def processing_block(org, binary, blocks, block_pad):
image_shape = org.shape
uicompos_all = []
for block in blocks:
# *** Step 2.1 *** check: examine if the block is valid layout block
if block.block_is_top_or_bottom_bar(image_shape, C.THRESHOLD_TOP_BOTTOM_BAR):
continue
if block.block_is_uicompo(image_shape, C.THRESHOLD_COMPO_MAX_SCALE):
uicompos_all.append(block)
# *** Step 2.2 *** binary map processing: erase children block -> clipping -> remove lines(opt)
binary_copy = binary.copy()
for i in block.children:
blocks[i].block_erase_from_bin(binary_copy, block_pad)
block_clip_bin = block.compo_clipping(binary_copy)
# det.line_removal(block_clip_bin, show=True)
# *** Step 2.3 *** component extraction: detect components in block binmap -> convert position to relative
uicompos = det.component_detection(block_clip_bin)
Compo.cvt_compos_relative_pos(uicompos, block.bbox.col_min, block.bbox.row_min)
uicompos_all += uicompos
return uicompos_all
def compo_detection(input_img_path, output_root, uied_params=None,
resize_by_height=600, block_pad=4,
classifier=None, show=False):
if uied_params is None:
uied_params = {'param-grad':5, 'param-block':5, 'param-minarea':150}
else:
uied_params = json.loads(uied_params)
print(uied_params)
start = time.clock()
name = 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['param-grad']))
# *** Step 2 *** element detection
det.rm_line(binary, show=show)
# det.rm_line_v_h(binary, show=show)
uicompos = det.component_detection(binary)
# *** Step 4 *** results refinement
# uicompos = det.rm_top_or_bottom_corners(uicompos, org.shape)
file.save_corners_json(pjoin(ip_root, name + '_all.json'), uicompos)
# uicompos = det.merge_text(uicompos, org.shape)
draw.draw_bounding_box(org, uicompos, show=show, name='no-merge')
uicompos = det.merge_intersected_corner(uicompos, org)
Compo.compos_update(uicompos, org.shape)
Compo.compos_containment(uicompos)
draw.draw_bounding_box(org, uicompos, show=show, name='no-nesting')
# *** Step 5 ** nesting inspection
uicompos += nesting_inspection(org, grey, uicompos)
uicompos = det.compo_filter(uicompos, min_area=int(uied_params['param-minarea']))
Compo.compos_update(uicompos, org.shape)
draw.draw_bounding_box(org, uicompos, show=show, name='ip-nesting', write_path=pjoin(ip_root, 'result.jpg'))
# *** 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(seg.clipping(org, uicompos), uicompos)
draw.draw_bounding_box_class(org, uicompos, show=show, name='cls', write_path=pjoin(ip_root, 'result.jpg'))
Compo.compos_update(uicompos, org.shape)
draw.draw_bounding_box(org, uicompos, show=show, name='final', write_path=pjoin(output_root, 'result.jpg'))
file.save_corners_json(pjoin(ip_root, name + '.json'), uicompos)
file.save_corners_json(pjoin(output_root, 'compo.json'), uicompos)
seg.dissemble_clip_img_fill(pjoin(output_root, 'clips'), org, uicompos)
print("[Compo Detection Completed in %.3f s] %s" % (time.clock() - start, input_img_path))
if show:
cv2.destroyAllWindows()
def compo_detection(input_img_path,
output_root,
num=0,
resize_by_height=600,
block_pad=4,
classifier=None,
show=False,
write_img=True):
start = time.clock()
name = 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,
show=show,
write_path=pjoin(ip_root, name + '_binary.png') if write_img else None)
binary_org = binary.copy()
# *** Step 2 *** block processing: detect block -> calculate hierarchy -> detect components in block
blocks = blk.block_division(
grey,
org,
show=show,
write_path=pjoin(ip_root, name + '_block.png') if write_img else None)
blk.block_hierarchy(blocks)
uicompos_in_blk = processing_block(org, binary, blocks, block_pad)
# *** Step 3 *** non-block part processing: remove lines -> erase blocks from binary -> detect left components
det.rm_line(binary, show=show)
blk.block_bin_erase_all_blk(binary, blocks, block_pad)
uicompos_not_in_blk = det.component_detection(binary)
uicompos = uicompos_in_blk + uicompos_not_in_blk
# *** Step 4 *** results refinement: remove top and bottom compos -> merge words into line
uicompos = det.rm_top_or_bottom_corners(uicompos, org.shape)
file.save_corners_json(pjoin(ip_root, name + '_all.json'), uicompos)
uicompos = det.merge_text(uicompos, org.shape)
draw.draw_bounding_box(org, uicompos, show=show)
# uicompos = det.merge_intersected_corner(uicompos, org.shape)
Compo.compos_containment(uicompos)
# draw.draw_bounding_box(org, uicompos, show=show, write_path=pjoin(ip_root, name + '_ip.png') if write_img else None)
# # *** 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(seg.clipping(org, uicompos), uicompos)
draw.draw_bounding_box_class(org,
uicompos,
show=show,
write_path=pjoin(ip_root,
name + '_cls.png'))
# uicompos = det.compo_filter(uicompos, org)
draw.draw_bounding_box(org, uicompos, show=show)
file.save_corners_json(pjoin(ip_root, name + '.json'), uicompos)
print("[Compo Detection Completed in %.3f s] %d %s" %
(time.clock() - start, num, input_img_path))
# Record run time
open('time.txt', 'a').write(str(round(time.clock() - start, 3)) + '\n')
if show:
cv2.destroyAllWindows()