from file_utils import time_start, timer_end
is_ctpn = True
is_uied = True
is_merge = True
PATH_IMG_INPUT = 'data\\input\\web\\6.png'
PATH_LABEL_COMPO = 'data\\output\\compo.json'
PATH_LABEL_TEXT = 'data\\output\\ocr.txt'
PATH_CTPN_DRAWN = 'data\\output\\ctpn.png'
PATH_UIED_DRAWN = 'data\\output\\uied.png'
PATH_UIED_BIN = 'data\\output\\gradient.png'
PATH_MERGE = 'data\\output\\merged.png'
PATH_COMPONENT = 'data\\output\\components'
img_section = (3000, 1500) # selected img section, height and width
start = time_start()
if is_ctpn:
import ocr
ocr.ctpn(PATH_IMG_INPUT, PATH_LABEL_TEXT, PATH_CTPN_DRAWN, img_section)
if is_uied:
import ui
ui.uied(PATH_IMG_INPUT, PATH_LABEL_COMPO, PATH_UIED_DRAWN, PATH_UIED_BIN, img_section)
if is_merge:
import merge
merge.incorporate(PATH_IMG_INPUT, PATH_LABEL_COMPO, PATH_LABEL_TEXT, PATH_MERGE, img_section, is_clip=True, clip_path=PATH_COMPONENT)
timer_end(start)
continue
input_path_img = line.split()[0]
print(input_path_img)
# *** start processing ***
start = time.clock()
# set output paths
# for image detection
label_compo = pjoin(C.ROOT_LABEL_UIED, str(index) + '.json')
img_uied_drawn = pjoin(C.ROOT_IMG_DRAWN_UIED, str(index) + '.png')
img_uied_grad = pjoin(C.ROOT_IMG_GRADIENT_UIED, str(index) + '.png')
# for text recognition (ctpn)
label_text = pjoin(C.ROOT_LABEL_CTPN, str(index) + '.txt')
img_ctpn_drawn = pjoin(C.ROOT_IMG_DRAWN_CTPN, str(index) + '.png')
# for incorporated results
img_merge = pjoin(C.ROOT_IMG_MERGE, str(index) + '.png')
label_merge = pjoin(C.ROOT_OUTPUT, 'label.txt')
if is_ctpn:
ocr.ctpn(input_path_img, label_text, img_ctpn_drawn, img_section)
if is_uied:
ui.uied(input_path_img, label_compo, img_uied_drawn, img_uied_grad,
img_section)
if is_merge:
merge.incorporate(input_path_img, label_compo, label_text, img_merge,
label_merge, img_section, is_clip)
print('%d Time Taken:%.3f s\n' % (index, time.clock() - start))
index += 1