PPOCR-Det

Table of contents

• 1. Description
• 2. Current Support Platform
• 3. Pretrained Model
• 4. Convert to RKNN
• 5. Python Demo
• 6. Android Demo
  • 6.1 Compile and Build
  • 6.2 Push demo files to device
  • 6.3 Run demo
• 7. Linux Demo
  • 7.1 Compile && Build
  • 7.2 Push demo files to device
  • 7.3 Run demo
• 8. Expected Results

1. Description

The model used in this example comes from the following open source projects:

https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.7

2. Current Support Platform

RK3562, RK3566, RK3568, RK3576, RK3588, RV1126B, RV1109, RV1126, RK1808, RK3399PRO

3. Pretrained Model

Download link:

../ppocrv4_det.onnx

Download with shell command:

cd model
./download_model.sh

(Optional)PADDLE to ONNX : Please refer Paddle_2_ONNX.md

4. Convert to RKNN

Usage:

cd python
python convert.py <onnx_model> <TARGET_PLATFORM> <dtype(optional)> <output_rknn_path(optional)>

# such as: 
python convert.py ../model/ppocrv4_det.onnx rk3588
# output model will be saved as ../model/ppocrv4_det.rknn

Description:

• <onnx_model>: Specify ONNX model path.
• <TARGET_PLATFORM>: Specify NPU platform name. Support Platform refer here.
• <dtype>(optional): Specify as i8 or fp. i8 for doing quantization, fp for no quantization. Default is i8.
• <output_rknn_path>(optional): Specify save path for the RKNN model, default save in the same directory as ONNX model with name ppocrv4_det.rknn

Attention:

• mmse quantized_algorithm can improve precision of ppocrv4_det rknn model, while it will increase the convert time.

• Our experiment show mmse can bring +0.7%@precision / +1.7%recall / +1.2%@hmean improvement compared to normal quantized_algorithm.

5. Python Demo

Usage:

cd python

# Inference with ONNX model
python ppocr_det.py --model_path <onnx_model>
# such as: python ppocr_det.py --model_path ../model/ppocrv4_det.onnx 

# Inference with RKNN model
python ppocr_det.py --model_path <rknn_model> --target <TARGET_PLATFORM>
# such as: python ppocr_det.py --model_path ../model/ppocrv4_det.rknn --target rk3588

Description:

• <TARGET_PLATFORM>: Specify NPU platform name. Such as 'rk3588'.

• <onnx_model / rknn_model>: specified as the model path.

6. Android Demo

6.1 Compile and Build

Usage:

# go back to the rknn_model_zoo root directory
cd ../../
export ANDROID_NDK_PATH=<android_ndk_path>

./build-android.sh -t <TARGET_PLATFORM> -a <ARCH> -d PPOCR-Det

# such as 
./build-android.sh -t rk3588 -a arm64-v8a -d PPOCR-Det

Description:

• <android_ndk_path>: Specify Android NDK path.
• <TARGET_PLATFORM>: Specify NPU platform name. Support Platform refer here.
• <ARCH>: Specify device system architecture. To query device architecture, refer to the following command:

  # Query architecture. For Android, ['arm64-v8a' or 'armeabi-v7a'] should shown in log.
  adb shell cat /proc/version
  

6.2 Push demo files to device

With device connected via USB port, push demo files to devices:

adb root
adb remount
adb push install/<TARGET_PLATFORM>_android_<ARCH>/rknn_PPOCR-Det_demo/ /data/

6.3 Run demo

adb shell
cd /data/rknn_PPOCR-Det_demo

export LD_LIBRARY_PATH=./lib
./rknn_ppocr_det_demo model/ppocrv4_det.rknn model/test.jpg

• After running, the result was saved as out.jpg. To check the result on host PC, pull back result referring to the following command:

  adb pull /data/rknn_PPOCR-Det_demo/out.jpg
  

7. Linux Demo

7.1 Compile and Build

usage

# go back to the rknn_model_zoo root directory
cd ../../

# if GCC_COMPILER not found while building, please set GCC_COMPILER path
(optional)export GCC_COMPILER=<GCC_COMPILER_PATH>

./build-linux.sh -t <TARGET_PLATFORM> -a <ARCH> -d PPOCR-Det

# such as 
./build-linux.sh -t rk3588 -a aarch64 -d PPOCR-Det
# such as 
./build-linux.sh -t rv1126 -a armhf -d PPOCR-Det

Description:

• <GCC_COMPILER_PATH>: Specified as GCC_COMPILER path.

• <TARGET_PLATFORM> : Specify NPU platform name. Support Platform refer here.

• <ARCH>: Specify device system architecture. To query device architecture, refer to the following command:

  # Query architecture. For Linux, ['aarch64' or 'armhf'] should shown in log.
  adb shell cat /proc/version
  

7.2 Push demo files to device

• If device connected via USB port, push demo files to devices:

adb push install/<TARGET_PLATFORM>_linux_<ARCH>/rknn_PPOCR-Det_demo/ /userdata/

• For other boards, use scp or other approaches to push all files under install/<TARGET_PLATFORM>_linux_<ARCH>/rknn_PPOCR-Det_demo/ to userdata.

7.3 Run demo

adb shell
cd /userdata/rknn_PPOCR-Det_demo

export LD_LIBRARY_PATH=./lib
./rknn_ppocr_det_demo model/ppocrv4_det.rknn model/test.jpg

• After running, the result was saved as out.jpg. To check the result on host PC, pull back result referring to the following command:

  adb pull /userdata/rknn_PPOCR-Det_demo/out.jpg
  

8. Expected Results

This example will print the box and corresponding scores of the test image detect results, as follows:

[0]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.990687
[1]: [(27, 428), (371, 428), (371, 446), (27, 446)] 0.865046
[2]: [(25, 397), (361, 395), (362, 413), (26, 415)] 0.947012
[3]: [(367, 368), (474, 366), (476, 386), (368, 388)] 0.988151
[4]: [(27, 363), (282, 365), (281, 384), (26, 382)] 0.952980
[5]: [(25, 334), (342, 334), (342, 352), (25, 352)] 0.955272
...

• Note: Different platforms, different versions of tools and drivers may have slightly different results.