在windows 10下，使用python3.8.0，在pycharm 2022.1.3社区版运行正常。

backend/main.py

@@ -1,4 +1,4 @@
-
+# coding=utf-8
 import os
 import sys
 
@@ -14,7 +14,8 @@
 from backend.webInterface import tr_index
 from backend.tools import log
 import logging
-logger = logging.getLogger(log.LOGGER_ROOT_NAME+'.'+__name__)
+
+logger = logging.getLogger(log.LOGGER_ROOT_NAME + '.' + __name__)
 
 current_path = os.path.dirname(__file__)
 settings = dict(
@@ -34,7 +35,6 @@ def make_app():
 
 
 if __name__ == "__main__":
-
     port = 8089
     app = make_app()
     server = tornado.httpserver.HTTPServer(app)

backend/tools/get_host_ip.py

@@ -1,4 +1,4 @@
-
+# coding=utf-8
 
 import socket
 

backend/webInterface/tr_index.py

@@ -1,4 +1,4 @@
-
+# coding=utf-8
 
 import tornado.web
 import tornado.gen

backend/webInterface/tr_run.py

@@ -1,4 +1,4 @@
-
+# coding=utf-8
 
 import time
 from model import  OcrHandle

config.py

@@ -1,34 +1,29 @@
-import os
-
+# coding=utf-8
 
+import os
 
 filt_path = os.path.abspath(__file__)
 father_path = os.path.abspath(os.path.dirname(filt_path) + os.path.sep + ".")
 
 # dbnet 参数
-dbnet_max_size = 6000 #长边最大长度
-pad_size = 0 #检测是pad尺寸，有些文档文字充满整个屏幕检测有误，需要pad
-
+dbnet_max_size = 6000  # 长边最大长度
+pad_size = 0  # 检测是pad尺寸，有些文档文字充满整个屏幕检测有误，需要pad
 
 # crnn参数
 crnn_lite = True
 model_path = os.path.join(father_path, "models/dbnet.onnx")
 is_rgb = True
 crnn_model_path = os.path.join(father_path, "models/crnn_lite_lstm.onnx")
 
-
-
 # angle
 angle_detect = True
 angle_detect_num = 30
 angle_net_path = os.path.join(father_path, "models/angle_net.onnx")
 
-
-max_post_time = 100 # ip 访问最大次数
+max_post_time = 100  # ip 访问最大次数
 
 from crnn.keys import alphabetChinese as alphabet
 
-
-white_ips = [] #白名单
+white_ips = []  # 白名单
 
 version = 'api/v1'

crnn/CRNN.py

@@ -1,4 +1,6 @@
-from PIL import  Image
+# coding=utf-8
+
+from PIL import Image
 import numpy as np
 import cv2
 from .keys import alphabetChinese as alphabet
@@ -10,19 +12,19 @@
 
 converter = strLabelConverter(''.join(alphabet))
 
+
 def softmax(x):
     x_row_max = x.max(axis=-1)
-    x_row_max = x_row_max.reshape(list(x.shape)[:-1]+[1])
+    x_row_max = x_row_max.reshape(list(x.shape)[:-1] + [1])
     x = x - x_row_max
     x_exp = np.exp(x)
-    x_exp_row_sum = x_exp.sum(axis=-1).reshape(list(x.shape)[:-1]+[1])
+    x_exp_row_sum = x_exp.sum(axis=-1).reshape(list(x.shape)[:-1] + [1])
     softmax = x_exp / x_exp_row_sum
     return softmax
 
 
 class CRNNHandle:
     def __init__(self, model_path):
-
         self.sess = rt.InferenceSession(model_path)
 
     def predict(self, image):
@@ -43,21 +45,17 @@ def predict(self, image):
 
         preds = preds[0]
 
+        length = preds.shape[0]
+        preds = preds.reshape(length, -1)
 
-        length  = preds.shape[0]
-        preds = preds.reshape(length,-1)
-
-        preds = np.argmax(preds,axis=1)
+        preds = np.argmax(preds, axis=1)
 
         preds = preds.reshape(-1)
 
-
         sim_pred = converter.decode(preds, length, raw=False)
 
         return sim_pred
 
-
-
     def predict_rbg(self, im):
         """
         预测
@@ -77,14 +75,12 @@ def predict_rbg(self, im):
 
         preds = preds[0]
 
-
-        length  = preds.shape[0]
-        preds = preds.reshape(length,-1)
+        length = preds.shape[0]
+        preds = preds.reshape(length, -1)
 
         # preds = softmax(preds)
 
-
-        preds = np.argmax(preds,axis=1)
+        preds = np.argmax(preds, axis=1)
 
         preds = preds.reshape(-1)
 
@@ -93,8 +89,7 @@ def predict_rbg(self, im):
         return sim_pred
 
 
-
 if __name__ == "__main__":
     im = Image.open("471594277244_.pic.jpg")
     crnn_handle = CRNNHandle(model_path="../models/crnn_lite_lstm_bk.onnx")
-    print(crnn_handle.predict(im))
+    print(crnn_handle.predict(im))

model.py

@@ -1,6 +1,7 @@
+# coding=utf-8
 from config import *
 from crnn import CRNNHandle
-from angnet import  AngleNetHandle
+from angnet import AngleNetHandle
 from utils import draw_bbox, crop_rect, sorted_boxes, get_rotate_crop_image
 from PIL import Image
 import numpy as np
@@ -10,15 +11,15 @@
 import time
 import traceback
 
-class  OcrHandle(object):
+
+class OcrHandle(object):
     def __init__(self):
         self.text_handle = DBNET(model_path)
         self.crnn_handle = CRNNHandle(crnn_model_path)
         if angle_detect:
             self.angle_handle = AngleNetHandle(angle_net_path)
 
-
-    def crnnRecWithBox(self,im, boxes_list,score_list):
+    def crnnRecWithBox(self, im, boxes_list, score_list):
         """
         crnn模型，ocr识别
         @@model,
@@ -43,18 +44,16 @@ def crnnRecWithBox(self,im, boxes_list,score_list):
             angle_res = self.angle_handle.predict_rbgs(line_imgs)
 
         count = 1
-        for index, (box ,score) in enumerate(zip(boxes_list,score_list)):
+        for index, (box, score) in enumerate(zip(boxes_list, score_list)):
 
             tmp_box = copy.deepcopy(box)
             partImg_array = get_rotate_crop_image(im, tmp_box.astype(np.float32))
 
-
             partImg = Image.fromarray(partImg_array).convert("RGB")
 
             if angle_detect and angle_res:
                 partImg = partImg.rotate(180)
 
-
             if not is_rgb:
                 partImg = partImg.convert('L')
 
@@ -68,15 +67,14 @@ def crnnRecWithBox(self,im, boxes_list,score_list):
                 continue
 
             if simPred.strip() != '':
-                results.append([tmp_box,"{}、 ".format(count)+  simPred,score])
+                results.append([tmp_box, "{}、 ".format(count) + simPred, score])
                 count += 1
 
         return results
 
-
-    def text_predict(self,img,short_size):
-        boxes_list, score_list = self.text_handle.process(np.asarray(img).astype(np.uint8),short_size=short_size)
-        result = self.crnnRecWithBox(np.array(img), boxes_list,score_list)
+    def text_predict(self, img, short_size):
+        boxes_list, score_list = self.text_handle.process(np.asarray(img).astype(np.uint8), short_size=short_size)
+        result = self.crnnRecWithBox(np.array(img), boxes_list, score_list)
 
         return result
 

requirements.txt

@@ -1,7 +1,15 @@
-tornado==5.1.1
-numpy==1.19.1
-opencv_python==4.3.0.36
-onnxruntime==1.4.0
-Shapely==1.7.0
-pyclipper==1.2.0
-Pillow==7.2.0
+tornado==6.2
+numpy==1.23.1
+opencv_python==4.6.0.66
+onnxruntime==1.12.0
+Shapely==1.8.2
+pyclipper==1.3.0.post3
+Pillow==9.2.0
+coloredlogs==15.0.1
+flatbuffers==2.0
+humanfriendly==10.0
+mpmath==1.2.1
+packaging==21.3
+pyparsing==3.0.9
+pyreadline3==3.4.1
+sympy==1.10.1

utils.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 import numpy as np
 import cv2
 from PIL import Image
@@ -12,16 +13,16 @@ def rotate_cut_img(im, degree, x_center, y_center, w, h, leftAdjust=False, right
         right = 1
     if leftAdjust:
         left = 1
-    
+
     box = (max(1, x_center - w / 2 - left * alph * (w / 2))
            , y_center - h / 2,  # ymin
            min(x_center + w / 2 + right * alph * (w / 2), im.size[0] - 1)
            , y_center + h / 2)  # ymax
-    
+
     newW = box[2] - box[0]
     newH = box[3] - box[1]
     tmpImg = im.rotate(degree, center=(x_center, y_center)).crop(box)
-    
+
     return tmpImg, newW, newH
 
 
@@ -32,7 +33,7 @@ def crop_rect(img, rect, alph=0.15):
     # print(rect)
     center, size, angle = rect[0], rect[1], rect[2]
     min_size = min(size)
-    
+
     if angle > -45:
         center, size = tuple(map(int, center)), tuple(map(int, size))
         # angle-=270
@@ -90,10 +91,10 @@ def sort_box(boxs):
         x2, y2 = sorted(newBox[:2], key=lambda x: x[1])[0]
         index = newBox.index([x2, y2])
         newBox.pop(index)
-        
+
         newBox = sorted(newBox, key=lambda x: -x[1])
         x3, y3 = sorted(newBox[:2], key=lambda x: x[0])[0]
-        
+
         res.append([x1, y1, x2, y2, x3, y3, x4, y4])
     return res
 
@@ -116,7 +117,7 @@ def solve(box):
     cy = (y1 + y3 + y4 + y2) / 4.0
     w = (np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + np.sqrt((x3 - x4) ** 2 + (y3 - y4) ** 2)) / 2
     h = (np.sqrt((x2 - x3) ** 2 + (y2 - y3) ** 2) + np.sqrt((x1 - x4) ** 2 + (y1 - y4) ** 2)) / 2
-    
+
     sinA = (h * (x1 - cx) - w * (y1 - cy)) * 1.0 / (h * h + w * w) * 2
     angle = np.arcsin(sinA)
     return angle, w, h, cx, cy
@@ -135,8 +136,8 @@ def sorted_boxes(dt_boxes):
     _boxes = list(sorted_boxes)
 
     for i in range(num_boxes - 1):
-        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
-            (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
+                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
             tmp = _boxes[i]
             _boxes[i] = _boxes[i + 1]
             _boxes[i + 1] = tmp
@@ -154,8 +155,8 @@ def get_rotate_crop_image(img, points):
     points[:, 1] = points[:, 1] - top
     img_crop_width = int(np.linalg.norm(points[0] - points[1]))
     img_crop_height = int(np.linalg.norm(points[0] - points[3]))
-    pts_std = np.float32([[0, 0], [img_crop_width, 0],\
-        [img_crop_width, img_crop_height], [0, img_crop_height]])
+    pts_std = np.float32([[0, 0], [img_crop_width, 0], \
+                          [img_crop_width, img_crop_height], [0, img_crop_height]])
 
     M = cv2.getPerspectiveTransform(points, pts_std)
     dst_img = cv2.warpPerspective(