(Old)Pre-inference function

To register a Pre-inference functions, Project creation needs to be completed first.

Please check the following information after creating a project.

ANNOTATION_MANAGER_USER_ID : User account ID for Manager at annotation tool
ANNOTATION_MANAGER_ACCESS_TOKEN : Access token for Manager at annotation tool
ANNOTATION_ORGANIZATION_ID : Organization ID for annotation tool
ANNOTATION_PROJECT_ID : ID for Annotation project
ABEJA_PLATFORM_USER_ID : ABEJA Platform User ID starting from “user-“
ABEJA_PLATFORM_PERSONAL_ACCESS_TOKEN : Access token for ABEJA Platform user

How to register result of pre-inference

1. Execute a POST request to the pre-inference registration endpoint.

Schema is different for each template. Please refer to [SAMPLE] ANNOTATION RESULT SCHEMA about schema information required for pre-registration.

https://annotation-tool.abeja.io//api/v1/organizations/<organization-id>/projects/<project-id>/tasks/<task-id>/preinferences

2. Execute following command. Please refer to sample code for various files below.

$ pip install -r requirements.txt
$ wget https://github.com/yuyu2172/share-weights/releases/download/0.0.3/ssd300_voc0712_2017_06_06.npz
$ export ANNOTATION_ORGANIZATION_ID=<your-organization-id>
$ export ANNOTATION_ACCESS_USER_ID=<your-user-id>
$ export ANNOTATION_ACCESS_TOKEN=<your-access-token>
$ export ANNOTATION_PROJECT_ID=<your-project-id>
$ export ABEJA_PLATFORM_USER_ID=<your-ABEJA-Platform-user-id>
$ export ABEJA_PLATFORM_PERSONAL_ACCESS_TOKEN=<your-ABEJA-Platform-access-token>

$ python register_preinference.py

3. Once it succeeds, inference result will be on the work page.

Sample code

From script, Register a pre-inference result for each task. Following is sample code by using chainercv.

labeling for project

{
  "attributes": [
    {
      "id": 0,
      "name": "voc",
      "categories": [
        {
          "id": 0,
          "name": "aeroplane"
        },
        {
          "id": 1,
          "name": "bicycle"
        },
        {
          "id": 2,
          "name": "bird"
        },
        {
          "id": 3,
          "name": "boat"
        },
        {
          "id": 4,
          "name": "bottle"
        },
        {
          "id": 5,
          "name": "bus"
        },
        {
          "id": 6,
          "name": "car"
        },
        {
          "id": 7,
          "name": "cat"
        },
        {
          "id": 8,
          "name": "chair"
        },
        {
          "id": 9,
          "name": "cow"
        },
        {
          "id": 10,
          "name": "diningtable"
        },
        {
          "id": 11,
          "name": "dog"
        },
        {
          "id": 12,
          "name": "horse"
        },
        {
          "id": 13,
          "name": "motorbike"
        },
        {
          "id": 14,
          "name": "person"
        },
        {
          "id": 15,
          "name": "pottedplant"
        },
        {
          "id": 16,
          "name": "sheep"
        },
        {
          "id": 17,
          "name": "sofa"
        },
        {
          "id": 18,
          "name": "train"
        },
        {
          "id": 19,
          "name": "tvmonitor"
        }
      ]
    }
  ],
  "isMultipleSelection": false
}

requirements.txt

chainercv==0.11.0
abeja-sdk==0.1.3

register_inference.py

import os
import io
from urllib.parse import urljoin

from abeja.datalake import Client as DatalakeClient
import requests
from PIL import Image
import numpy as np
from chainercv.datasets import voc_bbox_label_names
from chainercv.links import SSD300

from chainercv.visualizations import vis_bbox


ANNOTATION_API = os.environ.get('ANNOTATION_API', 'https://annotation-tool.abeja.io')
ANNOTATION_ACCESS_USER_ID = os.environ.get('ANNOTATION_ACCESS_USER_ID')
ANNOTATION_ACCESS_TOKEN = os.environ.get('ANNOTATION_ACCESS_TOKEN')

ANNOTATION_ORGANIZATION_ID = os.environ.get('ANNOTATION_ORGANIZATION_ID')
ANNOTATION_PROJECT_ID = os.environ.get('ANNOTATION_PROJECT_ID')

headers = {
    'api-access-user-id': ANNOTATION_ACCESS_USER_ID,
    'api-access-token': ANNOTATION_ACCESS_TOKEN
}

FIXED_CATEGORY_ID = 0


def main():
    # ABEJA Platformの組織IDを取得します
    organization_url = urljoin(ANNOTATION_API, "/api/v1/organizations/{}".format(ANNOTATION_ORGANIZATION_ID))
    res = requests.get(organization_url, headers=headers)
    res.raise_for_status()
    platform_organization_id = res.json()['id']

    # ABEJA Platform上でアノテーション対象ファイルが格納されているChannelのIDを取得します
    organization_url = urljoin(ANNOTATION_API, "/api/v1/organizations/{}/projects/{}".format(ANNOTATION_ORGANIZATION_ID, ANNOTATION_PROJECT_ID))
    res = requests.get(organization_url, headers=headers)
    res.raise_for_status()
    platform_datalake_channel_id = res.json()['data_lake_channels'][0]['channel_id']

    # ABEJA PlatformのSDKを使用します
    client = DatalakeClient(organization_id=platform_organization_id)
    datalake_channel = client.get_channel(platform_datalake_channel_id)

    # wget https://github.com/yuyu2172/share-weights/releases/download/0.0.3/ssd300_voc0712_2017_06_06.npz
    pretrained_model = 'ssd300_voc0712_2017_06_06.npz'
    model = SSD300( n_fg_class=len(voc_bbox_label_names), pretrained_model=pretrained_model)

    task_url = urljoin(ANNOTATION_API, "/api/v1/organizations/{}/projects/{}/tasks/".format(ANNOTATION_ORGANIZATION_ID, ANNOTATION_PROJECT_ID))
    page = 1
    while True:
        res = requests.get(task_url, headers=headers, params={'page': page})
        res.raise_for_status()
        res_body = res.json()
        if len(res_body) == 0:
            break
        for task in res_body:
            metadata = task['metadata'][0]
            # タスクのアノテーション対象画像ファイルをダウンロードします
            file = datalake_channel.get_file(metadata['file_id'])
            img_io = io.BytesIO(file.get_content())
            img = np.array(Image.open(img_io))
            img = img.transpose(2, 0, 1)
            try:
                # 推論を行います
                bboxes, labels, scores = model.predict([img])
            except:
                continue
            information = []
            for bbox, label in zip(bboxes, labels):
                for b, l in zip(bbox, label):
                    y_min, x_min, y_max, x_max = tuple(b.tolist())
                    rect = [
                        x_min,
                        y_min,
                        x_max,
                        y_max
                    ]
                    information.append({
                        'rect': rect,
                        'classes': [
                            {
                                'id': int(l),
                                'name': voc_bbox_label_names[l],
                                'category_id': FIXED_CATEGORY_ID
                            }
                        ]
                    })
            if len(information) == 0:
                continue
            preinference_url = urljoin(task_url, "{}/preinferences".format(str(task['id'])))
            res = requests.post(preinference_url, json={'information': information}, headers=headers)
        page = page + 1


if __name__ == '__main__':
    main()

Updated on 26 Jun 2017