Content Comparison

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Create a folder yolov8s-pose in path ~/c3v/Models. Please ensure the folder name is the same as the ONNX file name.

~/c3v/Models$ mkdir yolov8s-pose && cd yolov8s-pose

2. Copy the ONNX file and input.jpg which resolution is 640x640 to the folder yolov8s. These two files will be used as input files during model conversion.

~/c3v/Models$ cp yolov8s-pose.onnx yolov8s-pose/
~/c3v/Models$ cp input.jpg yolov8s-pose/

3. Create a dataset.txt file, the content of dataset.txt is the input.jpg file name.

./input.jpg

4. Create inputs_outputs.txt file and get the information from yolov8s-pose .onnx via netron tool/webpage. Here is the onnx file:

   View file
   name yolov8s-pose_onnx.zip

   .

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Select the three operators within the red box as the output. write --input-size-list and --outputs informations to inputs_outputs.txt:

--outputs  '/model.22/Sigmoid_output_0 /model.22/Mul_2_output_0 /model.22/Sigmoid_1_output_0 /model.22/Mul_4_output_0'

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Execute the command in the console or terminal, and wait for it to complete. It will import and translate an NN model to NN formats.

./pegasus_import.sh yolov8s-pose

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Select one quantized type for your need, such as uint8 / int16 / bf16 / pcq. In this sample we use int16.

./pegasus_quantize.sh yolov8s-pose int16

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Inference the NN model with the quantization data type.

./pegasus_inference.sh yolov8s-pose int16

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Export the quantized application for device deployment. Please modify the pegasus_export_ovx.sh for the nb file generating, and add both 3 lines marked in the red box.

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./pegasus_export_ovx.sh yolov8s-pose int16

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The function needs to be modified：vnn_PostProcessYolov8sPoseInt16

vsi_status vnn_PostProcessYolov8sPoseInt16(vsi_nn_graph_t *graph)
{
    vsi_status status = VSI_FAILURE;

#if DETECT_RESULT_IMPL
    /*detect result sample implement*/
    post_proc_init(graph);
    post_proc_process(graph);
    post_proc_deinit();

#else
    /* Show the top5 result */
    status = show_top5(graph, vsi_nn_GetTensor(graph, graph->output.tensors[0]));
    TEST_CHECK_STATUS(status, final);

    /* Save all output tensor data to txt file */
    save_output_data(graph);

final:
#endif

    return VSI_SUCCESS;
}

For detailed function implementation, please refer to the following file:

we needs to be unzipped and placed in ~/c3v/Models/yolov8s-pose/wksp/yolov8s_int16_nbg_unify Folder.

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• Example of SDK Includes Path：

INCLUDES+=-I$(NN_SDK_DIR)/include/ \
-I$(NN_SDK_DIR)/include/CL \
-I$(NN_SDK_DIR)/include/VX \
-I$(NN_SDK_DIR)/include/ovxlib \
-I$(NN_SDK_DIR)/include/jpeg

Example of SDK Link Libraries：

LIBS+=-lOpenVX -lOpenVXU -lCLC -lVSC -lGAL -ljpeg -lovxlib

3. Example flow of the program build and run

Unzipped

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If you want to build the project in c3v directly, please modify these contents of Makefile:

BIN=yolov8s-pose-int16

# 2.build in c3v
NN_SDK_DIR=/usr

CC=gcc
CXX=g++

then copy the whole folder yolov8s-pose_int16_nbg_unify to the c3v Linux system. Then using make to compile the project.

cd /sample/yolov8s-pose_int16_nbg_unify
make -j

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The param2 is the image that is for detection. Please prepare the image file which format is jpg and the pixel size is 640 * 640.

./yolov8s-pose-int16 ./network_binary.nb ./input.jpg

The result is like this:

/mnt/yolov8s-pose_int16_nbg_unify # ./yolov8s-pose-int16 ./network_binary.nb
../input.jpg
Create Neural Network: 59ms or 59044us
Verify...
Verify Graph: 24ms or 24933us
Start run graph [1] times...
Run the 1 time: 122.44ms or 122443.93us
vxProcessGraph execution time:
Total   122.66ms or 122658.48us
Average 122.66ms or 122658.48us
obj: L: 0 P:0.93, [(0, 42) - (200, 599)]
obj: L: 0 P:0.91, [(309, 279) - (180, 361)]
obj: L: 0 P:0.58, [(344, 171) - (170, 301)]

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If you want to build the project in host Linux, please modify these contents of Makefile:

BIN=yolov8s-pose-int16

# 1.cross compile
NN_SDK_DIR=Path to NN SDK directory
TOOLCHAIN=Path to toolchain directory

CROSS_COMPILE=$(TOOLCHAIN)/aarch64-none-linux-gnu-
CC=$(CROSS_COMPILE)gcc
CXX=$(CROSS_COMPILE)g++

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TOOLCHAIN: The cross-compile toolchain path. which format may be like this:

TOOLCHAIN=/pub/toolchain/crossgcc/gcc-arm-9.2-2019.12-x86_64-aarch64-none-linux-gnu/bin

then using make to compile the project.

make

Copy the application, network_binary.nb file and related libraries into C3V Linux and run:

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The param2 is the image that is for detection. Please prepare the image file which format is jpg and the pixel size is 640 * 640.

./yolov8s-pose-int16 ./network_binary.nb ./input.jpg

The result is like this:

/mnt/yolov8s-pose_int16_nbg_unify # ./yolov8s-pose-int16 ./network_binary.nb
../input.jpg
Create Neural Network: 59ms or 59044us
Verify...
Verify Graph: 24ms or 24933us
Start run graph [1] times...
Run the 1 time: 122.44ms or 122443.93us
vxProcessGraph execution time:
Total   122.66ms or 122658.48us
Average 122.66ms or 122658.48us
obj: L: 0 P:0.93, [(0, 42) - (200, 599)]
obj: L: 0 P:0.91, [(309, 279) - (180, 361)]
obj: L: 0 P:0.58, [(344, 171) - (170, 301)]

3.2. ImageWriter Tool

If you want to show the detection results in an image, we suggest using ImageWriter tools.

Install the related environment

sudo apt update
sudo apt install libopencv-dev

Please download

cd imageWriter
make -j

Then you can run the imageWriter application directly on c3v:

Param1 is the image which is the same as yolov8s-pose-int16 param2. The yolov8s-pose-int16 is the application that is built in step 3.1. build in c3v.

Param2 is the file pose_results.raw which was generated after the program yolov8s-pose-int16 runs.

Param3 is the output name, which format is jpg.

./imageWriter ./input.jpg ./pose_results.raw ./output.jpg

The result is like this:

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