def pre_processing(input_path):
# *** Step 1 *** pre-processing: gray, gradient, binary
org, gray = pre.read_img(input_path, (0, 3000)) # cut out partial img
binary = pre.preprocess(gray)
return org, binary
if int(index) > end_index:
break
start = time.clock()
# set paths
print(input_path)
print(time.ctime())
out_img_draw = pyjoin(C.ROOT_IMG_DRAWN, index + '.png')
out_img_clean = pyjoin(C.ROOT_IMG_CLEAN, index + '.png')
out_img_gradient = pyjoin(C.ROOT_IMG_GRADIENT, index + '.png')
out_img_segment = pyjoin(C.ROOT_IMG_SEGMENT, index)
out_label = pyjoin(C.ROOT_LABEL, index)
# *** Step 1 *** pre-processing: gray, gradient, binary
org, gray = pre.read_img(input_path, (0, 2600)) # cut out partial img
if org is None or gray is None: continue
binary = pre.preprocess(gray, 1)
# *** Step 2 *** processing: get connected areas -> get boundary -> rectangle check
boundary_rec, boundary_all = det.boundary_detection(
binary, C.THRESHOLD_MIN_OBJ_AREA, C.THRESHOLD_MIN_REC_PARAMETER,
C.THRESHOLD_MIN_REC_EVENNESS, C.THRESHOLD_MAX_LINE_THICKNESS,
C.THRESHOLD_MIN_LIN_LENGTH, C.THRESHOLD_MAX_IMG_DENT_RATIO)
# get corner of boundaries -> img or block check
corners_rec = det.get_corner(boundary_rec)
corners_block, corners_img = det.block_or_img(
binary, corners_rec, C.THRESHOLD_MAX_BLOCK_BORDER_THICKNESS,
C.THRESHOLD_MAX_BLOCK_CROSS_POINT)
# refine img component
corners_img = det.img_refine2(corners_img, C.THRESHOLD_MAX_IMG_EDGE_RATIO,
from code_generation import DIV
import line
import line_to_block as l2b
import code_generation as code
from config import CONFIG as cfg
import ip_preprocessing as pre
C = cfg()
org, gray = pre.read_img('input/4.png', (0, 3000)) # cut out partial img
lines_v = line.read_lines('output/lines_v.csv')
lines_h = line.read_lines('output/lines_h.csv')
blocks_h = l2b.divide_blocks_by_lines(lines_h, org.shape[0], C.BLOCK_MIN_HEIGHT)
hierarchies_h = l2b.hierarchy_blocks(blocks_h)
print(hierarchies_h)
# html = code.gen_html2(blocks_h)
# open('output/y.html', 'w').write(html)
# code.gen_css(blocks_h)
y + t >= column - 1 or
(np.sum(binary[head:end, y + t]) / 255) /
(end - head) < max_cross_point):
clear_right = True
if clear_left and clear_right:
lines_v.append(((y, head), (y, end)))
break
if x < row - 1:
x += 1
if y < column - 1:
y += 1
return lines_h, lines_v
org, gray = pre.read_img('input/6.png', (2000, 2600)) # cut out partial img
binary = pre.preprocess(gray, 1)
lines_h, lines_v = search_line(binary)
draw_line(org, lines_h, (0, 255, 0))
draw_line(org, lines_v, (0, 0, 255))
broad = np.zeros(org.shape, dtype=np.uint8)
draw_line(broad, lines_h, (0, 255, 0))
draw_line(broad, lines_v, (0, 0, 255))
new_bin = rm_line(binary, [lines_h, lines_v])
cv2.imwrite('output/labeled.png', org)
cv2.imwrite('output/lines.png', broad)
cv2.imwrite('output/grad.png', binary)
cv2.imwrite('output/rm_line.png', new_bin)
