first commit

README.md

 # chineseocr_lite_onnx
+## 论文
+chineseocr_lite通过det、rec、cls三个模型分别实现字符检测、字符识别和字符方向分类的应用
+
+det模型主要用DB算法，参考论文如下：
+
+https://arxiv.org/pdf/1911.08947.pdf
+
+rec模型主要用crnn算法，参考论文如下：
+
+https://arxiv.org/pdf/1507.05717.pdf
+
+cls模型用resnet实现通用分类，参考论文如下：
+
+https://arxiv.org/pdf/1512.03385.pdf
+## 模型结构
+det:
+
+![image](https://developer.hpccube.com/codes/modelzoo/chineseocr_lite_onnx/-/raw/main/configs/dbnet-arc.png)
+
+rec:
+
+![image](https://developer.hpccube.com/codes/modelzoo/chineseocr_lite_onnx/-/raw/main/configs/crnn-arc.png)
+
+cls:
+
+![image](https://developer.hpccube.com/codes/modelzoo/chineseocr_lite_onnx/-/raw/main/configs/resnet-arc.png)
+## 算法原理
+det->cls->rec->text
+## 数据集
+推荐使用icdar2015数据集[icdar2015](https://rrc.cvc.uab.es/?ch=4&com=downloads)。
+
+检测模型训练集文件结构
+```
+/PaddleOCR/train_data/icdar2015/text_localization/
+  └─ icdar_c4_train_imgs/         Training data of icdar dataset
+  └─ ch4_test_images/             Testing data of icdar dataset
+  └─ train_icdar2015_label.txt    Training annotation of icdar dataset
+  └─ test_icdar2015_label.txt     Test annotation of icdar dataset
+```
+识别模型训练集文件结构
+```
+|-train_data
+  |-rec
+    |- rec_gt_train.txt
+    |- train
+        |- word_001.png
+        |- word_002.jpg
+        |- word_003.jpg
+        | ...
+    |-ic15_data
+        |- rec_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+## 环境配置
+在[光源](https://www.sourcefind.cn/#/service-details)可拉取训练以及推理的docker镜像，在[光合开发者社区](https://cancon.hpccube.com:65024/4/main/)可下载paddle安装包用于模型测试。chineseocr_lite_onnx推荐的镜像如下：
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:ort1.14.0_migraphx3.0.0-dtk22.10.1
+```
+获取最新的dtk并安装依赖
+```
+cd /opt
+wget http://10.0.50.210:8000/jenkins/rocm/23.04.1/centos7/DTK-23.04.1-rc4-centos7-x86_64.tar.gz
+tar -zxvf DTK-23.04.1-rc4-centos7-x86_64.tar.gz
+source /opt/dtk-23.04.1/env.sh
+cd chineseocr_lite_onnx
+pip3 install -r requirements.txt
+```
+## 测试
+检测模型
+```
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/eval.py -c configs/det_mv3_db.yml -o Global.pretrained_model=./models/dbnet.onnx
+```
+识别模型
+```
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/eval.py -c configs/ch_PP-OCRv3_rec.yml -o Global.pretrained_model=./models/crnn_lite_lstm.onnx
+```
+## 推理
+```
+python3 main.py --img_dir="./images/" --det_model_dir="./models/dbnet.onnx" --rec_model_dir="./models/crnn_lite_lstm.onnx" --cls_model_dir="./models/angle_net.onnx" --use_angle_cls=1 --warmup=1
+```
+## result
+![image](https://developer.hpccube.com/codes/modelzoo/chineseocr_lite_onnx/-/raw/main/dbnet/test.jpg)
+### 性能和准确率数据
+
+检测模型测试
+| Model | Precision | Recall |
+| :------: | :------: |:------: |
+| det | 0.6969 | 0.2291  |
+
+识别模型测试
+| Model | Acc | 
+| :------: | :------: |
+| rec | 0.1160 | 
+## 应用场景
+### 算法类别
+ocr
+### 热点应用行业
+工业制造、金融、交通、教育、医疗
+## 源码仓库及问题反馈
+https://developer.hpccube.com/codes/modelzoo/chineseocr_lite_onnx
+## 参考
+* [chineseocr_lite](https://github.com/DayBreak-u/chineseocr_lite)
 

angnet/__init__.py

+from .angle import AngleNetHandle

angnet/angle.py

+from PIL import  Image
+import numpy as np
+import cv2
+
+import onnxruntime as rt
+import time
+
+class AngleNetHandle:
+    def __init__(self, model_path,size_h = 32, size_w = 192):
+
+        self.sess = rt.InferenceSession(model_path, providers=[('ROCMExecutionProvider', {'device_id': '4'}),'CPUExecutionProvider'])
+        self.size_h = size_h
+        self.size_w = size_w
+
+    def predict_rbg(self, im):
+        """
+        预测
+        """
+        scale = im.size[1] * 1.0 / self.size_h
+        w = im.size[0] / scale
+        w = int(w)
+        img = im.resize((w, self.size_h), Image.BILINEAR)
+
+        if w < self.size_w:
+            imgnew = Image.new('RGB', (self.size_w, self.size_h), (255))
+            imgnew.paste(img, (0, 0, w, self.size_h))
+        else :
+            imgnew = img.crop((0, 0, self.size_w,   self.size_h))
+
+        img = np.array(imgnew, dtype=np.float32)
+
+        img -= 127.5
+        img /= 127.5
+        image = img.transpose(2, 0, 1)
+        transformed_image = np.expand_dims(image, axis=0)
+
+
+        preds = self.sess.run(["out"], {"input": transformed_image.astype(np.float32)})
+
+        pred = np.argmax(preds[0])
+
+        return pred
+
+    def predict_rbgs(self, imgs):
+        nlen = len(imgs)
+        res_sum = sum([self.predict_rbg(im) for im in imgs])
+        return  res_sum < nlen//2
+    
+    def __call__(self, im):
+        """
+        预测
+        """
+        scale = im.shape[0] * 1.0 / self.size_h
+        w = im.shape[1] / scale
+        w = int(w)
+        img_resize = cv2.resize(im, (w, self.size_h))
+
+        imgnew = np.zeros((self.size_h, self.size_w, 3), dtype=np.float32)
+        if w < self.size_w:
+            imgnew[:, 0:w, :] = img_resize
+        else :
+            imgnew = img_resize[:, 0:self.size_w, :]
+
+        img = np.array(imgnew, dtype=np.float32)
+
+        img -= 127.5
+        img /= 127.5
+        image = img.transpose(2, 0, 1)
+        transformed_image = np.expand_dims(image, axis=0)
+
+
+        preds = self.sess.run(["out"], {"input": transformed_image.astype(np.float32)})
+
+        pred = np.argmax(preds[0])
+
+        return pred
+
+
+if __name__ == "__main__":
+    crnn_handle = AngleNetHandle(model_path="./models/angle_net.onnx")
+    import glob
+    imgs = glob.glob("/Users/yanghuiyu/Desktop/myself/OCR/mbv3_crnn/test_imgs/*p*g")
+    for im_path in imgs:
+        im = Image.open(im_path).convert("RGB")
+
+        print(im_path , crnn_handle.predict_rbg(im))
\ No newline at end of file

configs/ch_PP-OCRv3_rec.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_ppocr_v3
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  character_dict_path: crnn/key.txt
+  max_text_length: &max_text_length 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_ppocrv3.txt
+
+
+Architecture:
+  model_type: rec
+  algorithm: SVTR
+  Transform:
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+    last_conv_stride: [1, 2]
+    last_pool_type: avg
+  Head:
+    name: MultiHead
+    head_list:
+      - CTCHead:
+          Neck:
+            name: svtr
+            dims: 64
+            depth: 2
+            hidden_dims: 120
+            use_guide: True
+          Head:
+            fc_decay: 0.00001
+      - SARHead:
+          enc_dim: 512
+          max_text_length: *max_text_length
+
+PostProcess:  
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+  ignore_space: False
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/rec/ic15_data/
+    label_file_list:
+    - ./train_data/rec/ic15_data/rec_gt_test.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - MultiLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 256]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_sar
+        - length
+        - valid_ratio
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 4

configs/det_mv3_db.yml

+Global:
+  use_gpu: true
+  use_xpu: false
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  model_type: det
+  algorithm: DB
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: DBFPN
+    out_channels: 256
+  Head:
+    name: DBHead
+    k: 50
+
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForSingle:
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
+    use_shared_memory: False

crnn/CRNN.py

+from PIL import  Image
+import numpy as np
+import cv2
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+from keys import alphabetChinese as alphabet
+
+import onnxruntime as rt
+from util import strLabelConverter, resizeNormalize
+import os
+import time
+import math
+
+converter = strLabelConverter(''.join(alphabet))
+
+
+def _check_image_file(path):
+    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+    return any([path.lower().endswith(e) for e in img_end])
+
+def get_image_file_list(img_file):
+    imgs_lists = []
+    if img_file is None or not os.path.exists(img_file):
+        raise Exception("not found any img file in {}".format(img_file))
+
+    if os.path.isfile(img_file) and _check_image_file(img_file):
+        imgs_lists.append(img_file)
+    elif os.path.isdir(img_file):
+        for single_file in os.listdir(img_file):
+            file_path = os.path.join(img_file, single_file)
+            if os.path.isfile(file_path) and _check_image_file(file_path):
+                imgs_lists.append(file_path)
+    if len(imgs_lists) == 0:
+        raise Exception("not found any img file in {}".format(img_file))
+    imgs_lists = sorted(imgs_lists)
+    return imgs_lists
+
+
+def softmax(x):
+    x_row_max = x.max(axis=-1)
+    x_row_max = x_row_max.reshape(list(x.shape)[:-1]+[1])
+    x = x - x_row_max
+    x_exp = np.exp(x)
+    x_exp_row_sum = x_exp.sum(axis=-1).reshape(list(x.shape)[:-1]+[1])
+    softmax = x_exp / x_exp_row_sum
+    return softmax
+
+
+class CRNNHandle:
+    def __init__(self, model_path):
+
+        self.sess = rt.InferenceSession(model_path, providers=[('ROCMExecutionProvider', {'device_id': '3'}),'CPUExecutionProvider'])
+
+    def predict(self, image):
+        """
+        预测
+        """
+        scale = image.size[1] * 1.0 / 32
+        w = image.size[0] / scale
+        w = int(w)
+        transformer = resizeNormalize((w, 32))
+
+        image = transformer(image)
+
+        image = image.transpose(2, 0, 1)
+        transformed_image = np.expand_dims(image, axis=0)
+
+        preds = self.sess.run(["out"], {"input": transformed_image.astype(np.float32)})
+
+        preds = preds[0]
+
+
+        length  = preds.shape[0]
+        preds = preds.reshape(length,-1)
+
+        preds = np.argmax(preds,axis=1)
+
+        preds = preds.reshape(-1)
+
+
+        sim_pred = converter.decode(preds, length, raw=False)
+
+        return sim_pred
+
+
+
+    def predict_rbg(self, im):
+        """
+        预测
+        """
+        scale = im.size[1] * 1.0 / 32
+        w = im.size[0] / scale
+        w = int(w)
+
+        img = im.resize((w, 32), Image.BILINEAR)
+        img = np.array(img, dtype=np.float32)
+        img -= 127.5
+        img /= 127.5
+        image = img.transpose(2, 0, 1)
+        transformed_image = np.expand_dims(image, axis=0)
+
+        preds = self.sess.run(["out"], {"input": transformed_image.astype(np.float32)})
+
+        preds = preds[0]
+
+
+        length  = preds.shape[0]
+        preds = preds.reshape(length,-1)
+
+        # preds = softmax(preds)
+
+
+        preds = np.argmax(preds,axis=1)
+
+        preds = preds.reshape(-1)
+
+        sim_pred = converter.decode(preds, length, raw=False)
+
+        return sim_pred
+    
+    
+    def resize_norm_img_section(self, img, max_wh_ratio):
+        # print("rec resize for section")
+        imgC, imgH, imgW = 3, 32, 320
+        
+        rec_precision_level = os.environ.get("OCR_REC_PRECISION")
+        max_w = imgH * 48
+        # max_w = 2304
+        if rec_precision_level =='0':
+            imgW = max_w
+        elif rec_precision_level == '1':
+            imgW = int((imgH * max_wh_ratio))
+            if imgW <= max_w / 2:
+                imgW = max_w / 2
+            else:
+                imgW = max_w
+        elif rec_precision_level == '2':
+            imgW = int((imgH * max_wh_ratio))
+            if imgW <= max_w / 4:
+                imgW = max_w / 4
+            elif imgW > max_w / 4 and imgW <= max_w / 2:
+                imgW = max_w / 2
+            elif imgW > max_w / 2 and imgW <= 3 * max_w / 4:
+                imgW = 3 * max_w / 4
+            else:
+                imgW = max_w
+        else:
+            imgW = int((imgH * max_wh_ratio))
+            if imgW <= max_w / 6:
+                imgW = max_w / 6
+            elif imgW > max_w / 6 and imgW <= max_w / 3:
+                imgW = max_w / 3
+            elif imgW > max_w / 3 and imgW <= max_w / 2:
+                imgW = max_w / 2
+            elif imgW > max_w / 2 and imgW <= 2 * max_w / 3:
+                imgW = 2 * max_w / 3
+            elif imgW > 2 *max_w / 3 and imgW <= 5 * max_w / 6:
+                imgW = 5 * max_w / 6
+            else:
+                imgW = max_w
+
+        imgW = int(imgW)
+        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 __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 = [''] * img_num
+        if img_num <= 0:
+            return rec_res, 0
+        max_batnum = 24
+        min_batnum = 8
+        if os.environ.get("OCR_REC_MAX_BATNUM") is not None:
+            max_batnum = int(os.environ.get("OCR_REC_MAX_BATNUM"))
+        if os.environ.get("OCR_REC_MIN_BATNUM") is not None:
+            min_batnum = int(os.environ.get("OCR_REC_MIN_BATNUM"))
+        assert max_batnum / min_batnum == int(max_batnum / min_batnum), "max_batnum must be multiple of min_batnum."
+        img_num_left = img_num
+        img_no_count = 0
+        st = time.time()
+        if img_num_left > max_batnum:
+            batch_num = max_batnum
+            batch_num = int(batch_num)
+            for beg_img_no in range(img_no_count, int(img_num_left / batch_num) * batch_num, batch_num):
+                end_img_no = beg_img_no + batch_num
+                norm_img_batch = []
+                max_wh_ratio = 0
+                for ino in range(beg_img_no, end_img_no):
+                    h, w = img_list[indices[ino]].shape[0:2]
+                    wh_ratio = w * 1.0 / h
+                    max_wh_ratio = max(max_wh_ratio, wh_ratio)
+                for ino in range(beg_img_no, end_img_no):
+                    norm_img = self.resize_norm_img_section(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, axis=0)
+                norm_img_batch = norm_img_batch.copy()
+
+                input_dict = {}
+                input_dict["input"] = norm_img_batch
+                outputs = self.sess.run(["out"], input_dict)
+                preds_ = outputs[0]
+                
+                for rno in range(batch_num):
+                    preds = preds_[:,rno:rno + 1,:]
+                    length  = preds.shape[0]
+                    preds = preds.reshape(length,-1)
+
+                    preds = np.argmax(preds,axis=1)
+
+                    preds = preds.reshape(-1)
+
+                    sim_pred = converter.decode(preds, length, raw=False)
+                    rec_res[indices[beg_img_no + rno]] = sim_pred
+                    
+            img_no_count = int(img_num_left / batch_num) * batch_num
+            img_num_left = img_num_left - int(img_num_left / batch_num) * batch_num
+                    
+        batch_num = math.ceil(img_num_left / min_batnum) * min_batnum
+        batch_num = int(batch_num)
+        Dnum = batch_num - img_num_left
+        for dno in range(Dnum):
+            indices = np.append(indices,img_num + dno)
+            rec_res.append('')
+        
+        beg_img_no = img_no_count
+        end_img_no = img_num
+        norm_img_batch = []
+        max_wh_ratio = 0
+        for ino in range(beg_img_no, end_img_no):
+            h, w = img_list[indices[ino]].shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for ino in range(beg_img_no, end_img_no):
+            norm_img = self.resize_norm_img_section(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)
+        if norm_img_batch.shape[0] != batch_num:
+            img_tmp = np.zeros((batch_num - norm_img_batch.shape[0], norm_img_batch.shape[1], norm_img_batch.shape[2], norm_img_batch.shape[3]), dtype=np.float32)
+            norm_img_batch = np.concatenate([norm_img_batch, img_tmp])
+        norm_img_batch = norm_img_batch.copy()
+
+        input_dict = {}
+        input_dict["input"] = norm_img_batch
+        outputs = self.sess.run(["out"], input_dict)
+        preds_ = outputs[0]
+        
+        for rno in range(img_num - img_no_count):
+            preds = preds_[:,rno:rno + 1,:]
+            length  = preds.shape[0]
+            preds = preds.reshape(length,-1)
+
+            preds = np.argmax(preds,axis=1)
+
+            preds = preds.reshape(-1)
+
+            sim_pred = converter.decode(preds, length, raw=False)
+            rec_res[indices[beg_img_no + rno]] = sim_pred
+
+        return rec_res, time.time() - st
+
+
+
+if __name__ == "__main__":
+    image_file_list = get_image_file_list("warmup_images_rec")
+    crnn_handle = CRNNHandle(model_path="./models/crnn_lite_lstm.onnx")
+    
+    img_list = []
+    for image_file in image_file_list:
+        img = cv2.imread(image_file)
+        img_list.append(img)
+        
+        im = Image.open(image_file)
+        print(crnn_handle.predict_rbg(im))
+        
+        # img_list_tmp = [img]
+        # rec_res, _ = crnn_handle(img_list_tmp)
+        # print(rec_res[0])
+        
+    rec_res, _ = crnn_handle(img_list)
+    for i in range(len(image_file_list)):
+        print(rec_res[i])
+    

crnn/__init__.py

+from .CRNN import CRNNHandle

crnn/keys.py

+alphabetChinese =  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