examples

examples

Tengine Lite 的 examples 将提供简单的、好玩的 demo。

分类任务 - tm_classification.c

Tengine Lite 兼容 Tengine 原有的 C API 供用户使用，这里我们使用 C API 展示如何运行 tm_classification 例程运行 MobileNet v1 分类网络模型，实现指定图片分类的功能。让你快速上手Tengine Lite C API。这里，我们使用在这个撸猫时代行业从业者大爱的 tiger cat 作为测试图片。

模型在此处可以找到：Tengine model zoo 兼容原有 Tengine 的模型示例仓库（密码：hhgc）。

源码参考

tm_classification.c

编译

build.sh 编译脚本默认配置已实现自动编译 examples 中的 demo 程序，以 x86 平台为例，demo 存放在 ./build/install/bin/ 目录下。

bug1989@DESKTOP-SGN0H2A:/mnt/d/ubuntu/gitlab/build-linux$ tree install
install
├── bin
│   ├── tm_benchmark
│   ├── tm_classification
│   ├── tm_mobilenet_ssd
│   ├── tm_retinaface
│   └── tm_yolov3_tiny
├── include
│   └── tengine_c_api.h
└── lib
    └── libtengine-lite.so

运行结果

将测试图片和模型文件放在 Tengine-Lite 根目录下，运行：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_classification -m models/mobilenet.tmfile -i images/cat.jpg -g 224,224 -s 0.017,0.017,0.017 -w 104.007,116.669,122.679

结果如下：

model file : ./temp/models/mobilenet_v1.tmfile
image file : ./temp/images/cat.jpg
img_h, img_w, scale[3], mean[3] : 224 224 , 0.017 0.017 0.017, 104.0 116.7 122.7
Repeat 1 times, thread 1, avg time 656.76 ms, max_time 656.76 ms, min_time 656.76 ms
--------------------------------------
8.574148, 282
7.880116, 277
7.812579, 278
7.286453, 263
6.357488, 281
--------------------------------------

人脸关键点检测任务 - tm_landmark.cpp

使用图片：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_landmark -m models/landmark.tmfile -i images/mobileface02.jpg -r 1 -t 1

结果如下：

tengine-lite library version: 0.2-dev
Repeat [1] min 17.461 ms, max 17.461 ms, avg 17.461 ms

ssd目标检测任务 - tm_mobilenet_ssd.cpp

使用图片：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_mobilenet_ssd -m models/mobilenet_ssd.tmfile -i images/ssd_dog.jpg -r 1 -t 1

结果如下：

tengine-lite library version: 0.2-dev
Repeat 1 times, thread 1, avg time 206.30 ms, max_time 206.30 ms, min_time 206.30 ms
--------------------------------------
detect result num: 3
dog	:99.8%
BOX:( 138 , 209 ),( 324 , 541 )
car	:99.7%
BOX:( 467 , 72 ),( 687 , 171 )
bicycle	:99.5%
BOX:( 107 , 141 ),( 574 , 415 )
======================================
[DETECTED IMAGE SAVED]:
======================================

retinaface人脸检测任务 - tm_refinaface.cpp

使用图片：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_retinaface -m models/retinaface.tmfile -i images/mtcnn_face4.jpg -r 1 -t 1

结果如下：

tengine-lite library version: 0.2-dev
img_h, img_w : 316, 474
Repeat 1 times, thread 1, avg time 75.72 ms, max_time 75.72 ms, min_time 75.72 ms
--------------------------------------
detected face num: 4
BOX 0.99:( 38.9179 , 86.3346 ),( 45.7028 , 63.2934 )
BOX 0.99:( 168.12 , 86.14 ),( 37.5249 , 47.7839 )
BOX 0.98:( 383.673 , 56.4136 ),( 77.176 , 83.8093 )
BOX 0.98:( 289.365 , 103.773 ),( 38.0025 , 47.6989 )

yolact实例分割任务 - tm_yolact.cpp

使用图片：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolact -m models/yolact.tmfile -i images/ssd_car.jpg -r 1 -t 1

结果如下：

tengine-lite library version: 0.2-dev
Repeat 1 times, thread 1, avg time 15833.47 ms, max_time 15833.47 ms, min_time 15833.47 ms
--------------------------------------
6 = 0.99966 at 130.82 57.77 340.78 x 237.36
3 = 0.99675 at 323.39 194.97 175.57 x 132.96
1 = 0.33431 at 191.24 195.78 103.06 x 179.22

yolov3目标检测任务 - tm_yolov3.cpp

使用图片：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolact -m models/yolov3_tiny.tmfile -i images/ssd_dog.jpg -r 1 -t 1

结果如下：

tengine-lite library version: 0.2-dev
Repeat 1 times, thread 1, avg time 262.52 ms, max_time 262.52 ms, min_time 262.52 ms
--------------------------------------
num_detections,4
16: 57%
left = 129,right = 369,top = 186,bot = 516
2: 65%
left = 465,right = 677,top = 74,bot = 171
1: 60%
left = 205,right = 576,top = 153,bot = 447

人体姿态识别任务 - tm_openpose.cpp

使用图片：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_openpose -m models/openpose_coco.tmfile -i image/pose.jpg -r 1 -t 1

结果如下：

tengine-lite library version: 0.2-dev
Repeat 1 times, thread 1, avg time 15350.25 ms, max_time 15350.25 ms, min_time 15350.25 ms
--------------------------------------
KeyPoints Coordinate:
0:[292.174, 55.6522]
1:[306.087, 125.217]
2:[250.435, 139.13]
3:[236.522, 222.609]
4:[222.609, 306.087]
5:[361.739, 125.217]
6:[403.478, 208.696]
7:[417.391, 292.174]
8:[264.348, 306.087]
9:[264.348, 431.304]
10:[264.348, 570.435]
11:[347.826, 306.087]
12:[375.652, 431.304]
13:[333.913, 542.609]
14:[278.261, 41.7391]
15:[306.087, 41.7391]
16:[264.348, 55.6522]
17:[320, 55.6522]

人体姿态识别结果会保存为图片，名称为：Output-Keypionts.jpg和Output-Skeleton.jpg。

我们将持续更新各种有趣的 demo ，敬请期待......