diff --git a/ocrweb_multi/README.md b/ocrweb_multi/README.md new file mode 100644 index 0000000..e5a8228 --- /dev/null +++ b/ocrweb_multi/README.md @@ -0,0 +1,2 @@ +## RapidOCR多语言支持Web服务 +RapidOCR Multilanguage Web Demo diff --git a/ocrweb_multi/config.yaml b/ocrweb_multi/config.yaml new file mode 100644 index 0000000..363c4ed --- /dev/null +++ b/ocrweb_multi/config.yaml @@ -0,0 +1,106 @@ +debug: '1' + +server: + host: 127.0.0.1 + port: 9003 + token: null + +global: + use_cuda: false + verbose: false + # Details of the params: https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html + cuda_provider: + device_id: 0 + arena_extend_strategy: kNextPowerOfTwo + cudnn_conv_algo_search: EXHAUSTIVE + do_copy_in_default_stream: true + +# 模型配置 +models: + # 位置检测模型 + detect: + det_en: + path: models/en_PP-OCRv3_det_infer.onnx + config: &detectConfig + pre_process: + - class: DetResizeForTest + limit_side_len: 736 + limit_type: min + - class: NormalizeImage + std: [0.229, 0.224, 0.225] + mean: [0.485, 0.456, 0.406] + # 1 / 255 + scale: 0.00392156862745098 + order: hwc + - class: ToCHWImage + - class: KeepKeys + keep_keys: ["image", "shape"] + post_process: + thresh: 0.3 + box_thresh: 0.5 + max_candidates: 1000 + unclip_ratio: 1.6 + use_dilation: true + det_ch: + path: models/ch_PP-OCRv3_det_infer.onnx + config: *detectConfig + det_ml: + path: models/ch_PP-OCRv3_det_infer.onnx + config: *detectConfig + # 方向检测模型 + classify: + cls_ml: + path: models/ch_ppocr_mobile_v2.0_cls_infer.meta.onnx + config: + batch_size: 8 + score_thresh: 0.9 + # 文字识别模型 + recognize: + rec_ch: + path: models/ch_PP-OCRv3_rec_infer.meta.onnx + config: &recognizeConfig + batch_size: 8 + rec_cht: + path: models/chinese_cht_PP-OCRv3_rec_infer.meta.onnx + config: *recognizeConfig + rec_en: + path: models/en_PP-OCRv3_rec_infer.meta.onnx + config: *recognizeConfig + rec_ja: + path: models/japan_PP-OCRv3_rec_infer.meta.onnx + config: *recognizeConfig + +# 多语言配置 +languages: + ch: + name: 中文 + models: + detect: det_ch + classify: cls_ml + recognize: rec_ch + config: &languageConfig + text_score: 0.5 + use_angle_cls: true + verbose: false + min_height: 30 + cht: + name: 繁体中文 + models: + detect: det_ch + classify: cls_ml + recognize: rec_cht + config: *languageConfig + ja: + name: 日文 + models: + detect: det_ch + classify: cls_ml + recognize: rec_ja + config: *languageConfig + en: + name: 英文 + models: + detect: det_en + classify: cls_ml + recognize: rec_en + config: *languageConfig diff --git a/ocrweb_multi/main.py b/ocrweb_multi/main.py new file mode 100644 index 0000000..a5c7511 --- /dev/null +++ b/ocrweb_multi/main.py @@ -0,0 +1,71 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import logging +import cv2 +import numpy as np +from flask import Flask, send_file, request, make_response +from waitress import serve + + +from rapidocr.main import detect_recognize +from utils.config import conf +from utils.utils import tojson, parse_bool + +app = Flask(__name__) +log = logging.getLogger('app') +# 设置上传文件大小 +app.config['MAX_CONTENT_LENGTH'] = 3 * 1024 * 1024 + + +@app.route('/') +def index(): + return send_file('static/index.html') + + +def json_response(data, *args): + return make_response(tojson(data), {"content-type": 'application/json'}, *args) + + +@app.route('/lang') +def get_languages(): + """返回可用语言列表""" + data = [ + {'code': key, 'name': val['name']} for key, val in conf['languages'].items() + ] + result = {'msg': 'OK', 'data': data} + log.info('Send langs: %s', data) + return json_response(result) + + +@app.route('/ocr', methods=['POST', 'GET']) +def ocr(): + """执行文字识别""" + if conf['server'].get('token'): + if request.values.get('token') != conf['server']['token']: + return json_response({'msg': 'invalid token'}, 403) + + lang = request.values.get('lang') or 'ch' + detect = parse_bool(request.values.get('detect') or 'true') + classify = parse_bool(request.values.get('classify') or 'true') + + image_file = request.files.get('image') + nparr = np.frombuffer(image_file.stream.read(), np.uint8) + image = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + log.info('Input: image %s, lang=%s, detect=%s, classify=%s', image.shape, lang, detect, classify) + if image.ndim == 2: + image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) + result = detect_recognize(image, lang=lang, detect=detect, classify=classify) + log.info('OCR Done %s %s', result['ts'], len(result['results'])) + return json_response({'msg': 'OK', 'data': result}) + + +if __name__ == '__main__': + logging.basicConfig(level='INFO') + logging.getLogger('waitress').setLevel(logging.INFO) + if parse_bool(conf.get('debug', '0')): + # Debug + app.run(host=conf['server']['host'], port=conf['server']['port'], debug=True) + else: + # Deploy with waitress + serve(app, host=conf['server']['host'], port=conf['server']['port']) diff --git a/ocrweb_multi/models/.gitkeep b/ocrweb_multi/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/ocrweb_multi/rapidocr/__init__.py b/ocrweb_multi/rapidocr/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocrweb_multi/rapidocr/classify.py b/ocrweb_multi/rapidocr/classify.py new file mode 100644 index 0000000..5e1ac33 --- /dev/null +++ b/ocrweb_multi/rapidocr/classify.py @@ -0,0 +1,122 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import copy +import math +import json +from typing import List + +import cv2 +import numpy as np +from utils.utils import OrtInferSession + + +class ClsPostProcess(object): + """Convert between text-label and text-index""" + + def __init__(self, label_list): + super(ClsPostProcess, self).__init__() + self.label_list = label_list + + def __call__(self, preds, label=None): + pred_idxs = preds.argmax(axis=1) + decode_out = [ + (self.label_list[idx], preds[i, idx]) for i, idx in enumerate(pred_idxs) + ] + if label is None: + return decode_out + + label = [(self.label_list[idx], 1.0) for idx in label] + return decode_out, label + + +class TextClassifier(object): + def __init__(self, path, config): + self.cls_batch_num = config['batch_size'] + self.cls_thresh = config['score_thresh'] + + session_instance = OrtInferSession(path) + self.session = session_instance.session + metamap = self.session.get_modelmeta().custom_metadata_map + + self.cls_image_shape = json.loads(metamap['shape']) + + labels = json.loads(metamap['labels']) + self.postprocess_op = ClsPostProcess(labels) + self.input_name = session_instance.get_input_name() + + def resize_norm_img(self, img): + img_c, img_h, img_w = self.cls_image_shape + h, w = img.shape[:2] + ratio = w / float(h) + if math.ceil(img_h * ratio) > img_w: + resized_w = img_w + else: + resized_w = int(math.ceil(img_h * ratio)) + + resized_image = cv2.resize(img, (resized_w, img_h)) + resized_image = resized_image.astype('float32') + if img_c == 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 + padding_im = np.zeros((img_c, img_h, img_w), dtype=np.float32) + padding_im[:, :, :resized_w] = resized_image + return padding_im + + def __call__(self, img_list: List[np.ndarray]): + if isinstance(img_list, np.ndarray): + img_list = [img_list] + + img_list = copy.deepcopy(img_list) + + # Calculate the aspect ratio of all text bars + width_list = [img.shape[1] / float(img.shape[0]) for img in img_list] + + # Sorting can speed up the cls process + indices = np.argsort(np.array(width_list)) + + img_num = len(img_list) + cls_res = [['', 0.0]] * img_num + batch_num = self.cls_batch_num + for beg_img_no in range(0, img_num, batch_num): + end_img_no = min(img_num, beg_img_no + batch_num) + 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) + + norm_img_batch = [] + for ino in range(beg_img_no, end_img_no): + norm_img = self.resize_norm_img(img_list[indices[ino]]) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32) + + onnx_inputs = {self.input_name: norm_img_batch} + prob_out = self.session.run(None, onnx_inputs)[0] + cls_result = self.postprocess_op(prob_out) + + for rno in range(len(cls_result)): + label, score = cls_result[rno] + cls_res[indices[beg_img_no + rno]] = [label, score] + if label == '180' and score > self.cls_thresh: + img_list[indices[beg_img_no + rno]] = cv2.rotate( + img_list[indices[beg_img_no + rno]], 1 + ) + return img_list, cls_res diff --git a/ocrweb_multi/rapidocr/detect.py b/ocrweb_multi/rapidocr/detect.py new file mode 100644 index 0000000..46d202a --- /dev/null +++ b/ocrweb_multi/rapidocr/detect.py @@ -0,0 +1,99 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com + +import numpy as np + +from utils.utils import OrtInferSession +from .detect_process import DBPostProcess, create_operators, transform + + +class TextDetector(object): + def __init__(self, path, config): + self.preprocess_op = create_operators(config['pre_process']) + self.postprocess_op = DBPostProcess(**config['post_process']) + + session_instance = OrtInferSession(path) + self.session = session_instance.session + self.input_name = session_instance.get_input_name() + + def __call__(self, img): + if img is None: + raise ValueError('img is None') + + ori_im_shape = img.shape[:2] + + data = {'image': img} + data = transform(data, self.preprocess_op) + img, shape_list = data + if img is None: + return None, 0 + + img = np.expand_dims(img, axis=0).astype(np.float32) + shape_list = np.expand_dims(shape_list, axis=0) + + preds = self.session.run(None, {self.input_name: img}) + + post_result = self.postprocess_op(preds[0], shape_list) + + dt_boxes = post_result[0]['points'] + dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im_shape) + return dt_boxes + + def order_points_clockwise(self, pts): + """ + reference from: + https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py + sort the points based on their x-coordinates + """ + xSorted = pts[np.argsort(pts[:, 0]), :] + + # grab the left-most and right-most points from the sorted + # x-roodinate points + leftMost = xSorted[:2, :] + rightMost = xSorted[2:, :] + + # now, sort the left-most coordinates according to their + # y-coordinates so we can grab the top-left and bottom-left + # points, respectively + leftMost = leftMost[np.argsort(leftMost[:, 1]), :] + (tl, bl) = leftMost + + rightMost = rightMost[np.argsort(rightMost[:, 1]), :] + (tr, br) = rightMost + + rect = np.array([tl, tr, br, bl], dtype="float32") + return rect + + def clip_det_res(self, points, img_height, img_width): + for pno in range(points.shape[0]): + points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) + points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) + return points + + def filter_tag_det_res(self, dt_boxes, image_shape): + """对检测结果进行过滤""" + img_height, img_width = image_shape[:2] + dt_boxes_new = [] + for box in dt_boxes: + box = self.order_points_clockwise(box) + box = self.clip_det_res(box, img_height, img_width) + rect_width = int(np.linalg.norm(box[0] - box[1])) + rect_height = int(np.linalg.norm(box[0] - box[3])) + if rect_width <= 3 or rect_height <= 3: + continue + dt_boxes_new.append(box) + return dt_boxes_new diff --git a/ocrweb_multi/rapidocr/detect_process.py b/ocrweb_multi/rapidocr/detect_process.py new file mode 100644 index 0000000..0a01530 --- /dev/null +++ b/ocrweb_multi/rapidocr/detect_process.py @@ -0,0 +1,380 @@ +""" +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from copy import deepcopy +import sys +import warnings + +import cv2 +import numpy as np +import pyclipper +import six +from shapely.geometry import Polygon + + +class DecodeImage(object): + """decode image""" + + def __init__(self, img_mode='RGB', channel_first=False): + self.img_mode = img_mode + self.channel_first = channel_first + + def __call__(self, data): + img = data['image'] + if six.PY2: + assert ( + type(img) is str and len(img) > 0 + ), "invalid input 'img' in DecodeImage" + else: + assert ( + type(img) is bytes and len(img) > 0 + ), "invalid input 'img' in DecodeImage" + + img = np.frombuffer(img, dtype='uint8') + 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, f'invalid shape of image[{img.shape}]' + img = img[:, :, ::-1] + + if self.channel_first: + img = img.transpose((2, 0, 1)) + data['image'] = img + return data + + +class NormalizeImage(object): + """normalize image such as substract mean, divide std""" + + def __init__(self, scale=None, mean=None, std=None, order='chw'): + 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 = np.array(data['image']).astype(np.float32) + data['image'] = (img * self.scale - self.mean) / self.std + return data + + +class ToCHWImage(object): + """convert hwc image to chw image""" + + def __init__(self): + pass + + def __call__(self, data): + img = data['image'] + data['image'] = img.transpose((2, 0, 1)) + return data + + +class KeepKeys(object): + def __init__(self, keep_keys): + 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 DetResizeForTest(object): + def __init__(self, **kwargs): + super(DetResizeForTest, self).__init__() + self.resize_type = 0 + if 'image_shape' in kwargs: + self.image_shape = kwargs['image_shape'] + self.resize_type = 1 + 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 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 resize_image_type1(self, img): + resize_h, resize_w = self.image_shape + 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, 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, _ = 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 = 1.0 + else: + 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 = 1.0 + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = int(round(resize_h / 32) * 32) + resize_w = int(round(resize_w / 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: + 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 + + # Fix the longer side + 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] + + +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): + """ + create operators based on the config + """ + op_map = globals() + ops = [] + for args in op_param_list: + args = deepcopy(args) + op_class = op_map[args.pop('class')] + ops.append(op_class(**args)) + return ops + + +def draw_text_det_res(dt_boxes, img_path): + src_im = cv2.imread(img_path) + for box in dt_boxes: + box = np.array(box).astype(np.int32).reshape(-1, 2) + cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) + return src_im + + +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, + ): + self.thresh = thresh + self.box_thresh = box_thresh + self.max_candidates = max_candidates + self.unclip_ratio = unclip_ratio + self.min_size = 3 + + if use_dilation: + self.dilation_kernel = np.array([[1, 1], [1, 1]]) + else: + self.dilation_kernel = None + + 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) + score = self.box_score_fast(pred, points.reshape(-1, 2)) + if self.box_thresh > score: + continue + + box = self.unclip(points).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(np.int16)) + scores.append(score) + return np.array(boxes, dtype=np.int16), scores + + def unclip(self, box): + unclip_ratio = self.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): + h, w = bitmap.shape[:2] + box = _box.copy() + xmin = np.clip(np.floor(box[:, 0].min()).astype(int), 0, w - 1) + xmax = np.clip(np.ceil(box[:, 0].max()).astype(int), 0, w - 1) + ymin = np.clip(np.floor(box[:, 1].min()).astype(int), 0, h - 1) + ymax = np.clip(np.ceil(box[:, 1].max()).astype(int), 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(np.int32), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def __call__(self, pred, shape_list): + 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] + boxes, scores = self.boxes_from_bitmap( + pred[batch_index], mask, src_w, src_h + ) + + boxes_batch.append({'points': boxes}) + return boxes_batch diff --git a/ocrweb_multi/rapidocr/main.py b/ocrweb_multi/rapidocr/main.py new file mode 100644 index 0000000..500d097 --- /dev/null +++ b/ocrweb_multi/rapidocr/main.py @@ -0,0 +1,57 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import copy +from functools import lru_cache +from pathlib import Path + +import numpy as np +import cv2 + +from utils.config import conf +from rapidocr.rapid_ocr_api import TextSystem + + +@lru_cache(maxsize=None) +def load_language_model(lang='ch'): + models = conf['languages'][lang] + print('model', models) + return TextSystem(models) + + +def detect_recognize(image, lang='ch', detect=True, classify=True): + model = load_language_model(lang) + results, ts = model(image, detect=detect, classify=classify) + ts['total'] = sum(ts.values()) + return {'ts': ts, 'results': results} + + +def check_and_read_gif(img_path): + if Path(img_path).name[-3:] in ['gif', 'GIF']: + gif = cv2.VideoCapture(img_path) + ret, frame = gif.read() + if not ret: + print("Cannot read {}. This gif image maybe corrupted.") + return None, False + if len(frame.shape) == 2 or frame.shape[-1] == 1: + frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) + imgvalue = frame[:, :, ::-1] + return imgvalue, True + return None, False + + +def draw_text_det_res(dt_boxes, raw_im): + src_im = copy.deepcopy(raw_im) + for i, box in enumerate(dt_boxes): + box = np.array(box).astype(np.int32).reshape(-1, 2) + cv2.polylines(src_im, [box], True, color=(0, 0, 255), thickness=1) + cv2.putText( + src_im, + str(i), + (int(box[0][0]), int(box[0][1])), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (0, 0, 0), + 2, + ) + return src_im diff --git a/ocrweb_multi/rapidocr/rapid_ocr_api.py b/ocrweb_multi/rapidocr/rapid_ocr_api.py new file mode 100644 index 0000000..08bafc1 --- /dev/null +++ b/ocrweb_multi/rapidocr/rapid_ocr_api.py @@ -0,0 +1,160 @@ +# !/usr/bin/env python +# -*- encoding: utf-8 -*- +import copy + +import cv2 +import numpy as np + +from functools import lru_cache +from utils.config import conf +from utils.utils import Ticker +from .classify import TextClassifier +from .detect import TextDetector +from .recognize import TextRecognizer + + +def get_rotate_crop_image(img, points): + """根据box定义, 从图像中截取相应的部分, 通过透视变换转换为标准长方形图像""" + 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], + ] + ) + transform = cv2.getPerspectiveTransform(points, pts_std) + dst_img = cv2.warpPerspective( + img, + transform, + (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC, + ) + dst_img_height, dst_img_width = dst_img.shape[:2] + # 将竖向的文字方向转为横向, 仅当 高>1.5*宽 时进行转换 + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + + +@lru_cache(maxsize=None) +def load_onnx_model(step, name): + model_config = conf['models'][step][name] + model_class = { + 'detect': TextDetector, + 'classify': TextClassifier, + 'recognize': TextRecognizer, + }[step] + return model_class(model_config['path'], model_config.get('config')) + + +class TextSystem(object): + def __init__(self, config): + super(TextSystem).__init__() + self.config = config + self.text_score = config['config']['text_score'] + self.min_height = config['config']['min_height'] + + models = config['models'] + self.text_detector = load_onnx_model('detect', models['detect']) + self.text_recognizer = load_onnx_model('recognize', models['recognize']) + self.text_cls = load_onnx_model('classify', models['classify']) + + def __call__(self, img: np.ndarray, detect=True, classify=True): + ticker = Ticker() + h, w = img.shape[:2] + if not detect or h < self.min_height: + dt_boxes, img_crop_list = self.get_boxes_img_without_det(img, h, w) + ticker.tick('detect') + else: + dt_boxes = self.text_detector(img) + ticker.tick('detect') + + if dt_boxes is None or len(dt_boxes) < 1: + return [], ticker.maps + if conf['global']['verbose']: + print(f'boxes num: {len(dt_boxes)}') + + dt_boxes = self.sorted_boxes(dt_boxes) + + img_crop_list = self.get_crop_img_list(img, dt_boxes) + ticker.tick('post-detect') + + if classify: + # 进行子图像角度修正 + img_crop_list, _ = self.text_cls(img_crop_list) + ticker.tick('classify') + if conf['global']['verbose']: + print(f'cls num: {len(img_crop_list)}') + + recog_result = self.text_recognizer(img_crop_list) + ticker.tick('recognize') + if conf['global']['verbose']: + print(f'rec_res num: {len(recog_result)}') + + results = self.filter_boxes_rec_by_score(dt_boxes, recog_result) + ticker.tick('post-recognize') + return results, ticker.maps + + def get_boxes_img_without_det(self, img, h, w): + x0, y0, x1, y1 = 0, 0, w, h + dt_boxes = np.array([[x0, y0], [x1, y0], [x1, y1], [x0, y1]]) + dt_boxes = dt_boxes[np.newaxis, ...] + img_crop_list = [img] + return dt_boxes, img_crop_list + + def get_crop_img_list(self, img, dt_boxes): + img_crop_list = [] + for box in dt_boxes: + tmp_box = copy.deepcopy(box) + img_crop = get_rotate_crop_image(img, tmp_box) + img_crop_list.append(img_crop) + return img_crop_list + + @staticmethod + def sorted_boxes(dt_boxes): + """对文本框检测结果进行排序, 调整为从上到下、从左到右 + + args: + dt_boxes(array): detected text boxes with shape [4, 2] + return: + sorted boxes(array) with shape [4, 2] + """ + + class AlignBox(object): + def __init__(self, data) -> None: + self.data = data + self.x = data[0][0] + self.y = data[0][1] + + def __lt__(self, other: 'AlignBox'): + dy = self.y - other.y + # y差距小于10, 视为相等, 根据x排序 + if abs(dy) < 10: + return self.x < other.x + # 否则根据y排序 + return dy < 0 + + align_boxes = sorted([AlignBox(b) for b in dt_boxes]) + return [b.data for b in align_boxes] + + def filter_boxes_rec_by_score(self, dt_boxes, rec_res): + results = [] + for box, rec_reuslt in zip(dt_boxes, rec_res): + text, score = rec_reuslt + if score >= self.text_score: + results.append({'box': box, 'text': text, 'score': score}) + return results diff --git a/ocrweb_multi/rapidocr/recognize.py b/ocrweb_multi/rapidocr/recognize.py new file mode 100644 index 0000000..f77a6f2 --- /dev/null +++ b/ocrweb_multi/rapidocr/recognize.py @@ -0,0 +1,160 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import json +import math +import time +from typing import List + +import cv2 +import numpy as np + +from utils.utils import get_resource_path, OrtInferSession + + +class CTCLabelDecode(object): + """Convert between text-label and text-index""" + + def __init__(self, characters: List[str]): + super(CTCLabelDecode, self).__init__() + + self.characters = characters + self.characters.append(' ') + + dict_character = self.add_special_char(self.characters) + self.character = dict_character + + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + + def __call__(self, preds, label=None): + 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 + + def get_ignored_tokens(self): + return [0] # for ctc blank + + 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): + char_list = [] + conf_list = [] + for idx in range(len(text_index[batch_idx])): + if text_index[batch_idx][idx] in ignored_tokens: + continue + if is_remove_duplicate: + # only for predict + if ( + idx > 0 + and text_index[batch_idx][idx - 1] == text_index[batch_idx][idx] + ): + continue + char_list.append(self.character[int(text_index[batch_idx][idx])]) + if text_prob is not None: + conf_list.append(text_prob[batch_idx][idx]) + else: + conf_list.append(1) + # avoid `Mean of empty slice.` warning + score = np.mean(conf_list) if conf_list else 0 + text = ''.join(char_list) + result_list.append((text, score)) + return result_list + + +class TextRecognizer(object): + def __init__(self, path, config): + self.rec_batch_num = config.get('rec_batch_num', 6) + + session_instance = OrtInferSession(path) + self.session = session_instance.session + + metamap = session_instance.session.get_modelmeta().custom_metadata_map + chars = metamap['dictionary'].splitlines() + self.postprocess_op = CTCLabelDecode(chars) + self.rec_image_shape = json.loads(metamap['shape']) + self.input_name = session_instance.get_input_name() + + def resize_norm_img(self, img, max_wh_ratio): + img_channel, img_height, img_width = self.rec_image_shape + assert img_channel == img.shape[2] + + img_width = int((32 * max_wh_ratio)) + max_wh_ratio = 1 + + h, w = img.shape[:2] + ratio = w / float(h) + if math.ceil(img_height * ratio) > img_width: + resized_w = img_width + else: + resized_w = int(math.ceil(img_height * ratio)) + + resized_image = cv2.resize(img, (resized_w, img_height)) + 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((img_channel, img_height, img_width), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + return padding_im + + def __call__(self, img_list: List[np.ndarray]): + if isinstance(img_list, np.ndarray): + img_list = [img_list] + + # Calculate the aspect ratio of all text bars + width_list = [img.shape[1] / float(img.shape[0]) for img in img_list] + + # Sorting can speed up the recognition process + indices = np.argsort(np.array(width_list)) + + img_num = len(img_list) + rec_res = [['', 0.0]] * img_num + + batch_num = self.rec_batch_num + for beg_img_no in range(0, img_num, batch_num): + end_img_no = min(img_num, beg_img_no + batch_num) + 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) + + norm_img_batch = [] + 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_batch.append(norm_img[np.newaxis, :]) + norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32) + + onnx_inputs = {self.input_name: norm_img_batch} + preds = self.session.run(None, onnx_inputs)[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 diff --git a/ocrweb_multi/requirements.txt b/ocrweb_multi/requirements.txt new file mode 100644 index 0000000..b84c321 --- /dev/null +++ b/ocrweb_multi/requirements.txt @@ -0,0 +1,9 @@ +onnxruntime>=1.7.0 +opencv-python-headless==4.5.4.60 +six>=1.15.0 +pyclipper>=1.2.1 +numpy>=1.19.1 +Shapely>=1.7.1 +Flask>=2.1.2 +PyYAML +waitress diff --git a/ocrweb_multi/static/css/main.css b/ocrweb_multi/static/css/main.css new file mode 100644 index 0000000..0064733 --- /dev/null +++ b/ocrweb_multi/static/css/main.css @@ -0,0 +1,150 @@ +html { + height: 100%; + margin: 0; +} + +body { + background-color: #ebedef; + min-height: 100%; + margin: 0; +} + +.btn-gen { + background-color: #00a1d6; + text-align: center; + border-radius: 18px; + margin: 0 5px 0 5px; + font-size: 15px; + padding: 5px 10px; + height: 20px; + min-width: 120px; + text-transform: uppercase; + color: #fff; + border: none; +} + +.btn-gen:hover, +.btn-gen:focus { + border-color: #23AAEE; + background-color: #23AAEE; + color: white; + cursor: pointer; +} + +.row { + margin: 15px; +} + +.small { + font-size: 0.8em; +} + +.verysmall { + font-size: 0.5em; +} + +.area { + text-align: center; + height: auto; + margin: auto; +} + +.leftarea { + float: left; + width: 50%; + height: auto; + position: relative; +} + +.rightarea { + float: left; + width: 50%; + height: auto; + overflow-y: auto; +} + +.table { + width: auto; + height: auto; + margin: 0 auto; + +} + +.span_title { + width: 98%; + height: 36px; + margin-top: 4px; + line-height: 32px; + background-color: #00a1d6; + border: 1px solid #00a1d6; + border-radius: 20px; + color: #fff; + display: inline-block; + text-align: center; + font-size: 22px; + transition: .3s; + box-sizing: border-box; + cursor: default; +} + +.uplodNote { + font-size: 10px; + color: #A1A1A1; +} + +a { + text-decoration: none; +} + +#input-hint { + margin: auto; + cursor: pointer; + +} + +#result_view { + position: relative; + width: 95%; + margin: auto; +} + +#result_view img, +#result_view canvas { + width: 100%; + height: 100%; +} + +#result_view canvas { + position: absolute; + left: 0; + top: 0; +} + +/* wrapper */ +#wrapper { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; + background: + linear-gradient(#1a98ca, #1a98ca), + linear-gradient(90deg, #ffffff33 1px, transparent 0, transparent 19px), + linear-gradient(#ffffff33 1px, transparent 0, transparent 19px), + linear-gradient(transparent, #1a98ca); + background-size: 100% 1.5%, 10% 100%, 100% 8%, 100% 100%; + background-repeat: no-repeat, repeat, repeat, no-repeat; + background-position: 0% 100%, 0 0, 0 0, 0 0; + /* 初始位置 */ + clip-path: polygon(0% 0%, 100% 0%, 100% 1.5%, 0% 1.5%); + /* 添加动画效果 */ + animation: move 1s infinite linear; +} + +@keyframes move { + to { + background-position: 0 100%, 0 0, 0 0, 0 0; + /* 终止位置 */ + clip-path: polygon(0% 0%, 100% 0%, 100% 100%, 0% 100%); + } +} \ No newline at end of file diff --git a/ocrweb_multi/static/favicon.ico b/ocrweb_multi/static/favicon.ico new file mode 100644 index 0000000..b30495c Binary files /dev/null and b/ocrweb_multi/static/favicon.ico differ diff --git a/ocrweb_multi/static/hint.svg b/ocrweb_multi/static/hint.svg new file mode 100644 index 0000000..f651b0d --- /dev/null +++ b/ocrweb_multi/static/hint.svg @@ -0,0 +1,4 @@ + + 未选择图片 + \ No newline at end of file diff --git a/ocrweb_multi/static/index.html b/ocrweb_multi/static/index.html new file mode 100644 index 0000000..1c4aedd --- /dev/null +++ b/ocrweb_multi/static/index.html @@ -0,0 +1,221 @@ + + + + + + RapidOCR Demo + + + + + + +
+

+ Rapid🗲OCR Demo +   + 欢迎Star  + 了解Demo +

+
+
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+ 选择图片 支持拖拽/粘贴/点击; 支持常见图片格式; 大小不超过3M +
+
+ 选择语言: + +
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+ 文 本 检 测 结 果

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+ 目标图片 + + +
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+
+ 文 本 识 别 结 果 +

+
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+
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