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import cv2
import numpy as np
from random import randint as rint
import time
import detect_compo.lib_ip.ip_preprocessing as pre
import detect_compo.lib_ip.ip_detection as det
import detect_compo.lib_ip.ip_draw as draw
import detect_compo.lib_ip.ip_segment as seg
from detect_compo.lib_ip.Block import Block
from config.CONFIG_UIED import Config
C = Config()
def block_hierarchy(blocks):
for i in range(len(blocks) - 1):
for j in range(i + 1, len(blocks)):
relation = blocks[i].compo_relation(blocks[j])
if relation == -1:
blocks[j].children.append(i)
if relation == 1:
blocks[i].children.append(j)
return
def block_bin_erase_all_blk(binary, blocks, pad=0, show=False):
'''
erase the block parts from the binary map
:param binary: binary map of original image
:param blocks_corner: corners of detected layout block
:param show: show or not
:param pad: expand the bounding boxes of blocks
:return: binary map without block parts
'''
bin_org = binary.copy()
for block in blocks:
block.block_erase_from_bin(binary, pad)
if show:
cv2.imshow('before', bin_org)
cv2.imshow('after', binary)
cv2.waitKey()
def block_division(grey, org, grad_thresh,
show=False, write_path=None,
step_h=10, step_v=10,
line_thickness=C.THRESHOLD_LINE_THICKNESS,
min_rec_evenness=C.THRESHOLD_REC_MIN_EVENNESS,
max_dent_ratio=C.THRESHOLD_REC_MAX_DENT_RATIO,
min_block_height_ratio=C.THRESHOLD_BLOCK_MIN_HEIGHT):
'''
:param grey: grey-scale of original image
:return: corners: list of [(top_left, bottom_right)]
-> top_left: (column_min, row_min)
-> bottom_right: (column_max, row_max)
'''
blocks = []
mask = np.zeros((grey.shape[0]+2, grey.shape[1]+2), dtype=np.uint8)
broad = np.zeros((grey.shape[0], grey.shape[1], 3), dtype=np.uint8)
broad_all = broad.copy()
row, column = grey.shape[0], grey.shape[1]
for x in range(0, row, step_h):
for y in range(0, column, step_v):
if mask[x, y] == 0:
# region = flood_fill_bfs(grey, x, y, mask)
# flood fill algorithm to get background (layout block)
mask_copy = mask.copy()
ff = cv2.floodFill(grey, mask, (y, x), None, grad_thresh, grad_thresh, cv2.FLOODFILL_MASK_ONLY)
# ignore small regions
if ff[0] < 500: continue
mask_copy = mask - mask_copy
region = np.reshape(cv2.findNonZero(mask_copy[1:-1, 1:-1]), (-1, 2))
region = [(p[1], p[0]) for p in region]
block = Block(region, grey.shape)
# draw.draw_region(region, broad_all)
# if block.height < 40 and block.width < 40:
# continue
if block.height < 30:
continue
# print(block.area / (row * column))
if block.area / (row * column) > 0.9:
continue
elif block.area / (row * column) > 0.7:
block.redundant = True
# get the boundary of this region
# ignore lines
if block.compo_is_line(line_thickness):
continue
# ignore non-rectangle as blocks must be rectangular
if not block.compo_is_rectangle(min_rec_evenness, max_dent_ratio):
continue
# if block.height/row < min_block_height_ratio:
# continue
blocks.append(block)
# draw.draw_region(region, broad)
if show:
cv2.imshow('flood-fill all', broad_all)
cv2.imshow('block', broad)
cv2.waitKey()
if write_path is not None:
cv2.imwrite(write_path, broad)
return blocks
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