def view_detect_result_json(reslut_file_root,
img_file_root,
classifier=None,
show=True):
result_files = glob(pjoin(reslut_file_root, '*.json'))
result_files = sorted(result_files,
key=lambda x: int(x.split('\\')[-1].split('_')[0]))
print('Loading %d detection results' % len(result_files))
for reslut_file in result_files:
start_index = 0
end_index = 100000
index = reslut_file.split('\\')[-1].split('_')[0]
if int(index) < start_index:
continue
if int(index) > end_index:
break
org = cv2.imread(pjoin(img_file_root, index + '.jpg'))
print(index)
compos = json.load(open(reslut_file, 'r'))['compos']
bboxes = []
for compo in compos:
bboxes.append([(compo['column_min'], compo['row_min']),
(compo['column_max'], compo['row_max'])])
if classifier is not None:
classes = classifier.predict(seg.clipping(org, bboxes))
else:
classes = np.full(len(bboxes), 'ImageView')
if show:
draw_bounding_box(org, bboxes, classes, show=True)
def processing_block(org, binary, blocks_corner, classifier):
'''
:param org: original image
:param binary: binary map of original image
:param blocks_corner: list of corners of blocks
[(top_left, bottom_right)]
-> top_left: (column_min, row_min)
-> bottom_right: (column_max, row_max)
:param classifier: cnn model
:return: boundaries of detected components in blocks;
[up, bottom, left, right]
-> up, bottom: list of [(column_index, min/max row border)]
-> left, right: list of [(row_index, min/max column border)]
corners of detected components in blocks;
corresponding classes of components;
'''
blocks_clip_org = seg.clipping(org, blocks_corner, shrink=3)
blocks_clip_bin = seg.clipping(binary, blocks_corner, shrink=3)
all_compos_boundary = []
all_compos_corner = []
all_compos_class = []
for i in range(len(blocks_corner)):
# *** Substep 1.1 *** pre-processing: get block information -> binarization
block_corner = blocks_corner[i]
if blk.block_is_top_or_bottom_bar(blocks_corner[i], org.shape): continue
block_clip_org = blocks_clip_org[i]
block_clip_bin = blocks_clip_bin[i]
# *** Substep 1.2 *** object extraction: extract components boundary -> get bounding box corner
compos_boundary = det.boundary_detection(block_clip_bin)
compos_corner = det.get_corner(compos_boundary)
# *** Substep 1.3 *** classification: clip components -> classify components
compos_clip = seg.clipping(block_clip_org, compos_corner)
compos_class = classifier.predict(compos_clip)
# *** Substep 1.4 *** refining: merge overlapping components -> convert the corners to holistic value in entire image
compos_corner, compos_class = det.merge_corner(compos_corner, compos_class)
compos_corner = util.corner_cvt_relative_position(compos_corner, block_corner[0][0], block_corner[0][1])
if len(compos_boundary) > 0:
all_compos_boundary += compos_boundary
all_compos_corner += compos_corner
all_compos_class += compos_class
return all_compos_boundary, all_compos_corner, all_compos_class
def processing(org, binary, classifier, inspect_img=False):
# *** Substep 2.1 *** object detection: get connected areas -> get boundary -> get corners
compos_boundary = det.boundary_detection(binary)
compos_corner = det.get_corner(compos_boundary)
# *** Substep 2.2 *** classification: clip components -> classify components
compos_clip = seg.clipping(org, compos_corner)
compos_class = classifier.predict(compos_clip)
# *** Substep 2.3 *** refining: merge overlapping components -> search components on background image
compos_corner, compos_class = det.merge_corner(compos_corner, compos_class)
if inspect_img:
compos_corner, compos_class = det.compo_on_img(processing, org, binary, classifier, compos_corner, compos_class)
return compos_boundary, compos_corner, compos_class
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()