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import cv2
from os.path import join as pjoin
import time
import json
import numpy as np
import detect_compo.lib_ip.ip_preprocessing as pre
import detect_compo.lib_ip.ip_draw as draw
import detect_compo.lib_ip.ip_detection as det
import detect_compo.lib_ip.file_utils as file
import detect_compo.lib_ip.Component as Compo
from config.CONFIG_UIED import Config
C = Config()
def nesting_inspection(org, grey, compos, ffl_block):
'''
Inspect all big compos through block division by flood-fill
:param ffl_block: gradient threshold for flood-fill
:return: nesting compos
'''
nesting_compos = []
for i, compo in enumerate(compos):
if compo.height > 50:
replace = False
clip_grey = compo.compo_clipping(grey)
n_compos = det.nested_components_detection(clip_grey, org, grad_thresh=ffl_block, show=False)
Compo.cvt_compos_relative_pos(n_compos, compo.bbox.col_min, compo.bbox.row_min)
for n_compo in n_compos:
if n_compo.redundant:
compos[i] = n_compo
replace = True
break
if not replace:
nesting_compos += n_compos
return nesting_compos
def compo_detection(input_img_path, output_root, uied_params,
resize_by_height=800, classifier=None, show=False, wai_key=0):
start = time.perf_counter()
name = input_img_path.split('/')[-1][:-4] if '/' in input_img_path else input_img_path.split('\\')[-1][:-4]
ip_root = file.build_directory(pjoin(output_root, "ip"))
# *** Step 1 *** pre-processing: read img -> get binary map
org, grey = pre.read_img(input_img_path, resize_by_height)
binary = pre.binarization(org, grad_min=int(uied_params['min-grad']))
# *** Step 2 *** element detection
det.rm_line(binary, show=show, wait_key=wai_key)
uicompos = det.component_detection(binary, min_obj_area=int(uied_params['min-ele-area']))
# *** Step 3 *** results refinement
uicompos = det.compo_filter(uicompos, min_area=int(uied_params['min-ele-area']), img_shape=binary.shape)
uicompos = det.merge_intersected_compos(uicompos)
det.compo_block_recognition(binary, uicompos)
if uied_params['merge-contained-ele']:
uicompos = det.rm_contained_compos_not_in_block(uicompos)
Compo.compos_update(uicompos, org.shape)
Compo.compos_containment(uicompos)
# *** Step 4 ** nesting inspection: check if big compos have nesting element
uicompos += nesting_inspection(org, grey, uicompos, ffl_block=uied_params['ffl-block'])
Compo.compos_update(uicompos, org.shape)
draw.draw_bounding_box(org, uicompos, show=show, name='merged compo', write_path=pjoin(ip_root, name + '.jpg'), wait_key=wai_key)
# *** Step 5 *** image inspection: recognize image -> remove noise in image -> binarize with larger threshold and reverse -> rectangular compo detection
# if classifier is not None:
# classifier['Image'].predict(seg.clipping(org, uicompos), uicompos)
# draw.draw_bounding_box_class(org, uicompos, show=show)
# uicompos = det.rm_noise_in_large_img(uicompos, org)
# draw.draw_bounding_box_class(org, uicompos, show=show)
# det.detect_compos_in_img(uicompos, binary_org, org)
# draw.draw_bounding_box(org, uicompos, show=show)
# if classifier is not None:
# classifier['Noise'].predict(seg.clipping(org, uicompos), uicompos)
# draw.draw_bounding_box_class(org, uicompos, show=show)
# uicompos = det.rm_noise_compos(uicompos)
# *** Step 6 *** element classification: all category classification
# if classifier is not None:
# classifier['Elements'].predict([compo.compo_clipping(org) for compo in uicompos], uicompos)
# draw.draw_bounding_box_class(org, uicompos, show=show, name='cls', write_path=pjoin(ip_root, 'result.jpg'))
# draw.draw_bounding_box_class(org, uicompos, write_path=pjoin(output_root, 'result.jpg'))
# *** Step 7 *** save detection result
Compo.compos_update(uicompos, org.shape)
file.save_corners_json(pjoin(ip_root, name + '.json'), uicompos)
print("[Compo Detection Completed in %.3f s] Input: %s Output: %s" % (time.perf_counter() - start, input_img_path, pjoin(ip_root, name + '.json')))
return uicompos
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