def test_resnet50_v1b():
model_name = 'resnet50_v1b'
img_fp = os.path.join(example_dir, 'beauty2.jpg')
std = CnStd(model_name)
box_info_list = std.detect(img_fp)
img = mx.image.imread(img_fp, 1)
box_info_list2 = std.detect(img)
assert len(box_info_list) == len(box_info_list2)
def test_multiple_instances(model_name):
global INSTANCE_ID
print('test multiple instances for model_name: %s' % model_name)
img_fp = os.path.join(example_dir, 'beauty2.jpg')
INSTANCE_ID += 1
print('instance id: %d' % INSTANCE_ID)
std1 = CnStd(model_name, name='instance-%d' % INSTANCE_ID)
box_info_list = std1.detect(img_fp)
INSTANCE_ID += 1
std2 = CnStd(model_name, name='instance-%d' % INSTANCE_ID)
box_info_list2 = std2.detect(img_fp)
assert len(box_info_list) == len(box_info_list2)
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 identify(image):
std = CnStd(model_name='resnet50_v1b', root='C:/Users/HP/AppData/Roaming/Python/Python36/site-packages/cnstd')
global cn_ocr
box_info_list = std.detect(image)
ocr_res=[]
for box_info in box_info_list:
cropped_img = box_info['cropped_img']
ocr_res.extend(cn_ocr.ocr_for_single_line(cropped_img))
print('ocr result1: %s' % ''.join(ocr_res))
return ocr_res
def identify(image):
std = CnStd(model_name='resnet50_v1b',
root='C:/Users/dell/AppData/Roaming/cnstd/0.1.0/mobilenetv3')
global cn_ocr
ocr_res = []
box_info_list = std.detect(image)
ocr_res = []
for box_info in box_info_list:
cropped_img = box_info['cropped_img']
ocr_res.extend(cn_ocr.ocr_for_single_line(cropped_img))
print('ocr result1: %s' % ''.join(ocr_res))
return ocr_res
async def Img2Text(img: Image, ocr_model: CnOcr, std: CnStd) -> str:
url = img.url
async with aiohttp.ClientSession() as session:
async with session.get(url=url) as resp:
img_content = await resp.read()
img = IMG.open(BytesIO(img_content)).convert("RGB")
img = numpy.array(img)
box_info_list = std.detect(img)
res = []
for box_info in box_info_list:
cropped_img = box_info['cropped_img'] # 检测出的文本框
ocr_res = ocr_model.ocr_for_single_line(cropped_img)
res.append([ocr_res])
print(res)
return "".join(await flat(res))
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 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 target_function(idx, ls, prefix):
# 使得每一张卡都能被充分用到
# 提交的时候由于只有一张卡，需要全部都改成0
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
std = CnStd(context='gpu')
cn_ocr = CnOcr(context='gpu')
result = dict()
for file_name in tqdm(ls):
file_path = os.path.join(prefix, file_name)
box_info_list = std.detect(file_path)
output = ''
for box_info in box_info_list:
cropped_img = box_info['cropped_img'] # 检测出的文本框
if type(cropped_img) != type(None):
ocr_res = cn_ocr.ocr_for_single_line(cropped_img)
output += ''.join(ocr_res)
# print('ocr result: %s' % ''.join(ocr_res))
output = output.replace(' ', '')
output = re.sub("[^\u4e00-\u9fa5]", "", output)
result[file_name] = output
with open('./output_%d.json' % (idx), 'w', encoding='utf-8') as w:
w.write(json.dumps(result, ensure_ascii=False, indent=2))
class PickStuNumber:
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')
@staticmethod
def __format_path(path: str):
return os.path.abspath(path).replace('\\', '/')
@staticmethod
def __get_suffix(path: str) -> str:
"""
获取后缀
:param path: 图片路径
:return: 是否为图片
"""
return path.split('.')[-1]
def __is_image(self, path: str) -> bool:
return self.__get_suffix(path) in self.__ext
@staticmethod
def __cutter(path: str, show_img: bool = False) -> numpy.ndarray:
"""
切割图片
:param path: 图片路径
:param show_img: 是否需要展示图片
:return: 图片对应的 ndarray
"""
print(path)
# 以灰度模式读取图片
origin_img = cv2.imread(path, 0)
if show_img:
# 自由拉伸窗口
# cv2.namedWindow('bin img', 0)
cv2.imshow('origin img', origin_img)
# 切出一部分，取值是经验值
origin_img = origin_img[:origin_img.shape[0] // 2]
# 二值化
_, origin_img = cv2.threshold(origin_img, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
if show_img:
# 自由拉伸窗口
# cv2.namedWindow('bin img', 0)
cv2.imshow('bin img', origin_img)
# 形态学转换，主要为了检测出那个红色的 banner
kernel = numpy.ones((15, 15), dtype=numpy.uint8)
# img = cv2.erode(img, kernel=kernel, iterations=1)
img = cv2.dilate(origin_img, kernel=kernel, iterations=2)
# 边缘检测
contours, _ = cv2.findContours(img, 1, 2)
# 找出第二大的，即红色的 banner
contours = sorted(contours, key=cv2.contourArea, reverse=True)
if len(contours) > 1:
# 获取包围 banner 的矩形数据
x, y, w, h = cv2.boundingRect(contours[1])
# 目前所有的数值设定使用的是经验值
if w * h > 250000:
# 需要识别的学号部分
# 左上角坐标
left_top_x = x
left_top_y = y + h + 20
# 右下角坐标
right_down_x = x + w
right_down_y = y + h + 190
img = origin_img[left_top_y:right_down_y,
left_top_x:right_down_x]
else:
img = origin_img[120:]
else:
img = origin_img[120:]
# 对切出的图片进行再次处理，以便图像识别
kernel = numpy.ones((2, 2), dtype=numpy.uint8)
# 腐蚀以加粗
img = cv2.erode(img, kernel=kernel, iterations=1)
# 重新映射回 rgb
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
if show_img:
# 自由拉伸窗口
# cv2.namedWindow('final img', 0)
cv2.imshow('final img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
return img
def __ocr_number(self, img_list: List[numpy.ndarray]):
"""
识别数字
:param img_list:
:return:
"""
return self.__ocr.ocr_for_single_lines(img_list)
def __std_number(self, img: numpy.ndarray):
"""
定位数字
:param img:
:return:
"""
return [i['cropped_img'] for i in self.__std.detect(img)]
@staticmethod
def __handle_result_list(result_list: List[List[str]]) -> [str, bool]:
"""
处理结果列表
:param result_list: 结果列表
:return: 结果，是否有效
"""
result = result_list[0]
if len(result) < 12 and len(result_list) > 1:
for i in result_list:
if len(i) >= 12:
result = i
result = ''.join(result[:12] if len(result) >= 12 else result)
print(result, re.match(r'\d{12}', result) is not None)
return result, re.match(r'\d{12}', result) is not None
def __handle_dup_name(self, name, path):
dup_keys = self.__dup_name_dict.get(name)
# 如设置过，即表明有重复的
if dup_keys:
# 设置重复的为 True，只要第一次重复时设置即可
if 1 == len(dup_keys):
self.__info_dict[dup_keys[0]]['dup'] = True
# 将本次的 path 也添加进去
self.__dup_name_dict[name].append(path)
return True
else:
self.__dup_name_dict[name] = [path]
return False
def __handle_info(self, key, value):
"""
处理每条信息
:param key:
:param value:
"""
name, is_legal = self.__handle_result_list(value)
self.__info_dict[key] = {
'name': name,
'suffix': self.__get_suffix(key),
'legal': is_legal,
'dup': self.__handle_dup_name(name, key)
}
def print_info(self):
"""
打印图片信息
:return:
"""
beeprint.pp(self.__info_dict)
return self
def print_dup(self):
"""
打印重复图片信息
:return:
"""
beeprint.pp(self.__dup_name_dict)
return self
def write_out(self,
path: str = '.',
out_path_suc: str = 'output_suc',
out_path_dup: str = 'output_dup',
out_path_fail: str = 'output_fail'):
"""
输出重命名后的图片到文件夹
:param path: 文件夹路径
:param out_path_suc: 合规且不重复图片所在的文件夹
:param out_path_dup: 合规但是重复图片所在的文件夹
:param out_path_fail: 其它图片所在文件夹
:return: self
"""
# 处理路径
path = self.__format_path(path)
if os.path.isdir(path):
# 拼接文件路径
suc = os.path.join(path, out_path_suc)
fail = os.path.join(path, out_path_fail)
dup = os.path.join(path, out_path_dup)
# 创建结果文件夹
not os.path.exists(suc) and os.makedirs(suc)
not os.path.exists(fail) and os.makedirs(fail)
not os.path.exists(dup) and os.makedirs(dup)
# 将图片输出到相应的文件夹
for key, value in self.__info_dict.items():
# 合规且不重复
if value.get('legal') is True and value.get('dup') is False:
copyfile(
key,
os.path.join(
suc, f'{value.get("name")}.{value.get("suffix")}'))
# 合规但是重复
elif value.get('legal') is True and value.get('dup') is True:
index = self.__dup_name_dict[value.get("name")].index(key)
copyfile(
key,
os.path.join(
dup,
f'{value.get("name")}.{index}.{value.get("suffix")}'
))
else:
copyfile(
key,
os.path.join(
fail, f'{value.get("name")}.{value.get("suffix")}'
or os.path.split(key)[1]))
else:
print(f'“{path}” 并非一个合法的路径！')
return self
class picture(QWidget):
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 openimage(self):
imgName, imgType = QFileDialog.getOpenFileName(
self, "打开图片", "", "*.jpg;;*.png;;All Files(*)")
if imgName and imgType:
# 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)
box_info_list = self.std.detect(imgName)
result = ''
for box_info in box_info_list:
cropped_img = box_info['cropped_img'] # 检测出的文本框
# cv2.imshow('1', cropped_img)
# cv2.waitKey(0)
ocr_res = self.cn_ocr.ocr_for_single_line(cropped_img)
result += ''.join(ocr_res)
# print('ocr result: %s' % ''.join(ocr_res))
# print(result)
self.label_text.setText(result)
self.label_wait.setText('↑点击文字，ctrl+a全选、ctrl+c复制、ctrl+v粘贴')
jpg = QtGui.QPixmap(imgName).scaled(self.label.width(),
self.label.height())
self.label.setPixmap(jpg)
with open('history.txt', 'a', encoding='utf8') as f:
f.write(result + '\n')
from cnocr import CnOcr
import cnocr
from cnstd import CnStd
std=CnStd()
ocr=CnOcr()
box_info_list=std.detect('E:\\Work Place\\pocr\\pic\\2.png')
res=ocr.ocr('E:\\Work Place\\pocr\\pic\\1.png')
for box_info in box_info_list:
crp_img=box_info['cropped_img']
ocr_res=ocr.ocr_for_single_line(crp_img)
print('result: %s' % ''.join(ocr_res))
print('初始化失败，未找到预加载模型')
return None
def has_chinese(word):
for ch in word:
if '\u4e00' <= ch <= '\u9fff':
return True
return False
if __name__ == '__main__':
ocr = CnOcr()
std = CnStd()
path = '/images/life.jpg'
img = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.array(img)
# res = ocr.ocr(img)
box_info_list = std.detect(img)
res = ''
for box_info in box_info_list:
cropped_img = box_info['cropped_img']
ocr_res = ocr.ocr_for_single_line(cropped_img)
res += ''.join(ocr_res) + '\n'
# print('ocr result: %s' % ''.join(ocr_res))
print("Predicted Chars:", res)
#-*- codeing = utf-8 -*-
#@Time : 2020/12/30 下午10:19
#@Author : 江啸栋19262010049
#@File : run.py
#@Software : PyCharm
# from cnstd import CnStd
# std = CnStd()
# box_info_list = std.detect('examples/taobao.jpg')
from cnstd import CnStd
from cnocr import CnOcr
std = CnStd()
cn_ocr = CnOcr()
box_info_list = std.detect('examples/taobao.jpg')
for box_info in box_info_list:
cropped_img = box_info['cropped_img']
ocr_res = cn_ocr.ocr_for_single_line(cropped_img)
print('ocr result: %s' % ''.join(ocr_res))