def run_batch(usage_root_dir, output_root_dir):
models = eval.load()
usage_name = os.path.basename(os.path.normpath(usage_root_dir))
for ir_data_dir in glob.glob(usage_root_dir + '/*/ir_data_auto/'):
app_root_name = os.path.basename(os.path.normpath(ir_data_dir.replace('ir_data_auto/', '')))
for img in glob.glob(ir_data_dir + '*-screen.jpg'):
output_dir = os.path.join(output_root_dir, usage_name, app_root_name, os.path.basename(img).split('.')[0])
run_single(img, output_dir, models)
# set input image path
input_path_img = 'data/input/11300.jpg'
output_root = 'data/output'
resized_height = resize_height_by_longest_edge(input_path_img)
is_ip = False
is_clf = False
is_ocr = False
is_merge = True
if is_ocr:
import detect_text_east.ocr_east as ocr
import detect_text_east.lib_east.eval as eval
os.makedirs(pjoin(output_root, 'ocr'), exist_ok=True)
models = eval.load()
ocr.east(input_path_img,
output_root,
models,
resize_by_height=resized_height,
show=False)
if is_ip:
import detect_compo.ip_region_proposal as ip
os.makedirs(pjoin(output_root, 'ip'), exist_ok=True)
# switch of the classification func
classifier = None
if is_clf:
classifier = {}
from cnn.CNN import CNN
# classifier['Image'] = CNN('Image')
is_clf = args.classify
is_ip = not args.skip_ip
is_ocr = not args.skip_ocr
is_merge = not args.skip_merge
# Load deep learning models in advance
print('Load models...')
# Setup
# Load deep learning models in advance
ocr_model = None
if is_ocr:
import detect_text_east.ocr_east as ocr
import detect_text_east.lib_east.eval as ocr_eval
os.makedirs(pjoin(output_root, 'ocr'), exist_ok=True)
ocr_model = ocr_eval.load()
compo_classifier = None
if is_ip:
import detect_compo.ip_region_proposal as ip
os.makedirs(pjoin(output_root, 'ip'), exist_ok=True)
if is_clf:
from cnn.CNN import CNN
compo_classifier = {'Elements': CNN('Elements')}
# compo_classifier['Image'] = CNN('Image')
# compo_classifier['Noise'] = CNN('Noise')
if is_merge:
import merge
print('... Done\n')
run_times, run_clocks = [], []
def fun(filename, output):
input_path_img = filename
output_root = output
key_params = {
'min-grad': 10,
'ffl-block': 5,
'min-ele-area': 50,
'merge-contained-ele': True,
'max-word-inline-gap': 4,
'max-line-gap': 4
}
resized_height = resize_height_by_longest_edge(input_path_img)
is_ip = True
is_clf = True
is_ocr = True
is_merge = True
is_html = True
is_tes = True
color = True
new_html = True
directory = 'out_' + input_path_img.split('/')[-1].split('.')[0]
path = os.path.join(output_root, directory).replace('\\', '/')
os.makedirs(path, exist_ok=True)
if is_ocr:
import detect_text_east.ocr_east as ocr
import detect_text_east.lib_east.eval as eval
os.makedirs(pjoin(path, 'ocr'), exist_ok=True)
models = eval.load()
ocr.east(input_path_img,
path,
models,
key_params['max-word-inline-gap'],
resize_by_height=resized_height,
show=False)
if is_ip:
import detect_compo.ip_region_proposal as ip
os.makedirs(pjoin(path, 'ip'), exist_ok=True)
# switch of the classification func
classifier = None
ip.compo_detection(input_path_img,
path,
key_params,
classifier=classifier,
resize_by_height=resized_height,
show=False)
if is_clf:
classifier = {}
from cnn.CNN import CNN
# classifier['Image'] = CNN('Image')
classifier['Elements'] = CNN('Elements')
# classifier['Noise'] = CNN('Noise')
ip.compo_detection(input_path_img,
path,
key_params,
classifier=classifier,
resize_by_height=resized_height,
show=False)
if is_merge:
import merge
name = input_path_img.split('/')[-1][:-4]
#print(name)
compo_path = pjoin(path, 'ip', str(name) + '.json')
ocr_path = pjoin(path, 'ocr', str(name) + '.json')
merge.incorporate(input_path_img,
compo_path,
ocr_path,
path,
params=key_params,
resize_by_height=resized_height,
show=False)
if is_html:
from obj.Compos_DF import ComposDF
# from obj.Compo_HTML import *
# from obj.List import *
# from obj.Block import *
# from obj.Group import *
# from obj.React import *
# from obj.Page import *
# from obj.Tree import *
import lib.draw as draw
from lib.list_item_gethering import gather_lists_by_pair_and_group
# add path to compos.json name = 'data/input/wireframe/o3/compo'
try:
compos = ComposDF(json_file=path + '/compo'
'.json',
img_file=input_path_img)
img = compos.img.copy()
img_shape = compos.img_shape
img_re = cv2.resize(img, img_shape)
# ***Step 1*** repetitive list recognition
compos.repetitive_group_recognition()
compos.pair_groups()
compos.list_item_partition()
# ***Step 2*** mark compos in same group as a single list, mark compos in same group_pair as a multiple list
lists, non_listed_compos = gather_lists_by_pair_and_group(
compos.compos_dataframe[1:])
generate_lists_html_css(lists)
# ***Step 3*** slice compos as blocks
compos_html = [li.list_obj for li in lists] + non_listed_compos
blocks, non_blocked_compos = slice_blocks(compos_html, 'v')
# ***Step 4*** assembly html and css as web page, and react program
html, css = export_html_and_css(blocks, path + '/page')
blk, index = export_react_program(blocks, path + '/react')
tree = export_tree(blocks, path + '/tree')
#ADD PATH
print("CONVERT TO HTML SAVED TO PATH:", path + '/page')
except:
('cant')
if is_tes:
# get current dir
with open(path + '/compo.json') as f: # make this path variable
#parse json
data = json.load(f)
# get clips
for i in data['compos']:
if i['clip_path'] == "REDUNDANT":
continue
else:
clip_path = i['clip_path']
# get path of project directory +"tesseract E:\\smart-ui\\uied\\final\\" E:\\smart-ui\\uied\\final\\
command = 'cmd /c ' + "tesseract " + clip_path.replace(
"/", "\\") + " stdout -l eng > temp.txt"
os.system(command) #"E:\\smart-ui\\uied\\final\\
a = open("temp.txt", "r")
var = a.read()
# set var
i["ocr"] = var
# make new json
with open(path + '/compo_ocr.json', 'w') as json_file:
json.dump(data, json_file)
if color:
#print(km.cluster_centers_[0][0])
with open(path + '/compo_ocr.json') as f:
data = json.load(f)
for i in data['compos']:
if i['clip_path'] == "REDUNDANT":
continue
else:
clip_path = i['clip_path'] #"E:\\smart-ui\\uied\\final\\"+
img = cv2.imread(clip_path.replace(
"/", "\\")) ### set directory path
#rgb = img[0][0];
all_pixels = img.reshape((-1, 3))
from sklearn.cluster import KMeans
k = 2
km = KMeans(n_clusters=k)
km.fit(all_pixels)
rgb = km.cluster_centers_[0]
rgb = rgb[::-1]
rgb = rgb.astype(int)
i["color"] = '#%02x%02x%02x' % tuple(rgb)
with open(path + '/compo_html.json', 'w') as json_file:
json.dump(data, json_file)
if new_html:
htmltext = """<!DOCTYPE html>
<html>
<head>
<title>HTML, CSS and JavaScript demo</title>
</head>
<body>"""
char = ['\n', '\f', '\\', '/', ']', '[', '(', ")"]
with open(path + '/compo_html.json') as f: # make this path variable
#parse json
data = json.load(f)
# get clips
for i in data['compos']:
if i['clip_path'] == "REDUNDANT":
continue
else:
div = '<div style="background-color:' + i[
'color'] + '; position: absolute; top:' + str(
i["row_min"]) + 'px; left:' + str(
i["column_min"]
) + 'px; border:3px solid black; height:' + str(
i["height"]) + 'px; width:' + str(
i["width"]) + 'px;">' + ''.join([
c for c in i['ocr'] if c not in char
]) + '</div>'
htmltext = htmltext + div
htmltext = htmltext + "</body></html>"
Html_file = open(path + '/output.html', "w", encoding="utf-8")
Html_file.write(htmltext)
Html_file.close()
#print(htmltext)
#json output
st.header("Generated Json ")
with open(path + '/compo_html.json') as f:
data = json.load(f)
st.write(data)
st.header("Generated Json with classification details")
with open(path + '/ip/clf_' + name + '.json') as f:
data = json.load(f)
for i in data['compos']:
if i['class'] == "Background":
continue
else:
clas = i['class']
fid = i['id']
i['clip_path'] = path + '/new/' + clas + '/' + str(
fid) + '.jpg'
st.write(data)
# st.write(json.dumps(data))
st.header("Generated Html")
hp = path + '/output.html'
HtmlFile = open(hp, 'r', encoding='utf-8')
source_code = HtmlFile.read()
st.markdown(source_code, unsafe_allow_html=True)
#image output
st.header("Output Classification")
imagee = cv2.imread(path + "/ip/result.jpg")
#cv2.imshow('Image', imagee)
st.image(imagee, caption='annotated result')
