Source code for mmocr.utils.box_util
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
[docs]def is_on_same_line(box_a, box_b, min_y_overlap_ratio=0.8):
"""Check if two boxes are on the same line by their y-axis coordinates.
Two boxes are on the same line if they overlap vertically, and the length
of the overlapping line segment is greater than min_y_overlap_ratio * the
height of either of the boxes.
Args:
box_a (list), box_b (list): Two bounding boxes to be checked
min_y_overlap_ratio (float): The minimum vertical overlapping ratio
allowed for boxes in the same line
Returns:
The bool flag indicating if they are on the same line
"""
a_y_min = np.min(box_a[1::2])
b_y_min = np.min(box_b[1::2])
a_y_max = np.max(box_a[1::2])
b_y_max = np.max(box_b[1::2])
# Make sure that box a is always the box above another
if a_y_min > b_y_min:
a_y_min, b_y_min = b_y_min, a_y_min
a_y_max, b_y_max = b_y_max, a_y_max
if b_y_min <= a_y_max:
if min_y_overlap_ratio is not None:
sorted_y = sorted([b_y_min, b_y_max, a_y_max])
overlap = sorted_y[1] - sorted_y[0]
min_a_overlap = (a_y_max - a_y_min) * min_y_overlap_ratio
min_b_overlap = (b_y_max - b_y_min) * min_y_overlap_ratio
return overlap >= min_a_overlap or \
overlap >= min_b_overlap
else:
return True
return False
[docs]def stitch_boxes_into_lines(boxes, max_x_dist=10, min_y_overlap_ratio=0.8):
"""Stitch fragmented boxes of words into lines.
Note: part of its logic is inspired by @Johndirr
(https://github.com/faustomorales/keras-ocr/issues/22)
Args:
boxes (list): List of ocr results to be stitched
max_x_dist (int): The maximum horizontal distance between the closest
edges of neighboring boxes in the same line
min_y_overlap_ratio (float): The minimum vertical overlapping ratio
allowed for any pairs of neighboring boxes in the same line
Returns:
merged_boxes(list[dict]): List of merged boxes and texts
"""
if len(boxes) <= 1:
return boxes
merged_boxes = []
# sort groups based on the x_min coordinate of boxes
x_sorted_boxes = sorted(boxes, key=lambda x: np.min(x['box'][::2]))
# store indexes of boxes which are already parts of other lines
skip_idxs = set()
i = 0
# locate lines of boxes starting from the leftmost one
for i in range(len(x_sorted_boxes)):
if i in skip_idxs:
continue
# the rightmost box in the current line
rightmost_box_idx = i
line = [rightmost_box_idx]
for j in range(i + 1, len(x_sorted_boxes)):
if j in skip_idxs:
continue
if is_on_same_line(x_sorted_boxes[rightmost_box_idx]['box'],
x_sorted_boxes[j]['box'], min_y_overlap_ratio):
line.append(j)
skip_idxs.add(j)
rightmost_box_idx = j
# split line into lines if the distance between two neighboring
# sub-lines' is greater than max_x_dist
lines = []
line_idx = 0
lines.append([line[0]])
for k in range(1, len(line)):
curr_box = x_sorted_boxes[line[k]]
prev_box = x_sorted_boxes[line[k - 1]]
dist = np.min(curr_box['box'][::2]) - np.max(prev_box['box'][::2])
if dist > max_x_dist:
line_idx += 1
lines.append([])
lines[line_idx].append(line[k])
# Get merged boxes
for box_group in lines:
merged_box = {}
merged_box['text'] = ' '.join(
[x_sorted_boxes[idx]['text'] for idx in box_group])
x_min, y_min = float('inf'), float('inf')
x_max, y_max = float('-inf'), float('-inf')
for idx in box_group:
x_max = max(np.max(x_sorted_boxes[idx]['box'][::2]), x_max)
x_min = min(np.min(x_sorted_boxes[idx]['box'][::2]), x_min)
y_max = max(np.max(x_sorted_boxes[idx]['box'][1::2]), y_max)
y_min = min(np.min(x_sorted_boxes[idx]['box'][1::2]), y_min)
merged_box['box'] = [
x_min, y_min, x_max, y_min, x_max, y_max, x_min, y_max
]
merged_boxes.append(merged_box)
return merged_boxes