.seg_shrink_ratio,
binary=False)
mask = np.ones(pad_shape, dtype=np.int32)
mask[:resize_shape[0], resize_shape[1]:] = 0
results['gt_kernels'] = BitmapMasks([binary_target, seg_target, mask],
pad_shape[0], pad_shape[1])
results['mask_fields'] = ['gt_kernels']
return results