From cacd36c5e1b4d5077cdc02dade1f46bae92fb762 Mon Sep 17 00:00:00 2001 From: KevinHuSh Date: Wed, 21 Feb 2024 16:32:38 +0800 Subject: [PATCH] use onnx models, new deepdoc (#68) --- api/apps/conversation_app.py | 2 +- api/apps/dialog_app.py | 78 +- api/db/db_models.py | 2 - deepdoc/__init__.py | 0 {rag => deepdoc}/parser/__init__.py | 3 +- {rag => deepdoc}/parser/docx_parser.py | 0 {rag => deepdoc}/parser/excel_parser.py | 0 {rag => deepdoc}/parser/pdf_parser.py | 48 +- deepdoc/visual/__init__.py | 2 + deepdoc/visual/ocr.py | 561 ++ deepdoc/visual/ocr.res | 6623 +++++++++++++++++++++++ deepdoc/visual/operators.py | 710 +++ deepdoc/visual/postprocess.py | 354 ++ deepdoc/visual/recognizer.py | 139 + deepdoc/visual/seeit.py | 83 + rag/app/book.py | 25 +- rag/app/laws.py | 23 +- rag/app/manual.py | 8 +- rag/app/naive.py | 20 +- rag/app/paper.py | 20 +- rag/app/presentation.py | 21 +- rag/app/qa.py | 18 +- rag/app/resume.py | 87 +- rag/app/table.py | 19 +- rag/nlp/huchunk.py | 18 +- rag/svr/task_broker.py | 2 +- 26 files changed, 8730 insertions(+), 136 deletions(-) create mode 100644 deepdoc/__init__.py rename {rag => deepdoc}/parser/__init__.py (99%) rename {rag => deepdoc}/parser/docx_parser.py (100%) rename {rag => deepdoc}/parser/excel_parser.py (100%) rename {rag => deepdoc}/parser/pdf_parser.py (98%) create mode 100644 deepdoc/visual/__init__.py create mode 100644 deepdoc/visual/ocr.py create mode 100644 deepdoc/visual/ocr.res create mode 100644 deepdoc/visual/operators.py create mode 100644 deepdoc/visual/postprocess.py create mode 100644 deepdoc/visual/recognizer.py create mode 100644 deepdoc/visual/seeit.py diff --git a/api/apps/conversation_app.py b/api/apps/conversation_app.py index ad1745a37..e6e33d063 100644 --- a/api/apps/conversation_app.py +++ b/api/apps/conversation_app.py @@ -198,7 +198,7 @@ def chat(dialog, messages, **kwargs): 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: diff --git a/api/apps/dialog_app.py b/api/apps/dialog_app.py index 083d4129c..cc6f9810a 100644 --- a/api/apps/dialog_app.py +++ b/api/apps/dialog_app.py @@ -33,38 +33,17 @@ def set_dialog(): 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} @@ -74,30 +53,40 @@ def set_dialog(): {"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"]) @@ -122,7 +111,7 @@ def set_dialog(): 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"]) @@ -130,20 +119,22 @@ def get(): 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"]) @@ -154,12 +145,11 @@ def list(): @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) \ No newline at end of file + return server_error_response(e) diff --git a/api/db/db_models.py b/api/db/db_models.py index 282a5660b..0e032fcaa 100644 --- a/api/db/db_models.py +++ b/api/db/db_models.py @@ -529,8 +529,6 @@ class Dialog(DataBaseModel): 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") diff --git a/deepdoc/__init__.py b/deepdoc/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/rag/parser/__init__.py b/deepdoc/parser/__init__.py similarity index 99% rename from rag/parser/__init__.py rename to deepdoc/parser/__init__.py index d2b499c90..886f4ab9e 100644 --- a/rag/parser/__init__.py +++ b/deepdoc/parser/__init__.py @@ -1,4 +1,3 @@ -import copy import random from .pdf_parser import HuParser as PdfParser @@ -10,7 +9,7 @@ import re 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)", diff --git a/rag/parser/docx_parser.py b/deepdoc/parser/docx_parser.py similarity index 100% rename from rag/parser/docx_parser.py rename to deepdoc/parser/docx_parser.py diff --git a/rag/parser/excel_parser.py b/deepdoc/parser/excel_parser.py similarity index 100% rename from rag/parser/excel_parser.py rename to deepdoc/parser/excel_parser.py diff --git a/rag/parser/pdf_parser.py b/deepdoc/parser/pdf_parser.py similarity index 98% rename from rag/parser/pdf_parser.py rename to deepdoc/parser/pdf_parser.py index 79611a7bd..576687b18 100644 --- a/rag/parser/pdf_parser.py +++ b/deepdoc/parser/pdf_parser.py @@ -1,7 +1,6 @@ # -*- coding: utf-8 -*- import os import random -from functools import partial import fitz import requests @@ -15,6 +14,7 @@ from PIL import Image 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 @@ -26,13 +26,32 @@ logging.getLogger("pdfminer").setLevel(logging.WARNING) 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(): @@ -56,7 +75,7 @@ class HuParser: 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() @@ -382,7 +401,7 @@ class HuParser: 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 @@ -452,7 +471,7 @@ class HuParser: 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 = [] @@ -517,8 +536,8 @@ class HuParser: 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 @@ -557,11 +576,12 @@ class HuParser: 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] @@ -1741,7 +1761,7 @@ class HuParser: # 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] @@ -1754,7 +1774,7 @@ class HuParser: 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() diff --git a/deepdoc/visual/__init__.py b/deepdoc/visual/__init__.py new file mode 100644 index 000000000..e53762a90 --- /dev/null +++ b/deepdoc/visual/__init__.py @@ -0,0 +1,2 @@ +from .ocr import OCR +from .recognizer import Recognizer \ No newline at end of file diff --git a/deepdoc/visual/ocr.py b/deepdoc/visual/ocr.py new file mode 100644 index 000000000..65b2c2ddf --- /dev/null +++ b/deepdoc/visual/ocr.py @@ -0,0 +1,561 @@ +# 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)) diff --git a/deepdoc/visual/ocr.res b/deepdoc/visual/ocr.res new file mode 100644 index 000000000..b62de6619 --- /dev/null +++ b/deepdoc/visual/ocr.res @@ -0,0 +1,6623 @@ +' +疗 +绚 +诚 +娇 +溜 +题 +贿 +者 +廖 +更 +纳 +加 +奉 +公 +一 +就 +汴 +计 +与 +路 +房 +原 +妇 +2 +0 +8 +- +7 +其 +> +: +] +, +， +骑 +刈 +全 +消 +昏 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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 \ No newline at end of file diff --git a/deepdoc/visual/postprocess.py b/deepdoc/visual/postprocess.py new file mode 100644 index 000000000..b3d1ac781 --- /dev/null +++ b/deepdoc/visual/postprocess.py @@ -0,0 +1,354 @@ +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 diff --git a/deepdoc/visual/recognizer.py b/deepdoc/visual/recognizer.py new file mode 100644 index 000000000..09ccbb34a --- /dev/null +++ b/deepdoc/visual/recognizer.py @@ -0,0 +1,139 @@ +# 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 diff --git a/deepdoc/visual/seeit.py b/deepdoc/visual/seeit.py new file mode 100644 index 000000000..70e547f0c --- /dev/null +++ b/deepdoc/visual/seeit.py @@ -0,0 +1,83 @@ +# 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 diff --git a/rag/app/book.py b/rag/app/book.py index 75b9f088a..c9996aeb3 100644 --- a/rag/app/book.py +++ b/rag/app/book.py @@ -1,15 +1,24 @@ +# 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__( @@ -21,7 +30,7 @@ class Pdf(HuParser): 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) @@ -53,7 +62,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None, **k 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))) diff --git a/rag/app/laws.py b/rag/app/laws.py index 0c4bca164..f24987086 100644 --- a/rag/app/laws.py +++ b/rag/app/laws.py @@ -1,16 +1,27 @@ +# 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 @@ -35,7 +46,7 @@ class Docx(HuDocxParser): 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__( @@ -47,7 +58,7 @@ class Pdf(HuParser): 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() diff --git a/rag/app/manual.py b/rag/app/manual.py index e8a9dada1..9b051ec93 100644 --- a/rag/app/manual.py +++ b/rag/app/manual.py @@ -1,12 +1,12 @@ 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__( @@ -18,7 +18,7 @@ class Pdf(HuParser): 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) diff --git a/rag/app/naive.py b/rag/app/naive.py index 8c80d5f05..aceb22f26 100644 --- a/rag/app/naive.py +++ b/rag/app/naive.py @@ -1,13 +1,25 @@ +# 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__( @@ -19,7 +31,7 @@ class Pdf(HuParser): 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() diff --git a/rag/app/paper.py b/rag/app/paper.py index 4f464ac5d..ac9afd226 100644 --- a/rag/app/paper.py +++ b/rag/app/paper.py @@ -1,16 +1,28 @@ +# 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__() @@ -26,7 +38,7 @@ class Pdf(HuParser): 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) diff --git a/rag/app/presentation.py b/rag/app/presentation.py index afcb8f23a..2cb660663 100644 --- a/rag/app/presentation.py +++ b/rag/app/presentation.py @@ -1,11 +1,22 @@ +# 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): @@ -58,7 +69,7 @@ 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__() @@ -74,7 +85,7 @@ class Pdf(HuParser): 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: diff --git a/rag/app/qa.py b/rag/app/qa.py index 9d55d1bda..34615a8e0 100644 --- a/rag/app/qa.py +++ b/rag/app/qa.py @@ -1,13 +1,25 @@ -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) diff --git a/rag/app/resume.py b/rag/app/resume.py index fd9dc0337..8b4ca0133 100644 --- a/rag/app/resume.py +++ b/rag/app/resume.py @@ -1,59 +1,82 @@ -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 [] diff --git a/rag/app/table.py b/rag/app/table.py index c80b3fb01..635284308 100644 --- a/rag/app/table.py +++ b/rag/app/table.py @@ -1,3 +1,15 @@ +# 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 @@ -8,11 +20,12 @@ from openpyxl import load_workbook 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) diff --git a/rag/nlp/huchunk.py b/rag/nlp/huchunk.py index ba81a46a7..bb2d46f34 100644 --- a/rag/nlp/huchunk.py +++ b/rag/nlp/huchunk.py @@ -1,3 +1,15 @@ +# 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 @@ -443,13 +455,13 @@ if __name__ == "__main__": 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]) diff --git a/rag/svr/task_broker.py b/rag/svr/task_broker.py index e5010426a..1204713d6 100644 --- a/rag/svr/task_broker.py +++ b/rag/svr/task_broker.py @@ -21,7 +21,7 @@ from datetime import datetime 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