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Fix typos (#14936)
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@ -64,7 +64,7 @@ x1, y1, x2, y2, x3, y3, x4, y4, annotation
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1. config the `dataset['train']['dataset'['data_path']'`,`dataset['validate']['dataset'['data_path']`in [config/icdar2015_resnet18_fpn_DBhead_polyLR.yaml](cconfig/icdar2015_resnet18_fpn_DBhead_polyLR.yaml)
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* . single gpu train
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```bash
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bash singlel_gpu_train.sh
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bash single_gpu_train.sh
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```
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* . Multi-gpu training
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```bash
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@ -4,5 +4,5 @@
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from .util import *
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from .metrics import *
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from .schedulers import *
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from .cal_recall.script import cal_recall_precison_f1
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from .cal_recall.script import cal_recall_precision_f1
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from .ocr_metric import get_metric
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@ -1,6 +1,6 @@
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# -*- coding: utf-8 -*-
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# @Time : 1/16/19 6:40 AM
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# @Author : zhoujun
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from .script import cal_recall_precison_f1
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from .script import cal_recall_precision_f1
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__all__ = ["cal_recall_precison_f1"]
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__all__ = ["cal_recall_precision_f1"]
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@ -394,7 +394,7 @@ def evaluate_method(gtFilePath, submFilePath, evaluationParams):
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return resDict
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def cal_recall_precison_f1(gt_path, result_path, show_result=False):
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def cal_recall_precision_f1(gt_path, result_path, show_result=False):
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p = {"g": gt_path, "s": result_path}
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result = rrc_evaluation_funcs.main_evaluation(
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p, default_evaluation_params, validate_data, evaluate_method, show_result
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@ -66,7 +66,7 @@ app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们
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<img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400">
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模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model successed`表明模型运行成功。
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模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model succeeded`表明模型运行成功。
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模型的运行结果显示在运行结果显示区，显示格式为
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```text
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@ -256,7 +256,7 @@ public class MainActivity extends AppCompatActivity {
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// Load test image from path and run model
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tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
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tvInputSetting.scrollTo(0, 0);
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tvStatus.setText("STATUS: load model successed");
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tvStatus.setText("STATUS: load model succeeded");
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}
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@ -265,7 +265,7 @@ public class MainActivity extends AppCompatActivity {
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}
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public void onRunModelSuccessed() {
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tvStatus.setText("STATUS: run model successed");
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tvStatus.setText("STATUS: run model succeeded");
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// Obtain results and update UI
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tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
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Bitmap outputImage = predictor.outputImage();
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@ -314,7 +314,7 @@ class MakeShrinkMap(object):
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padding = pyclipper.PyclipperOffset()
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padding.AddPath(subject, pyclipper.JT_ROUND,
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pyclipper.ET_CLOSEDPOLYGON)
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shrinked = []
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shrunk = []
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# Increase the shrink ratio every time we get multiple polygon returned back
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possible_ratios = np.arange(self.shrink_ratio, 1,
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@ -323,19 +323,19 @@ class MakeShrinkMap(object):
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for ratio in possible_ratios:
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distance = polygon_shape.area * (
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1 - np.power(ratio, 2)) / polygon_shape.length
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shrinked = padding.Execute(-distance)
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if len(shrinked) == 1:
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shrunk = padding.Execute(-distance)
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if len(shrunk) == 1:
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break
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if shrinked == []:
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if shrunk == []:
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cv2.fillPoly(mask,
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polygon.astype(np.int32)[np.newaxis, :, :], 0)
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ignore_tags[i] = True
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continue
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for each_shirnk in shrinked:
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shirnk = np.array(each_shirnk).reshape(-1, 2)
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cv2.fillPoly(gt, [shirnk.astype(np.int32)], 1)
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for each_shrink in shrunk:
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shrink = np.array(each_shrink).reshape(-1, 2)
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cv2.fillPoly(gt, [shrink.astype(np.int32)], 1)
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if self.num_classes > 1: # 绘制分类的mask
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cv2.fillPoly(gt_class, polygon.astype(np.int32)[np.newaxis, :, :], classes[i])
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@ -64,7 +64,7 @@ When you tap **Run Model**, the demo executes the corresponding model(s) in your
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<img src="./images/run_det_cls_rec.jpg" width="400">
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The status display area shows the current model status (e.g., `run model successed`), indicating that the model ran successfully. The recognition results are formatted as follows:
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The status display area shows the current model status (e.g., `run model succeeded`), indicating that the model ran successfully. The recognition results are formatted as follows:
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```text
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Serial Number: Det: (x1,y1)(x2,y2)(x3,y3)(x4,y4) Rec: Recognized Text, Confidence Score Cls: Classification Label, Classification Score
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@ -63,7 +63,7 @@ app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们
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<img src="./images/run_det_cls_rec.jpg" width="400">
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模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model successed`表明模型运行成功。
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模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model succeeded`表明模型运行成功。
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模型的运行结果显示在运行结果显示区，显示格式为
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@ -2,7 +2,7 @@
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The All-in-One development tool [PaddleX](https://github.com/PaddlePaddle/PaddleX/tree/release/3.0-beta1), based on the advanced technology of PaddleOCR, supports **low-code full-process** development capabilities in the OCR field. Through low-code development, simple and efficient model use, combination, and customization can be achieved. This will significantly **reduce the time consumption** of model development, **lower its development difficulty**, and greatly accelerate the application and promotion speed of models in the industry. Features include:
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* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **48 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 10 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 30+ single-function modules, making it convenient for developers to use **model combinations**.
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* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **48 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 10 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 30+ single-function modules, making it convenient for developers to use **model combinations**.
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* 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**.
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