format_convert/utils.py

@@ -38,6 +38,23 @@ if get_platform() == "Linux":
     import resource
 import math
 
+from shapely.geometry import Polygon
+
+def has_intersection(poly1, poly2):
+    """
+    判断两个四边形是否有交集。
+    参数:
+        poly1, poly2: list of tuples, 每个tuple表示一个顶点的(x, y)坐标。
+        例如: [(x1, y1), (x2, y2), (x3, y3), (x4, y4)]
+    返回:
+        bool: 如果两个四边形有交集则返回True，否则返回False。
+    """
+    # 创建Shapely多边形对象
+    polygon1 = Polygon(poly1)
+    polygon2 = Polygon(poly2)
+
+    # 使用intersects方法判断是否有交集
+    return polygon1.intersects(polygon2)
 
 def judge_error_code(_list, code=[0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16]):
     """

ocr/paddleocr.py

@@ -193,6 +193,47 @@ def parse_args(mMain=True, add_help=True):
         else:
             use_gpu = True
 
+        # return argparse.Namespace(
+        #     use_gpu=use_gpu,
+        #     ir_optim=True,
+        #     use_tensorrt=False,
+        #     gpu_mem=8000,
+        #     image_dir='',
+        #     det_algorithm='DB',
+        #     det_model_dir=None,
+        #     det_limit_side_len=1280,
+        #     det_limit_type='max',
+        #     det_db_thresh=0.1,
+        #     # det_db_box_thresh 漏行 调小
+        #     det_db_box_thresh=0.1,
+        #     # det_db_unclip_ratio 检测框的贴近程度
+        #     det_db_unclip_ratio=2.5,
+        #     # 对文字膨胀操作
+        #     use_dilation=False,
+        #     det_east_score_thresh=0.8,
+        #     det_east_cover_thresh=0.1,
+        #     det_east_nms_thresh=0.2,
+        #     rec_algorithm='CRNN',
+        #     rec_model_dir=None,
+        #     rec_image_shape="3, 32, 1000",
+        #     rec_char_type='ch',
+        #     rec_batch_num=30,
+        #     max_text_length=128,
+        #     rec_char_dict_path='ocr/ppocr/utils/ppocr_keys_v1.txt',
+        #     use_space_char=True,
+        #     drop_score=0.5,
+        #     cls_model_dir=None,
+        #     cls_image_shape="3, 32, 1000",
+        #     label_list=['0', '180'],
+        #     cls_batch_num=30,
+        #     cls_thresh=0.9,
+        #     enable_mkldnn=False,
+        #     use_zero_copy_run=True,
+        #     use_pdserving=False,
+        #     lang='ch',
+        #     det=True,
+        #     rec=True,
+        #     use_angle_cls=False)
         return argparse.Namespace(
             use_gpu=use_gpu,
             ir_optim=True,
@@ -203,11 +244,14 @@ def parse_args(mMain=True, add_help=True):
             det_model_dir=None,
             det_limit_side_len=1280,
             det_limit_type='max',
-            det_db_thresh=0.1,
+            # det_db_thresh=0.1,
+            det_db_thresh=0.2,# torch
             # det_db_box_thresh 漏行 调小
-            det_db_box_thresh=0.1,
+            # det_db_box_thresh=0.1,
+            det_db_box_thresh=0.5,# torch
             # det_db_unclip_ratio 检测框的贴近程度
-            det_db_unclip_ratio=2.5,
+            # det_db_unclip_ratio=2.5,
+            det_db_unclip_ratio=2.5,# torch
             # 对文字膨胀操作
             use_dilation=False,
             det_east_score_thresh=0.8,
@@ -219,8 +263,10 @@ def parse_args(mMain=True, add_help=True):
             rec_char_type='ch',
             rec_batch_num=30,
             max_text_length=128,
-            rec_char_dict_path='ocr/ppocr/utils/ppocr_keys_v1.txt',
-            use_space_char=True,
+            # rec_char_dict_path='ocr/ppocr/utils/ppocr_keys_v1.txt',
+            # use_space_char=True,
+            rec_char_dict_path='ocr/ppocr/utils/char_std_7551.txt',
+            use_space_char=False,
             drop_score=0.5,
             cls_model_dir=None,
             cls_image_shape="3, 32, 1000",
@@ -256,20 +302,24 @@ class PaddleOCR(predict_system.TextSystem):
 
         # init model dir
         if postprocess_params.det_model_dir is None:
+            # postprocess_params.det_model_dir = os.path.join(
+            #     BASE_DIR, '{}/det'.format(VERSION))
             postprocess_params.det_model_dir = os.path.join(
-                BASE_DIR, '{}/det'.format(VERSION))
+                BASE_DIR, 'pytorch/det/det.pth') # torch
         if postprocess_params.rec_model_dir is None:
+            # postprocess_params.rec_model_dir = os.path.join(
+            #     BASE_DIR, '{}/rec/{}'.format(VERSION, lang))
             postprocess_params.rec_model_dir = os.path.join(
-                BASE_DIR, '{}/rec/{}'.format(VERSION, lang))
+                BASE_DIR, 'pytorch/rec/rec.pth')# torch
         if postprocess_params.cls_model_dir is None:
             postprocess_params.cls_model_dir = os.path.join(
                 BASE_DIR, '{}/cls'.format(VERSION))
         logger.info(postprocess_params)
 
         # download model
-        maybe_download(postprocess_params.det_model_dir, model_urls['det'])
-        maybe_download(postprocess_params.rec_model_dir,
-                       model_urls['rec'][lang]['url'])
+        # maybe_download(postprocess_params.det_model_dir, model_urls['det'])
+        # maybe_download(postprocess_params.rec_model_dir,
+        #                model_urls['rec'][lang]['url'])
         maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
 
         if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:

ocr/ppocr/postprocess/db_postprocess.py

@@ -69,7 +69,8 @@ class DBPostProcess(object):
             if sside < self.min_size:
                 continue
             points = np.array(points)
-            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            # score = self.box_score_fast(pred, points.reshape(-1, 2)) # fast 近似计算得分
+            score = self.box_score_slow(pred, contour) # slow
             if self.box_thresh > score:
                 continue
 
@@ -133,6 +134,27 @@ class DBPostProcess(object):
         cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
+    def box_score_slow(self, bitmap, contour):
+        '''
+        box_score_slow: use polyon mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        contour = contour.copy()
+        contour = np.reshape(contour, (-1, 2))
+
+        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
+        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
+        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
+        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+
+        contour[:, 0] = contour[:, 0] - xmin
+        contour[:, 1] = contour[:, 1] - ymin
+
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
     def __call__(self, outs_dict, shape_list):
         pred = outs_dict['maps']
         if isinstance(pred, paddle.Tensor):

ocr/ppocr/utils/char_std_7551.txt

@@ -0,0 +1,7551 @@
+'
+疗
+绚
+诚
+娇
+溜
+题
+贿
+者
+廖
+更
+纳
+加
+奉
+公
+一
+就
+汴
+计
+与
+路
+房
+原
+妇
+2
+0
+8
+-
+7
+其
+>
+:
+]
+,
+，
+骑
+刈
+全
+消
+昏
+傈
+安
+久
+钟
+嗅
+不
+影
+处
+驽
+蜿
+资
+关
+椤
+地
+瘸
+专
+问
+忖
+票
+嫉
+炎
+韵
+要
+月
+田
+节
+陂
+鄙
+捌
+备
+拳
+伺
+眼
+网
+盎
+大
+傍
+心
+东
+愉
+汇
+蹿
+科
+每
+业
+里
+航
+晏
+字
+平
+录
+先
+1
+3
+彤
+鲶
+产
+稍
+督
+腴
+有
+象
+岳
+注
+绍
+在
+泺
+文
+定
+核
+名
+水
+过
+理
+让
+偷
+率
+等
+这
+发
+”
+为
+含
+肥
+酉
+相
+鄱
+七
+编
+猥
+锛
+日
+镀
+蒂
+掰
+倒
+辆
+栾
+栗
+综
+涩
+州
+雌
+滑
+馀
+了
+机
+块
+司
+宰
+甙
+兴
+矽
+抚
+保
+用
+沧
+秩
+如
+收
+息
+滥
+页
+疑
+埠
+!
+！
+姥
+异
+橹
+钇
+向
+下
+跄
+的
+椴
+沫
+国
+绥
+獠
+报
+开
+民
+蜇
+何
+分
+凇
+长
+讥
+藏
+掏
+施
+羽
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+讲
+派
+嘟
+人
+提
+浼
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+古
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+唇
+饯
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+钭
+範
+琭
+箨
+昫
+耩
+缡
+岵
+迺
+暎
+蔴
+轾
+糸
+塥
+馇
+圊
+睥
+鈊
+铫
+俤
+砟
+韨
+鶯
+塅
+犄
+矼
+骉
+翃
+璠
+鋆
+牤
+湧
+劢
+瑱
+圬
+菉
+镡
+崟
+笪
+廘
+硐
+辚
+囝
+滹
+埏
+俙
+靭
+琇
+聶
+澴
+蘅
+褡
+笳
+桫
+烔
+磙
+诖
+倞
+鞥
+璘
+樘
+苧
+郉
+翀
+焩
+酴
+曌
+夿
+劼
+饫
+掛
+蔵
+枟
+鮦
+麴
+岠
+焓
+缷
+駜
+漴
+舣
+蛱
+凃
+翚
+婥
+銛
+禇
+圪
+柃
+艽
+镆
+橺
+鞔
+舨
+蝥
+钫
+漈
+鄧
+玎
+洸
+蒨
+驖
+贶
+谠
+ф
+舳
+蒔
+僕
+棬
+側
+锞
+頣
+琎
+锍
+鉮
+崾
+浛
+埝
+邙
+甍
+硎
+菰
+蓁
+浯
+韡
+苳
+硭
+鎳
+翾
+鷇
+艋
+鹍
+禢
+埴
+昉
+桴
+査
+琍
+垐
+忭
+枨
+釭
+瘛
+淠
+漷
+泃
+蚈
+妉
+舾
+∮
+沺
+撖
+菭
+奤
+犭
+竝
+骢
+湔
+锜
+嶝
+挏
+沨
+蕻
+朊
+枘
+梶
+逷
+顸
+竑
+踅
+佾
+瑢
+鹁
+朣
+屺
+闩
+槠
+甦
+玠
+玭
+勍
+汎
+迴
+厐
+剀
+胓
+勔
+侉
+澥
+鼐
+嘏
+仉
+柽
+澂
+塚
+陔
+堽
+玙
+伲
+鋉

ocr/tools/infer/predict_det_pytorch.py

@@ -0,0 +1,435 @@
+# encoding=utf8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import io
+import logging
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../")
+import requests
+from format_convert import _global
+from format_convert.utils import judge_error_code, log, namespace_to_dict, get_platform, file_lock
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+import cv2
+import numpy as np
+import time
+import sys
+os.environ['FLAGS_eager_delete_tensor_gb'] = '0'
+import ocr.tools.infer.utility as utility
+from ocr.ppocr.utils.logging import get_logger
+from ocr.ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ocr.ppocr.data import create_operators, transform
+from ocr.ppocr.postprocess import build_post_process
+from format_convert.max_compute_config import max_compute
+
+import torch
+from torch import nn
+from ocr.tools.infer.torch_det_model import DB_ResNet_18
+import gc
+
+MAX_COMPUTE = max_compute
+logger = get_logger()
+
+
+class TextDetector(object):
+    shrink_memory_count = 0
+
+    def __init__(self, args):
+        self.args = args
+        self.det_algorithm = args.det_algorithm
+        pre_process_list = [{
+            'DetResizeForTest': None
+        }, {
+            'NormalizeImage': {
+                'std': [0.229, 0.224, 0.225],
+                'mean': [0.485, 0.456, 0.406],
+                'scale': '1./255.',
+                'order': 'hwc'
+            }
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': ['image', 'shape']
+            }
+        }]
+        postprocess_params = {}
+        if self.det_algorithm == "DB":
+            postprocess_params['name'] = 'DBPostProcess'
+            postprocess_params["thresh"] = args.det_db_thresh
+            postprocess_params["box_thresh"] = args.det_db_box_thresh
+            postprocess_params["max_candidates"] = 1000
+            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+            postprocess_params["use_dilation"] = args.use_dilation
+        elif self.det_algorithm == "EAST":
+            postprocess_params['name'] = 'EASTPostProcess'
+            postprocess_params["score_thresh"] = args.det_east_score_thresh
+            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
+            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
+        elif self.det_algorithm == "SAST":
+            pre_process_list[0] = {
+                'DetResizeForTest': {
+                    'resize_long': args.det_limit_side_len
+                }
+            }
+            postprocess_params['name'] = 'SASTPostProcess'
+            postprocess_params["score_thresh"] = args.det_sast_score_thresh
+            postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
+            self.det_sast_polygon = args.det_sast_polygon
+            if self.det_sast_polygon:
+                postprocess_params["sample_pts_num"] = 6
+                postprocess_params["expand_scale"] = 1.2
+                postprocess_params["shrink_ratio_of_width"] = 0.2
+            else:
+                postprocess_params["sample_pts_num"] = 2
+                postprocess_params["expand_scale"] = 1.0
+                postprocess_params["shrink_ratio_of_width"] = 0.3
+        else:
+            logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
+            sys.exit(0)
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+
+        det_model_path = args.det_model_dir
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = DB_ResNet_18()
+        mode_state_dict = torch.load(det_model_path, self.device)['state_dict']
+        if str(self.device) == 'cpu':  # cpu处理时精度调整，加速推理
+            for name, value in mode_state_dict.items():
+                if get_platform() != "Windows":
+                    value = value.double()
+                value = torch.where((value < 1.0e-23) & (value > 0.0), 1.0e-23, value)
+                value = torch.where((value > -1.0e-23) & (value < 0.0), -1.0e-23, value)
+                mode_state_dict[name] = value
+
+        model.load_state_dict(mode_state_dict)
+
+        self.predictor = model
+        self.predictor.to(self.device)
+        self.predictor.eval()
+
+        # self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
+        #     args, 'det', logger)  # paddle.jit.load(args.det_model_dir)
+        # self.predictor.eval()
+
+    def order_points_clockwise(self, pts):
+        """
+        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+        # sort the points based on their x-coordinates
+        """
+        xSorted = pts[np.argsort(pts[:, 0]), :]
+
+        # grab the left-most and right-most points from the sorted
+        # x-roodinate points
+        leftMost = xSorted[:2, :]
+        rightMost = xSorted[2:, :]
+
+        # now, sort the left-most coordinates according to their
+        # y-coordinates so we can grab the top-left and bottom-left
+        # points, respectively
+        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+        (tl, bl) = leftMost
+
+        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+        (tr, br) = rightMost
+
+        rect = np.array([tl, tr, br, bl], dtype="float32")
+        return rect
+
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points
+
+    def filter_tag_det_res(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.order_points_clockwise(box)
+            box = self.clip_det_res(box, img_height, img_width)
+            rect_width = int(np.linalg.norm(box[0] - box[1]))
+            rect_height = int(np.linalg.norm(box[0] - box[3]))
+            if rect_width <= 3 or rect_height <= 3:
+                continue
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.clip_det_res(box, img_height, img_width)
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def __call__(self, img):
+        # cv2.imwrite("/data2/znj/format_conversion_maxcompute/ocr/temp_image/temp.jpg",img)
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img, shape_list = data
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        shape_list = np.expand_dims(shape_list, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        # self.input_tensor.copy_from_cpu(img)
+        img = torch.from_numpy(img).float()
+        img = img.to(self.device)
+        try:
+            # 加锁，防止太多大图片同时预测，爆显存
+            if ori_im.shape[0] > 1024 and ori_im.shape[1] > 1024 and get_platform() != "Windows" and not max_compute:
+                time2 = time.time()
+                lock_file_sub = 'ocr'
+                lock_file = os.path.abspath(os.path.dirname(__file__)) + "/" + lock_file_sub + ".lock"
+                f = file_lock(lock_file)
+                log("get file_lock " + lock_file_sub + " time " + str(time.time()-time2))
+                with torch.no_grad():
+                    out = self.predictor(img)
+                f.close()
+            else:
+                with torch.no_grad():
+                    out = self.predictor(img)
+        except RuntimeError:
+            log("ocr/tools/infer/predict_det.py predict.run error! maybe no gpu memory!")
+            log("predictor shrink memory!")
+            # self.predictor.clear_intermediate_tensor()
+            # self.predictor.try_shrink_memory()
+            if str(self.device)!='cpu':
+                torch.cuda.empty_cache()
+                gc.collect()
+            raise RuntimeError
+
+        # outputs = []
+        # for output_tensor in self.output_tensors:
+        #     output = output_tensor.copy_to_cpu()
+        #     outputs.append(output)
+        out = out.cpu().numpy()
+
+        preds = {}
+        preds['maps'] = out
+
+        # if self.det_algorithm == "EAST":
+        #     preds['f_geo'] = outputs[0]
+        #     preds['f_score'] = outputs[1]
+        # elif self.det_algorithm == 'SAST':
+        #     preds['f_border'] = outputs[0]
+        #     preds['f_score'] = outputs[1]
+        #     preds['f_tco'] = outputs[2]
+        #     preds['f_tvo'] = outputs[3]
+        # elif self.det_algorithm == 'DB':
+        #     preds['maps'] = outputs[0]
+        # else:
+        #     raise NotImplementedError
+        post_result = self.postprocess_op(preds, shape_list)
+        dt_boxes = post_result[0]['points']
+        if self.det_algorithm == "SAST" and self.det_sast_polygon:
+            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
+        else:
+            dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+        elapse = time.time() - starttime
+
+        # 释放内存
+        # print("TextDetector", self.predictor)
+        # if TextDetector.shrink_memory_count % 100 == 0:
+            # print("TextDetector shrink memory")
+        # self.predictor.clear_intermediate_tensor()
+        # self.predictor.try_shrink_memory()
+        # TextDetector.shrink_memory_count += 1
+        if str(self.device) != 'cpu':
+            torch.cuda.empty_cache()
+            # gc.collect()
+
+        return dt_boxes, elapse
+
+
+class TextDetector2(object):
+    shrink_memory_count = 0
+
+    def __init__(self, args):
+        self.args = args
+        self.det_algorithm = args.det_algorithm
+        pre_process_list = [{
+            'DetResizeForTest': None
+        }, {
+            'NormalizeImage': {
+                'std': [0.229, 0.224, 0.225],
+                'mean': [0.485, 0.456, 0.406],
+                'scale': '1./255.',
+                'order': 'hwc'
+            }
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': ['image', 'shape']
+            }
+        }]
+        postprocess_params = {}
+        if self.det_algorithm == "DB":
+            postprocess_params['name'] = 'DBPostProcess'
+            postprocess_params["thresh"] = args.det_db_thresh
+            postprocess_params["box_thresh"] = args.det_db_box_thresh
+            postprocess_params["max_candidates"] = 1000
+            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+            postprocess_params["use_dilation"] = args.use_dilation
+        else:
+            logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
+            sys.exit(0)
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+
+    def order_points_clockwise(self, pts):
+        """
+        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+        # sort the points based on their x-coordinates
+        """
+        xSorted = pts[np.argsort(pts[:, 0]), :]
+
+        # grab the left-most and right-most points from the sorted
+        # x-roodinate points
+        leftMost = xSorted[:2, :]
+        rightMost = xSorted[2:, :]
+
+        # now, sort the left-most coordinates according to their
+        # y-coordinates so we can grab the top-left and bottom-left
+        # points, respectively
+        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+        (tl, bl) = leftMost
+
+        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+        (tr, br) = rightMost
+
+        rect = np.array([tl, tr, br, bl], dtype="float32")
+        return rect
+
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points
+
+    def filter_tag_det_res(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.order_points_clockwise(box)
+            box = self.clip_det_res(box, img_height, img_width)
+            rect_width = int(np.linalg.norm(box[0] - box[1]))
+            rect_height = int(np.linalg.norm(box[0] - box[3]))
+            if rect_width <= 3 or rect_height <= 3:
+                continue
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.clip_det_res(box, img_height, img_width)
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def __call__(self, img):
+        from format_convert.convert_need_interface import from_gpu_interface_redis
+        # 预处理
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img, shape_list = data
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        shape_list = np.expand_dims(shape_list, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        # # 压缩numpy
+        # compressed_array = io.BytesIO()
+        # np.savez_compressed(compressed_array, img)
+        # compressed_array.seek(0)
+        # img = compressed_array.read()
+
+        # 调用GPU接口
+        _dict = {"inputs": img, "args": str(namespace_to_dict(self.args)), "md5": _global.get("md5")}
+        result = from_gpu_interface_redis(_dict, model_type="ocr", predictor_type="det")
+        if judge_error_code(result):
+            logging.error("from_gpu_interface failed! " + str(result))
+            raise requests.exceptions.RequestException
+
+        _preds = result.get("preds")
+        gpu_time = result.get("gpu_time")
+
+        # # 解压numpy
+        # decompressed_array = io.BytesIO()
+        # decompressed_array.write(_preds)
+        # decompressed_array.seek(0)
+        # _preds = np.load(decompressed_array, allow_pickle=True)['arr_0']
+        # log("inputs.shape" + str(_preds.shape))
+
+        # 后处理
+        preds = {}
+        if self.det_algorithm == 'DB':
+            preds['maps'] = _preds
+        else:
+            raise NotImplementedError
+
+        post_result = self.postprocess_op(preds, shape_list)
+        dt_boxes = post_result[0]['points']
+        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+        elapse = time.time() - starttime
+
+        log("ocr model predict time - det - time " + str(gpu_time))
+        return dt_boxes, elapse
+
+
+if __name__ == "__main__":
+    args = utility.parse_args()
+    image_file_list = get_image_file_list(args.image_dir)
+    text_detector = TextDetector(args)
+    count = 0
+    total_time = 0
+    draw_img_save = "./inference_results"
+    if not os.path.exists(draw_img_save):
+        os.makedirs(draw_img_save)
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        dt_boxes, elapse = text_detector(img)
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+        src_im = utility.draw_text_det_res(dt_boxes, image_file)
+        img_name_pure = os.path.split(image_file)[-1]
+        img_path = os.path.join(draw_img_save,
+                                "det_res_{}".format(img_name_pure))
+        cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
+    if count > 1:
+        logger.info("Avg Time: {}".format(total_time / (count - 1)))

ocr/tools/infer/predict_rec_pytorch.py

@@ -0,0 +1,464 @@
+# encoding=utf8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import io
+import logging
+import os
+import sys
+# __dir__ = os.path.dirname(os.path.abspath(__file__))
+import zlib
+
+import requests
+# sys.path.append(__dir__)
+# sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../")
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+import cv2
+import numpy as np
+import math
+import time
+import traceback
+os.environ['FLAGS_eager_delete_tensor_gb'] = '0'
+import paddle
+
+import ocr.tools.infer.utility as utility
+from ocr.ppocr.postprocess import build_post_process
+from ocr.ppocr.utils.logging import get_logger
+from ocr.ppocr.utils.utility import get_image_file_list, check_and_read_gif
+
+from format_convert.utils import judge_error_code, log, namespace_to_dict,get_platform
+from format_convert import _global
+
+import torch
+from torch import nn
+from ocr.tools.infer.torch_rec_model import Rec_ResNet_34
+import gc
+
+logger = get_logger()
+
+
+class TextRecognizer(object):
+    shrink_memory_count = 0
+
+    def __init__(self, args):
+        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
+        self.character_type = args.rec_char_type
+        self.rec_batch_num = args.rec_batch_num
+        self.rec_algorithm = args.rec_algorithm
+        postprocess_params = {
+            'name': 'CTCLabelDecode',
+            "character_type": args.rec_char_type,
+            "character_dict_path": args.rec_char_dict_path,
+            # "use_space_char": args.use_space_char
+            "use_space_char": False
+        }
+        # if self.rec_algorithm == "SRN":
+        #     postprocess_params = {
+        #         'name': 'SRNLabelDecode',
+        #         "character_type": args.rec_char_type,
+        #         "character_dict_path": args.rec_char_dict_path,
+        #         "use_space_char": args.use_space_char
+        #     }
+        # elif self.rec_algorithm == "RARE":
+        #     postprocess_params = {
+        #         'name': 'AttnLabelDecode',
+        #         "character_type": args.rec_char_type,
+        #         "character_dict_path": args.rec_char_dict_path,
+        #         "use_space_char": args.use_space_char
+        #     }
+        self.postprocess_op = build_post_process(postprocess_params)
+        # self.predictor, self.input_tensor, self.output_tensors = \
+        #     utility.create_predictor(args, 'rec', logger)
+
+        rec_model_path = args.rec_model_dir
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = Rec_ResNet_34()
+        mode_state_dict = torch.load(rec_model_path, self.device)['state_dict']
+        if str(self.device)=='cpu': # cpu处理时精度调整，加速推理
+            for name, value in mode_state_dict.items():
+                if get_platform() != "Windows":
+                    value = value.double()
+                value = torch.where((value < 1.0e-23) & (value > 0.0), 1.0e-23, value)
+                value = torch.where((value > -1.0e-23) & (value < 0.0), -1.0e-23, value)
+                mode_state_dict[name] = value
+
+        model.load_state_dict(mode_state_dict)
+
+        self.predictor = model
+        self.predictor.to(self.device)
+        self.predictor.eval()
+
+    def resize_norm_img(self, img, max_wh_ratio):
+        h, w = img.shape[:2]
+        imgC, imgH, imgW = self.rec_image_shape
+        assert imgC == img.shape[2]
+        # print('max_wh_ratio', max_wh_ratio)
+        if max_wh_ratio < 0.1:
+            # if h > imgW:
+            #     resized_image = cv2.resize(img, (w, imgW))
+            # else:
+            #     resized_image = img
+
+            # max_wh_ratio h是w的10倍，直接跳过
+            resized_w = None
+        else:
+            if self.character_type == "ch":
+                imgW = int((32 * max_wh_ratio))
+
+            ratio = w / float(h)
+            if math.ceil(imgH * ratio) > imgW:
+                resized_w = imgW
+            else:
+                resized_w = int(math.ceil(imgH * ratio))
+
+            try:
+                resized_image = cv2.resize(img, (resized_w, imgH))
+            except:
+                log("predict_rec.py resize_norm_img resize shape " + str((resized_w, imgH, imgW, h, w, ratio, max_wh_ratio)) + ' ' + str(self.rec_image_shape))
+                raise
+
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+        if resized_w is not None:
+            padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im
+
+    def resize_norm_img_srn(self, img, image_shape):
+        imgC, imgH, imgW = image_shape
+
+        img_black = np.zeros((imgH, imgW))
+        im_hei = img.shape[0]
+        im_wid = img.shape[1]
+
+        if im_wid <= im_hei * 1:
+            img_new = cv2.resize(img, (imgH * 1, imgH))
+        elif im_wid <= im_hei * 2:
+            img_new = cv2.resize(img, (imgH * 2, imgH))
+        elif im_wid <= im_hei * 3:
+            img_new = cv2.resize(img, (imgH * 3, imgH))
+        else:
+            img_new = cv2.resize(img, (imgW, imgH))
+
+        img_np = np.asarray(img_new)
+        img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+        img_black[:, 0:img_np.shape[1]] = img_np
+        img_black = img_black[:, :, np.newaxis]
+
+        row, col, c = img_black.shape
+        c = 1
+
+        return np.reshape(img_black, (c, row, col)).astype(np.float32)
+
+    def srn_other_inputs(self, image_shape, num_heads, max_text_length):
+
+        imgC, imgH, imgW = image_shape
+        feature_dim = int((imgH / 8) * (imgW / 8))
+
+        encoder_word_pos = np.array(range(0, feature_dim)).reshape(
+            (feature_dim, 1)).astype('int64')
+        gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
+            (max_text_length, 1)).astype('int64')
+
+        gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
+        gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
+            [-1, 1, max_text_length, max_text_length])
+        gsrm_slf_attn_bias1 = np.tile(
+            gsrm_slf_attn_bias1,
+            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
+
+        gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
+            [-1, 1, max_text_length, max_text_length])
+        gsrm_slf_attn_bias2 = np.tile(
+            gsrm_slf_attn_bias2,
+            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
+
+        encoder_word_pos = encoder_word_pos[np.newaxis, :]
+        gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
+
+        return [
+            encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+            gsrm_slf_attn_bias2
+        ]
+
+    def process_image_srn(self, img, image_shape, num_heads, max_text_length):
+        norm_img = self.resize_norm_img_srn(img, image_shape)
+        norm_img = norm_img[np.newaxis, :]
+
+        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
+            self.srn_other_inputs(image_shape, num_heads, max_text_length)
+
+        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
+        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
+        encoder_word_pos = encoder_word_pos.astype(np.int64)
+        gsrm_word_pos = gsrm_word_pos.astype(np.int64)
+
+        return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+                gsrm_slf_attn_bias2)
+
+    def __call__(self, img_list):
+        img_num = len(img_list)
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        i = 0
+        for img in img_list:
+            # cv2.imwrite('D:/myProject/format_conversion_maxcompute/ocr/test/'+str(i)+'.jpg',img)
+            # i+=1
+            # cv2.imshow('img', img)
+            # cv2.waitKey(1000)
+            width_list.append(img.shape[1] / float(img.shape[0]))
+        # Sorting can speed up the recognition process
+        indices = np.argsort(np.array(width_list))
+
+        # rec_res = []
+        rec_res = [['', 0.0]] * img_num
+        batch_num = self.rec_batch_num
+        elapse = 0
+        for beg_img_no in range(0, img_num, batch_num):
+            end_img_no = min(img_num, beg_img_no + batch_num)
+            norm_img_batch = []
+            max_wh_ratio = 0
+            for ino in range(beg_img_no, end_img_no):
+                # h, w = img_list[ino].shape[0:2]
+                h, w = img_list[indices[ino]].shape[0:2]
+                wh_ratio = w * 1.0 / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+            # print('max_wh_ratio',max_wh_ratio)
+            for ino in range(beg_img_no, end_img_no):
+                if self.rec_algorithm != "SRN":
+                    # print('max_wh_ratio', max_wh_ratio)
+                    norm_img = self.resize_norm_img(img_list[indices[ino]],
+                                                    max_wh_ratio)
+                    # cv2.imshow('img', norm_img.transpose(1,2,0))
+                    # cv2.waitKey(1000)
+                    norm_img = norm_img[np.newaxis, :]
+                    norm_img_batch.append(norm_img)
+                else:
+                    # norm_img = self.process_image_srn(
+                    #     img_list[indices[ino]], self.rec_image_shape, 8, 25)
+                    # encoder_word_pos_list = []
+                    # gsrm_word_pos_list = []
+                    # gsrm_slf_attn_bias1_list = []
+                    # gsrm_slf_attn_bias2_list = []
+                    # encoder_word_pos_list.append(norm_img[1])
+                    # gsrm_word_pos_list.append(norm_img[2])
+                    # gsrm_slf_attn_bias1_list.append(norm_img[3])
+                    # gsrm_slf_attn_bias2_list.append(norm_img[4])
+                    # norm_img_batch.append(norm_img[0])
+                    pass
+            norm_img_batch = np.concatenate(norm_img_batch)
+            norm_img_batch = norm_img_batch.copy()
+
+            if self.rec_algorithm == "SRN":
+                # starttime = time.time()
+                # encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
+                # gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
+                # gsrm_slf_attn_bias1_list = np.concatenate(
+                #     gsrm_slf_attn_bias1_list)
+                # gsrm_slf_attn_bias2_list = np.concatenate(
+                #     gsrm_slf_attn_bias2_list)
+                #
+                # inputs = [
+                #     norm_img_batch,
+                #     encoder_word_pos_list,
+                #     gsrm_word_pos_list,
+                #     gsrm_slf_attn_bias1_list,
+                #     gsrm_slf_attn_bias2_list,
+                # ]
+                # input_names = self.predictor.get_input_names()
+                # for i in range(len(input_names)):
+                #     input_tensor = self.predictor.get_input_handle(input_names[
+                #         i])
+                #     input_tensor.copy_from_cpu(inputs[i])
+                # self.predictor.run()
+                # outputs = []
+                # for output_tensor in self.output_tensors:
+                #     output = output_tensor.copy_to_cpu()
+                #     outputs.append(output)
+                # preds = {"predict": outputs[2]}
+                pass
+            else:
+                starttime = time.time()
+
+                tensor = torch.from_numpy(norm_img_batch).float()
+                start_time = time.time()
+                tensor = tensor.to(self.device)
+                with torch.no_grad():
+                    out = self.predictor(tensor)
+                logging.info("ocr model predict time - rec" + str(time.time()-start_time))
+                out = out.cpu().numpy()
+                preds = out
+
+            # print("tools/infer/predict_rec preds", preds)
+            rec_result = self.postprocess_op(preds)
+            for rno in range(len(rec_result)):
+                # print("predict_rec", img_num, batch_num, beg_img_no,
+                #       indices[beg_img_no + rno], len(rec_res))
+                rec_res[indices[beg_img_no + rno]] = rec_result[rno]
+            elapse += time.time() - starttime
+            # 释放内存
+            # self.predictor.clear_intermediate_tensor()
+            # self.predictor.try_shrink_memory()
+
+            # gc.collect()
+            if str(self.device)!='cpu':
+                torch.cuda.empty_cache()
+            #     gc.collect()
+        return rec_res, elapse
+
+
+class TextRecognizer2(object):
+    shrink_memory_count = 0
+
+    def __init__(self, args):
+        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
+        self.character_type = args.rec_char_type
+        self.rec_batch_num = args.rec_batch_num
+        self.rec_algorithm = args.rec_algorithm
+        postprocess_params = {
+            'name': 'CTCLabelDecode',
+            "character_type": args.rec_char_type,
+            "character_dict_path": args.rec_char_dict_path,
+            "use_space_char": args.use_space_char
+        }
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.args = args
+        # self.predictor, self.input_tensor, self.output_tensors = \
+        #     utility.create_predictor(args, 'rec', logger)
+
+    def resize_norm_img(self, img, max_wh_ratio):
+        imgC, imgH, imgW = self.rec_image_shape
+        assert imgC == img.shape[2]
+        if self.character_type == "ch":
+            imgW = int((32 * max_wh_ratio))
+        h, w = img.shape[:2]
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        # print("predict_rec.py resize_norm_img resize shape", (resized_w, imgH))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+        padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im
+
+    def __call__(self, img_list):
+        from format_convert.convert_need_interface import from_gpu_interface_redis
+        img_num = len(img_list)
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        for img in img_list:
+            width_list.append(img.shape[1] / float(img.shape[0]))
+
+        # Sorting can speed up the recognition process
+        indices = np.argsort(np.array(width_list))
+
+        rec_res = [['', 0.0]] * img_num
+        batch_num = self.rec_batch_num
+        elapse = 0
+        all_gpu_time = 0
+        for beg_img_no in range(0, img_num, batch_num):
+            # 预处理
+            end_img_no = min(img_num, beg_img_no + batch_num)
+            norm_img_batch = []
+            max_wh_ratio = 0
+            for ino in range(beg_img_no, end_img_no):
+                h, w = img_list[indices[ino]].shape[0:2]
+                wh_ratio = w * 1.0 / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(img_list[indices[ino]],
+                                                max_wh_ratio)
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+            norm_img_batch = np.concatenate(norm_img_batch)
+            norm_img_batch = norm_img_batch.copy()
+            starttime = time.time()
+
+            # # 压缩numpy
+            # compressed_array = io.BytesIO()
+            # np.savez_compressed(compressed_array, norm_img_batch)
+            # compressed_array.seek(0)
+            # norm_img_batch = compressed_array.read()
+
+            # 调用GPU接口
+            _dict = {"inputs": norm_img_batch, "args": str(namespace_to_dict(self.args)), "md5": _global.get("md5")}
+            result = from_gpu_interface_redis(_dict, model_type="ocr", predictor_type="rec")
+            if judge_error_code(result):
+                logging.error("from_gpu_interface failed! " + str(result))
+                raise requests.exceptions.RequestException
+
+            preds = result.get("preds")
+            gpu_time = result.get("gpu_time")
+            all_gpu_time += round(gpu_time, 2)
+
+            # # 解压numpy
+            # decompressed_array = io.BytesIO()
+            # decompressed_array.write(preds)
+            # decompressed_array.seek(0)
+            # preds = np.load(decompressed_array, allow_pickle=True)['arr_0']
+            # log("inputs.shape" + str(preds.shape))
+
+            # 后处理
+            rec_result = self.postprocess_op(preds)
+            for rno in range(len(rec_result)):
+                rec_res[indices[beg_img_no + rno]] = rec_result[rno]
+            elapse += time.time() - starttime
+
+        log("ocr model predict time - rec - time " + str(all_gpu_time) + " - num " + str(img_num))
+        return rec_res, elapse
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    text_recognizer = TextRecognizer(args)
+    valid_image_file_list = []
+    img_list = []
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        valid_image_file_list.append(image_file)
+        img_list.append(img)
+    try:
+        rec_res, predict_time = text_recognizer(img_list)
+    except:
+        logger.info(traceback.format_exc())
+        logger.info(
+            "ERROR!!!! \n"
+            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
+            "If your model has tps module:  "
+            "TPS does not support variable shape.\n"
+            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
+        exit()
+    for ino in range(len(img_list)):
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
+                                               rec_res[ino]))
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
+        len(img_list), predict_time))
+
+
+if __name__ == "__main__":
+    main(utility.parse_args())

ocr/tools/infer/predict_system.py

@@ -28,12 +28,15 @@ import time
 from PIL import Image
 os.environ['FLAGS_eager_delete_tensor_gb'] = '0'
 import utility as utility
-import ocr.tools.infer.predict_rec as predict_rec
-import ocr.tools.infer.predict_det as predict_det
+# import ocr.tools.infer.predict_rec as predict_rec
+import ocr.tools.infer.predict_rec_pytorch as predict_rec # pytorch rec model
+# import ocr.tools.infer.predict_det as predict_det
+import ocr.tools.infer.predict_det_pytorch as predict_det # pytorch det model
 import ocr.tools.infer.predict_cls as predict_cls
 from ocr.ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ocr.ppocr.utils.logging import get_logger
 from ocr.tools.infer.utility import draw_ocr_box_txt
+from format_convert.utils import has_intersection
 
 logger = get_logger()
 
@@ -76,7 +79,8 @@ class TextSystem(object):
             borderMode=cv2.BORDER_REPLICATE,
             flags=cv2.INTER_CUBIC)
         dst_img_height, dst_img_width = dst_img.shape[0:2]
-        if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        # if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        if dst_img_height * 1.0 / dst_img_width >= 2.0:
             dst_img = np.rot90(dst_img)
         return dst_img
 
@@ -87,6 +91,8 @@ class TextSystem(object):
             logger.info(bno, rec_res[bno])
 
     def __call__(self, img):
+        # cv2.imshow('img',img)
+        # cv2.waitKey(0)
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
         logger.info("dt_boxes num : {}, elapse : {}".format(
@@ -112,6 +118,12 @@ class TextSystem(object):
             len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         filter_boxes, filter_rec_res = [], []
+
+        # dt_boxes 上下重合检测框修正
+        # t1 = time.time()
+        dt_boxes = boxex_points_fixup(dt_boxes)
+        # print("boxex_points_fixup cost:",time.time()-t1)
+
         for box, rec_reuslt in zip(dt_boxes, rec_res):
             text, score = rec_reuslt
             if score >= self.drop_score:
@@ -119,6 +131,53 @@ class TextSystem(object):
                 filter_rec_res.append(rec_reuslt)
         return filter_boxes, filter_rec_res
 
+def boxex_points_fixup(dt_boxes):
+    # 检查框全部转换为矩形
+    # for i in range(len(dt_boxes)):
+    #     box1 = dt_boxes[i]
+    #     x_list = [box1[0][0],box1[1][0],box1[2][0],box1[3][0]]
+    #     y_list = [box1[0][1],box1[1][1],box1[2][1],box1[3][1]]
+    #     x_max = max(x_list)
+    #     x_min = min(x_list)
+    #     y_max = max(y_list)
+    #     y_min = min(y_list)
+    #     dt_boxes[i] = np.array([[x_min,y_min],[x_max,y_min],[x_max,y_max],[x_min,y_max]])
+
+
+    for i in range(len(dt_boxes)):
+        box1 = dt_boxes[i]
+        box1_point3 = box1[2]
+        box1_point4 = box1[3] # 四边形底边的两点坐标
+        bottom_line = (min(box1_point3[0],box1_point4[0]),max(box1_point3[0],box1_point4[0]))
+        bottom_line_len = abs(bottom_line[1]-bottom_line[0])
+
+        for j in range(i+1,len(dt_boxes)):
+            box2 = dt_boxes[j]
+            box2_point1 = box2[0]
+            box2_point2 = box2[1] # 四边形顶边的两点坐标
+            top_line = (min(box2_point1[0], box2_point2[0]), max(box2_point1[0], box2_point2[0]))
+            top_line_len = abs(top_line[1]-top_line[0])
+            if has_intersection(box1, box2):  # 四边形框是否有交集
+                if not (min(top_line)>=max(bottom_line) or min(bottom_line)>=max(top_line)):  # x轴方向上有交集
+                    # 求重合部分y中间值
+                    mid_y = ((box2_point1[1] + box2_point2[1]) / 2 + (box1_point3[1] + box1_point4[1]) / 2) // 2
+                    if not mid_y:
+                        continue
+                    max_line_len = max(bottom_line_len,top_line_len)
+                    cross_line_len = bottom_line_len + top_line_len - \
+                                     (max(bottom_line[1],bottom_line[0],top_line[1],top_line[0]) - min(bottom_line[1],bottom_line[0],top_line[1],top_line[0]))
+                    # print(cross_line_len,max_line_len,cross_line_len/max_line_len)
+                    if cross_line_len/max_line_len>=0.55: # 重合比例
+                        box1[2] = [box1_point3[0],mid_y]
+                        box1[3] = [box1_point4[0],mid_y]
+                        box2[0] = [box2_point1[0],mid_y]
+                        box2[1] = [box2_point2[0],mid_y]
+                        break
+
+
+
+    return dt_boxes
+
 
 def sorted_boxes(dt_boxes):
     """
@@ -187,3 +246,5 @@ def main(args):
 
 if __name__ == "__main__":
     main(utility.parse_args())
+
+    pass

ocr/tools/infer/torch_det_model.py

@@ -0,0 +1,358 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+import os
+from torch.nn import functional as F
+import torch.nn.init as init
+import logging
+
+
+class HSwish(nn.Module):
+    def forward(self, x):
+        out = x * F.relu6(x + 3, inplace=True) / 6
+        return out
+
+class ConvBNACT(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, act=None):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
+                              stride=stride, padding=padding, groups=groups,
+                              bias=False)
+        self.bn = nn.BatchNorm2d(out_channels)
+        if act == 'relu':
+            self.act = nn.ReLU(inplace=True)
+        elif act == 'hard_swish':
+            self.act = HSwish()
+        elif act is None:
+            self.act = None
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.act is not None:
+            x = self.act(x)
+        return x
+
+
+class ConvBNACTWithPool(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, groups=1, act=None):
+        super().__init__()
+        # self.pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self.pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+
+        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1,
+                              padding=(kernel_size - 1) // 2,
+                              groups=groups,
+                              bias=False)
+        self.bn = nn.BatchNorm2d(out_channels)
+        if act is None:
+            self.act = None
+        else:
+            self.act = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.pool(x)
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.act is not None:
+            x = self.act(x)
+        return x
+
+
+class ShortCut(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, name, if_first=False):
+        super().__init__()
+        assert name is not None, 'shortcut must have name'
+
+        self.name = name
+        if in_channels != out_channels or stride != 1:
+            if if_first:
+                self.conv = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
+                                      padding=0, groups=1, act=None)
+            else:
+                self.conv = ConvBNACTWithPool(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
+                                              groups=1, act=None)
+        elif if_first:
+            self.conv = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
+                                  padding=0, groups=1, act=None)
+        else:
+            self.conv = None
+
+    def forward(self, x):
+        if self.conv is not None:
+            x = self.conv(x)
+        return x
+
+
+class BottleneckBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, if_first, name):
+        super().__init__()
+        assert name is not None, 'bottleneck must have name'
+        self.name = name
+        self.conv0 = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0,
+                               groups=1, act='relu')
+        self.conv1 = ConvBNACT(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=stride,
+                               padding=1, groups=1, act='relu')
+        self.conv2 = ConvBNACT(in_channels=out_channels, out_channels=out_channels * 4, kernel_size=1, stride=1,
+                               padding=0, groups=1, act=None)
+        self.shortcut = ShortCut(in_channels=in_channels, out_channels=out_channels * 4, stride=stride,
+                                 if_first=if_first, name=f'{name}_branch1')
+        self.relu = nn.ReLU(inplace=True)
+        self.output_channels = out_channels * 4
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        y = self.conv2(y)
+        y = y + self.shortcut(x)
+        return self.relu(y)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, if_first, name):
+        super().__init__()
+        assert name is not None, 'block must have name'
+        self.name = name
+
+        self.conv0 = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride,
+                               padding=1, groups=1, act='relu')
+        self.conv1 = ConvBNACT(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1,
+                               groups=1, act=None)
+        self.shortcut = ShortCut(in_channels=in_channels, out_channels=out_channels, stride=stride,
+                                 name=f'{name}_branch1', if_first=if_first, )
+        self.relu = nn.ReLU(inplace=True)
+        self.output_channels = out_channels
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        y = y + self.shortcut(x)
+        return self.relu(y)
+
+class ResNet(nn.Module):
+    def __init__(self, in_channels, layers, out_indices=[0, 1, 2, 3], pretrained=True, **kwargs):
+        """
+        the Resnet backbone network for detection module.
+        Args:
+            params(dict): the super parameters for network build
+        """
+        super().__init__()
+        supported_layers = {
+            18: {'depth': [2, 2, 2, 2], 'block_class': BasicBlock},
+            34: {'depth': [3, 4, 6, 3], 'block_class': BasicBlock},
+            50: {'depth': [3, 4, 6, 3], 'block_class': BottleneckBlock},
+            101: {'depth': [3, 4, 23, 3], 'block_class': BottleneckBlock},
+            152: {'depth': [3, 8, 36, 3], 'block_class': BottleneckBlock},
+            200: {'depth': [3, 12, 48, 3], 'block_class': BottleneckBlock}
+        }
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+        depth = supported_layers[layers]['depth']
+        block_class = supported_layers[layers]['block_class']
+        self.use_supervised = kwargs.get('use_supervised', False)
+        self.out_indices = out_indices
+        num_filters = [64, 128, 256, 512]
+        self.conv1 = nn.Sequential(
+            ConvBNACT(in_channels=in_channels, out_channels=32, kernel_size=3, stride=2, padding=1, act='relu'),
+            ConvBNACT(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1, act='relu'),
+            ConvBNACT(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1, act='relu')
+        )
+        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.stages = nn.ModuleList()
+        self.out_channels = []
+        tmp_channels = []
+        in_ch = 64
+        for block_index in range(len(depth)):
+            block_list = []
+            for i in range(depth[block_index]):
+                if layers >= 50:
+                    if layers in [101, 152, 200] and block_index == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block_index + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block_index + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block_index + 2) + chr(97 + i)
+                else:
+                    conv_name = f'res{str(block_index + 2)}{chr(97 + i)}'
+                block_list.append(block_class(in_channels=in_ch, out_channels=num_filters[block_index],
+                                              stride=2 if i == 0 and block_index != 0 else 1,
+                                              if_first=block_index == i == 0, name=conv_name))
+                in_ch = block_list[-1].output_channels
+            tmp_channels.append(in_ch)
+            self.stages.append(nn.Sequential(*block_list))
+        for idx, ch in enumerate(tmp_channels):
+            if idx in self.out_indices:
+                self.out_channels.append(ch)
+        if pretrained:
+            ckpt_path = f'./weights/resnet{layers}_vd.pth'
+            logger = logging.getLogger('torchocr')
+            if os.path.exists(ckpt_path):
+                logger.info('load imagenet weights')
+                self.load_state_dict(torch.load(ckpt_path))
+            else:
+                logger.info(f'{ckpt_path} not exists')
+        if self.use_supervised:
+            ckpt_path = f'./weights/res_supervised_140w_387e.pth'
+            logger = logging.getLogger('torchocr')
+            if os.path.exists(ckpt_path):
+                logger.info('load supervised weights')
+                self.load_state_dict(torch.load(ckpt_path))
+            else:
+                logger.info(f'{ckpt_path} not exists')
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pool1(x)
+        out = []
+        for idx, stage in enumerate(self.stages):
+            x = stage(x)
+            if idx in self.out_indices:
+                out.append(x)
+        return out
+
+def weights_init(m):
+
+    if isinstance(m, nn.Conv2d):
+        init.kaiming_normal_(m.weight.data)
+        if m.bias is not None:
+            init.normal_(m.bias.data)
+    elif isinstance(m, nn.ConvTranspose2d):
+        init.kaiming_normal_(m.weight.data)
+        if m.bias is not None:
+            init.normal_(m.bias.data)
+    elif isinstance(m, nn.BatchNorm2d):
+        init.normal_(m.weight.data, mean=1, std=0.02)
+        init.constant_(m.bias.data, 0)
+
+class DB_fpn(nn.Module):
+    def __init__(self, in_channels, out_channels=256, **kwargs):
+        """
+        :param in_channels: 基础网络输出的维度
+        :param kwargs:
+        """
+        super().__init__()
+        inplace = True
+        self.out_channels = out_channels
+        # reduce layers
+        self.in2_conv = nn.Conv2d(in_channels[0], self.out_channels, kernel_size=1, bias=False)
+        self.in3_conv = nn.Conv2d(in_channels[1], self.out_channels, kernel_size=1, bias=False)
+        self.in4_conv = nn.Conv2d(in_channels[2], self.out_channels, kernel_size=1, bias=False)
+        self.in5_conv = nn.Conv2d(in_channels[3], self.out_channels, kernel_size=1, bias=False)
+        # Smooth layers
+        self.p5_conv = nn.Conv2d(self.out_channels, self.out_channels // 4, kernel_size=3, padding=1, bias=False)
+        self.p4_conv = nn.Conv2d(self.out_channels, self.out_channels // 4, kernel_size=3, padding=1, bias=False)
+        self.p3_conv = nn.Conv2d(self.out_channels, self.out_channels // 4, kernel_size=3, padding=1, bias=False)
+        self.p2_conv = nn.Conv2d(self.out_channels, self.out_channels // 4, kernel_size=3, padding=1, bias=False)
+
+        self.in2_conv.apply(weights_init)
+        self.in3_conv.apply(weights_init)
+        self.in4_conv.apply(weights_init)
+        self.in5_conv.apply(weights_init)
+        self.p5_conv.apply(weights_init)
+        self.p4_conv.apply(weights_init)
+        self.p3_conv.apply(weights_init)
+        self.p2_conv.apply(weights_init)
+
+    def _interpolate_add(self, x, y):
+        return F.interpolate(x, scale_factor=2) + y
+
+    def _interpolate_cat(self, p2, p3, p4, p5):
+        p3 = F.interpolate(p3, scale_factor=2)
+        p4 = F.interpolate(p4, scale_factor=4)
+        p5 = F.interpolate(p5, scale_factor=8)
+        return torch.cat([p5, p4, p3, p2], dim=1)
+
+    def forward(self, x):
+        c2, c3, c4, c5 = x
+        in5 = self.in5_conv(c5)
+        in4 = self.in4_conv(c4)
+        in3 = self.in3_conv(c3)
+        in2 = self.in2_conv(c2)
+
+        out4 = self._interpolate_add(in5, in4)
+        out3 = self._interpolate_add(out4, in3)
+        out2 = self._interpolate_add(out3, in2)
+
+        p5 = self.p5_conv(in5)
+        p4 = self.p4_conv(out4)
+        p3 = self.p3_conv(out3)
+        p2 = self.p2_conv(out2)
+
+        x = self._interpolate_cat(p2, p3, p4, p5)
+        return x
+
+class Head(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels // 4, kernel_size=3, padding=1,
+                               bias=False)
+        # self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels // 4, kernel_size=5, padding=2,
+        #                        bias=False)
+        self.conv_bn1 = nn.BatchNorm2d(in_channels // 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.ConvTranspose2d(in_channels=in_channels // 4, out_channels=in_channels // 4, kernel_size=2,
+                                        stride=2)
+        self.conv_bn2 = nn.BatchNorm2d(in_channels // 4)
+        self.conv3 = nn.ConvTranspose2d(in_channels=in_channels // 4, out_channels=1, kernel_size=2, stride=2)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv_bn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.conv_bn2(x)
+        x = self.relu(x)
+        x = self.conv3(x)
+        x = torch.sigmoid(x)
+        return x
+
+class DBHead(nn.Module):
+    """
+    Differentiable Binarization (DB) for text detection:
+        see https://arxiv.org/abs/1911.08947
+    args:
+        params(dict): super parameters for build DB network
+    """
+
+    def __init__(self, in_channels, k=50):
+        super().__init__()
+        self.k = k
+        self.binarize = Head(in_channels)
+        self.thresh = Head(in_channels)
+        self.binarize.apply(weights_init)
+        self.thresh.apply(weights_init)
+
+    def step_function(self, x, y):
+        return torch.reciprocal(1 + torch.exp(-self.k * (x - y)))
+
+    def forward(self, x):
+        shrink_maps = self.binarize(x)
+        if not self.training:
+            return shrink_maps
+        threshold_maps = self.thresh(x)
+        binary_maps = self.step_function(shrink_maps, threshold_maps)
+        y = torch.cat((shrink_maps, threshold_maps, binary_maps), dim=1)
+        return y
+
+class DB_ResNet_18(nn.Module):
+    def __init__(self, ):
+        super().__init__()
+
+        self.backbone = ResNet(in_channels=3,layers=18,pretrained=False)
+
+        self.neck = DB_fpn(in_channels=self.backbone.out_channels,out_channels=256)
+
+        self.head = DBHead(self.neck.out_channels)
+
+
+    def forward(self, x):
+        x = self.backbone(x)
+        x = self.neck(x)
+        x = self.head(x)
+        return x
+
+
+

ocr/tools/infer/torch_rec_model.py

@@ -0,0 +1,311 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+from torch.nn import functional as F
+
+
+class HSwish(nn.Module):
+    def forward(self, x):
+        out = x * F.relu6(x + 3, inplace=True) / 6
+        return out
+
+class ConvBNACT(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, act=None):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
+                              stride=stride, padding=padding, groups=groups,
+                              bias=False)
+        self.bn = nn.BatchNorm2d(out_channels)
+        if act == 'relu':
+            self.act = nn.ReLU()
+        elif act == 'hard_swish':
+            self.act = HSwish()
+        elif act is None:
+            self.act = None
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.act is not None:
+            x = self.act(x)
+        return x
+
+
+class ConvBNACTWithPool(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, groups=1, act=None):
+        super().__init__()
+        self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
+
+        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1,
+                              padding=(kernel_size - 1) // 2,
+                              groups=groups,
+                              bias=False)
+        self.bn = nn.BatchNorm2d(out_channels)
+        if act is None:
+            self.act = None
+        else:
+            self.act = nn.ReLU()
+
+    def forward(self, x):
+        x = self.pool(x)
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.act is not None:
+            x = self.act(x)
+        return x
+
+
+class ShortCut(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, name, if_first=False):
+        super().__init__()
+        assert name is not None, 'shortcut must have name'
+
+        self.name = name
+        if in_channels != out_channels or stride[0] != 1:
+            if if_first:
+                self.conv = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
+                                      padding=0, groups=1, act=None)
+            else:
+                self.conv = ConvBNACTWithPool(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
+                                              stride=stride, groups=1, act=None)
+        elif if_first:
+            self.conv = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
+                                  padding=0, groups=1, act=None)
+        else:
+            self.conv = None
+
+
+    def forward(self, x):
+        if self.conv is not None:
+            x = self.conv(x)
+        return x
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, if_first, name):
+        super().__init__()
+        assert name is not None, 'block must have name'
+        self.name = name
+
+        self.conv0 = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride,
+                               padding=1, groups=1, act='relu')
+        self.conv1 = ConvBNACT(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1,
+                               groups=1, act=None)
+        self.shortcut = ShortCut(in_channels=in_channels, out_channels=out_channels, stride=stride,
+                                 name=f'{name}_branch1', if_first=if_first, )
+        self.relu = nn.ReLU()
+        self.output_channels = out_channels
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        y = y + self.shortcut(x)
+        return self.relu(y)
+
+
+class BottleneckBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, if_first, name):
+        super().__init__()
+        assert name is not None, 'bottleneck must have name'
+        self.name = name
+        self.conv0 = ConvBNACT(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0,
+                               groups=1, act='relu')
+        self.conv1 = ConvBNACT(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=stride,
+                               padding=1, groups=1, act='relu')
+        self.conv2 = ConvBNACT(in_channels=out_channels, out_channels=out_channels * 4, kernel_size=1, stride=1,
+                               padding=0, groups=1, act=None)
+        self.shortcut = ShortCut(in_channels=in_channels, out_channels=out_channels * 4, stride=stride,
+                                 if_first=if_first, name=f'{name}_branch1')
+        self.relu = nn.ReLU()
+        self.output_channels = out_channels * 4
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        y = self.conv2(y)
+        y = y + self.shortcut(x)
+        return self.relu(y)
+
+
+class ResNet(nn.Module):
+    def __init__(self, in_channels, layers, **kwargs):
+        super().__init__()
+        supported_layers = {
+            18: {'depth': [2, 2, 2, 2], 'block_class': BasicBlock},
+            34: {'depth': [3, 4, 6, 3], 'block_class': BasicBlock},
+            50: {'depth': [3, 4, 6, 3], 'block_class': BottleneckBlock},
+            101: {'depth': [3, 4, 23, 3], 'block_class': BottleneckBlock},
+            152: {'depth': [3, 8, 36, 3], 'block_class': BottleneckBlock},
+            200: {'depth': [3, 12, 48, 3], 'block_class': BottleneckBlock}
+        }
+        assert layers in supported_layers, "supported layers are {} but input layer is {}".format(supported_layers,
+                                                                                                  layers)
+
+        depth = supported_layers[layers]['depth']
+        block_class = supported_layers[layers]['block_class']
+
+        num_filters = [64, 128, 256, 512]
+        self.conv1 = nn.Sequential(
+            ConvBNACT(in_channels=in_channels, out_channels=32, kernel_size=3, stride=1, padding=1, act='relu'),
+            ConvBNACT(in_channels=32, out_channels=32, kernel_size=3, stride=1, act='relu', padding=1),
+            ConvBNACT(in_channels=32, out_channels=64, kernel_size=3, stride=1, act='relu', padding=1)
+        )
+
+        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.stages = nn.ModuleList()
+        in_ch = 64
+        for block_index in range(len(depth)):
+            block_list = []
+            for i in range(depth[block_index]):
+                if layers >= 50:
+                    if layers in [101, 152, 200] and block_index == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block_index + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block_index + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block_index + 2) + chr(97 + i)
+                else:
+                    conv_name = f'res{str(block_index + 2)}{chr(97 + i)}'
+                if i == 0 and block_index != 0:
+                    stride = (2, 1)
+                else:
+                    stride = (1, 1)
+                block_list.append(block_class(in_channels=in_ch, out_channels=num_filters[block_index],
+                                              stride=stride,
+                                              if_first=block_index == i == 0, name=conv_name))
+                in_ch = block_list[-1].output_channels
+            self.stages.append(nn.Sequential(*block_list))
+        self.out_channels = in_ch
+        self.out = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pool1(x)
+        for stage in self.stages:
+            x = stage(x)
+        x = self.out(x)
+        return x
+
+
+class Im2Seq(nn.Module):
+    def __init__(self, in_channels, **kwargs):
+        super().__init__()
+        self.out_channels = in_channels
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        assert H == 1
+        x = x.reshape(B, C, H * W)
+        x = x.permute((0, 2, 1))
+        return x
+
+
+
+class CTCHead(nn.Module):
+    def __init__(self,
+                 in_channels=192,
+                 out_channels=6624,
+                 fc_decay=0.0004,
+                 mid_channels=None,
+                 return_feats=False,
+                 **kwargs):
+        super(CTCHead, self).__init__()
+        if mid_channels is None:
+            self.fc = nn.Linear(
+                in_channels,
+                out_channels)
+        else:
+            self.fc1 = nn.Linear(
+                in_channels,
+                mid_channels)
+            self.fc2 = nn.Linear(
+                mid_channels,
+                out_channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.mid_channels = mid_channels
+        self.return_feats = return_feats
+        self.apply(self._init_weights)
+        print('---------model weight inits-----------')
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            stdv = 1.0 / math.sqrt(self.in_channels * 1.0)
+            nn.init.uniform_(m.weight, -stdv, stdv)
+            nn.init.uniform_(m.bias, -stdv, stdv)
+
+    def forward(self, x):
+        if self.mid_channels is None:
+            predicts = self.fc(x)
+        else:
+            x = self.fc1(x)
+            predicts = self.fc2(x)
+
+        if self.return_feats:
+            result = (predicts, x)
+        else:
+            result = (predicts, None)
+        return result[0]
+
+
+class EncoderWithRNN(nn.Module):
+    def __init__(self, in_channels,**kwargs):
+        super(EncoderWithRNN, self).__init__()
+        hidden_size = kwargs.get('hidden_size', 256)
+        self.out_channels = hidden_size * 2
+        self.lstm = nn.LSTM(in_channels, hidden_size, bidirectional=True, num_layers=2,batch_first=True)
+
+    def forward(self, x):
+        self.lstm.flatten_parameters()
+        x, _ = self.lstm(x)
+        return x
+
+class SequenceEncoder(nn.Module):
+    def __init__(self, in_channels, encoder_type='rnn',  **kwargs):
+        super(SequenceEncoder, self).__init__()
+        self.encoder_reshape = Im2Seq(in_channels)
+        self.out_channels = self.encoder_reshape.out_channels
+        if encoder_type == 'reshape':
+            self.only_reshape = True
+        else:
+            support_encoder_dict = {
+                'reshape': Im2Seq,
+                'rnn': EncoderWithRNN
+            }
+            assert encoder_type in support_encoder_dict, '{} must in {}'.format(
+                encoder_type, support_encoder_dict.keys())
+
+            self.encoder = support_encoder_dict[encoder_type](
+                self.encoder_reshape.out_channels,**kwargs)
+            self.out_channels = self.encoder.out_channels
+            self.only_reshape = False
+
+    def forward(self, x):
+        x = self.encoder_reshape(x)
+        if not self.only_reshape:
+            x = self.encoder(x)
+
+        return x
+
+
+class Rec_ResNet_34(nn.Module):
+    def __init__(self,class_nums=None):
+        super(Rec_ResNet_34, self).__init__()
+        self.backbone = ResNet(in_channels=3,layers=34)
+        hidden_size = 256
+        self.neck = SequenceEncoder(in_channels=512,encoder_type='rnn',hidden_size=hidden_size)
+        if class_nums:
+            self.class_nums = class_nums
+        else:
+            self.class_nums = 7551
+        self.head = CTCHead(in_channels=hidden_size*2,out_channels=self.class_nums + 1,mid_channels=None)
+        # self.head = CTCHead(in_channels=2304,out_channels=7546,mid_channels=200)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        x = self.neck(x)
+        x = self.head(x)
+        return x