def text_line_segmentation_NN(image,
scale,
mask=None,
use_binary=False,
debug_image=None,
offset=(0, 0)):
h, w = image.shape[:2]
if debug_image is None:
debug_image = image
if len(debug_image.shape) < 3:
debug_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
if len(image.shape) > 2:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if use_binary:
image = morph.thresh_sauvola(image, k=0.15) * 255
if mask is not None:
image = (image + mask * 255).astype('uint8')
labels, _ = morph.label(image == 0)
objects = morph.find_objects(labels)
height_map = [
sl.height(o) for o in objects if sl.height(o) > 6
and sl.height(o) < 100 and sl.aspect_normalized(o) < 8
]
avg_h = max(np.nan_to_num(np.mean(height_map)), scale * 0.6)
block = Block(image, avg_h)
words = block.getWordBoundingBoxes()
lines = filter_and_merge_overlap_boxes(words,
max(avg_h, scale * 1.2) * 0.3)
lines = filter_and_merge_overlap_boxes(lines,
max(avg_h, scale * 1.2) * 0.3,
use_merge_same_line_only=True)
offset_x, offset_y = offset
# filter line by size
lines = [
l for l in lines if l[3] - l[1] > avg_h * 0.3 and l[3] - l[1] < avg_h *
2.5 and l[2] - l[0] > avg_h * 0.5
]
lines = [sl.pad_box(l, 0, (h, w)) for l in lines]
lines = [[
l[0] + offset_x, l[1] + offset_y, l[2] + offset_x, l[3] + offset_y
] for l in lines]
debug_image = block.paint(None)
return lines, debug_image
def detect_table(image, scale, maxsize=10, debug_path=None):
h, w = image.shape[:2]
if len(image.shape) > 2 and image.shape[2] >= 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image
# kernel = np.ones((5,5),np.uint8)
# gray = 255-cv2.morphologyEx(255-gray, cv2.MORPH_CLOSE, kernel)
binary = 1 - morph.thresh_sauvola(gray, k=0.05)
junk_cc, _ = filter_junk_cc(binary, scale, maxsize)
junk_cc = morph.r_closing(junk_cc, (5, 5))
print('calculating combine sep...')
combine_sep = compute_combine_seps(junk_cc, scale)
# using closing morphology to connect disconnected edges
close_thes = int(scale * 0.15)
closed_sep = morph.r_closing(combine_sep, (close_thes, close_thes))
if debug_path is not None:
cv2.imwrite(filename[:-4] + '_bin.png',
((1 - junk_cc) * 255).astype('uint8'))
cv2.imwrite(filename[:-4] + '_sep.png',
(closed_sep * 255).astype('uint8'))
labels, _ = morph.label(closed_sep)
objects = morph.find_objects(labels)
# result table list
boxes = []
for i, b in enumerate(objects):
if sl.width(b) > maxsize * scale or sl.area(
b) > scale * scale * 10 or (sl.aspect_normalized(b) > 6
and sl.max_dim(b) > scale * 1.5):
density = np.sum(combine_sep[b])
density = density / sl.area(b)
if (sl.area(b) > scale * scale * 10 and sl.min_dim(b) > scale * 1.0
and sl.max_dim(b) > scale * 8 and density < 0.4):
# calculate projection to determine table border
w = sl.width(b)
h = sl.height(b)
region = (labels[b] == i + 1).astype('uint8')
border_pad = max(w, h)
border_thres = scale * 2
proj_x = np.sum(region, axis=0)
proj_y = np.sum(region, axis=1)
proj_x[3:] += proj_x[:-3]
proj_y[3:] += proj_y[:-3]
sep_x = np.sort([j[0] for j in np.argwhere(proj_x > 0.75 * h)])
sep_y = np.sort([j[0] for j in np.argwhere(proj_y > 0.4 * w)])
# skip if sep count < 2
if len(sep_x) < 1 or len(sep_y) < 1: continue
border_left, border_right, border_top, border_bottom = None, None, None, None
if sep_x[0] < border_pad:
border_left = sep_x[0]
if sep_x[-1] > w - border_pad:
border_right = sep_x[-1]
if sep_y[0] < border_pad:
border_top = sep_y[0]
if sep_y[-1] > h - border_pad:
border_bottom = sep_y[-1]
# print_info(border_top, border_bottom, border_left, border_right)
if all([
j is not None for j in
[border_top, border_bottom, border_left, border_right]
]):
border_right = b[1].stop - b[1].start
boxes.append([
b[1].start + border_left, b[0].start + border_top,
b[1].start + border_right, b[0].start + border_bottom
])
# boxes.append(([b[1].start, b[0].start, b[1].stop, b[0].stop]))
return boxes
