1edgeboard-191:~/root/workspace/PaddleLiteDemo# ls
2C++ README.md  configs  Python  res 

1 configs ✗ tree
2.
3├── classification
4│   ├── Inceptionv2
5│   ├── Inceptionv3
6│   ├── mobilenetv1
7│   └── resnet50
8│       ├── image.json
9│       ├── multi_channel_camera.json
10│       ├── rtspcamera.json
11│       └── usbcamera.json
12├── detection
13│   ├── mobilenet-ssd
14│   ├── mobilenet-ssd-640
15│   ├── mobilenet-yolov3
16│   ├── vgg-ssd
17│   └── yolov3
18│       ├── image.json
19│       ├── multi_channel_camera.json
20│       ├── rtspcamera.json
21│       └── usb_camera.json
22└── segmentation
23    └── deeplabv3
24        ├── image.json
25        ├── multi_channel_camera.json
26        ├── rtspcamera.json
27        └── usbcamera.json
28
2913 directories, 12 files

1{
2    "model_config": "../../res/modles/detection/yolov3",
3    "input": {
4        "type": "usb_camera",
5        "path": "/dev/video2"
6    },
7    "debug": {
8        "display_enable": true,
9        "predict_log_enable": true,
10        "predict_time_log_enable": true
11    }
12}

1{
2	"network_type":"YOLOV3",
3
4	"model_file_name":"model",
5	"params_file_name":"params",
6
7	"labels_file_name":"label_list.txt",
8
9	"format":"RGB",
10	"input_width":608,
11	"input_height":608,
12		
13	"mean":[123.675, 116.28, 103.53],
14    "scale":[0.0171248, 0.017507, 0.0174292],
15	"threshold":0.3
16}

1cd /root/workspace/PaddleLiteDemo/Python

1cd /root/workspace/PaddleLiteDemo/Python
2python3 detection.py ../configs/detection/yolov3/usb_camera.json

1 configs ? tree
2.
3|——paddlelite.cpython-35m-aarch64-linux-gnu.so
4|——edgeboard
5        |——__init__.py
6        |——predictor.py
7        |——preprocess.py
8
91 directories, 4 files

1class PaddleLitePredictor(object):
2    """ 这个类是对pybind封装的Predictor的进一步封装 """
3
4    def __init__(self):
5        self.predictor = None
6
7    def set_model_file(self, path):
8        """配置模型文件的路径，一般都是"model"或者"__model__" """
9        
10
11    def set_param_file(self, path):
12        """配置模型文件的路径，一般都是"params"或者"__params__" """
13
14    def set_model_dir(self, path):
15        """配置模型文件的目录"""
16
17    def load(self):
18        """根据上面三个模型路径相关的api设置，初始化C++侧的predictor并将模型装载到内存"""
19
20    def get_input(self, index):
21        """获取到predictor的第index位置处的输入空tensor，预处理后的结果最终会拷贝到这个tensor中"""
22
23    def set_input(self, data, index):
24        """将预处理后的输入数据numpy.array设置到predictor的第index个输入tensor中"""
25
26    def run(self):
27        """完成一次模型推理"""
28
29    def get_output(self, index):
30        """取出第index位置的输出结果"""

1class ImageTransformer(object):
2    """这个类是对fpga预处理功能的封装，包括resize，格式转换和均值方差操作"""
3
4    def __init__(self, image, mean, scale, dst_shape, dst_format, src_format="BGR"):
5        """初始化预处理参数"""
6
7
8    def set_mean_scale(self, mean, scale):
9        """
10        out = (x / 255 - mean) * scale
11        """
12
13    def read_image(self, image):
14        """读入原图"""
15        
16    def set_dst_format(self, dst_format):
17        """
18        默认原图是BGR, 如果需要转RGB，要在这里设置
19        """
20
21    def set_src_format(self, src_format):
22        """
23        默认是BGR
24        """
25
26    def set_shape_config(self, src_shape, dst_shape):
27        """
28        fpga的预处理只支持到1080p，如果输入尺寸超过这个限制，只能先做cpu resize
29        """
30
31    def transforms(self, input_tensor):
32        """
33        从predictor中拿到输入tensor，将预处理后的结果写入这个tensor
34        """