def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name",
help="model name",
type=str,
default='conv-lite-fc')
parser.add_argument("--model_epoch",
type=int,
default=None,
help="model epoch")
parser.add_argument("-f", "--file", help="Path to the image file")
parser.add_argument(
"-s",
"--single-line",
default=False,
help="Whether the image only includes one-line characters",
)
args = parser.parse_args()
ocr = CnOcr(model_name=args.model_name, model_epoch=args.model_epoch)
if args.single_line:
res = ocr.ocr_for_single_line(args.file)
else:
res = ocr.ocr(args.file)
logger.info("Predicted Chars: %s", res)
def __init__(self, parent=None):
super(GUIMainWin, self).__init__(parent)
# Basic Setups
self.setupUi(self)
self.appExceptionHandler = ExceptHookHandler(
self,
logFile=os.path.join(os.path.expanduser('~'),
"ArknightsRecruitmentHelper", "log.txt"))
self.setWindowTitle(globalVersion)
# Key Initializations
self.handle = 0
self.initializeHandleSelector()
self.tagImageSceneList = [
QGraphicsScene(),
QGraphicsScene(),
QGraphicsScene(),
QGraphicsScene(),
QGraphicsScene()
]
self.tagTypeList = ["", "", "", "", ""]
# Handler Connections
self.handleSelectorComboBox.activated[str].connect(self.onHandleSelect)
self.updateHandleSelectorListButton.clicked.connect(
self.initializeHandleSelector)
self.manualRecognizeAndAnalyzeButton.clicked.connect(
self.recognizeAndAnalyze)
self.updateDataButton.clicked.connect(self.updateData)
self.ocrInstance = CnOcr(model_name='conv-lite-fc')
def __init__(self, font='HYZiKuTangChangLinTiW-2.ttf'):
self.img_font = ImageFont.truetype(font, 60)
self.fill_color = "#000000"
self.ocr = CnOcr()
if not os.path.isdir('jpg'):
os.mkdir('jpg')
pass
def pathCallBack():
# , ('All Files', '*')
filePath = filedialog.askopenfilename(title='Select picture to OCR', filetypes=[('PNG', '*.png'), ('JPG', '*.jpg'),('JPEG', '*.jpeg'), ('bmp', '*.bmp')])
if (filePath != ''):
strPath.set(filePath)
ocr = CnOcr()
img_fp = ''
# img_fp = 'D://download/multi-line_cn1.png'
img_fp = filePath
img = mx.image.imread(img_fp, 1)
res = ocr.ocr(img)
# print("Predicted Chars:", res)
# print(type(res))
strResult = ""
for s in res:
strResult = strResult + ''.join('%s' % id for id in s) + "\n"
txtResult.delete(0.0, tk.END)
txtResult.insert(tk.INSERT, strResult)
txtResult.update()
def __init__(self, path: str, show_img: bool = False):
self.__ext = {'jpg', 'jpeg'}
self.__ocr = CnOcr(model_name='densenet-lite-gru',
cand_alphabet=string.digits,
name=path)
self.__std = CnStd(name=path)
self.__info_dict = {}
self.__dup_name_dict = {}
# 先对路径进行替换
path = self.__format_path(path)
# 根据传入的路径判断操作
if os.path.isdir(path) or os.path.isfile(path):
files = [self.__format_path(os.path.join(path, f)) for f in os.listdir(path) if
(os.path.isfile(os.path.join(path, f)) and self.__is_image(f))] \
if os.path.isdir(path) \
else [path]
for file in tqdm(files):
self.__handle_info(
file,
self.__ocr_number(
self.__std_number(self.__cutter(file, show_img))))
else:
print(f'获取数据错误，“{path}”既不是文件也不是文件夹')
def getOCR(img):
"""使用CnOcr识别图片内容，按行输出结果至res列表的函数
"""
global result_str, result
buffer = QtCore.QBuffer()
buffer.open(QtCore.QBuffer.ReadWrite)
img.save(buffer, "PNG")
pil_img = Image.open(io.BytesIO(buffer.data()))
image_array = np.array(pil_img)
buffer.close()
ocr = CnOcr()
res = ocr.ocr(image_array)
try:
# 拼接字符串并保存至results列表
result_str = []
for iter_item in res:
result_str.append(''.join(iter_item))
result = "\n".join(result_str)
except Exception as e:
print(f"INFO: Unable to read text from image, did not copy")
notify(f"Unable to read text from image, did not copy")
if result:
pyperclip.copy(result)
print(f'INFO: Copied "{result}" to the clipboard')
notify(f'Copied "{result}" to the clipboard')
else:
print(f"INFO: Unable to read text from image, did not copy")
notify(f"Unable to read text from image, did not copy")
class dyOCR():
def __init__(self):
from cnocr import CnOcr
self.ocr = CnOcr()
def resFromFilePath(self, fp):
get = self.ocr.ocr(fp)
if get == []:
return "-1 暂时不能识别"
get = "".join(get[0])
get = get.replace("o", "0")
get = get.replace("O", "0")
return get
def resFromB64(self, b64):
img_data = base64.b64decode(b64)
# nparr = np.fromstring(img_data, np.uint8)
nparr = np.frombuffer(img_data, np.uint8)
img_np = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
get = self.ocr.ocr(img_np)
if get == []:
return "-1 暂时不能识别"
get = "".join(get[0])
get = get.replace("o", "0")
get = get.replace("O", "0")
return get
def get_list(self):
std=CnStd()
ocr=CnOcr()
ocr._model_name='conv-lite-fc'
print(ocr._model_name)
ocr_res2=ocr.ocr(img_fp=self.filepath)
box_info_list=std.detect(self.filepath,pse_threshold=0.7,pse_min_area=150,context='gpu',height_border=0.10)
image=Image.open(self.filepath)
fontstyle=ImageFont.truetype('./simhei.ttf',13,encoding='utf-8')
draw=ImageDraw.Draw(image)
for box_info in box_info_list:
print('a')
print('a')
print(box_info)
info_box=box_info['box']
crp_img=box_info['cropped_img']
ocr_res1=ocr.ocr_for_single_line(crp_img)
print('result: %s' % ''.join(str(ocr_res1)))
x1,y1=info_box[0,0],info_box[0,1]
x2,y2=info_box[1,0],info_box[1,1]
x3,y3=info_box[2,0],info_box[2,1]
x4,y4=info_box[3,0],info_box[3,1]
draw.polygon([(x1,y1),(x4,y4),(x3,y3),(x2,y2)],outline=(255,0,0))
draw.text((x4,y4),str(ocr_res1),(200,0,0),font=fontstyle)
image.show()
print(ocr_res2)
return box_info_list
def aaa(request):
if request.method == 'GET':
return render(request, 'aaa.html')
file = request.FILES.get('file')
b = b''
for c in file.chunks():
b += c
try:
ocr = CnOcr()
except:
pass
sep = ','
img_data = plt.imread(BytesIO(b), "png")
# print(type(img_data))
# print(img_data.shape)
image = img_data[:, :, :3]
# print(image.shape)
image = (image * 255).astype(np.int)
try:
res = ocr.ocr_for_single_line(image)
msg = sep.join(res).replace(',', '')
data = {'code': 200, 'msg': msg}
return JsonResponse(data)
except:
msg = '图片名含有非转义字符，建议使用数字字母的形式'
data = {'code': 400, 'msg': msg}
return JsonResponse(data)
def OCR():
ocr = CnOcr()
QR_Image = getScreenImage()
#res = ocr.ocr_for_single_line(QR_Image)
res = ocr.ocr(QR_Image)
print("Predicted Chars:", res)
easygui.msgbox(res)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--file", help="Path to the CAPTCHA image file")
args = parser.parse_args()
ocr = CnOcr()
res = ocr.ocr_for_single_line(args.file)
print("Predicted Chars:", res)
def test_cand_alphabet():
img_fp = os.path.join(example_dir, 'hybrid.png')
ocr = CnOcr(cand_alphabet=NUMBERS)
pred = ocr.ocr(img_fp)
pred = [''.join(line_p) for line_p, _ in pred]
print("Predicted Chars:", pred)
assert len(pred) == 1 and pred[0] == '012345678'
def cnocr_ocr(pic_path):
from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr(pic_path)
for i in res[:-1]:
for j in i[0]:
print(j, end='')
print()
for i in res[-1][0]:
print(i, end='')
def __init__(self, font='shaoq.woff'):
self.img_font = ImageFont.truetype(font, 60)
self.fill_color = "#000000"
self.font = TTFont('shaoq.woff')
self.keys = list(key for key in self.font['glyf'].keys()
if key.startswith('uni'))
self.ocr = CnOcr()
if not os.path.isdir('jpg'):
os.mkdir('jpg')
pass
def extract_img(path: str):
ocr = CnOcr()
res = ocr.ocr(path)
lines = []
for obj in res:
line = "".join(obj[0])
lines.append(line)
data = "\n".join(lines)
os_remove(path)
return data
def ocr_image(fname):
print('OCR %s' % fname)
global _cnocr
if _cnocr is None:
_cnocr = CnOcr()
res = _cnocr.ocr(fname)
text = '\n'.join([''.join(t) for t in res])
return text
def cnocr_ocr(pic_path):
from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr(pic_path)
for r in res[:-1]:
for c in r:
print(c, end='')
print()
for r in res[-1]:
for c in r:
print(c, end='')
def start_engine(model_path='models'):
if OCR_MODEL_NAME + '.zip' in os.listdir(
model_path) and STD_MODEL_NAME + '.zip' in os.listdir(model_path):
print('初始化模型中...')
ocr_root = os.path.join(data_dir(mode='cnocr'), ocr_version)
ocr_root = os.path.join(ocr_root, OCR_MODEL_NAME)
os.makedirs(ocr_root, exist_ok=True)
ocr_zip_file_path = os.path.join(ocr_root, OCR_MODEL_NAME + '.zip')
shutil.copyfile(os.path.join('models', OCR_MODEL_NAME + '.zip'),
ocr_zip_file_path)
with zipfile.ZipFile(ocr_zip_file_path) as zf:
zf.extractall(os.path.dirname(ocr_root))
os.remove(ocr_zip_file_path)
std_root = os.path.join(data_dir(mode='cnstd'), std_version)
std_root = os.path.join(std_root, STD_MODEL_NAME)
os.makedirs(std_root, exist_ok=True)
std_zip_file_path = os.path.join(std_root, STD_MODEL_NAME + '.zip')
shutil.copyfile(os.path.join('models', STD_MODEL_NAME + '.zip'),
std_zip_file_path)
with zipfile.ZipFile(std_zip_file_path) as zf:
zf.extractall(os.path.dirname(std_root))
os.remove(std_zip_file_path)
ocr_core = CnOcr()
std_core = CnStd()
print('初始化完成')
return ocr_core, std_core
else:
print('初始化失败，未找到预加载模型')
return None
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--file", help="Path to the image file")
parser.add_argument(
"-s",
"--single-line",
default=False,
help="Whether the image only includes one-line characters")
args = parser.parse_args()
ocr = CnOcr()
if args.single_line:
res = ocr.ocr_for_single_line(args.file)
else:
res = ocr.ocr(args.file)
print("Predicted Chars:", res)
class FTC(object):
# font_to_chinese
def __init__(self, font='HYZiKuTangChangLinTiW-2.ttf'):
self.img_font = ImageFont.truetype(font, 60)
self.fill_color = "#000000"
self.ocr = CnOcr()
if not os.path.isdir('jpg'):
os.mkdir('jpg')
pass
def make_all_font_img(self):
img = Image.new('RGB', (70 * 20, 80), color='white')
draw = ImageDraw.Draw(img)
draw.text((0, 0), u'abcdefghigklmnopqrstuvwxyz', font=self.img_font, fill=self.fill_color)
img.show()
img.save('jpg/1.jpg')
def make_img(self):
img = Image.new('RGB', (140, 80), color='white')
draw = ImageDraw.Draw(img)
draw.text((0, 0), chr(0xe41b), font=self.img_font, fill=self.fill_color)
# img.show()
# img.save('jpg/1.jpg')
# print(pytesseract.image_to_string(img, lang='chi_sim'))
pass
def ret_key_font(self):
self.make_all_font_img()
res = self.ocr.ocr('jpg/1.jpg')
return res
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name",
help="model name",
type=str,
default='conv-lite-fc')
parser.add_argument("--model_epoch",
type=int,
default=None,
help="model epoch")
parser.add_argument("-f", "--file", help="Path to the image file or dir")
parser.add_argument(
"-s",
"--single-line",
default=False,
help="Whether the image only includes one-line characters",
)
args = parser.parse_args()
ocr = CnOcr(model_name=args.model_name, model_epoch=args.model_epoch)
ocr_func = ocr.ocr_for_single_line if args.single_line else ocr.ocr
fp_list = []
if os.path.isfile(args.file):
fp_list.append(args.file)
elif os.path.isdir(args.file):
fn_list = glob.glob1(args.file, '*g')
fp_list = [os.path.join(args.file, fn) for fn in fn_list]
for fp in fp_list:
res = ocr_func(fp)
logger.info('\n' + '=' * 10 + fp + '=' * 10)
res = [''.join(line_p) for line_p in res]
logger.info('\n' + '\n'.join(res))
class textRecongiser:
ocr = CnOcr()
def __init__(self, file_name, temp_save_path):
f_suffix = file_name.split('.')[-1]
self.file_name = file_name.replace('.' + f_suffix, '')
self.temp_pic_dir = temp_save_path
self.output_text_dir = subtile_dir
def gen_subtitle(self):
total_content = []
files = os.listdir(self.temp_pic_dir)
for f in files:
t = self.ocr.ocr(self.temp_pic_dir + '/' + f)
for tt in t:
line = ''.join(tt) + '\n'
total_content.append( line )
# write into file
with open(self.output_text_dir + self.file_name + '.txt', 'w', encoding='utf8') as f:
f.write(self._processContent(total_content))
f.close()
def _processContent(self, content_array):
final_content = ''
t_content_array = []
for c in content_array:
if c not in t_content_array:
t_content_array.append(c)
# print(t_content_array)
for t in t_content_array:
final_content += t
return final_content.strip('\n')
def predict(model_name, pretrained_model_fp, context, img_file_or_dir,
single_line):
ocr = CnOcr(model_name=model_name,
model_fp=pretrained_model_fp,
context=context)
ocr_func = ocr.ocr_for_single_line if single_line else ocr.ocr
fp_list = []
if os.path.isfile(img_file_or_dir):
fp_list.append(img_file_or_dir)
elif os.path.isdir(img_file_or_dir):
fn_list = glob.glob1(img_file_or_dir, '*g')
fp_list = [os.path.join(img_file_or_dir, fn) for fn in fn_list]
for fp in fp_list:
start_time = time.time()
logger.info('\n' + '=' * 10 + fp + '=' * 10)
res = ocr_func(fp)
logger.info('time cost: %f' % (time.time() - start_time))
logger.info(res)
if single_line:
res = [res]
for line_res in res:
preds, prob = line_res
logger.info('\npred: %s, with probability %f' %
(''.join(preds), prob))
def txtOcr():
if request.method == 'POST':
file = request.files['file']
if file and allowed_file(file.filename):
filename = secure_filename(''.join(lazy_pinyin(file.filename)))
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
file_url = url_for('uploaded_file', filename=filename)
import mxnet as mx
from cnocr import CnOcr
ocr = CnOcr()
img_fp = os.path.join(app.config['UPLOAD_FOLDER'], filename)
img = mx.image.imread(img_fp, 1)
res = ocr.ocr(img)
print("Predicted Chars:", res)
return html + '<br><img src=' + file_url + '><p>预测结果如下：' + str(
res) + '</p>'
return html
def orc_text(path_image):
ocr = CnOcr()
image = cv2.imread(path_image, cv2.IMREAD_GRAYSCALE)
height_image, width_image = image.shape
_, binary_image = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY_INV)
height_projection = get_horizontal_projection(binary_image)
text_lines = get_text_lines(height_projection)
text_list = []
for line_index, text_line in enumerate(text_lines):
text_line_image = binary_image[text_line[0]:text_line[1],
0:width_image]
vertical_projection = get_vertical_projection(text_line_image)
text_words = get_text_word(vertical_projection)
text_line_word_image = image[text_line[0]:text_line[1],
text_words[0][0]:text_words[-1][1]]
res = ocr.ocr_for_single_line(text_line_word_image)
text_list.append(''.join(res))
return text_list
def extract_content(save_dir_path):
std = CnStd()
cn_ocr = CnOcr()
base_path = os.path.abspath(os.path.dirname(__file__))
pic_base_dir = os.path.join(base_path, "received")
pic_path_list = (glob.glob(os.path.join(pic_base_dir, "*.png")) +
glob.glob(os.path.join(pic_base_dir, "*.jpg")) +
glob.glob(os.path.join(pic_base_dir, "*.jpeg")))
workbook = xlwt.Workbook()
for index, pic_path in enumerate(pic_path_list):
sheet = workbook.add_sheet('sheet{}'.format(index),
cell_overwrite_ok=True)
box_info_list = std.detect(pic_path)
for box_info in box_info_list:
x_list, y_list = [], []
for (x, y) in box_info['box']:
x_list.append(x)
y_list.append(y)
top, bottom, left, right = min(y_list), max(y_list), min(
x_list), max(x_list)
top_row, bottom_row, left_column, right_column = int(
top // 80), int(bottom // 80), int(left // 60), int(right //
60)
cropped_img = box_info['cropped_img'] # 检测出的文本框
ocr_res = ''.join(cn_ocr.ocr_for_single_line(cropped_img))
try:
logger.info(
"top_row:{}, bottom_row:{}, left_column:{}, right_column:{}, ocr_res:{}",
top_row,
bottom_row,
left_column,
right_column,
ocr_res,
feature="f-strings")
sheet.write_merge(top_row, bottom_row, left_column,
right_column, ocr_res)
except Exception as e:
print(e)
xls_base_dir = os.path.join(base_path, save_dir_path)
xls_path = os.path.join(xls_base_dir, "res.xls")
workbook.save(xls_path)
def __init__(self):
super(picture, self).__init__()
self.resize(350, 350)
self.setWindowTitle("图片转文字")
self.label = QLabel(self)
# self.label.setText("显示图片")
self.label.setScaledContents(True)
self.label.setFixedSize(300, 200)
self.label.move(25, 60)
self.label.setStyleSheet(
"QLabel{background:white;}"
"QLabel{color:rgb(300,300,300,120);font-size:10px;font-weight:bold;font-family:宋体;}"
)
btn = QPushButton(self)
btn.setText("打开图片")
btn.move(135, 20)
btn.clicked.connect(self.openimage)
self.label_text = QLabel(self)
self.label_text.setFixedSize(300, 30)
self.label_text.move(25, 270)
self.label_text.setTextInteractionFlags(
Qt.TextSelectableByMouse) ###可复制
self.label_wait = QLabel(self)
self.label_wait.setFixedSize(300, 30)
self.label_wait.move(25, 300)
# 标签1的背景填充更改为True，否则无法显示背景
self.label_wait.setAutoFillBackground(True)
# # 实例化背景对象，进行相关背景颜色属性设置
# palette = QPalette()
# palette.setColor(QPalette.Window, Qt.green)
# # 标签1加载背景
# self.label_wait.setPalette(palette)
# 设置文本居中显示
self.label_wait.setAlignment(Qt.AlignCenter)
self.label_wait.setText('tips：识别过程可能会卡住，需几秒到几十秒不等')
self.std = CnStd()
self.cn_ocr = CnOcr()
def multLineOcr(request):
imgs = request.POST.get("img")
img = imgs.replace(" ", "+")
res = base64.b64decode(img)
# res = base64.urlsafe_b64encode(img)
nparr = np.fromstring(res, np.uint8)
img = cv2.imdecode(nparr, cv2.COLOR_BGR2RGB)
imgb = img
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# cv2.imwrite("img.png", imgb)
ocr = CnOcr()
res = ocr.ocr(img)
return HttpResponse(
json.dumps(res)
)
def ocr():
if 'file' not in request.files:
return jsonify(code=-1, message='no file error'), 400
file = request.files['file']
_uuid = str(uuid.uuid1())
file_name = '/tmp/ocr/' + _uuid
file.save(file_name)
ocr = CnOcr(name=_uuid)
std = CnStd(name=_uuid)
box_info_list = std.detect(file_name)
lines = []
for box_info in box_info_list:
cropped_img = box_info['cropped_img'] # 检测出的文本框
ocr_res = ocr.ocr_for_single_line(cropped_img)
lines.append(''.join(ocr_res))
return jsonify(code=0, message='ok', data=lines)
def cn_ocr(self, _range, delay_time=5):
"""
cn ocr识别数字
:param delay_time: ocr识别延迟时间
:param _range: 验证码截图区域坐标(左x,左y,右x,右y)
:return: 识别到的数字
"""
global sms_code
self.get_code_pic(_range)
cn_ocr = CnOcr(model_name="conv-lite-fc",
context="cpu",
root="conv-lite-fc")
ret = cn_ocr.ocr("ios_code_pic.png")
result = ""
for v in ret:
result += "".join(v)
find_all = re.findall(r'\'[\d]{6}\'', str(result))
if len(find_all) != 1:
find_all = re.findall(r'([\d]{6})[\u3002]', str(result))
if len(find_all) != 1:
find_all = re.findall(r'(您的验证码为[\d]{6})', str(result))
# 识别结果
self.logger.info("CnOCR识别结果：" + result)
if len(find_all) == 1:
code = find_all[0].strip("'")
if sms_code == code:
self.logger.info("暂未获取到最新验证码，%d秒后重试" % delay_time)
time.sleep(delay_time)
return self.cn_ocr(_range, delay_time)
else:
sms_code = code
return code
else:
self.logger.info("暂未获取到最新验证码，%d秒后重试" % delay_time)
time.sleep(delay_time)
return self.cn_ocr(_range, delay_time)
