# yolov3_darknet53_pedestrian

|Module Name|yolov3_darknet53_pedestrian|
| :--- | :---: |
|Category|object detection|
|Network|YOLOv3|
|Dataset|百度自建大规模行人Dataset|
|Fine-tuning supported or not|No|
|Module Size|238MB|
|Latest update date|2021-03-15|
|Data indicators|-|


## I.Basic Information

- ### Application Effect Display
  - Sample results：
     <p align="center">
     <img src="https://user-images.githubusercontent.com/22424850/131492636-714c697c-3275-4c8c-a83a-cf971a91ba98.jpg"   width='50%' hspace='10'/>
     <br />
     </p>

- ### Module Introduction

  - 行人检测是计算机视觉技术中的目标检测问题，用于判断图像中是否存在行人并给予精确定位，定位结果用矩形框表示.行人检测技术有很强的使用价值，它可以与行人跟踪、行人重识别等技术结合，应用于汽车无人驾驶系统、智能视频监控、人体行为分析、客流统计系统、智能交通等领域.yolov3_darknet53_pedestrian Module的网络为YOLOv3, 其中backbone为DarkNet53, 采用百度自建大规模车辆数据集训练得到，目前仅支持预测.


## II.Installation

- ### 1、Environmental Dependence  

  - paddlepaddle >= 1.6.2  

  - paddlehub >= 1.6.0  | [How to install PaddleHub]()

- ### 2、Installation

  - ```shell
    $ hub install yolov3_darknet53_pedestrian
    ```
  - In case of any problems during installation, please refer to: [Windows_Quickstart]() | [Linux_Quickstart]() | [Mac_Quickstart]()

## III.Module API Prediction

- ### 1、Command line Prediction

  - ```shell
    $ hub run yolov3_darknet53_pedestrian --input_path "/PATH/TO/IMAGE"
    ```
  - If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example

  - ```python
    import paddlehub as hub
    import cv2

    pedestrian_detector = hub.Module(name="yolov3_darknet53_pedestrian")
    result = pedestrian_detector.object_detection(images=[cv2.imread('/PATH/TO/IMAGE')])
    # or
    # result = pedestrian_detector.object_detection(paths=['/PATH/TO/IMAGE'])
    ```

- ### 3、API

  - ```python
    def object_detection(paths=None,
                         images=None,
                         batch_size=1,
                         use_gpu=False,
                         output_dir='yolov3_pedestrian_detect_output',
                         score_thresh=0.2,
                         visualization=True)  
    ```

    - 预测API，检测输入图片中的所有行人的位置.

    - **Parameters**

      - paths (list[str]): image path;
      - images (list\[numpy.ndarray\]): image data, ndarray.shape is in the format [H, W, C], BGR;
      - batch_size (int): the size of batch;
      - use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**
      - output_dir (str): save path of images;
      - score\_thresh (float): 识别置信度的阈值；<br/>
      - visualization (bool): Whether to save the results as picture files;

      **NOTE:** choose one parameter to provide data from paths and images


    - **Return**

      - res (list\[dict\]): classication results, each element in the list is dict, key is the label name, and value is the corresponding probability
        - data (list): 检测结果，list的每一个元素为 dict，各字段为:
          - confidence (float): 识别的置信度
          - label (str): 标签
          - left (int): 边界框的左上角x坐标
          - top (int): 边界框的左上角y坐标
          - right (int): 边界框的右下角x坐标
          - bottom (int): 边界框的右下角y坐标
        - save\_path (str, optional): 识别结果的保存路径 (仅当visualization=True时存在)

  - ```python
    def save_inference_model(dirname,
                             model_filename=None,
                             params_filename=None,
                             combined=True)
    ```
    - 将模型保存到指定路径.

    - **Parameters**

      - dirname: 存在模型的目录名称； <br/>
      - model\_filename: 模型文件名称，默认为\_\_model\_\_； <br/>
      - params\_filename: Parameters文件名称，默认为\_\_params\_\_(仅当`combined`为True时生效)；<br/>
      - combined: 是否将Parameters保存到统一的一个文件中.


## IV.Server Deployment

- PaddleHub Serving can deploy an online service of object detection.

- ### Step 1: Start PaddleHub Serving

  - Run the startup command：
  - ```shell
    $ hub serving start -m yolov3_darknet53_pedestrian
    ```

  - The servitization API is now deployed and the default port number is 8866.

  - **NOTE:**  If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.

- ### Step 2: Send a predictive request

  - With a configured server, use the following lines of code to send the prediction request and obtain the result

  - ```python
    import requests
    import json
    import cv2
    import base64


    def cv2_to_base64(image):
      data = cv2.imencode('.jpg', image)[1]
      return base64.b64encode(data.tostring()).decode('utf8')

    # Send an HTTP request
    data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]}
    headers = {"Content-type": "application/json"}
    url = "http://127.0.0.1:8866/predict/yolov3_darknet53_pedestrian"
    r = requests.post(url=url, headers=headers, data=json.dumps(data))

    # print prediction results
    print(r.json()["results"])
    ```


## V.Release Note

* 1.0.0

  First release  

* 1.0.2

   Fix the problem of reading numpy

  - ```shell
    $ hub install yolov3_darknet53_pedestrian==1.0.2
    ```