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deeplabv3p_xception65_humanseg

Module Name deeplabv3p_xception65_humanseg
Category Image segmentation
Network deeplabv3p
Dataset Baidu self-built dataset
Fine-tuning supported or not No
Module Size 162MB
Data indicators -
Latest update date 2021-02-26

I. Basic Information

  • Application Effect Display

    • Sample results:

  • Module Introduction

    • DeepLabv3+ model is trained by Baidu self-built dataset, which can be used for portrait segmentation.


  • For more information, please refer to: deeplabv3p

II. Installation

  • 1、Environmental Dependence

    • paddlepaddle >= 2.0.0

    • paddlehub >= 2.0.0

  • 2、Installation

III. Module API Prediction

  • 1、Command line Prediction

    • hub run deeplabv3p_xception65_humanseg --input_path "/PATH/TO/IMAGE"
    • If you want to call the Hub module through the command line, please refer to: PaddleHub Command Line Instruction

  • 2、Prediction Code Example

    • import paddlehub as hub
import cv2

human_seg = hub.Module(name="deeplabv3p_xception65_humanseg")
result = human_seg.segmentation(images=[cv2.imread('/PATH/TO/IMAGE')])

  • 3.API

    • def segmentation(images=None,
                 paths=None,
                 batch_size=1,
                 use_gpu=False,
                 visualization=False,
                 output_dir='humanseg_output')

      • Prediction API, generating segmentation result.

      • Parameter

        • images (list[numpy.ndarray]): Image data, ndarray.shape is in the format [H, W, C], BGR.
        • paths (list[str]): Image path.
        • batch_size (int): Batch size.
        • use_gpu (bool): Use GPU or not. set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU
        • visualization (bool): Whether to save the recognition results as picture files.
        • output_dir (str): Save path of images.

      • Return

        • res (list[dict]): The list of recognition results, where each element is dict and each field is:
          • save_path (str, optional): Save path of the result.
          • data (numpy.ndarray): The result of portrait segmentation.
    • def save_inference_model(dirname)

      • Save the model to the specified path.

      • Parameters

        • dirname: Model save path.

IV. Server Deployment

  • PaddleHub Serving can deploy an online service of for human segmentation.

  • Step 1: Start PaddleHub Serving

    • Run the startup command:

      • $ hub serving start -m deeplabv3p_xception65_humanseg

    • 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

    • import requests
import json
import cv2
import base64
import numpy as np


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


def base64_to_cv2(b64str):
    data = base64.b64decode(b64str.encode('utf8'))
    data = np.fromstring(data, np.uint8)
    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
    return data

org_im = cv2.imread("/PATH/TO/IMAGE")
# Send an HTTP request
data = {'images':[cv2_to_base64(org_im)]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/deeplabv3p_xception65_humanseg"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
mask =cv2.cvtColor(base64_to_cv2(r.json()["results"][0]['data']), cv2.COLOR_BGR2GRAY)
rgba = np.concatenate((org_im, np.expand_dims(mask, axis=2)), axis=2)
cv2.imwrite("segment_human_server.png", rgba)

V. Release Note

  • 1.0.0

    First release

  • 1.1.0

    Improve prediction performance

  • 1.1.1

    Fix the bug of image value out of range

  • 1.2.0

    Remove fluid api

    • $ hub install deeplabv3p_xception65_humanseg==1.2.0