def processing(org, binary, clf, main=True):
if main:
# *** Step 2 *** object detection: get connected areas -> get boundary -> get corners
boundary_rec, boundary_non_rec = det.boundary_detection(binary, write_boundary=True)
corners_rec = det.get_corner(boundary_rec)
corners_non_rec = det.get_corner(boundary_non_rec)
# *** Step 3 *** data processing: identify blocks and compos from rectangles -> identify irregular compos
corners_block, corners_img, corners_compo = det.block_or_compo(org, binary, corners_rec)
det.compo_irregular(org, corners_non_rec, corners_img, corners_compo)
# *** Step 4 *** classification: clip and classify the components candidates -> ignore noises -> refine img
compos = seg.clipping(org, corners_compo)
compos_class = clf.predict(compos)
corners_compo, compos_class = det.strip_img(corners_compo, compos_class, corners_img)
# *** Step 5 *** result refinement
if is_shrink_img:
corners_img = det.img_shrink(org, binary, corners_img)
# *** Step 6 *** recursive inspection: search components nested in components
corners_block, corners_img, corners_compo, compos_class = det.compo_in_img(processing, org, binary, clf, corners_img, corners_block, corners_compo, compos_class)
# *** Step 7 *** ocr check and text detection from cleaned image
if is_ocr:
corners_block, _ = det.rm_text(org, corners_block, ['block' for i in range(len(corners_block))])
corners_img, _ = det.rm_text(org, corners_img, ['img' for i in range(len(corners_img))])
corners_compo, compos_class = det.rm_text(org, corners_compo, compos_class)
# *** Step 8 *** merge overlapped components
# corners_img = det.rm_img_in_compo(corners_img, corners_compo)
corners_img, _ = det.merge_corner(org, corners_img, ['img' for i in range(len(corners_img))], is_merge_nested_same=True)
corners_compo, compos_class = det.merge_corner(org, corners_compo, compos_class, is_merge_nested_same=True)
return corners_block, corners_img, corners_compo, compos_class
# *** used for img inspection ***
# only consider rectangular components
else:
boundary_rec, boundary_non_rec = det.boundary_detection(binary)
corners_rec = det.get_corner(boundary_rec)
corners_block, corners_img, corners_compo = det.block_or_compo(org, binary, corners_rec)
compos = seg.clipping(org, corners_compo)
compos_class = clf.predict(compos)
corners_compo, compos_class = det.strip_img(corners_compo, compos_class, corners_img)
return corners_block, corners_compo, compos_class
def processing(org, binary, main=True):
if main:
# *** Step 2 *** object detection: get connected areas -> get boundary -> get corners
boundary_rec, boundary_non_rec = det.boundary_detection(binary,
show=False)
corners_rec = det.get_corner(boundary_rec)
corners_non_rec = det.get_corner(boundary_non_rec)
# *** Step 3 *** data processing: identify blocks and compos from rectangles -> identify irregular compos
corners_block, corners_img, corners_compo = det.block_or_compo(
org, binary, corners_rec)
det.compo_irregular(org, corners_non_rec, corners_img, corners_compo)
corners_img, _ = det.rm_text(org, corners_img,
['img' for i in range(len(corners_img))])
# *** Step 4 *** classification: clip and classify the components candidates -> ignore noises -> refine img
compos = seg.clipping(org, corners_compo)
compos_class = CNN.predict(compos)
# corners_compo, compos_class = det.compo_filter(org, corners_compo, compos_class, is_icon)
corners_compo, compos_class = det.strip_img(corners_compo,
compos_class, corners_img)
# *** Step 5 *** result refinement
if is_merge:
corners_img, _ = det.merge_corner(
corners_img, ['img' for i in range(len(corners_img))])
corners_block, _ = det.rm_text(
org, corners_block, ['block' for i in range(len(corners_block))])
corners_img, _ = det.rm_text(org, corners_img,
['img' for i in range(len(corners_img))])
corners_compo, compos_class = det.rm_text(org, corners_compo,
compos_class)
if is_shrink_img:
corners_img = det.img_shrink(org, binary, corners_img)
# *** Step 6 *** text detection from cleaned image
img_clean = draw.draw_bounding_box(org,
corners_img,
color=(255, 255, 255),
line=-1)
corners_word = ocr.text_detection(org, img_clean)
corners_text = ocr.text_merge_word_into_line(org, corners_word)
# *** Step 7 *** img inspection: search components in img element
if is_img_inspect:
corners_block, corners_img, corners_compo, compos_class = det.compo_in_img(
processing, org, binary, corners_img, corners_block,
corners_compo, compos_class)
return corners_block, corners_img, corners_compo, compos_class, corners_text
# *** used for img inspection ***
# only consider rectangular components
else:
boundary_rec, boundary_non_rec = det.boundary_detection(binary)
corners_rec = det.get_corner(boundary_rec)
corners_block, corners_img, corners_compo = det.block_or_compo(
org, binary, corners_rec)
compos = seg.clipping(org, corners_compo)
compos_class = CNN.predict(compos)
corners_compo, compos_class = det.compo_filter(org, corners_compo,
compos_class, is_icon)
corners_compo, compos_class = det.strip_img(corners_compo,
compos_class, corners_img)
corners_block, _ = det.rm_text(
org, corners_block, ['block' for i in range(len(corners_block))])
corners_compo, compos_class = det.rm_text(org, corners_compo,
compos_class)
return corners_block, corners_compo, compos_class
binary = pre.preprocess(gray, 1)
# *** Step 2 *** object detection: get connected areas -> get boundary -> get corners
boundary_all, boundary_rec, boundary_nonrec = det.boundary_detection(binary)
# get corner of boundaries
corners_rec = det.get_corner(boundary_rec)
corners_nonrec = det.get_corner(boundary_nonrec)
# *** Step 3 *** process data: identify blocks and imgs from rectangles -> identify compos -> identify irregular imgs
# identify rectangular block and rectangular img from rectangular shapes
corners_block, corners_img = det.img_or_block(org, binary, corners_rec)
# identify potential buttons and input bars
corners_block, corners_compo = det.uicomponent_or_block(org, corners_block)
# shrink images with extra borders
if is_shrink_img:
corners_img = det.img_shrink(org, binary, corners_img)
# identify irregular-shape img from irregular shapes
corners_img += det.img_irregular(org, corners_nonrec)
# ignore too large and highly likely text areas
corners_img = det.img_refine(org, corners_img)
# merge overlapped corners, and remove nested corners
if is_merge_img:
corners_img = det.merge_corners(corners_img)
# detect components on img
corners_compo += det.uicomponent_in_img(org, binary, corners_img)
# remove text misrecognition
corners_block = det.rm_text(org, corners_block)
corners_img = det.rm_text(org, corners_img)
corners_compo = det.rm_text(org, corners_compo)
# *** Step 5 *** text detection from cleaned image
corners_rec = det.get_corner(boundary_rec)
corners_nonrec = det.get_corner(boundary_nonrec)
# *** Step 4 *** process data: identify blocks and imgs from rectangles -> identify compos -> identify irregular imgs
# identify rectangular block and rectangular img from rectangular shapes
corners_block, corners_img = det.img_or_block(
org, binary, corners_rec, C.THRESHOLD_BLOCK_MAX_BORDER_THICKNESS,
C.THRESHOLD_BLOCK_MAX_CROSS_POINT) # block check
# identify potential buttons and input bars
corners_block, corners_compo = det.uicomponent_or_block(
org, corners_block, C.THRESHOLD_UICOMPO_MAX_HEIGHT,
C.THRESHOLD_UICOMPO_MIN_EDGE_RATION, C.THRESHOLD_BLOCK_MIN_EDGE_LENGTH)
# shrink images with extra borders
if is_shrink_img:
corners_img = det.img_shrink(org, binary, corners_img,
C.THRESHOLD_LINE_MIN_LENGTH_H,
C.THRESHOLD_LINE_MIN_LENGTH_V,
C.THRESHOLD_LINE_THICKNESS)
# identify irregular-shape img from irregular shapes
corners_img += det.img_irregular(org, corners_nonrec,
C.THRESHOLD_IMG_MUST_HEIGHT,
C.THRESHOLD_IMG_MUST_WIDTH) # img assertion
# *** Step 5 *** refine results: refine img according to size -> OCR text area filter
# ignore too large and highly likely text areas
corners_img = det.img_refine(
org,
corners_img,
C.THRESHOLD_IMG_MAX_HEIGHT_RATIO, # ignore too large imgs
C.THRESHOLD_TEXT_EDGE_RATIO,
C.THRESHOLD_TEXT_HEIGHT) # ignore text areas
# merge overlapped corners, and remove nested corners