use onnx models, new deepdoc (#68)

api/apps/conversation_app.py

         return {"answer": prompt_config["empty_response"], "retrieval": kbinfos}
 
     kwargs["knowledge"] = "\n".join(knowledges)
-    gen_conf = dialog.llm_setting[dialog.llm_setting_type]
+    gen_conf = dialog.llm_setting
     msg = [{"role": m["role"], "content": m["content"]} for m in messages if m["role"] != "system"]
     used_token_count, msg = message_fit_in(msg, int(llm.max_tokens * 0.97))
     if "max_tokens" in gen_conf:

api/apps/dialog_app.py

     name = req.get("name", "New Dialog")
     description = req.get("description", "A helpful Dialog")
     language = req.get("language", "Chinese")
-    llm_setting_type = req.get("llm_setting_type", "Precise")
+    top_n = req.get("top_n", 6)
+    similarity_threshold = req.get("similarity_threshold", 0.1)
+    vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
     llm_setting = req.get("llm_setting", {
-        "Creative": {
-            "temperature": 0.9,
-            "top_p": 0.9,
-            "frequency_penalty": 0.2,
-            "presence_penalty": 0.4,
-            "max_tokens": 512
-        },
-        "Precise": {
-            "temperature": 0.1,
-            "top_p": 0.3,
-            "frequency_penalty": 0.7,
-            "presence_penalty": 0.4,
-            "max_tokens": 215
-        },
-        "Evenly": {
-            "temperature": 0.5,
-            "top_p": 0.5,
-            "frequency_penalty": 0.7,
-            "presence_penalty": 0.4,
-            "max_tokens": 215
-        },
-        "Custom": {
-            "temperature": 0.2,
-            "top_p": 0.3,
-            "frequency_penalty": 0.6,
-            "presence_penalty": 0.3,
-            "max_tokens": 215
-        },
+        "temperature": 0.1,
+        "top_p": 0.3,
+        "frequency_penalty": 0.7,
+        "presence_penalty": 0.4,
+        "max_tokens": 215
     })
-    prompt_config = req.get("prompt_config", {
+    default_prompt = {
         "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
 以下是知识库：
 {knowledge}
             {"key": "knowledge", "optional": False}
         ],
         "empty_response": "Sorry! 知识库中未找到相关内容！"
-    })
+    }
+    prompt_config = req.get("prompt_config", default_prompt)
 
-    if len(prompt_config["parameters"]) < 1:
-        return get_data_error_result(retmsg="'knowledge' should be in parameters")
+    if not prompt_config["system"]: prompt_config["system"] = default_prompt["system"]
+    # if len(prompt_config["parameters"]) < 1:
+    #     prompt_config["parameters"] = default_prompt["parameters"]
+    # for p in prompt_config["parameters"]:
+    #     if p["key"] == "knowledge":break
+    # else: prompt_config["parameters"].append(default_prompt["parameters"][0])
 
     for p in prompt_config["parameters"]:
-        if prompt_config["system"].find("{%s}"%p["key"]) < 0:
+        if p["optional"]: continue
+        if prompt_config["system"].find("{%s}" % p["key"]) < 0:
             return get_data_error_result(retmsg="Parameter '{}' is not used".format(p["key"]))
 
     try:
         e, tenant = TenantService.get_by_id(current_user.id)
-        if not e:return get_data_error_result(retmsg="Tenant not found!")
+        if not e: return get_data_error_result(retmsg="Tenant not found!")
         llm_id = req.get("llm_id", tenant.llm_id)
         if not dialog_id:
+            if not req.get("kb_ids"):return get_data_error_result(retmsg="Fail! Please select knowledgebase!")
             dia = {
                 "id": get_uuid(),
                 "tenant_id": current_user.id,
                 "name": name,
+                "kb_ids": req["kb_ids"],
                 "description": description,
                 "language": language,
                 "llm_id": llm_id,
-                "llm_setting_type": llm_setting_type,
                 "llm_setting": llm_setting,
-                "prompt_config": prompt_config
+                "prompt_config": prompt_config,
+                "top_n": top_n,
+                "similarity_threshold": similarity_threshold,
+                "vector_similarity_weight": vector_similarity_weight
             }
             if not DialogService.save(**dia): return get_data_error_result(retmsg="Fail to new a dialog!")
             e, dia = DialogService.get_by_id(dia["id"])
 def get():
     dialog_id = request.args["dialog_id"]
     try:
-        e,dia = DialogService.get_by_id(dialog_id)
+        e, dia = DialogService.get_by_id(dialog_id)
         if not e: return get_data_error_result(retmsg="Dialog not found!")
         dia = dia.to_dict()
         dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
     except Exception as e:
         return server_error_response(e)
 
+
 def get_kb_names(kb_ids):
     ids, nms = [], []
     for kid in kb_ids:
         e, kb = KnowledgebaseService.get_by_id(kid)
-        if not e or kb.status != StatusEnum.VALID.value:continue
+        if not e or kb.status != StatusEnum.VALID.value: continue
         ids.append(kid)
         nms.append(kb.name)
     return ids, nms
 
+
 @manager.route('/list', methods=['GET'])
 @login_required
 def list():
     try:
-        diags = DialogService.query(tenant_id=current_user.id, status=StatusEnum.VALID.value)
+        diags = DialogService.query(tenant_id=current_user.id, status=StatusEnum.VALID.value, reverse=True, order_by=DialogService.model.create_time)
         diags = [d.to_dict() for d in diags]
         for d in diags:
             d["kb_ids"], d["kb_names"] = get_kb_names(d["kb_ids"])
 
 @manager.route('/rm', methods=['POST'])
 @login_required
-@validate_request("dialog_id")
+@validate_request("dialog_ids")
 def rm():
     req = request.json
     try:
-        if not DialogService.update_by_id(req["dialog_id"], {"status": StatusEnum.INVALID.value}):
-            return get_data_error_result(retmsg="Dialog not found!")
+        DialogService.update_many_by_id([{"id": id, "status": StatusEnum.INVALID.value} for id in req["dialog_ids"]])
         return get_json_result(data=True)
     except Exception as e:
-        return server_error_response(e)
+        return server_error_response(e)

api/db/db_models.py

     icon = CharField(max_length=16, null=False, help_text="dialog icon")
     language = CharField(max_length=32, null=True, default="Chinese", help_text="English|Chinese")
     llm_id = CharField(max_length=32, null=False, help_text="default llm ID")
-    llm_setting_type = CharField(max_length=8, null=False, help_text="Creative|Precise|Evenly|Custom",
-                                 default="Creative")
     llm_setting = JSONField(null=False, default={"temperature": 0.1, "top_p": 0.3, "frequency_penalty": 0.7,
                                                  "presence_penalty": 0.4, "max_tokens": 215})
     prompt_type = CharField(max_length=16, null=False, default="simple", help_text="simple|advanced")

rag/parser/__init__.py → deepdoc/parser/__init__.py

-import copy
 import random
 
 from .pdf_parser import HuParser as PdfParser
 from nltk import word_tokenize
 
 from rag.nlp import stemmer, huqie
-from ..utils import num_tokens_from_string
+from rag.utils import num_tokens_from_string
 
 BULLET_PATTERN = [[
     r"第[零一二三四五六七八九十百0-9]+(分?编|部分)",

rag/parser/pdf_parser.py → deepdoc/parser/pdf_parser.py

 # -*- coding: utf-8 -*-
 import os
 import random
-from functools import partial
 
 import fitz
 import requests
 import numpy as np
 
 from api.db import ParserType
+from deepdoc.visual import OCR, Recognizer
 from rag.nlp import huqie
 from collections import Counter
 from copy import deepcopy
 
 class HuParser:
     def __init__(self):
-        from paddleocr import PaddleOCR
-        logging.getLogger("ppocr").setLevel(logging.ERROR)
-        self.ocr = PaddleOCR(use_angle_cls=False, lang="ch")
+        self.ocr = OCR()
         if not hasattr(self, "model_speciess"):
             self.model_speciess = ParserType.GENERAL.value
-        self.layouter = partial(self.__remote_call, self.model_speciess)
-        self.tbl_det = partial(self.__remote_call, "table_component")
+        self.layout_labels = [
+             "_background_",
+             "Text",
+             "Title",
+             "Figure",
+             "Figure caption",
+             "Table",
+             "Table caption",
+             "Header",
+             "Footer",
+             "Reference",
+             "Equation",
+        ]
+        self.tsr_labels = [
+            "table",
+            "table column",
+            "table row",
+            "table column header",
+            "table projected row header",
+            "table spanning cell",
+        ]
+        self.layouter = Recognizer(self.layout_labels, "layout", "/data/newpeak/medical-gpt/res/ppdet/")
+        self.tbl_det = Recognizer(self.tsr_labels, "tsr", "/data/newpeak/medical-gpt/res/ppdet.tbl/")
 
         self.updown_cnt_mdl = xgb.Booster()
         if torch.cuda.is_available():
         token = os.environ.get("INFINIFLOW_TOKEN")
         if not url or not token:
             logging.warning("INFINIFLOW_SERVER is not specified. To maximize the effectiveness, please visit https://github.com/infiniflow/ragflow, and sign in the our demo web site to get token. It's FREE! Using 'export' to set both environment variables: INFINIFLOW_SERVER and INFINIFLOW_TOKEN.")
-            return []
+            return [[] for _ in range(len(images))]
 
         def convert_image_to_bytes(PILimage):
             image = BytesIO()
 
         return layouts
 
-    def __table_paddle(self, images):
+    def __table_tsr(self, images):
         tbls = self.tbl_det(images, thr=0.5)
         res = []
         # align left&right for rows, align top&bottom for columns
         assert len(self.page_images) == len(tbcnt) - 1
         if not imgs:
             return
-        recos = self.__table_paddle(imgs)
+        recos = self.__table_tsr(imgs)
         tbcnt = np.cumsum(tbcnt)
         for i in range(len(tbcnt) - 1):  # for page
             pg = []
                 b["H_right"] = spans[ii]["x1"]
                 b["SP"] = ii
 
-    def __ocr_paddle(self, pagenum, img, chars, ZM=3):
-        bxs = self.ocr.ocr(np.array(img), cls=True)[0]
+    def __ocr(self, pagenum, img, chars, ZM=3):
+        bxs = self.ocr(np.array(img))
         if not bxs:
             self.boxes.append([])
             return
 
         self.boxes.append(bxs)
 
-    def _layouts_paddle(self, ZM):
+    def _layouts_rec(self, ZM):
         assert len(self.page_images) == len(self.boxes)
         # Tag layout type
         boxes = []
         layouts = self.layouter(self.page_images)
+        #save_results(self.page_images, layouts, self.layout_labels, output_dir='output/', threshold=0.7)
         assert len(self.page_images) == len(layouts)
         for pn, lts in enumerate(layouts):
             bxs = self.boxes[pn]
             #     else:
             #         self.page_cum_height.append(
             #             np.max([c["bottom"] for c in chars]))
-            self.__ocr_paddle(i + 1, img, chars, zoomin)
+            self.__ocr(i + 1, img, chars, zoomin)
 
         if not self.is_english and not any([c for c in self.page_chars]) and self.boxes:
             bxes = [b for bxs in self.boxes for b in bxs]
 
     def __call__(self, fnm, need_image=True, zoomin=3, return_html=False):
         self.__images__(fnm, zoomin)
-        self._layouts_paddle(zoomin)
+        self._layouts_rec(zoomin)
         self._table_transformer_job(zoomin)
         self._text_merge()
         self._concat_downward()

deepdoc/visual/__init__.py

+from .ocr import OCR
+from .recognizer import Recognizer

deepdoc/visual/ocr.py

+#  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 copy
+import time
+import os
+
+from huggingface_hub import snapshot_download
+
+from .operators import *
+import numpy as np
+import onnxruntime as ort
+
+from api.utils.file_utils import get_project_base_directory
+from .postprocess import build_post_process
+from rag.settings import cron_logger
+
+
+def transform(data, ops=None):
+    """ transform """
+    if ops is None:
+        ops = []
+    for op in ops:
+        data = op(data)
+        if data is None:
+            return None
+    return data
+
+
+def create_operators(op_param_list, global_config=None):
+    """
+    create operators based on the config
+
+    Args:
+        params(list): a dict list, used to create some operators
+    """
+    assert isinstance(
+        op_param_list, list), ('operator config should be a list')
+    ops = []
+    for operator in op_param_list:
+        assert isinstance(operator,
+                          dict) and len(operator) == 1, "yaml format error"
+        op_name = list(operator)[0]
+        param = {} if operator[op_name] is None else operator[op_name]
+        if global_config is not None:
+            param.update(global_config)
+        op = eval(op_name)(**param)
+        ops.append(op)
+    return ops
+
+
+def load_model(model_dir, nm):
+    model_file_path = os.path.join(model_dir, nm + ".onnx")
+    if not os.path.exists(model_file_path):
+        raise ValueError("not find model file path {}".format(
+            model_file_path))
+    sess = ort.InferenceSession(model_file_path)
+    return sess, sess.get_inputs()[0]
+
+
+class TextRecognizer(object):
+    def __init__(self, model_dir):
+        self.rec_image_shape = [int(v) for v in "3, 48, 320".split(",")]
+        self.rec_batch_num = 16
+        postprocess_params = {
+            'name': 'CTCLabelDecode',
+            "character_dict_path": os.path.join(get_project_base_directory(), "rag/res", "ocr.res"),
+            "use_space_char": True
+        }
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor = load_model(model_dir, 'rec')
+
+    def resize_norm_img(self, img, max_wh_ratio):
+        imgC, imgH, imgW = self.rec_image_shape
+
+        assert imgC == img.shape[2]
+        imgW = int((imgH * max_wh_ratio))
+        w = self.input_tensor.shape[3:][0]
+        if isinstance(w, str):
+            pass
+        elif w is not None and w > 0:
+            imgW = w
+        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))
+
+        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 resize_norm_img_vl(self, img, image_shape):
+
+        imgC, imgH, imgW = image_shape
+        img = img[:, :, ::-1]  # bgr2rgb
+        resized_image = cv2.resize(
+            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        return resized_image
+
+    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 resize_norm_img_sar(self, img, image_shape,
+                            width_downsample_ratio=0.25):
+        imgC, imgH, imgW_min, imgW_max = image_shape
+        h = img.shape[0]
+        w = img.shape[1]
+        valid_ratio = 1.0
+        # make sure new_width is an integral multiple of width_divisor.
+        width_divisor = int(1 / width_downsample_ratio)
+        # resize
+        ratio = w / float(h)
+        resize_w = math.ceil(imgH * ratio)
+        if resize_w % width_divisor != 0:
+            resize_w = round(resize_w / width_divisor) * width_divisor
+        if imgW_min is not None:
+            resize_w = max(imgW_min, resize_w)
+        if imgW_max is not None:
+            valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
+            resize_w = min(imgW_max, resize_w)
+        resized_image = cv2.resize(img, (resize_w, imgH))
+        resized_image = resized_image.astype('float32')
+        # norm
+        if image_shape[0] == 1:
+            resized_image = resized_image / 255
+            resized_image = resized_image[np.newaxis, :]
+        else:
+            resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        resize_shape = resized_image.shape
+        padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
+        padding_im[:, :, 0:resize_w] = resized_image
+        pad_shape = padding_im.shape
+
+        return padding_im, resize_shape, pad_shape, valid_ratio
+
+    def resize_norm_img_spin(self, img):
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        # return padding_im
+        img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
+        img = np.array(img, np.float32)
+        img = np.expand_dims(img, -1)
+        img = img.transpose((2, 0, 1))
+        mean = [127.5]
+        std = [127.5]
+        mean = np.array(mean, dtype=np.float32)
+        std = np.array(std, dtype=np.float32)
+        mean = np.float32(mean.reshape(1, -1))
+        stdinv = 1 / np.float32(std.reshape(1, -1))
+        img -= mean
+        img *= stdinv
+        return img
+
+    def resize_norm_img_svtr(self, img, image_shape):
+
+        imgC, imgH, imgW = image_shape
+        resized_image = cv2.resize(
+            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+        return resized_image
+
+    def resize_norm_img_abinet(self, img, image_shape):
+
+        imgC, imgH, imgW = image_shape
+
+        resized_image = cv2.resize(
+            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+        resized_image = resized_image.astype('float32')
+        resized_image = resized_image / 255.
+
+        mean = np.array([0.485, 0.456, 0.406])
+        std = np.array([0.229, 0.224, 0.225])
+        resized_image = (
+            resized_image - mean[None, None, ...]) / std[None, None, ...]
+        resized_image = resized_image.transpose((2, 0, 1))
+        resized_image = resized_image.astype('float32')
+
+        return resized_image
+
+    def norm_img_can(self, img, image_shape):
+
+        img = cv2.cvtColor(
+            img, cv2.COLOR_BGR2GRAY)  # CAN only predict gray scale image
+
+        if self.rec_image_shape[0] == 1:
+            h, w = img.shape
+            _, imgH, imgW = self.rec_image_shape
+            if h < imgH or w < imgW:
+                padding_h = max(imgH - h, 0)
+                padding_w = max(imgW - w, 0)
+                img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),
+                                    'constant',
+                                    constant_values=(255))
+                img = img_padded
+
+        img = np.expand_dims(img, 0) / 255.0  # h,w,c -> c,h,w
+        img = img.astype('float32')
+
+        return img
+
+    def __call__(self, img_list):
+        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
+        st = time.time()
+
+        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 = []
+            imgC, imgH, imgW = self.rec_image_shape[:3]
+            max_wh_ratio = imgW / imgH
+            # 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()
+
+            input_dict = {}
+            input_dict[self.input_tensor.name] = norm_img_batch
+            outputs = self.predictor.run(None, input_dict)
+            preds = outputs[0]
+            rec_result = self.postprocess_op(preds)
+            for rno in range(len(rec_result)):
+                rec_res[indices[beg_img_no + rno]] = rec_result[rno]
+
+        return rec_res, time.time() - st
+
+
+class TextDetector(object):
+    def __init__(self, model_dir):
+        pre_process_list = [{
+            'DetResizeForTest': {
+                'limit_side_len': 960,
+                'limit_type': "max",
+            }
+        }, {
+            '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 = {"name": "DBPostProcess", "thresh": 0.3, "box_thresh": 0.6, "max_candidates": 1000,
+                              "unclip_ratio": 1.5, "use_dilation": False, "score_mode": "fast", "box_type": "quad"}
+
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor = load_model(model_dir, 'det')
+
+        img_h, img_w = self.input_tensor.shape[2:]
+        if isinstance(img_h, str) or isinstance(img_w, str):
+            pass
+        elif img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
+            pre_process_list[0] = {
+                'DetResizeForTest': {
+                    'image_shape': [img_h, img_w]
+                }
+            }
+        self.preprocess_op = create_operators(pre_process_list)
+
+    def order_points_clockwise(self, pts):
+        rect = np.zeros((4, 2), dtype="float32")
+        s = pts.sum(axis=1)
+        rect[0] = pts[np.argmin(s)]
+        rect[2] = pts[np.argmax(s)]
+        tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
+        diff = np.diff(np.array(tmp), axis=1)
+        rect[1] = tmp[np.argmin(diff)]
+        rect[3] = tmp[np.argmax(diff)]
+        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:
+            if isinstance(box, list):
+                box = np.array(box)
+            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:
+            if isinstance(box, list):
+                box = np.array(box)
+            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):
+        ori_im = img.copy()
+        data = {'image': img}
+
+        st = time.time()
+        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()
+        input_dict = {}
+        input_dict[self.input_tensor.name] = img
+        outputs = self.predictor.run(None, input_dict)
+
+        post_result = self.postprocess_op({"maps": outputs[0]}, shape_list)
+        dt_boxes = post_result[0]['points']
+        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+
+        return dt_boxes, time.time() - st
+
+
+class OCR(object):
+    def __init__(self, model_dir=None):
+        """
+        If you have trouble downloading HuggingFace models, -_^ this might help!!
+
+        For Linux:
+        export HF_ENDPOINT=https://hf-mirror.com
+
+        For Windows:
+        Good luck
+        ^_-
+
+        """
+        if not model_dir:
+            model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
+
+        self.text_detector = TextDetector(model_dir)
+        self.text_recognizer = TextRecognizer(model_dir)
+        self.drop_score = 0.5
+        self.crop_image_res_index = 0
+
+    def get_rotate_crop_image(self, img, points):
+        '''
+        img_height, img_width = img.shape[0:2]
+        left = int(np.min(points[:, 0]))
+        right = int(np.max(points[:, 0]))
+        top = int(np.min(points[:, 1]))
+        bottom = int(np.max(points[:, 1]))
+        img_crop = img[top:bottom, left:right, :].copy()
+        points[:, 0] = points[:, 0] - left
+        points[:, 1] = points[:, 1] - top
+        '''
+        assert len(points) == 4, "shape of points must be 4*2"
+        img_crop_width = int(
+            max(
+                np.linalg.norm(points[0] - points[1]),
+                np.linalg.norm(points[2] - points[3])))
+        img_crop_height = int(
+            max(
+                np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[1] - points[2])))
+        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(
+            img,
+            M, (img_crop_width, img_crop_height),
+            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:
+            dst_img = np.rot90(dst_img)
+        return dst_img
+
+    def sorted_boxes(self, dt_boxes):
+        """
+        Sort text boxes in order from top to bottom, left to right
+        args:
+            dt_boxes(array):detected text boxes with shape [4, 2]
+        return:
+            sorted boxes(array) with shape [4, 2]
+        """
+        num_boxes = dt_boxes.shape[0]
+        sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
+        _boxes = list(sorted_boxes)
+
+        for i in range(num_boxes - 1):
+            for j in range(i, -1, -1):
+                if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
+                        (_boxes[j + 1][0][0] < _boxes[j][0][0]):
+                    tmp = _boxes[j]
+                    _boxes[j] = _boxes[j + 1]
+                    _boxes[j + 1] = tmp
+                else:
+                    break
+        return _boxes
+
+    def __call__(self, img, cls=True):
+        time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
+
+        if img is None:
+            return None, None, time_dict
+
+        start = time.time()
+        ori_im = img.copy()
+        dt_boxes, elapse = self.text_detector(img)
+        time_dict['det'] = elapse
+
+        if dt_boxes is None:
+            end = time.time()
+            time_dict['all'] = end - start
+            return None, None, time_dict
+        else:
+            cron_logger.debug("dt_boxes num : {}, elapsed : {}".format(
+                len(dt_boxes), elapse))
+        img_crop_list = []
+
+        dt_boxes = self.sorted_boxes(dt_boxes)
+
+        for bno in range(len(dt_boxes)):
+            tmp_box = copy.deepcopy(dt_boxes[bno])
+            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
+            img_crop_list.append(img_crop)
+
+        rec_res, elapse = self.text_recognizer(img_crop_list)
+        time_dict['rec'] = elapse
+        cron_logger.debug("rec_res num  : {}, elapsed : {}".format(
+            len(rec_res), elapse))
+
+        filter_boxes, filter_rec_res = [], []
+        for box, rec_result in zip(dt_boxes, rec_res):
+            text, score = rec_result
+            if score >= self.drop_score:
+                filter_boxes.append(box)
+                filter_rec_res.append(rec_result)
+        end = time.time()
+        time_dict['all'] = end - start
+
+        #for bno in range(len(img_crop_list)):
+        #    print(f"{bno}, {rec_res[bno]}")
+
+        return list(zip([a.tolist() for a in filter_boxes], filter_rec_res))

deepdoc/visual/operators.py

+#
+#  Copyright 2024 The InfiniFlow 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 sys
+import six
+import cv2
+import numpy as np
+import math
+from PIL import Image
+
+
+class DecodeImage(object):
+    """ decode image """
+
+    def __init__(self,
+                 img_mode='RGB',
+                 channel_first=False,
+                 ignore_orientation=False,
+                 **kwargs):
+        self.img_mode = img_mode
+        self.channel_first = channel_first
+        self.ignore_orientation = ignore_orientation
+
+    def __call__(self, data):
+        img = data['image']
+        if six.PY2:
+            assert isinstance(img, str) and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert isinstance(img, bytes) and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        img = np.frombuffer(img, dtype='uint8')
+        if self.ignore_orientation:
+            img = cv2.imdecode(img, cv2.IMREAD_IGNORE_ORIENTATION |
+                               cv2.IMREAD_COLOR)
+        else:
+            img = cv2.imdecode(img, 1)
+        if img is None:
+            return None
+        if self.img_mode == 'GRAY':
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif self.img_mode == 'RGB':
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+                img.shape)
+            img = img[:, :, ::-1]
+
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+
+        data['image'] = img
+        return data
+
+class StandardizeImage(object):
+    """normalize image
+    Args:
+        mean (list): im - mean
+        std (list): im / std
+        is_scale (bool): whether need im / 255
+        norm_type (str): type in ['mean_std', 'none']
+    """
+
+    def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
+        self.mean = mean
+        self.std = std
+        self.is_scale = is_scale
+        self.norm_type = norm_type
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        im = im.astype(np.float32, copy=False)
+        if self.is_scale:
+            scale = 1.0 / 255.0
+            im *= scale
+
+        if self.norm_type == 'mean_std':
+            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+            std = np.array(self.std)[np.newaxis, np.newaxis, :]
+            im -= mean
+            im /= std
+        return im, im_info
+
+
+class NormalizeImage(object):
+    """ normalize image such as substract mean, divide std
+    """
+
+    def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+        if isinstance(scale, str):
+            scale = eval(scale)
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        assert isinstance(img,
+                          np.ndarray), "invalid input 'img' in NormalizeImage"
+        data['image'] = (
+            img.astype('float32') * self.scale - self.mean) / self.std
+        return data
+
+
+class ToCHWImage(object):
+    """ convert hwc image to chw image
+    """
+
+    def __init__(self, **kwargs):
+        pass
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        data['image'] = img.transpose((2, 0, 1))
+        return data
+
+
+class Fasttext(object):
+    def __init__(self, path="None", **kwargs):
+        import fasttext
+        self.fast_model = fasttext.load_model(path)
+
+    def __call__(self, data):
+        label = data['label']
+        fast_label = self.fast_model[label]
+        data['fast_label'] = fast_label
+        return data
+
+
+class KeepKeys(object):
+    def __init__(self, keep_keys, **kwargs):
+        self.keep_keys = keep_keys
+
+    def __call__(self, data):
+        data_list = []
+        for key in self.keep_keys:
+            data_list.append(data[key])
+        return data_list
+
+
+class Pad(object):
+    def __init__(self, size=None, size_div=32, **kwargs):
+        if size is not None and not isinstance(size, (int, list, tuple)):
+            raise TypeError("Type of target_size is invalid. Now is {}".format(
+                type(size)))
+        if isinstance(size, int):
+            size = [size, size]
+        self.size = size
+        self.size_div = size_div
+
+    def __call__(self, data):
+
+        img = data['image']
+        img_h, img_w = img.shape[0], img.shape[1]
+        if self.size:
+            resize_h2, resize_w2 = self.size
+            assert (
+                img_h < resize_h2 and img_w < resize_w2
+            ), '(h, w) of target size should be greater than (img_h, img_w)'
+        else:
+            resize_h2 = max(
+                int(math.ceil(img.shape[0] / self.size_div) * self.size_div),
+                self.size_div)
+            resize_w2 = max(
+                int(math.ceil(img.shape[1] / self.size_div) * self.size_div),
+                self.size_div)
+        img = cv2.copyMakeBorder(
+            img,
+            0,
+            resize_h2 - img_h,
+            0,
+            resize_w2 - img_w,
+            cv2.BORDER_CONSTANT,
+            value=0)
+        data['image'] = img
+        return data
+
+
+class LinearResize(object):
+    """resize image by target_size and max_size
+    Args:
+        target_size (int): the target size of image
+        keep_ratio (bool): whether keep_ratio or not, default true
+        interp (int): method of resize
+    """
+
+    def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
+        if isinstance(target_size, int):
+            target_size = [target_size, target_size]
+        self.target_size = target_size
+        self.keep_ratio = keep_ratio
+        self.interp = interp
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        assert len(self.target_size) == 2
+        assert self.target_size[0] > 0 and self.target_size[1] > 0
+        im_channel = im.shape[2]
+        im_scale_y, im_scale_x = self.generate_scale(im)
+        im = cv2.resize(
+            im,
+            None,
+            None,
+            fx=im_scale_x,
+            fy=im_scale_y,
+            interpolation=self.interp)
+        im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
+        im_info['scale_factor'] = np.array(
+            [im_scale_y, im_scale_x]).astype('float32')
+        return im, im_info
+
+    def generate_scale(self, im):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+        Returns:
+            im_scale_x: the resize ratio of X
+            im_scale_y: the resize ratio of Y
+        """
+        origin_shape = im.shape[:2]
+        im_c = im.shape[2]
+        if self.keep_ratio:
+            im_size_min = np.min(origin_shape)
+            im_size_max = np.max(origin_shape)
+            target_size_min = np.min(self.target_size)
+            target_size_max = np.max(self.target_size)
+            im_scale = float(target_size_min) / float(im_size_min)
+            if np.round(im_scale * im_size_max) > target_size_max:
+                im_scale = float(target_size_max) / float(im_size_max)
+            im_scale_x = im_scale
+            im_scale_y = im_scale
+        else:
+            resize_h, resize_w = self.target_size
+            im_scale_y = resize_h / float(origin_shape[0])
+            im_scale_x = resize_w / float(origin_shape[1])
+        return im_scale_y, im_scale_x
+
+
+class Resize(object):
+    def __init__(self, size=(640, 640), **kwargs):
+        self.size = size
+
+    def resize_image(self, img):
+        resize_h, resize_w = self.size
+        ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        ratio_h = float(resize_h) / ori_h
+        ratio_w = float(resize_w) / ori_w
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        return img, [ratio_h, ratio_w]
+
+    def __call__(self, data):
+        img = data['image']
+        if 'polys' in data:
+            text_polys = data['polys']
+
+        img_resize, [ratio_h, ratio_w] = self.resize_image(img)
+        if 'polys' in data:
+            new_boxes = []
+            for box in text_polys:
+                new_box = []
+                for cord in box:
+                    new_box.append([cord[0] * ratio_w, cord[1] * ratio_h])
+                new_boxes.append(new_box)
+            data['polys'] = np.array(new_boxes, dtype=np.float32)
+        data['image'] = img_resize
+        return data
+
+
+class DetResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(DetResizeForTest, self).__init__()
+        self.resize_type = 0
+        self.keep_ratio = False
+        if 'image_shape' in kwargs:
+            self.image_shape = kwargs['image_shape']
+            self.resize_type = 1
+            if 'keep_ratio' in kwargs:
+                self.keep_ratio = kwargs['keep_ratio']
+        elif 'limit_side_len' in kwargs:
+            self.limit_side_len = kwargs['limit_side_len']
+            self.limit_type = kwargs.get('limit_type', 'min')
+        elif 'resize_long' in kwargs:
+            self.resize_type = 2
+            self.resize_long = kwargs.get('resize_long', 960)
+        else:
+            self.limit_side_len = 736
+            self.limit_type = 'min'
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if sum([src_h, src_w]) < 64:
+            img = self.image_padding(img)
+
+        if self.resize_type == 0:
+            # img, shape = self.resize_image_type0(img)
+            img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+        elif self.resize_type == 2:
+            img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+        else:
+            # img, shape = self.resize_image_type1(img)
+            img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+        data['image'] = img
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def image_padding(self, im, value=0):
+        h, w, c = im.shape
+        im_pad = np.zeros((max(32, h), max(32, w), c), np.uint8) + value
+        im_pad[:h, :w, :] = im
+        return im_pad
+
+    def resize_image_type1(self, img):
+        resize_h, resize_w = self.image_shape
+        ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        if self.keep_ratio is True:
+            resize_w = ori_w * resize_h / ori_h
+            N = math.ceil(resize_w / 32)
+            resize_w = N * 32
+        ratio_h = float(resize_h) / ori_h
+        ratio_w = float(resize_w) / ori_w
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        # return img, np.array([ori_h, ori_w])
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type0(self, img):
+        """
+        resize image to a size multiple of 32 which is required by the network
+        args:
+            img(array): array with shape [h, w, c]
+        return(tuple):
+            img, (ratio_h, ratio_w)
+        """
+        limit_side_len = self.limit_side_len
+        h, w, c = img.shape
+
+        # limit the max side
+        if self.limit_type == 'max':
+            if max(h, w) > limit_side_len:
+                if h > w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        elif self.limit_type == 'min':
+            if min(h, w) < limit_side_len:
+                if h < w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        elif self.limit_type == 'resize_long':
+            ratio = float(limit_side_len) / max(h, w)
+        else:
+            raise Exception('not support limit type, image ')
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = max(int(round(resize_h / 32) * 32), 32)
+        resize_w = max(int(round(resize_w / 32) * 32), 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except BaseException:
+            print(img.shape, resize_w, resize_h)
+            sys.exit(0)
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return img, [ratio_h, ratio_w]
+
+    def resize_image_type2(self, img):
+        h, w, _ = img.shape
+
+        resize_w = w
+        resize_h = h
+
+        if resize_h > resize_w:
+            ratio = float(self.resize_long) / resize_h
+        else:
+            ratio = float(self.resize_long) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return img, [ratio_h, ratio_w]
+
+
+class E2EResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(E2EResizeForTest, self).__init__()
+        self.max_side_len = kwargs['max_side_len']
+        self.valid_set = kwargs['valid_set']
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if self.valid_set == 'totaltext':
+            im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
+                img, max_side_len=self.max_side_len)
+        else:
+            im_resized, (ratio_h, ratio_w) = self.resize_image(
+                img, max_side_len=self.max_side_len)
+        data['image'] = im_resized
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def resize_image_for_totaltext(self, im, max_side_len=512):
+
+        h, w, _ = im.shape
+        resize_w = w
+        resize_h = h
+        ratio = 1.25
+        if h * ratio > max_side_len:
+            ratio = float(max_side_len) / resize_h
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def resize_image(self, im, max_side_len=512):
+        """
+        resize image to a size multiple of max_stride which is required by the network
+        :param im: the resized image
+        :param max_side_len: limit of max image size to avoid out of memory in gpu
+        :return: the resized image and the resize ratio
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(max_side_len) / resize_h
+        else:
+            ratio = float(max_side_len) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return im, (ratio_h, ratio_w)
+
+
+class KieResize(object):
+    def __init__(self, **kwargs):
+        super(KieResize, self).__init__()
+        self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[
+            'img_scale'][1]
+
+    def __call__(self, data):
+        img = data['image']
+        points = data['points']
+        src_h, src_w, _ = img.shape
+        im_resized, scale_factor, [ratio_h, ratio_w
+                                   ], [new_h, new_w] = self.resize_image(img)
+        resize_points = self.resize_boxes(img, points, scale_factor)
+        data['ori_image'] = img
+        data['ori_boxes'] = points
+        data['points'] = resize_points
+        data['image'] = im_resized
+        data['shape'] = np.array([new_h, new_w])
+        return data
+
+    def resize_image(self, img):
+        norm_img = np.zeros([1024, 1024, 3], dtype='float32')
+        scale = [512, 1024]
+        h, w = img.shape[:2]
+        max_long_edge = max(scale)
+        max_short_edge = min(scale)
+        scale_factor = min(max_long_edge / max(h, w),
+                           max_short_edge / min(h, w))
+        resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(h * float(
+            scale_factor) + 0.5)
+        max_stride = 32
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(img, (resize_w, resize_h))
+        new_h, new_w = im.shape[:2]
+        w_scale = new_w / w
+        h_scale = new_h / h
+        scale_factor = np.array(
+            [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
+        norm_img[:new_h, :new_w, :] = im
+        return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w]
+
+    def resize_boxes(self, im, points, scale_factor):
+        points = points * scale_factor
+        img_shape = im.shape[:2]
+        points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
+        points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
+        return points
+
+
+class SRResize(object):
+    def __init__(self,
+                 imgH=32,
+                 imgW=128,
+                 down_sample_scale=4,
+                 keep_ratio=False,
+                 min_ratio=1,
+                 mask=False,
+                 infer_mode=False,
+                 **kwargs):
+        self.imgH = imgH
+        self.imgW = imgW
+        self.keep_ratio = keep_ratio
+        self.min_ratio = min_ratio
+        self.down_sample_scale = down_sample_scale
+        self.mask = mask
+        self.infer_mode = infer_mode
+
+    def __call__(self, data):
+        imgH = self.imgH
+        imgW = self.imgW
+        images_lr = data["image_lr"]
+        transform2 = ResizeNormalize(
+            (imgW // self.down_sample_scale, imgH // self.down_sample_scale))
+        images_lr = transform2(images_lr)
+        data["img_lr"] = images_lr
+        if self.infer_mode:
+            return data
+
+        images_HR = data["image_hr"]
+        label_strs = data["label"]
+        transform = ResizeNormalize((imgW, imgH))
+        images_HR = transform(images_HR)
+        data["img_hr"] = images_HR
+        return data
+
+
+class ResizeNormalize(object):
+    def __init__(self, size, interpolation=Image.BICUBIC):
+        self.size = size
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        img = img.resize(self.size, self.interpolation)
+        img_numpy = np.array(img).astype("float32")
+        img_numpy = img_numpy.transpose((2, 0, 1)) / 255
+        return img_numpy
+
+
+class GrayImageChannelFormat(object):
+    """
+    format gray scale image's channel: (3,h,w) -> (1,h,w)
+    Args:
+        inverse: inverse gray image
+    """
+
+    def __init__(self, inverse=False, **kwargs):
+        self.inverse = inverse
+
+    def __call__(self, data):
+        img = data['image']
+        img_single_channel = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img_expanded = np.expand_dims(img_single_channel, 0)
+
+        if self.inverse:
+            data['image'] = np.abs(img_expanded - 1)
+        else:
+            data['image'] = img_expanded
+
+        data['src_image'] = img
+        return data
+
+
+class Permute(object):
+    """permute image
+    Args:
+        to_bgr (bool): whether convert RGB to BGR
+        channel_first (bool): whether convert HWC to CHW
+    """
+
+    def __init__(self, ):
+        super(Permute, self).__init__()
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        im = im.transpose((2, 0, 1)).copy()
+        return im, im_info
+
+
+class PadStride(object):
+    """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
+    Args:
+        stride (bool): model with FPN need image shape % stride == 0
+    """
+
+    def __init__(self, stride=0):
+        self.coarsest_stride = stride
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        coarsest_stride = self.coarsest_stride
+        if coarsest_stride <= 0:
+            return im, im_info
+        im_c, im_h, im_w = im.shape
+        pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
+        pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
+        padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
+        padding_im[:, :im_h, :im_w] = im
+        return padding_im, im_info
+
+
+def decode_image(im_file, im_info):
+    """read rgb image
+    Args:
+        im_file (str|np.ndarray): input can be image path or np.ndarray
+        im_info (dict): info of image
+    Returns:
+        im (np.ndarray):  processed image (np.ndarray)
+        im_info (dict): info of processed image
+    """
+    if isinstance(im_file, str):
+        with open(im_file, 'rb') as f:
+            im_read = f.read()
+        data = np.frombuffer(im_read, dtype='uint8')
+        im = cv2.imdecode(data, 1)  # BGR mode, but need RGB mode
+        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+    else:
+        im = im_file
+    im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
+    im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
+    return im, im_info
+
+
+def preprocess(im, preprocess_ops):
+    # process image by preprocess_ops
+    im_info = {
+        'scale_factor': np.array(
+            [1., 1.], dtype=np.float32),
+        'im_shape': None,
+    }
+    im, im_info = decode_image(im, im_info)
+    for operator in preprocess_ops:
+        im, im_info = operator(im, im_info)
+    return im, im_info

deepdoc/visual/postprocess.py

+import copy
+
+import numpy as np
+import cv2
+import paddle
+from shapely.geometry import Polygon
+import pyclipper
+
+
+def build_post_process(config, global_config=None):
+    support_dict = ['DBPostProcess', 'CTCLabelDecode']
+
+    config = copy.deepcopy(config)
+    module_name = config.pop('name')
+    if module_name == "None":
+        return
+    if global_config is not None:
+        config.update(global_config)
+    assert module_name in support_dict, Exception(
+        'post process only support {}'.format(support_dict))
+    module_class = eval(module_name)(**config)
+    return module_class
+
+
+class DBPostProcess(object):
+    """
+    The post process for Differentiable Binarization (DB).
+    """
+
+    def __init__(self,
+                 thresh=0.3,
+                 box_thresh=0.7,
+                 max_candidates=1000,
+                 unclip_ratio=2.0,
+                 use_dilation=False,
+                 score_mode="fast",
+                 box_type='quad',
+                 **kwargs):
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.max_candidates = max_candidates
+        self.unclip_ratio = unclip_ratio
+        self.min_size = 3
+        self.score_mode = score_mode
+        self.box_type = box_type
+        assert score_mode in [
+            "slow", "fast"
+        ], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
+
+        self.dilation_kernel = None if not use_dilation else np.array(
+            [[1, 1], [1, 1]])
+
+    def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        '''
+        _bitmap: single map with shape (1, H, W),
+            whose values are binarized as {0, 1}
+        '''
+
+        bitmap = _bitmap
+        height, width = bitmap.shape
+
+        boxes = []
+        scores = []
+
+        contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8),
+                                       cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+        for contour in contours[:self.max_candidates]:
+            epsilon = 0.002 * cv2.arcLength(contour, True)
+            approx = cv2.approxPolyDP(contour, epsilon, True)
+            points = approx.reshape((-1, 2))
+            if points.shape[0] < 4:
+                continue
+
+            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.box_thresh > score:
+                continue
+
+            if points.shape[0] > 2:
+                box = self.unclip(points, self.unclip_ratio)
+                if len(box) > 1:
+                    continue
+            else:
+                continue
+            box = box.reshape(-1, 2)
+
+            _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
+            if sside < self.min_size + 2:
+                continue
+
+            box = np.array(box)
+            box[:, 0] = np.clip(
+                np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height)
+            boxes.append(box.tolist())
+            scores.append(score)
+        return boxes, scores
+
+    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        '''
+        _bitmap: single map with shape (1, H, W),
+                whose values are binarized as {0, 1}
+        '''
+
+        bitmap = _bitmap
+        height, width = bitmap.shape
+
+        outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+                                cv2.CHAIN_APPROX_SIMPLE)
+        if len(outs) == 3:
+            img, contours, _ = outs[0], outs[1], outs[2]
+        elif len(outs) == 2:
+            contours, _ = outs[0], outs[1]
+
+        num_contours = min(len(contours), self.max_candidates)
+
+        boxes = []
+        scores = []
+        for index in range(num_contours):
+            contour = contours[index]
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+            points = np.array(points)
+            if self.score_mode == "fast":
+                score = self.box_score_fast(pred, points.reshape(-1, 2))
+            else:
+                score = self.box_score_slow(pred, contour)
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2)
+            box, sside = self.get_mini_boxes(box)
+            if sside < self.min_size + 2:
+                continue
+            box = np.array(box)
+
+            box[:, 0] = np.clip(
+                np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height)
+            boxes.append(box.astype("int32"))
+            scores.append(score)
+        return np.array(boxes, dtype="int32"), scores
+
+    def unclip(self, box, unclip_ratio):
+        poly = Polygon(box)
+        distance = poly.area * unclip_ratio / poly.length
+        offset = pyclipper.PyclipperOffset()
+        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance))
+        return expanded
+
+    def get_mini_boxes(self, contour):
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+        if points[1][1] > points[0][1]:
+            index_1 = 0
+            index_4 = 1
+        else:
+            index_1 = 1
+            index_4 = 0
+        if points[3][1] > points[2][1]:
+            index_2 = 2
+            index_3 = 3
+        else:
+            index_2 = 3
+            index_3 = 2
+
+        box = [
+            points[index_1], points[index_2], points[index_3], points[index_4]
+        ]
+        return box, min(bounding_box[1])
+
+    def box_score_fast(self, bitmap, _box):
+        '''
+        box_score_fast: use bbox mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        box = _box.copy()
+        xmin = np.clip(np.floor(box[:, 0].min()).astype("int32"), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype("int32"), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype("int32"), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype("int32"), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+        box[:, 0] = box[:, 0] - xmin
+        box[:, 1] = box[:, 1] - ymin
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype("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("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):
+            pred = pred.numpy()
+        pred = pred[:, 0, :, :]
+        segmentation = pred > self.thresh
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
+            if self.dilation_kernel is not None:
+                mask = cv2.dilate(
+                    np.array(segmentation[batch_index]).astype(np.uint8),
+                    self.dilation_kernel)
+            else:
+                mask = segmentation[batch_index]
+            if self.box_type == 'poly':
+                boxes, scores = self.polygons_from_bitmap(pred[batch_index],
+                                                          mask, src_w, src_h)
+            elif self.box_type == 'quad':
+                boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+                                                       src_w, src_h)
+            else:
+                raise ValueError(
+                    "box_type can only be one of ['quad', 'poly']")
+
+            boxes_batch.append({'points': boxes})
+        return boxes_batch
+
+
+class BaseRecLabelDecode(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False):
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        self.reverse = False
+        self.character_str = []
+
+        if character_dict_path is None:
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        else:
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n").strip("\r\n")
+                    self.character_str.append(line)
+            if use_space_char:
+                self.character_str.append(" ")
+            dict_character = list(self.character_str)
+            if 'arabic' in character_dict_path:
+                self.reverse = True
+
+        dict_character = self.add_special_char(dict_character)
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+    def pred_reverse(self, pred):
+        pred_re = []
+        c_current = ''
+        for c in pred:
+            if not bool(re.search('[a-zA-Z0-9 :*./%+-]', c)):
+                if c_current != '':
+                    pred_re.append(c_current)
+                pred_re.append(c)
+                c_current = ''
+            else:
+                c_current += c
+        if c_current != '':
+            pred_re.append(c_current)
+
+        return ''.join(pred_re[::-1])
+
+    def add_special_char(self, dict_character):
+        return dict_character
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            selection = np.ones(len(text_index[batch_idx]), dtype=bool)
+            if is_remove_duplicate:
+                selection[1:] = text_index[batch_idx][1:] != text_index[
+                    batch_idx][:-1]
+            for ignored_token in ignored_tokens:
+                selection &= text_index[batch_idx] != ignored_token
+
+            char_list = [
+                self.character[text_id]
+                for text_id in text_index[batch_idx][selection]
+            ]
+            if text_prob is not None:
+                conf_list = text_prob[batch_idx][selection]
+            else:
+                conf_list = [1] * len(selection)
+            if len(conf_list) == 0:
+                conf_list = [0]
+
+            text = ''.join(char_list)
+
+            if self.reverse:  # for arabic rec
+                text = self.pred_reverse(text)
+
+            result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    def get_ignored_tokens(self):
+        return [0]  # for ctc blank
+
+
+class CTCLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(CTCLabelDecode, self).__init__(character_dict_path,
+                                             use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, tuple) or isinstance(preds, list):
+            preds = preds[-1]
+        if isinstance(preds, paddle.Tensor):
+            preds = preds.numpy()
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank'] + dict_character
+        return dict_character

deepdoc/visual/recognizer.py

+#  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 os
+import onnxruntime as ort
+from huggingface_hub import snapshot_download
+
+from .operators import *
+from rag.settings import cron_logger
+
+
+class Recognizer(object):
+    def __init__(self, label_list, task_name, model_dir=None):
+        """
+        If you have trouble downloading HuggingFace models, -_^ this might help!!
+
+        For Linux:
+        export HF_ENDPOINT=https://hf-mirror.com
+
+        For Windows:
+        Good luck
+        ^_-
+
+        """
+        if not model_dir:
+            model_dir = snapshot_download(repo_id="InfiniFlow/ocr")
+
+        model_file_path = os.path.join(model_dir, task_name + ".onnx")
+        if not os.path.exists(model_file_path):
+            raise ValueError("not find model file path {}".format(
+                model_file_path))
+        if ort.get_device() == "GPU":
+            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CUDAExecutionProvider'])
+        else:
+            self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider'])
+        self.label_list = label_list
+
+    def create_inputs(self, imgs, im_info):
+        """generate input for different model type
+        Args:
+            imgs (list(numpy)): list of images (np.ndarray)
+            im_info (list(dict)): list of image info
+        Returns:
+            inputs (dict): input of model
+        """
+        inputs = {}
+
+        im_shape = []
+        scale_factor = []
+        if len(imgs) == 1:
+            inputs['image'] = np.array((imgs[0],)).astype('float32')
+            inputs['im_shape'] = np.array(
+                (im_info[0]['im_shape'],)).astype('float32')
+            inputs['scale_factor'] = np.array(
+                (im_info[0]['scale_factor'],)).astype('float32')
+            return inputs
+
+        for e in im_info:
+            im_shape.append(np.array((e['im_shape'],)).astype('float32'))
+            scale_factor.append(np.array((e['scale_factor'],)).astype('float32'))
+
+        inputs['im_shape'] = np.concatenate(im_shape, axis=0)
+        inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
+
+        imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
+        max_shape_h = max([e[0] for e in imgs_shape])
+        max_shape_w = max([e[1] for e in imgs_shape])
+        padding_imgs = []
+        for img in imgs:
+            im_c, im_h, im_w = img.shape[:]
+            padding_im = np.zeros(
+                (im_c, max_shape_h, max_shape_w), dtype=np.float32)
+            padding_im[:, :im_h, :im_w] = img
+            padding_imgs.append(padding_im)
+        inputs['image'] = np.stack(padding_imgs, axis=0)
+        return inputs
+
+    def preprocess(self, image_list):
+        preprocess_ops = []
+        for op_info in [
+            {'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'},
+            {'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'},
+            {'type': 'Permute'},
+            {'stride': 32, 'type': 'PadStride'}
+        ]:
+            new_op_info = op_info.copy()
+            op_type = new_op_info.pop('type')
+            preprocess_ops.append(eval(op_type)(**new_op_info))
+
+        inputs = []
+        for im_path in image_list:
+            im, im_info = preprocess(im_path, preprocess_ops)
+            inputs.append({"image": np.array((im,)).astype('float32'), "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')})
+        return inputs
+
+
+    def __call__(self, image_list, thr=0.7, batch_size=16):
+        res = []
+        imgs = []
+        for i in range(len(image_list)):
+            if not isinstance(image_list[i], np.ndarray):
+                imgs.append(np.array(image_list[i]))
+            else: imgs.append(image_list[i])
+
+        batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size)
+        for i in range(batch_loop_cnt):
+            start_index = i * batch_size
+            end_index = min((i + 1) * batch_size, len(imgs))
+            batch_image_list = imgs[start_index:end_index]
+            inputs = self.preprocess(batch_image_list)
+            for ins in inputs:
+                bb = []
+                for b in self.ort_sess.run(None, ins)[0]:
+                    clsid, bbox, score = int(b[0]), b[2:], b[1]
+                    if score < thr:
+                        continue
+                    if clsid >= len(self.label_list):
+                        cron_logger.warning(f"bad category id")
+                        continue
+                    bb.append({
+                        "type": self.label_list[clsid].lower(),
+                        "bbox": [float(t) for t in bbox.tolist()],
+                        "score": float(score)
+                    })
+                res.append(bb)
+
+        #seeit.save_results(image_list, res, self.label_list, threshold=thr)
+
+        return res

deepdoc/visual/seeit.py

+#  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 os
+import PIL
+from PIL import ImageDraw
+
+
+def save_results(image_list, results, labels, output_dir='output/', threshold=0.5):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    for idx, im in enumerate(image_list):
+        im = draw_box(im, results[idx], labels, threshold=threshold)
+
+        out_path = os.path.join(output_dir, f"{idx}.jpg")
+        im.save(out_path, quality=95)
+        print("save result to: " + out_path)
+
+
+def draw_box(im, result, lables, threshold=0.5):
+    draw_thickness = min(im.size) // 320
+    draw = ImageDraw.Draw(im)
+    color_list = get_color_map_list(len(lables))
+    clsid2color = {n.lower():color_list[i] for i,n in enumerate(lables)}
+    result = [r for r in result if r["score"] >= threshold]
+
+    for dt in result:
+        color = tuple(clsid2color[dt["type"]])
+        xmin, ymin, xmax, ymax = dt["bbox"]
+        draw.line(
+            [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
+             (xmin, ymin)],
+            width=draw_thickness,
+            fill=color)
+
+        # draw label
+        text = "{} {:.4f}".format(dt["type"], dt["score"])
+        tw, th = imagedraw_textsize_c(draw, text)
+        draw.rectangle(
+            [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
+        draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
+    return im
+
+
+def get_color_map_list(num_classes):
+    """
+    Args:
+        num_classes (int): number of class
+    Returns:
+        color_map (list): RGB color list
+    """
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
+    return color_map
+
+
+def imagedraw_textsize_c(draw, text):
+    if int(PIL.__version__.split('.')[0]) < 10:
+        tw, th = draw.textsize(text)
+    else:
+        left, top, right, bottom = draw.textbbox((0, 0), text)
+        tw, th = right - left, bottom - top
+
+    return tw, th

rag/app/book.py

+#  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 copy
-import random
 import re
-import numpy as np
-from rag.parser import bullets_category, BULLET_PATTERN, is_english, tokenize, remove_contents_table, \
+from deepdoc.parser import bullets_category, is_english, tokenize, remove_contents_table, \
     hierarchical_merge, make_colon_as_title, naive_merge, random_choices
 from rag.nlp import huqie
-from rag.parser.docx_parser import HuDocxParser
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import PdfParser, DocxParser
 
 
-class Pdf(HuParser):
+class Pdf(PdfParser):
     def __call__(self, filename, binary=None, from_page=0,
                  to_page=100000, zoomin=3, callback=None):
         self.__images__(
 
         from timeit import default_timer as timer
         start = timer()
-        self._layouts_paddle(zoomin)
+        self._layouts_rec(zoomin)
         callback(0.47, "Layout analysis finished")
         print("paddle layouts:", timer() - start)
         self._table_transformer_job(zoomin)
     sections,tbls = [], []
     if re.search(r"\.docx?$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
-        doc_parser = HuDocxParser()
+        doc_parser = DocxParser()
         # TODO: table of contents need to be removed
         sections, tbls = doc_parser(binary if binary else filename, from_page=from_page, to_page=to_page)
         remove_contents_table(sections, eng=is_english(random_choices([t for t,_ in sections], k=200)))

rag/app/laws.py

+#  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 copy
 import re
 from io import BytesIO
 from docx import Document
-from rag.parser import bullets_category, is_english, tokenize, remove_contents_table, hierarchical_merge, \
+from deepdoc.parser import bullets_category, is_english, tokenize, remove_contents_table, hierarchical_merge, \
     make_colon_as_title
 from rag.nlp import huqie
-from rag.parser.docx_parser import HuDocxParser
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import PdfParser, DocxParser
 from rag.settings import cron_logger
 
 
-class Docx(HuDocxParser):
+class Docx(DocxParser):
     def __init__(self):
         pass
 
         return [l for l in lines if l]
 
 
-class Pdf(HuParser):
+class Pdf(PdfParser):
     def __call__(self, filename, binary=None, from_page=0,
                  to_page=100000, zoomin=3, callback=None):
         self.__images__(
 
         from timeit import default_timer as timer
         start = timer()
-        self._layouts_paddle(zoomin)
+        self._layouts_rec(zoomin)
         callback(0.77, "Layout analysis finished")
         cron_logger.info("paddle layouts:".format((timer()-start)/(self.total_page+0.1)))
         self._naive_vertical_merge()

rag/app/manual.py

 import copy
 import re
-from rag.parser import tokenize
+from deepdoc.parser import tokenize
 from rag.nlp import huqie
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import PdfParser
 from rag.utils import num_tokens_from_string
 
 
-class Pdf(HuParser):
+class Pdf(PdfParser):
     def __call__(self, filename, binary=None, from_page=0,
                  to_page=100000, zoomin=3, callback=None):
         self.__images__(
 
         from timeit import default_timer as timer
         start = timer()
-        self._layouts_paddle(zoomin)
+        self._layouts_rec(zoomin)
         callback(0.5, "Layout analysis finished.")
         print("paddle layouts:", timer() - start)
         self._table_transformer_job(zoomin)

rag/app/naive.py

+#  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 copy
 import re
 from rag.app import laws
-from rag.parser import is_english, tokenize, naive_merge
+from deepdoc.parser import is_english, tokenize, naive_merge
 from rag.nlp import huqie
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import PdfParser
 from rag.settings import cron_logger
 
 
-class Pdf(HuParser):
+class Pdf(PdfParser):
     def __call__(self, filename, binary=None, from_page=0,
                  to_page=100000, zoomin=3, callback=None):
         self.__images__(
 
         from timeit import default_timer as timer
         start = timer()
-        self._layouts_paddle(zoomin)
+        self._layouts_rec(zoomin)
         callback(0.77, "Layout analysis finished")
         cron_logger.info("paddle layouts:".format((timer() - start) / (self.total_page + 0.1)))
         self._naive_vertical_merge()

rag/app/paper.py

+#  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 copy
 import re
 from collections import Counter
 
 from api.db import ParserType
-from rag.parser import tokenize
+from deepdoc.parser import tokenize
 from rag.nlp import huqie
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import PdfParser
 import numpy as np
 from rag.utils import num_tokens_from_string
 
 
-class Pdf(HuParser):
+class Pdf(PdfParser):
     def __init__(self):
         self.model_speciess = ParserType.PAPER.value
         super().__init__()
 
         from timeit import default_timer as timer
         start = timer()
-        self._layouts_paddle(zoomin)
+        self._layouts_rec(zoomin)
         callback(0.47, "Layout analysis finished")
         print("paddle layouts:", timer() - start)
         self._table_transformer_job(zoomin)

rag/app/presentation.py

+#  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 copy
 import re
 from io import BytesIO
 from pptx import Presentation
-
-from rag.parser import tokenize, is_english
+from deepdoc.parser import tokenize, is_english
 from rag.nlp import huqie
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import PdfParser
 
 
 class Ppt(object):
         return [(txts[i], imgs[i]) for i in range(len(txts))]
 
 
-class Pdf(HuParser):
+class Pdf(PdfParser):
     def __init__(self):
         super().__init__()
 
         assert len(self.boxes) == len(self.page_images), "{} vs. {}".format(len(self.boxes), len(self.page_images))
         res = []
         #################### More precisely ###################
-        # self._layouts_paddle(zoomin)
+        # self._layouts_rec(zoomin)
         # self._text_merge()
         # pages = {}
         # for b in self.boxes:

rag/app/qa.py

-import random
+#  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 re
 from io import BytesIO
 from nltk import word_tokenize
 from openpyxl import load_workbook
-from rag.parser import is_english, random_choices
+from deepdoc.parser import is_english, random_choices
 from rag.nlp import huqie, stemmer
+from deepdoc.parser import ExcelParser
 
 
-class Excel(object):
+class Excel(ExcelParser):
     def __call__(self, fnm, binary=None, callback=None):
         if not binary:
             wb = load_workbook(fnm)

rag/app/resume.py

-import copy
+#  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 base64
+import datetime
 import json
-import os
 import re
+
+import pandas as pd
 import requests
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.settings import stat_logger
 from rag.nlp import huqie
-
+from deepdoc.parser.resume import refactor
+from deepdoc.parser.resume import step_one, step_two
 from rag.settings import cron_logger
 from rag.utils import rmSpace
 
 forbidden_select_fields4resume = [
     "name_pinyin_kwd", "edu_first_fea_kwd", "degree_kwd", "sch_rank_kwd", "edu_fea_kwd"
 ]
+def remote_call(filename, binary):
+    q = {
+        "header": {
+            "uid": 1,
+            "user": "kevinhu",
+            "log_id": filename
+        },
+        "request": {
+            "p": {
+                "request_id": "1",
+                "encrypt_type": "base64",
+                "filename": filename,
+                "langtype": '',
+                "fileori": base64.b64encode(binary.stream.read()).decode('utf-8')
+            },
+            "c": "resume_parse_module",
+            "m": "resume_parse"
+        }
+    }
+    for _ in range(3):
+        try:
+            resume = requests.post("http://127.0.0.1:61670/tog", data=json.dumps(q))
+            resume = resume.json()["response"]["results"]
+            resume = refactor(resume)
+            for k in ["education", "work", "project", "training", "skill", "certificate", "language"]:
+                if not resume.get(k) and k in resume: del resume[k]
+
+            resume = step_one.refactor(pd.DataFrame([{"resume_content": json.dumps(resume), "tob_resume_id": "x",
+                                                "updated_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]))
+            resume = step_two.parse(resume)
+            return resume
+        except Exception as e:
+            cron_logger.error("Resume parser error: "+str(e))
+    return {}
+
 
 def chunk(filename, binary=None, callback=None, **kwargs):
     """
     The supported file formats are pdf, docx and txt.
-    To maximize the effectiveness, parse the resume correctly,
-    please visit https://github.com/infiniflow/ragflow, and sign in the our demo web-site
-    to get token. It's FREE!
-    Set INFINIFLOW_SERVER and INFINIFLOW_TOKEN in '.env' file or
-    using 'export' to set both environment variables: INFINIFLOW_SERVER and INFINIFLOW_TOKEN in docker container.
+    To maximize the effectiveness, parse the resume correctly, please concat us: https://github.com/infiniflow/ragflow
     """
     if not re.search(r"\.(pdf|doc|docx|txt)$", filename, flags=re.IGNORECASE):
         raise NotImplementedError("file type not supported yet(pdf supported)")
 
-    url = os.environ.get("INFINIFLOW_SERVER")
-    token = os.environ.get("INFINIFLOW_TOKEN")
-    if not url or not token:
-        stat_logger.warning(
-            "INFINIFLOW_SERVER is not specified. To maximize the effectiveness, please visit https://github.com/infiniflow/ragflow, and sign in the our demo web site to get token. It's FREE! Using 'export' to set both environment variables: INFINIFLOW_SERVER and INFINIFLOW_TOKEN.")
-        return []
-
     if not binary:
         with open(filename, "rb") as f:
             binary = f.read()
 
-    def remote_call():
-        nonlocal filename, binary
-        for _ in range(3):
-            try:
-                res = requests.post(url + "/v1/layout/resume/", files=[(filename, binary)],
-                                    headers={"Authorization": token}, timeout=180)
-                res = res.json()
-                if res["retcode"] != 0:
-                    raise RuntimeError(res["retmsg"])
-                return res["data"]
-            except RuntimeError as e:
-                raise e
-            except Exception as e:
-                cron_logger.error("resume parsing:" + str(e))
-
     callback(0.2, "Resume parsing is going on...")
-    resume = remote_call()
+    resume = remote_call(filename, binary)
     if len(resume.keys()) < 7:
         callback(-1, "Resume is not successfully parsed.")
         return []

rag/app/table.py

+#  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 copy
 import re
 from io import BytesIO
 from dateutil.parser import parse as datetime_parse
 
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from rag.parser import is_english, tokenize
-from rag.nlp import huqie, stemmer
+from deepdoc.parser import is_english, tokenize
+from rag.nlp import huqie
+from deepdoc.parser import ExcelParser
 
 
-class Excel(object):
+class Excel(ExcelParser):
     def __call__(self, fnm, binary=None, callback=None):
         if not binary:
             wb = load_workbook(fnm)

rag/nlp/huchunk.py

+#  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 re
 import os
 import copy
     import sys
     sys.path.append(os.path.dirname(__file__) + "/../")
     if sys.argv[1].split(".")[-1].lower() == "pdf":
-        from parser import PdfParser
+        from deepdoc.parser import PdfParser
         ckr = PdfChunker(PdfParser())
     if sys.argv[1].split(".")[-1].lower().find("doc") >= 0:
-        from parser import DocxParser
+        from deepdoc.parser import DocxParser
         ckr = DocxChunker(DocxParser())
     if sys.argv[1].split(".")[-1].lower().find("xlsx") >= 0:
-        from parser import ExcelParser
+        from deepdoc.parser import ExcelParser
         ckr = ExcelChunker(ExcelParser())
 
     # ckr.html(sys.argv[1])

rag/svr/task_broker.py

 from api.db.db_models import Task
 from api.db.db_utils import bulk_insert_into_db
 from api.db.services.task_service import TaskService
-from rag.parser.pdf_parser import HuParser
+from deepdoc.parser import HuParser
 from rag.settings import cron_logger
 from rag.utils import MINIO
 from rag.utils import findMaxTm