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ace2p

Module Name ace2p
Category Image segmentation
Network ACE2P
Dataset LIP
Fine-tuning supported or not No
Module Size 259MB
Data indicators -
Latest update date 2021-02-26

I. Basic Information

  • Application Effect Display

    • Network architecture:


    • Color palette


    • Sample results:

  • Module Introduction

    • Human Parsing is a fine-grained semantic segmentation task that aims to identify the components (for example, body parts and clothing) of a human image at the pixel level. The PaddleHub Module uses ResNet101 as the backbone network, and accepts input image sizes of 473x473x3.

II. Installation

III. Module API Prediction

  • 1、Command line Prediction

  • 2、Prediction Code Example

    • import paddlehub as hub
import cv2

human_parser = hub.Module(name="ace2p")
result = human_parser.segmentation(images=[cv2.imread('/PATH/TO/IMAGE')])

  • 3、API

    • def segmentation(images=None,
                paths=None,
                batch_size=1,
                use_gpu=False,
                output_dir='ace2p_output',
                visualization=False):

      • Prediction API, used for human parsing.

      • 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
        • output_dir (str): Save path of output, default is 'ace2p_output'.
        • visualization (bool): Whether to save the recognition results as picture files.

      • 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 human parsing

  • Step 1: Start PaddleHub Serving

    • Run the startup command:

      • $ hub serving start -m ace2p

    • 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

      • 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


# 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/ace2p"
r = requests.post(url=url, headers=headers, data=json.dumps(data))

# print prediction results
print(base64_to_cv2(r.json()["results"][0]['data']))

五、更新历史

  • 1.0.0

    First release

  • 1.1.0

    Adapt to paddlehub2.0

  • 1.2.0

    Remove Fluid API

    $ hub install ace2p == 1.2.0