添加分类模型
This commit is contained in:
WenmuZhou
parent
567c74c508
commit
144b022fb6
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@ -1,21 +1,22 @@
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Global:
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algorithm: CLS
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use_gpu: false
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epoch_num: 30
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use_gpu: False
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epoch_num: 100
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: output/cls_mb3
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print_batch_step: 100
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save_model_dir: output/cls_mv3
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save_epoch_step: 3
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eval_batch_step: 100
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train_batch_size_per_card: 256
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test_batch_size_per_card: 256
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image_shape: [3, 32, 100]
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label_list: [0,180]
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eval_batch_step: 500
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train_batch_size_per_card: 512
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test_batch_size_per_card: 512
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image_shape: [3, 48, 192]
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label_list: ['0','180']
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distort: True
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reader_yml: ./configs/cls/cls_reader.yml
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pretrain_weights:
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checkpoints: /Users/zhoujun20/Desktop/code/class_model/cls_mb3_ultra_small_0.35/best_accuracy
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checkpoints:
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save_inference_dir:
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infer_img: /Users/zhoujun20/Desktop/code/PaddleOCR/doc/imgs_words/ch/word_1.jpg
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.cls_model,ClsModel
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@ -23,7 +24,7 @@ Architecture:
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Backbone:
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function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
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scale: 0.35
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model_name: Ultra_small
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model_name: small
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Head:
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function: ppocr.modeling.heads.cls_head,ClsHead
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@ -38,6 +39,6 @@ Optimizer:
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beta1: 0.9
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beta2: 0.999
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decay:
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function: piecewise_decay
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boundaries: [20,30]
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decay_rate: 0.1
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function: cosine_decay
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step_each_epoch: 1169
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total_epoch: 100
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@ -1,13 +1,13 @@
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TrainReader:
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reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
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num_workers: 1
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img_set_dir: /
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label_file_path: /Users/zhoujun20/Downloads/direction/rotate_ver/train.txt
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num_workers: 8
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img_set_dir: ./train_data/cls
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label_file_path: ./train_data/cls/train.txt
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EvalReader:
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reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
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img_set_dir: /
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label_file_path: /Users/zhoujun20/Downloads/direction/rotate_ver/train.txt
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img_set_dir: ./train_data/cls
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label_file_path: ./train_data/cls/test.txt
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TestReader:
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reader_function: ppocr.data.cls.dataset_traversal,SimpleReader
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@ -57,6 +57,8 @@ public:
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this->char_list_file.assign(config_map_["char_list_file"]);
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this->use_angle_cls = bool(stoi(config_map_["use_angle_cls"]));
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this->cls_model_dir.assign(config_map_["cls_model_dir"]);
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this->cls_thresh = stod(config_map_["cls_thresh"]);
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@ -88,6 +90,8 @@ public:
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std::string rec_model_dir;
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bool use_angle_cls;
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std::string char_list_file;
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std::string cls_model_dir;
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@ -58,7 +58,7 @@ public:
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void LoadModel(const std::string &model_dir);
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void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img,
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Classifier &cls);
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Classifier *cls);
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private:
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std::shared_ptr<PaddlePredictor> predictor_;
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@ -53,10 +53,15 @@ int main(int argc, char **argv) {
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config.cpu_math_library_num_threads, config.use_mkldnn,
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config.use_zero_copy_run, config.max_side_len, config.det_db_thresh,
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config.det_db_box_thresh, config.det_db_unclip_ratio, config.visualize);
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Classifier cls(config.cls_model_dir, config.use_gpu, config.gpu_id,
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Classifier *cls = nullptr;
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if (config.use_angle_cls == true) {
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cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.use_zero_copy_run,
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config.cls_thresh);
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}
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CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
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config.gpu_mem, config.cpu_math_library_num_threads,
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config.use_mkldnn, config.use_zero_copy_run,
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@ -17,7 +17,7 @@
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namespace PaddleOCR {
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void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
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cv::Mat &img, Classifier &cls) {
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cv::Mat &img, Classifier *cls) {
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cv::Mat srcimg;
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img.copyTo(srcimg);
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cv::Mat crop_img;
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@ -27,8 +27,9 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
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int index = 0;
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for (int i = boxes.size() - 1; i >= 0; i--) {
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crop_img = GetRotateCropImage(srcimg, boxes[i]);
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crop_img = cls.Run(crop_img);
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if (cls != nullptr) {
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crop_img = cls->Run(crop_img);
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}
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float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
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@ -4,23 +4,23 @@ gpu_id 0
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gpu_mem 4000
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cpu_math_library_num_threads 10
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use_mkldnn 0
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use_zero_copy_run 1
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use_zero_copy_run 0
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# det config
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max_side_len 960
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det_db_thresh 0.3
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det_db_box_thresh 0.5
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det_db_unclip_ratio 2.0
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det_model_dir ./inference/det_db
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det_model_dir ../model/det
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# cls config
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cls_model_dir ./inference/cls
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use_angle_cls 1
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cls_model_dir ../model/cls
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cls_thresh 0.9
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# rec config
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rec_model_dir ./inference/rec_crnn
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char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
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rec_model_dir ../model/rec
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char_list_file ../model/ppocr_keys_v1.txt
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# show the detection results
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visualize 1
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@ -0,0 +1,127 @@
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## 文字角度分类
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### 数据准备
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请按如下步骤设置数据集：
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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：
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```
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ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
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```
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请参考下文组织您的数据。
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- 训练集
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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。
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**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
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0和180分别表示图片的角度为0度和180度
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```
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" 图像文件名 图像标注信息 "
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train_data/cls/word_001.jpg 0
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train_data/cls/word_002.jpg 180
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```
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最终训练集应有如下文件结构：
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```
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|-train_data
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|-cls
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|- cls_gt_train.txt
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|- train
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|- word_001.png
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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- 测试集
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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：
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```
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|-train_data
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|-cls
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|- 和一个cls_gt_test.txt
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|- test
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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### 启动训练
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PaddleOCR提供了训练脚本、评估脚本和预测脚本。
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开始训练:
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*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*
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```
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# 设置PYTHONPATH路径
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export PYTHONPATH=$PYTHONPATH:.
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# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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# 启动训练
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python3 tools/train.py -c configs/cls/cls_mv3.yml
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```
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- 数据增强
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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`。
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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)。
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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：
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[randaugment.py.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
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[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
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*由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
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### 训练
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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` 。
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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。
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**注意，预测/评估时的配置文件请务必与训练一致。**
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### 评估
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评估数据集可以通过`configs/cls/cls_reader.yml` 修改EvalReader中的 `label_file_path` 设置。
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*注意* 评估时必须确保配置文件中 infer_img 字段为空
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```
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export CUDA_VISIBLE_DEVICES=0
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# GPU 评估， Global.checkpoints 为待测权重
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python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### 预测
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* 训练引擎的预测
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使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
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默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重：
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```
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# 预测分类结果
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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
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```
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预测图片：
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得到输入图像的预测结果：
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```
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infer_img: doc/imgs_words/en/word_1.png
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scores: [[0.93161047 0.06838956]]
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label: [0]
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```
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@ -0,0 +1,126 @@
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## TEXT ANGLE CLASSIFICATION
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### DATA PREPARATION
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Please organize the dataset as follows:
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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:
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```
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ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
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```
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please refer to the following to organize your data.
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- Training set
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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.
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* 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
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0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
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```
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" Image file name Image annotation "
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train_data/word_001.jpg 0
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train_data/word_002.jpg 180
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```
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The final training set should have the following file structure:
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```
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|-train_data
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|-cls
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|- cls_gt_train.txt
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|- train
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|- word_001.png
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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- Test set
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Similar to the training set, the test set also needs to be provided a folder
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containing all images (test) and a cls_gt_test.txt. The structure of the test set is as follows:
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```
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|-train_data
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|-cls
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|- cls_gt_test.txt
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|- test
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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### TRAINING
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PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
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Start training:
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```
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# Set PYTHONPATH path
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export PYTHONPATH=$PYTHONPATH:.
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# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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# Training icdar15 English data
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python3 tools/train.py -c configs/cls/cls_mv3.yml
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```
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- Data Augmentation
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PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
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The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
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Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
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[randaugment.py.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
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[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
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- Training
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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.
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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.
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**Note that the configuration file for prediction/evaluation must be consistent with the training.**
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### EVALUATION
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The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader.
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```
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export CUDA_VISIBLE_DEVICES=0
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# GPU evaluation, Global.checkpoints is the weight to be tested
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python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### PREDICTION
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* Training engine prediction
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Using the model trained by paddleocr, you can quickly get prediction through the following script.
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The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
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```
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# Predict English results
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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
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```
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Input image:
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Get the prediction result of the input image:
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```
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infer_img: doc/imgs_words/en/word_1.png
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scores: [[0.93161047 0.06838956]]
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label: [0]
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```
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@ -14,6 +14,7 @@
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import os
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import sys
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import math
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import random
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import numpy as np
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import cv2
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@ -23,7 +24,18 @@ from ppocr.utils.utility import get_image_file_list
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logger = initial_logger()
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|
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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 = []
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -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_class_run']
|
||||
__all__ = ['eval_cls_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)
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
|
|
@ -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))
|
||||
rec_res, elapse = self.text_recognizer(img_rotate_list)
|
||||
print("cls num : {}, elapse : {}".format(
|
||||
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
|
||||
|
|
|
|||
|
|
@ -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("--label_list", type=list, default=[0, 180])
|
||||
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("--cls_batch_num", type=int, default=30)
|
||||
|
||||
parser.add_argument("--enable_mkldnn", type=bool, default=False)
|
||||
parser.add_argument("--use_zero_copy_run", type=bool, default=False)
|
||||
parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
|
||||
parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -85,9 +85,10 @@ def main():
|
|||
feed={"image": img},
|
||||
fetch_list=fetch_varname_list,
|
||||
return_numpy=False)
|
||||
for k in predict:
|
||||
k = np.array(k)
|
||||
print(k)
|
||||
scores = np.array(predict[0])
|
||||
label = np.array(predict[1])
|
||||
logger.info('\t scores: {}'.format(scores))
|
||||
logger.info('\t label: {}'.format(label))
|
||||
# save for inference model
|
||||
target_var = []
|
||||
for key, values in outputs.items():
|
||||
|
|
|
|||
Loading…
Reference in New Issue