def clip_word(sentences, min_pix, output_path):
if not os.path.exists(output_path):
os.mkdir(output_path)
index = 0
words = []
for sentence in sentences:
prepros = pre.preprocess(sentence)
hist = [int(h / 255) for h in np.sum(prepros, 0)] # project vertically
front = -1
is_word = False
for cur, h in enumerate(hist):
if not is_word and h > min_pix:
front = cur
is_word = True
continue
if is_word and h < min_pix:
word = sentence[:, front:cur]
words.append(word)
is_word = False
cv2.imwrite(os.path.join(output_path,
str(index) + '.png'), word)
index += 1
return words
def main():
org = cv2.imread('data/4.jpg')
org = shrink(org)
grey = cv2.cvtColor(org, cv2.COLOR_BGR2GRAY)
binary = pre.preprocess(grey)
blocks_corner = block_division(grey, show=False)
block_erase(binary, blocks_corner)
def refine_text(org, corners_text, max_line_gap, min_word_length):
def refine(bin):
head = 0
rear = 0
gap = 0
get_word = False
for i in range(bin.shape[1]):
# find head
if not get_word and np.sum(bin[:, i]) != 0:
head = i
rear = i
get_word = True
continue
if get_word and np.sum(bin[:, i]) != 0:
rear = i
continue
if get_word and np.sum(bin[:, i]) == 0:
gap += 1
if gap > max_line_gap:
if (rear - head) > min_word_length:
corners_text_refine.append(
(head + col_min, row_min, rear + col_min, row_max))
gap = 0
get_word = False
if get_word and (rear - head) > min_word_length:
corners_text_refine.append(
(head + col_min, row_min, rear + col_min, row_max))
corners_text_refine = []
pad = 1
for corner in corners_text:
(col_min, row_min, col_max, row_max) = corner
col_min = max(col_min - pad, 0)
col_max = min(col_max + pad, org.shape[1])
row_min = max(row_min - pad, 0)
row_max = min(row_max + pad, org.shape[0])
clip = org[row_min:row_max, col_min:col_max]
clip_bin = preprocess(clip)
refine(clip_bin)
return corners_text_refine
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
import cv2
import time
# initialization
start = time.clock()
is_detect_line = False
is_merge_img = False
is_shrink_img = False
is_ocr = True
is_segment = False
is_save = True
# *** Step 1 *** pre-processing: gray, gradient, binary
org, gray = pre.read_img('input/5.png', (0, 600)) # cut out partial img
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)
import cv2
import time
# initialization
C = Config()
start = time.clock()
is_detect_line = False
is_merge_img = False
is_shrink_img = False
is_ocr = True
is_segment = False
is_save = True
# *** Step 1 *** pre-processing: gray, gradient, binary
org, gray = pre.read_img('input/5.png', (0, 600)) # cut out partial img
bin = pre.preprocess(gray, 1)
# *** Step 2 *** line detection: for better boundary detection
if is_detect_line:
line_h, line_v = det.line_detection(bin, C.THRESHOLD_LINE_MIN_LENGTH_H,
C.THRESHOLD_LINE_MIN_LENGTH_V,
C.THRESHOLD_LINE_THICKNESS)
bin_no_line = det.rm_line(bin, [line_h, line_v])
binary = bin_no_line
else:
binary = bin
# *** Step 3 *** object detection: get connected areas -> get boundary -> get corners
boundary_all, boundary_rec, boundary_nonrec = det.boundary_detection(
binary,
C.THRESHOLD_OBJ_MIN_AREA,
def split_word(img, hist, min_pix):
front = -1
is_word = False
for cur, h in enumerate(hist):
if not is_word and h > min_pix:
front = cur
is_word = True
continue
if is_word and h < min_pix:
word = img[:, front:cur]
is_word = False
cv2.imshow('word', word)
cv2.waitKey(0)
cv2.destroyWindow('word')
clip_paths = glob.glob('output\\1\\*.png')
for clip_path in clip_paths:
clip = cv2.imread(clip_path, 0)
p = pre.preprocess(clip)
hist = [int(h / 255) for h in np.sum(p, 0)]
cv2.imshow('org', clip)
split_word(clip, hist, 1)
break
