def main():
save = True
show = False
bad_num = 0
none_tree = 0
num = 0
start = 19558 # start point
end = 100000
img_root = 'E:\\Mulong\\Datasets\\gui\\rico\\combined\\all\\'
block_root = 'E:\\Mulong\\Datasets\\gui\\rico\\subtree\\rico-block\\json\\'
tree_root = 'E:\\Mulong\\Datasets\\gui\\rico\\subtree\\rico-tree-filtered\\widget-layout\\'
subtree_root = 'E:\\Mulong\\Datasets\\gui\\rico\\subtree\\rico-subtree\\widget-layout\\'
for index in range(start, end):
img_path = img_root + str(index) + '.jpg'
block_path = block_root + str(index) + '.json'
tree_path = tree_root + str(index) + '.json'
subtree_path = subtree_root + str(index) + '.json'
if not os.path.exists(block_path) or not os.path.exists(tree_path):
continue
start_time = time.clock()
img, _ = pre.read_img(img_path, resize_height=2560)
blocks = Block.load_blocks(block_path)
resize_block(blocks, det_height=800, tgt_height=2560, bias=0)
board_block = draw.draw_bounding_box(img, blocks, line=5)
try:
tree = Tree.load_tree(tree_path)
except:
bad_num += 1
print('*** F**k Json: %d %s ***' % (bad_num, tree_path))
continue
if tree is not None:
segments = segment_subtree(blocks, tree, img)
else:
none_tree += 1
print('*** Tree is None: %d %s ***' % (none_tree, tree_path))
continue
if show:
board_tree = np.full((2560, 1440, 3), 255,
dtype=np.uint8) # used for draw new labels
Tree.draw_tree(tree, board_tree, 0)
cv2.imshow('block', cv2.resize(board_block, (300, 500)))
cv2.imshow('tree', cv2.resize(board_tree, (300, 500)))
cv2.waitKey()
cv2.destroyAllWindows()
Tree.view_segments(segments, img)
if save:
jfile = open(subtree_path, 'w')
json.dump(segments, jfile, indent=4)
print('[%.3fs]: %d %s' % (time.clock() - start_time, num, img_path))
num += 1
def check_subtree(block, tree, org, test=False):
'''
check if the tree can be segmented by block
relation: -1 : block in tree
0 : block, tree are not intersected
1 : tree in block
2 : block, tree are intersected
3 : block and tree are same
'''
relation = block.relation(tree['bounds'])
if test:
print(relation, block.put_bbox(), tree['bounds'])
img = org.copy()
board_test_blk = draw.draw_bounding_box(img, [block], line=5)
board_test_tree = cv2.resize(img, (1440, 2560))
Tree.draw_tree(tree, board_test_tree)
cv2.imshow('tree-test', cv2.resize(board_test_tree, (300, 500)))
cv2.imshow('blk-test', cv2.resize(board_test_blk, (300, 500)))
cv2.waitKey()
# block contains tree or block and tree are same
if relation == 1 or relation == 3:
return [tree]
# non-intersected
elif relation == 0:
return None
# intersected or tree contains block, search children
else:
if 'children' not in tree:
return None
subtrees = []
for child in tree['children']:
subtree = check_subtree(block, child, org)
if subtree is not None:
if type(subtree) is list:
subtrees += subtree
if len(subtrees) > 0:
return subtrees
else:
return None
def block_add_bkg(blocks, org, img_shape, show=False):
board = np.full(img_shape[:2], 255, dtype=np.uint8)
for block in blocks:
block.block_fill_color(board, 0, flag='bbox')
blocks_bkg = block_division(board, is_background=True)
if show:
ratio = 2
board_bkg = draw.draw_bounding_box(org, blocks_bkg, name='block_bkg')
cv2.imshow(
'bkg_board',
cv2.resize(board_bkg, (int(
board_bkg.shape[1] / ratio), int(board_bkg.shape[0] / ratio))))
cv2.imshow(
'filled_board',
cv2.resize(
board,
(int(board.shape[1] / ratio), int(board.shape[0] / ratio))))
cv2.waitKey()
return blocks_bkg + blocks
def check_subtree(block, tree, test=False):
'''
relation: -1 : a in b
0 : a, b are not intersected
1 : b in a
2 : a, b are identical or intersected
'''
relation = block.relation(tree['bounds'])
if test:
print(relation, block.put_bbox(), tree['bounds'])
board_test_blk = draw.draw_bounding_box(img, [block], line=5)
board_test_tree = cv2.resize(img, (1440, 2560))
Tree.draw_tree(tree, board_test_tree, 0)
cv2.imshow('tree-test', cv2.resize(board_test_tree, (300, 500)))
cv2.imshow('blk-test', cv2.resize(board_test_blk, (300, 500)))
cv2.waitKey()
# block contains tree or block and tree are same
if relation == 1 or relation == 3:
return [tree]
# non-intersected
elif relation == 0:
return None
# else search children
else:
if 'children' not in tree:
return None
subtrees = []
for child in tree['children']:
subtree = check_subtree(block, child)
if subtree is not None:
if type(subtree) is list:
subtrees += subtree
if len(subtrees) > 0:
return subtrees
else:
return None
for index in range(start, end):
img_path = img_root + str(index) + '.jpg'
block_path = block_root + str(index) + '.json'
tree_path = tree_root + str(index) + '.json'
subtree_path = subtree_root + str(index) + '.json'
if not os.path.exists(block_path) or not os.path.exists(tree_path):
continue
start_time = time.clock()
img, _ = pre.read_img(img_path, resize_height=2560)
block_img = cv2.imread(block_root + str(index) + '_blk.png')
blocks = Block.load_blocks(block_path)
resize_block(blocks, det_height=800, tgt_height=2560, bias=0)
board_block = draw.draw_bounding_box(img, blocks, line=5)
try:
tree = Tree.load_tree(tree_path)
except:
bad_num += 1
print('*** F**k Json: %d %s ***' %(bad_num, tree_path))
continue
segments = segment_subtree(blocks, tree)
if show:
board_tree = np.full((2560, 1440, 3), 255, dtype=np.uint8) # used for draw new labels
Tree.draw_tree(tree, board_tree, 0)
cv2.imshow('blk_img', cv2.resize(block_img, (300, 500)))
cv2.imshow('block', cv2.resize(board_block, (300, 500)))
cv2.imshow('tree', cv2.resize(board_tree, (300, 500)))
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()
