添加分类模型

configs/cls/cls_mv3.yml

@@ -1,21 +1,22 @@
 Global:
   algorithm: CLS
-  use_gpu: false
+  use_gpu: False
-  epoch_num: 30
+  epoch_num: 100
   log_smooth_window: 20
-  print_batch_step: 10
+  print_batch_step: 100
-  save_model_dir: output/cls_mb3
+  save_model_dir: output/cls_mv3
   save_epoch_step: 3
-  eval_batch_step: 100
+  eval_batch_step: 500
-  train_batch_size_per_card: 256
+  train_batch_size_per_card: 512
-  test_batch_size_per_card: 256
+  test_batch_size_per_card: 512
-  image_shape: [3, 32, 100]
+  image_shape: [3, 48, 192]
-  label_list: [0,180]
+  label_list: ['0','180']
+  distort: True
   reader_yml: ./configs/cls/cls_reader.yml
   pretrain_weights:
-  checkpoints: /Users/zhoujun20/Desktop/code/class_model/cls_mb3_ultra_small_0.35/best_accuracy
+  checkpoints:
   save_inference_dir:
-  infer_img: /Users/zhoujun20/Desktop/code/PaddleOCR/doc/imgs_words/ch/word_1.jpg
+  infer_img:
   
 Architecture:
   function: ppocr.modeling.architectures.cls_model,ClsModel
@@ -23,7 +24,7 @@ Architecture:
 Backbone:
   function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
   scale: 0.35
-  model_name: Ultra_small
+  model_name: small
  
 Head:
   function: ppocr.modeling.heads.cls_head,ClsHead
@@ -38,6 +39,6 @@ Optimizer:
   beta1: 0.9
   beta2: 0.999
   decay:
-    function: piecewise_decay
+    function: cosine_decay
-    boundaries: [20,30]
+    step_each_epoch: 1169
-    decay_rate: 0.1
+    total_epoch: 100

configs/cls/cls_reader.yml

@@ -1,13 +1,13 @@
 TrainReader:
   reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
-  num_workers: 1
+  num_workers: 8
-  img_set_dir: /
+  img_set_dir: ./train_data/cls
-  label_file_path: /Users/zhoujun20/Downloads/direction/rotate_ver/train.txt
+  label_file_path: ./train_data/cls/train.txt
   
 EvalReader:
   reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
-  img_set_dir: /
+  img_set_dir: ./train_data/cls
-  label_file_path: /Users/zhoujun20/Downloads/direction/rotate_ver/train.txt
+  label_file_path: ./train_data/cls/test.txt
 
 TestReader:
   reader_function: ppocr.data.cls.dataset_traversal,SimpleReader

deploy/cpp_infer/include/config.h

@@ -57,6 +57,8 @@ public:
 
     this->char_list_file.assign(config_map_["char_list_file"]);
 
+    this->use_angle_cls = bool(stoi(config_map_["use_angle_cls"]));
+
     this->cls_model_dir.assign(config_map_["cls_model_dir"]);
 
     this->cls_thresh = stod(config_map_["cls_thresh"]);
@@ -88,6 +90,8 @@ public:
 
   std::string rec_model_dir;
 
+  bool use_angle_cls;
+
   std::string char_list_file;
 
   std::string cls_model_dir;

deploy/cpp_infer/include/ocr_rec.h

@@ -58,7 +58,7 @@ public:
   void LoadModel(const std::string &model_dir);
 
   void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img,
-           Classifier &cls);
+           Classifier *cls);
 
 private:
   std::shared_ptr<PaddlePredictor> predictor_;

deploy/cpp_infer/src/main.cpp

@@ -53,10 +53,15 @@ int main(int argc, char **argv) {
       config.cpu_math_library_num_threads, config.use_mkldnn,
       config.use_zero_copy_run, config.max_side_len, config.det_db_thresh,
       config.det_db_box_thresh, config.det_db_unclip_ratio, config.visualize);
-  Classifier cls(config.cls_model_dir, config.use_gpu, config.gpu_id,
+
+  Classifier *cls = nullptr;
+  if (config.use_angle_cls == true) {
+    cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
                          config.gpu_mem, config.cpu_math_library_num_threads,
                          config.use_mkldnn, config.use_zero_copy_run,
                          config.cls_thresh);
+  }
+
   CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
                      config.gpu_mem, config.cpu_math_library_num_threads,
                      config.use_mkldnn, config.use_zero_copy_run,

deploy/cpp_infer/src/ocr_rec.cpp

@@ -17,7 +17,7 @@
 namespace PaddleOCR {
 
 void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
-                         cv::Mat &img, Classifier &cls) {
+                         cv::Mat &img, Classifier *cls) {
   cv::Mat srcimg;
   img.copyTo(srcimg);
   cv::Mat crop_img;
@@ -27,8 +27,9 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
   int index = 0;
   for (int i = boxes.size() - 1; i >= 0; i--) {
     crop_img = GetRotateCropImage(srcimg, boxes[i]);
-
+    if (cls != nullptr) {
-    crop_img = cls.Run(crop_img);
+      crop_img = cls->Run(crop_img);
+    }
 
     float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
 

deploy/cpp_infer/tools/config.txt

@@ -4,23 +4,23 @@ gpu_id  0
 gpu_mem  4000
 cpu_math_library_num_threads  10
 use_mkldnn 0
-use_zero_copy_run 1
+use_zero_copy_run 0
 
 # det config
 max_side_len  960
 det_db_thresh  0.3
 det_db_box_thresh  0.5
 det_db_unclip_ratio  2.0
-det_model_dir  ./inference/det_db
+det_model_dir  ../model/det
 
 # cls config
-cls_model_dir ./inference/cls
+use_angle_cls 1
+cls_model_dir ../model/cls
 cls_thresh  0.9
 
 # rec config
-rec_model_dir  ./inference/rec_crnn
+rec_model_dir  ../model/rec
-char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
+char_list_file ../model/ppocr_keys_v1.txt
 
 # show the detection results
 visualize 1
-

doc/doc_ch/angle_class.md

@@ -0,0 +1,127 @@
+## 文字角度分类
+
+### 数据准备
+
+请按如下步骤设置数据集：
+
+训练数据的默认存储路径是 `PaddleOCR/train_data/cls`,如果您的磁盘上已有数据集，只需创建软链接至数据集目录：
+
+```
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
+```
+
+请参考下文组织您的数据。
+- 训练集
+
+首先请将训练图片放入同一个文件夹（train_images），并用一个txt文件（cls_gt_train.txt）记录图片路径和标签。
+
+**注意：** 默认请将图片路径和图片标签用 `\t` 分割，如用其他方式分割将造成训练报错
+
+0和180分别表示图片的角度为0度和180度
+
+```
+" 图像文件名                 图像标注信息 "
+
+train_data/cls/word_001.jpg   0
+train_data/cls/word_002.jpg   180
+```
+
+最终训练集应有如下文件结构：
+```
+|-train_data
+    |-cls
+        |- cls_gt_train.txt
+        |- train
+            |- word_001.png
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+- 测试集
+
+同训练集类似，测试集也需要提供一个包含所有图片的文件夹（test）和一个cls_gt_test.txt，测试集的结构如下所示：
+
+```
+|-train_data
+    |-cls
+        |- 和一个cls_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+### 启动训练
+
+PaddleOCR提供了训练脚本、评估脚本和预测脚本。
+
+开始训练:
+
+*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
+
+```
+# 设置PYTHONPATH路径
+export PYTHONPATH=$PYTHONPATH:.
+# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+# 启动训练
+python3 tools/train.py -c configs/cls/cls_mv3.yml
+```
+
+- 数据增强
+
+PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
+
+默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。
+
+训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考：
+[randaugment.py.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
+[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+
+*由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
+
+### 训练
+
+PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/cls_mv3/best_accuracy` 。
+
+如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
+
+**注意，预测/评估时的配置文件请务必与训练一致。**
+
+### 评估
+
+评估数据集可以通过`configs/cls/cls_reader.yml`  修改EvalReader中的 `label_file_path` 设置。
+
+*注意* 评估时必须确保配置文件中 infer_img 字段为空
+```
+export CUDA_VISIBLE_DEVICES=0
+# GPU 评估， Global.checkpoints 为待测权重
+python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+```
+
+### 预测
+
+* 训练引擎的预测
+
+使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
+
+默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重：
+
+```
+# 预测分类结果
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+```
+
+预测图片：
+
+![](../imgs_words/en/word_1.png)
+
+得到输入图像的预测结果：
+
+```
+infer_img: doc/imgs_words/en/word_1.png
+    scores: [[0.93161047 0.06838956]]
+    label: [0]
+```

doc/doc_en/angle_class_en.md

@@ -0,0 +1,126 @@
+## TEXT ANGLE CLASSIFICATION
+
+### DATA PREPARATION
+
+Please organize the dataset as follows:
+
+The default storage path for training data is `PaddleOCR/train_data/cls`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
+
+```
+ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
+```
+
+please refer to the following to organize your data.
+
+- Training set
+
+First put the training images in the same folder (train_images), and use a txt file (cls_gt_train.txt) to store the image path and label.
+
+* Note: by default, the image path and image label are split with `\t`, if you use other methods to split, it will cause training error
+
+0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
+
+```
+" Image file name           Image annotation "
+
+train_data/word_001.jpg   0
+train_data/word_002.jpg   180
+```
+
+The final training set should have the following file structure:
+
+```
+|-train_data
+    |-cls
+        |- cls_gt_train.txt
+        |- train
+            |- word_001.png
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+- Test set
+
+Similar to the training set, the test set also needs to be provided a folder
+containing all images (test) and a cls_gt_test.txt. The structure of the test set is as follows:
+
+```
+|-train_data
+    |-cls
+        |- cls_gt_test.txt
+        |- test
+            |- word_001.jpg
+            |- word_002.jpg
+            |- word_003.jpg
+            | ...
+```
+
+### TRAINING
+
+PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
+
+Start training:
+
+```
+# Set PYTHONPATH path
+export PYTHONPATH=$PYTHONPATH:.
+# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+# Training icdar15 English data
+python3 tools/train.py -c configs/cls/cls_mv3.yml
+```
+
+- Data Augmentation
+
+PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
+
+The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
+
+Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
+[randaugment.py.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
+[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+
+
+- Training
+
+PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/cls_mv3/best_accuracy` during the evaluation process.
+
+If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
+
+**Note that the configuration file for prediction/evaluation must be consistent with the training.**
+
+### EVALUATION
+
+The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader.
+
+```
+export CUDA_VISIBLE_DEVICES=0
+# GPU evaluation, Global.checkpoints is the weight to be tested
+python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+```
+
+### PREDICTION
+
+* Training engine prediction
+
+Using the model trained by paddleocr, you can quickly get prediction through the following script.
+
+The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
+
+```
+# Predict English results
+python3 tools/infer_rec.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+```
+
+Input image:
+
+![](../imgs_words/en/word_1.png)
+
+Get the prediction result of the input image:
+
+```
+infer_img: doc/imgs_words/en/word_1.png
+    scores: [[0.93161047 0.06838956]]
+    label: [0]
+```

ppocr/data/cls/dataset_traversal.py

@@ -14,6 +14,7 @@
 
 import os
 import sys
+import math
 import random
 import numpy as np
 import cv2
@@ -23,7 +24,18 @@ from ppocr.utils.utility import get_image_file_list
 
 logger = initial_logger()
 
-from ppocr.data.rec.img_tools import warp, resize_norm_img
+from ppocr.data.rec.img_tools import resize_norm_img, warp
+from ppocr.data.cls.randaugment import RandAugment
+
+
+def random_crop(img):
+    img_h, img_w = img.shape[:2]
+    if img_w > img_h * 4:
+        w = random.randint(img_h * 2, img_w)
+        i = random.randint(0, img_w - w)
+
+        img = img[:, i:i + w, :]
+    return img
 
 
 class SimpleReader(object):
@@ -39,7 +51,8 @@ class SimpleReader(object):
         self.image_shape = params['image_shape']
         self.mode = params['mode']
         self.infer_img = params['infer_img']
-        self.use_distort = False
+        self.use_distort = params['mode'] == 'train' and params['distort']
+        self.randaug = RandAugment()
         self.label_list = params['label_list']
         if "distort" in params:
             self.use_distort = params['distort'] and params['use_gpu']
@@ -76,6 +89,7 @@ class SimpleReader(object):
                     if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
                     norm_img = resize_norm_img(img, self.image_shape)
+
                     norm_img = norm_img[np.newaxis, :]
                     yield norm_img
             else:
@@ -97,6 +111,8 @@ class SimpleReader(object):
                 for img_id in range(process_id, img_num, self.num_workers):
                     label_infor = label_infor_list[img_id_list[img_id]]
                     substr = label_infor.decode('utf-8').strip("\n").split("\t")
+                    label = self.label_list.index(substr[1])
+
                     img_path = self.img_set_dir + "/" + substr[0]
                     img = cv2.imread(img_path)
                     if img is None:
@@ -105,12 +121,14 @@ class SimpleReader(object):
                     if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
 
-                    label = substr[1]
                     if self.use_distort:
+                        # if random.randint(1, 100)>= 50:
+                        #     img = random_crop(img)
                         img = warp(img, 10)
+                        img = self.randaug(img)
                     norm_img = resize_norm_img(img, self.image_shape)
                     norm_img = norm_img[np.newaxis, :]
-                    yield (norm_img, self.label_list.index(int(label)))
+                    yield (norm_img, label)
 
         def batch_iter_reader():
             batch_outs = []

ppocr/data/cls/randaugment.py

@@ -0,0 +1,135 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+import random
+import six
+
+
+class RawRandAugment(object):
+    def __init__(self, num_layers=2, magnitude=5, fillcolor=(128, 128, 128)):
+        self.num_layers = num_layers
+        self.magnitude = magnitude
+        self.max_level = 10
+
+        abso_level = self.magnitude / self.max_level
+        self.level_map = {
+            "shearX": 0.3 * abso_level,
+            "shearY": 0.3 * abso_level,
+            "translateX": 150.0 / 331 * abso_level,
+            "translateY": 150.0 / 331 * abso_level,
+            "rotate": 30 * abso_level,
+            "color": 0.9 * abso_level,
+            "posterize": int(4.0 * abso_level),
+            "solarize": 256.0 * abso_level,
+            "contrast": 0.9 * abso_level,
+            "sharpness": 0.9 * abso_level,
+            "brightness": 0.9 * abso_level,
+            "autocontrast": 0,
+            "equalize": 0,
+            "invert": 0
+        }
+
+        # from https://stackoverflow.com/questions/5252170/
+        # specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot,
+                                   Image.new("RGBA", rot.size, (128, ) * 4),
+                                   rot).convert(img.mode)
+
+        rnd_ch_op = random.choice
+
+        self.func = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, magnitude * rnd_ch_op([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, magnitude * rnd_ch_op([-1, 1]), 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, magnitude * img.size[0] * rnd_ch_op([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, 0, 1, magnitude * img.size[1] * rnd_ch_op([-1, 1])),
+                fillcolor=fillcolor),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "posterize": lambda img, magnitude:
+            ImageOps.posterize(img, magnitude),
+            "solarize": lambda img, magnitude:
+            ImageOps.solarize(img, magnitude),
+            "contrast": lambda img, magnitude:
+            ImageEnhance.Contrast(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "sharpness": lambda img, magnitude:
+            ImageEnhance.Sharpness(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "brightness": lambda img, magnitude:
+            ImageEnhance.Brightness(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "autocontrast": lambda img, magnitude:
+            ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img)
+        }
+
+    def __call__(self, img):
+        avaiable_op_names = list(self.level_map.keys())
+        for layer_num in range(self.num_layers):
+            op_name = np.random.choice(avaiable_op_names)
+            img = self.func[op_name](img, self.level_map[op_name])
+        return img
+
+
+class RandAugment(RawRandAugment):
+    """ RandAugment wrapper to auto fit different img types """
+
+    def __init__(self, *args, **kwargs):
+        if six.PY2:
+            super(RandAugment, self).__init__(*args, **kwargs)
+        else:
+            super().__init__(*args, **kwargs)
+
+    def __call__(self, img):
+        if not isinstance(img, Image.Image):
+            img = np.ascontiguousarray(img)
+            img = Image.fromarray(img)
+
+        if six.PY2:
+            img = super(RandAugment, self).__call__(img)
+        else:
+            img = super().__call__(img)
+
+        if isinstance(img, Image.Image):
+            img = np.asarray(img)
+
+        return img

tools/eval_utils/eval_cls_utils.py

@@ -16,12 +16,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import logging
 import numpy as np
 
-import paddle.fluid as fluid
+__all__ = ['eval_cls_run']
-
-__all__ = ['eval_class_run']
 
 import logging
 
@@ -52,7 +49,8 @@ def eval_cls_run(exe, eval_info_dict):
                        fetch_list=eval_info_dict['fetch_varname_list'], \
                        return_numpy=False)
         softmax_outs = np.array(outs[1])
-
+        if len(softmax_outs.shape) != 1:
+            softmax_outs = np.array(outs[0])
         acc, acc_num = cal_cls_acc(softmax_outs, label_list)
         total_acc_num += acc_num
         total_sample_num += len(label_list)

tools/infer/predict_cls.py

@@ -108,7 +108,7 @@ class TextClassifier(object):
                 score = prob_out[rno][label_idx]
                 label = self.label_list[label_idx]
                 cls_res[indices[beg_img_no + rno]] = [label, score]
-                if label == 180:
+                if '180' in label and score > 0.9999:
                     img_list[indices[beg_img_no + rno]] = cv2.rotate(
                         img_list[indices[beg_img_no + rno]], 1)
         return img_list, cls_res, predict_time
@@ -130,12 +130,6 @@ def main(args):
         img_list.append(img)
     try:
         img_list, cls_res, predict_time = text_classifier(img_list)
-        print(cls_res)
-        from matplotlib import pyplot as plt
-        for img, angle in zip(img_list, cls_res):
-            plt.title(str(angle))
-            plt.imshow(img)
-            plt.show()
     except Exception as e:
         print(e)
         exit()

tools/infer/predict_system.py

@@ -40,6 +40,8 @@ class TextSystem(object):
     def __init__(self, args):
         self.text_detector = predict_det.TextDetector(args)
         self.text_recognizer = predict_rec.TextRecognizer(args)
+        self.use_angle_cls = args.use_angle_cls
+        if self.use_angle_cls:
             self.text_classifier = predict_cls.TextClassifier(args)
 
     def get_rotate_crop_image(self, img, points):
@@ -95,10 +97,12 @@ class TextSystem(object):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
-        img_rotate_list, angle_list, elapse = self.text_classifier(
+        if self.use_angle_cls:
+            img_crop_list, angle_list, elapse = self.text_classifier(
                 img_crop_list)
-        print("cls num  : {}, elapse : {}".format(len(img_rotate_list), elapse))
+            print("cls num  : {}, elapse : {}".format(
-        rec_res, elapse = self.text_recognizer(img_rotate_list)
+                len(img_crop_list), elapse))
+        rec_res, elapse = self.text_recognizer(img_crop_list)
         print("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         return dt_boxes, rec_res

tools/infer/utility.py

@@ -15,6 +15,7 @@
 import argparse
 import os, sys
 from ppocr.utils.utility import initial_logger
+
 logger = initial_logger()
 from paddle.fluid.core import PaddleTensor
 from paddle.fluid.core import AnalysisConfig
@@ -72,13 +73,14 @@ def parse_args():
     parser.add_argument("--use_space_char", type=bool, default=True)
 
     # params for text classifier
+    parser.add_argument("--use_angle_cls", type=str2bool, default=True)
     parser.add_argument("--cls_model_dir", type=str)
-    parser.add_argument("--cls_image_shape", type=str, default="3, 32, 100")
+    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
-    parser.add_argument("--label_list", type=list, default=[0, 180])
+    parser.add_argument("--label_list", type=list, default=['0', '180'])
     parser.add_argument("--cls_batch_num", type=int, default=30)
 
-    parser.add_argument("--enable_mkldnn", type=bool, default=False)
+    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
-    parser.add_argument("--use_zero_copy_run", type=bool, default=False)
+    parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
     return parser.parse_args()
 
 

tools/infer_cls.py

@@ -85,9 +85,10 @@ def main():
                           feed={"image": img},
                           fetch_list=fetch_varname_list,
                           return_numpy=False)
-        for k in predict:
+        scores = np.array(predict[0])
-            k = np.array(k)
+        label = np.array(predict[1])
-            print(k)
+        logger.info('\t scores: {}'.format(scores))
+        logger.info('\t label: {}'.format(label))
     # save for inference model
     target_var = []
     for key, values in outputs.items():