Auto segmentation using Mask_RCNN (#767)
Reza Malek
6 years ago
committed by
Nikita Manovich
parent
310e208229
commit
d99140a0fb
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## [Keras+Tensorflow Mask R-CNN Segmentation](https://github.com/matterport/Mask_RCNN)
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### What is it?
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- This application allows you automatically to segment many various objects on images.
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- It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
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- It uses a pre-trained model on MS COCO dataset
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- It supports next classes (use them in "labels" row):
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```python
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'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
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'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
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'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
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'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
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'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
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'kite', 'baseball bat', 'baseball glove', 'skateboard',
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'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
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'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
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'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
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'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
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'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
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'teddy bear', 'hair drier', 'toothbrush'.
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```
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- Component adds "Run Auto Segmentation" button into dashboard.
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### Build docker image
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```bash
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# From project root directory
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docker-compose -f docker-compose.yml -f components/auto_segmentation/docker-compose.auto_segmentation.yml build
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```
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### Run docker container
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```bash
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# From project root directory
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docker-compose -f docker-compose.yml -f components/auto_segmentation/docker-compose.auto_segmentation.yml up -d
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```
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#
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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#
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version: "2.3"
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services:
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cvat:
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build:
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context: .
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args:
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AUTO_SEGMENTATION: "yes"
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#!/bin/bash
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#
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set -e
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cd ${HOME} && \
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git clone https://github.com/matterport/Mask_RCNN.git && \
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wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 && \
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mv mask_rcnn_coco.h5 Mask_RCNN/mask_rcnn_coco.h5
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# TODO remove useless files
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# tensorflow and Keras are installed globally
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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from cvat.settings.base import JS_3RDPARTY
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JS_3RDPARTY['dashboard'] = JS_3RDPARTY.get('dashboard', []) + ['auto_segmentation/js/dashboardPlugin.js']
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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# Register your models here.
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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from django.apps import AppConfig
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class AutoSegmentationConfig(AppConfig):
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name = 'auto_segmentation'
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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# Create your models here.
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/*
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* Copyright (C) 2018 Intel Corporation
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*
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* SPDX-License-Identifier: MIT
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*/
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/* global
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userConfirm:false
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showMessage:false
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*/
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window.addEventListener('dashboardReady', () => {
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function checkProcess(tid, button) {
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function checkCallback() {
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$.get(`/tensorflow/segmentation/check/task/${tid}`).done((statusData) => {
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if (['started', 'queued'].includes(statusData.status)) {
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const progress = Math.round(statusData.progress) || '0';
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button.text(`Cancel Auto Segmentation (${progress}%)`);
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setTimeout(checkCallback, 5000);
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} else {
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button.text('Run Auto Segmentation');
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button.removeClass('tfAnnotationProcess');
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button.prop('disabled', false);
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if (statusData.status === 'failed') {
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const message = `Tensorflow Segmentation failed. Error: ${statusData.stderr}`;
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showMessage(message);
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} else if (statusData.status !== 'finished') {
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const message = `Tensorflow segmentation check request returned status "${statusData.status}"`;
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showMessage(message);
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}
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}
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}).fail((errorData) => {
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const message = `Can not sent tensorflow segmentation check request. Code: ${errorData.status}. `
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+ `Message: ${errorData.responseText || errorData.statusText}`;
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showMessage(message);
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});
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}
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setTimeout(checkCallback, 5000);
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}
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function runProcess(tid, button) {
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$.get(`/tensorflow/segmentation/create/task/${tid}`).done(() => {
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showMessage('Process has started');
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button.text('Cancel Auto Segmentation (0%)');
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button.addClass('tfAnnotationProcess');
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checkProcess(tid, button);
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}).fail((errorData) => {
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const message = `Can not run Auto Segmentation. Code: ${errorData.status}. `
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+ `Message: ${errorData.responseText || errorData.statusText}`;
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showMessage(message);
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});
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}
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function cancelProcess(tid, button) {
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$.get(`/tensorflow/segmentation/cancel/task/${tid}`).done(() => {
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button.prop('disabled', true);
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}).fail((errorData) => {
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const message = `Can not cancel Auto Segmentation. Code: ${errorData.status}. `
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+ `Message: ${errorData.responseText || errorData.statusText}`;
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showMessage(message);
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});
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}
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function setupDashboardItem(item, metaData) {
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const tid = +item.attr('tid');
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const button = $('<button> Run Auto Segmentation </button>');
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button.on('click', () => {
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if (button.hasClass('tfAnnotationProcess')) {
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userConfirm('The process will be canceled. Continue?', () => {
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cancelProcess(tid, button);
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});
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} else {
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userConfirm('The current annotation will be lost. Are you sure?', () => {
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runProcess(tid, button);
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});
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}
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});
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button.addClass('dashboardTFAnnotationButton regular dashboardButtonUI');
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button.appendTo(item.find('div.dashboardButtonsUI'));
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if ((tid in metaData) && (metaData[tid].active)) {
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button.text('Cancel Auto Segmentation');
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button.addClass('tfAnnotationProcess');
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checkProcess(tid, button);
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}
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}
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const elements = $('.dashboardItem');
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const tids = Array.from(elements, el => +el.getAttribute('tid'));
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$.ajax({
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type: 'POST',
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url: '/tensorflow/segmentation/meta/get',
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data: JSON.stringify(tids),
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contentType: 'application/json; charset=utf-8',
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}).done((metaData) => {
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elements.each(function setupDashboardItemWrapper() {
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setupDashboardItem($(this), metaData);
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});
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}).fail((errorData) => {
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const message = `Can not get Auto Segmentation meta info. Code: ${errorData.status}. `
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+ `Message: ${errorData.responseText || errorData.statusText}`;
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showMessage(message);
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});
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});
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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# Create your tests here.
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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from django.urls import path
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from . import views
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urlpatterns = [
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path('create/task/<int:tid>', views.create),
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path('check/task/<int:tid>', views.check),
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path('cancel/task/<int:tid>', views.cancel),
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path('meta/get', views.get_meta_info),
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]
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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from django.http import HttpResponse, JsonResponse, HttpResponseBadRequest
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from rules.contrib.views import permission_required, objectgetter
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from cvat.apps.authentication.decorators import login_required
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from cvat.apps.engine.models import Task as TaskModel
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from cvat.apps.engine.serializers import LabeledDataSerializer
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from cvat.apps.engine.annotation import put_task_data
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import django_rq
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import fnmatch
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import json
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import os
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import rq
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import numpy as np
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from cvat.apps.engine.log import slogger
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import sys
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import skimage.io
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from skimage.measure import find_contours, approximate_polygon
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def load_image_into_numpy(image):
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(im_width, im_height) = image.size
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return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
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def run_tensorflow_auto_segmentation(image_list, labels_mapping, treshold):
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def _convert_to_int(boolean_mask):
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return boolean_mask.astype(np.uint8)
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def _convert_to_segmentation(mask):
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contours = find_contours(mask, 0.5)
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# only one contour exist in our case
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contour = contours[0]
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contour = np.flip(contour, axis=1)
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# Approximate the contour and reduce the number of points
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contour = approximate_polygon(contour, tolerance=2.5)
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segmentation = contour.ravel().tolist()
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return segmentation
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## INITIALIZATION
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# Root directory of the project
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ROOT_DIR = os.environ.get('AUTO_SEGMENTATION_PATH')
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# Import Mask RCNN
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sys.path.append(ROOT_DIR) # To find local version of the library
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import mrcnn.model as modellib
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# Import COCO config
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sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) # To find local version
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import coco
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# Directory to save logs and trained model
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MODEL_DIR = os.path.join(ROOT_DIR, "logs")
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# Local path to trained weights file
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COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
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if COCO_MODEL_PATH is None:
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raise OSError('Model path env not found in the system.')
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job = rq.get_current_job()
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## CONFIGURATION
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class InferenceConfig(coco.CocoConfig):
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# Set batch size to 1 since we'll be running inference on
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# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
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GPU_COUNT = 1
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IMAGES_PER_GPU = 1
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# Print config details
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config = InferenceConfig()
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config.display()
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## CREATE MODEL AND LOAD TRAINED WEIGHTS
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# Create model object in inference mode.
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model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
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# Load weights trained on MS-COCO
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model.load_weights(COCO_MODEL_PATH, by_name=True)
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## RUN OBJECT DETECTION
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result = {}
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for image_num, image_path in enumerate(image_list):
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job.refresh()
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if 'cancel' in job.meta:
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del job.meta['cancel']
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job.save()
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return None
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job.meta['progress'] = image_num * 100 / len(image_list)
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job.save_meta()
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image = skimage.io.imread(image_path)
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# for multiple image detection, "batch size" must be equal to number of images
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r = model.detect([image], verbose=1)
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r = r[0]
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# "r['rois'][index]" gives bounding box around the object
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for index, c_id in enumerate(r['class_ids']):
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if c_id in labels_mapping.keys():
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if r['scores'][index] >= treshold:
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mask = _convert_to_int(r['masks'][:,:,index])
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segmentation = _convert_to_segmentation(mask)
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label = labels_mapping[c_id]
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if label not in result:
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result[label] = []
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result[label].append(
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[image_num, segmentation])
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return result
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def make_image_list(path_to_data):
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def get_image_key(item):
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return int(os.path.splitext(os.path.basename(item))[0])
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image_list = []
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for root, _, filenames in os.walk(path_to_data):
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for filename in fnmatch.filter(filenames, '*.jpg'):
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image_list.append(os.path.join(root, filename))
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image_list.sort(key=get_image_key)
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return image_list
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def convert_to_cvat_format(data):
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result = {
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"tracks": [],
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"shapes": [],
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"tags": [],
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"version": 0,
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}
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for label in data:
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segments = data[label]
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for segment in segments:
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result['shapes'].append({
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"type": "polygon",
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"label_id": label,
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"frame": segment[0],
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"points": segment[1],
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"z_order": 0,
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"group": None,
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"occluded": False,
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"attributes": [],
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})
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return result
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def create_thread(tid, labels_mapping, user):
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try:
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# If detected object accuracy bigger than threshold it will returend
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TRESHOLD = 0.5
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# Init rq job
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job = rq.get_current_job()
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job.meta['progress'] = 0
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job.save_meta()
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# Get job indexes and segment length
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db_task = TaskModel.objects.get(pk=tid)
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# Get image list
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image_list = make_image_list(db_task.get_data_dirname())
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# Run auto segmentation by tf
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result = None
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slogger.glob.info("auto segmentation with tensorflow framework for task {}".format(tid))
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result = run_tensorflow_auto_segmentation(image_list, labels_mapping, TRESHOLD)
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if result is None:
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slogger.glob.info('auto segmentation for task {} canceled by user'.format(tid))
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return
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# Modify data format and save
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result = convert_to_cvat_format(result)
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serializer = LabeledDataSerializer(data = result)
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if serializer.is_valid(raise_exception=True):
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put_task_data(tid, user, result)
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slogger.glob.info('auto segmentation for task {} done'.format(tid))
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except Exception as ex:
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try:
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slogger.task[tid].exception('exception was occured during auto segmentation of the task', exc_info=True)
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except Exception:
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slogger.glob.exception('exception was occured during auto segmentation of the task {}'.format(tid), exc_into=True)
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raise ex
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@login_required
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def get_meta_info(request):
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try:
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queue = django_rq.get_queue('low')
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tids = json.loads(request.body.decode('utf-8'))
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result = {}
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for tid in tids:
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job = queue.fetch_job('auto_segmentation.create/{}'.format(tid))
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if job is not None:
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result[tid] = {
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"active": job.is_queued or job.is_started,
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"success": not job.is_failed
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}
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return JsonResponse(result)
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except Exception as ex:
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slogger.glob.exception('exception was occured during tf meta request', exc_into=True)
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return HttpResponseBadRequest(str(ex))
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@login_required
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@permission_required(perm=['engine.task.change'],
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fn=objectgetter(TaskModel, 'tid'), raise_exception=True)
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def create(request, tid):
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slogger.glob.info('auto segmentation create request for task {}'.format(tid))
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try:
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db_task = TaskModel.objects.get(pk=tid)
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queue = django_rq.get_queue('low')
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job = queue.fetch_job('auto_segmentation.create/{}'.format(tid))
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if job is not None and (job.is_started or job.is_queued):
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raise Exception("The process is already running")
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db_labels = db_task.label_set.prefetch_related('attributespec_set').all()
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db_labels = {db_label.id:db_label.name for db_label in db_labels}
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# COCO Labels
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auto_segmentation_labels = { "BG": 0,
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"person": 1, "bicycle": 2, "car": 3, "motorcycle": 4, "airplane": 5,
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"bus": 6, "train": 7, "truck": 8, "boat": 9, "traffic_light": 10,
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"fire_hydrant": 11, "stop_sign": 13, "parking_meter": 14, "bench": 15,
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"bird": 16, "cat": 17, "dog": 18, "horse": 19, "sheep": 20, "cow": 21,
|
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"elephant": 22, "bear": 23, "zebra": 24, "giraffe": 25, "backpack": 27,
|
||||
"umbrella": 28, "handbag": 31, "tie": 32, "suitcase": 33, "frisbee": 34,
|
||||
"skis": 35, "snowboard": 36, "sports_ball": 37, "kite": 38, "baseball_bat": 39,
|
||||
"baseball_glove": 40, "skateboard": 41, "surfboard": 42, "tennis_racket": 43,
|
||||
"bottle": 44, "wine_glass": 46, "cup": 47, "fork": 48, "knife": 49, "spoon": 50,
|
||||
"bowl": 51, "banana": 52, "apple": 53, "sandwich": 54, "orange": 55, "broccoli": 56,
|
||||
"carrot": 57, "hot_dog": 58, "pizza": 59, "donut": 60, "cake": 61, "chair": 62,
|
||||
"couch": 63, "potted_plant": 64, "bed": 65, "dining_table": 67, "toilet": 70,
|
||||
"tv": 72, "laptop": 73, "mouse": 74, "remote": 75, "keyboard": 76, "cell_phone": 77,
|
||||
"microwave": 78, "oven": 79, "toaster": 80, "sink": 81, "refrigerator": 83,
|
||||
"book": 84, "clock": 85, "vase": 86, "scissors": 87, "teddy_bear": 88, "hair_drier": 89,
|
||||
"toothbrush": 90
|
||||
}
|
||||
|
||||
labels_mapping = {}
|
||||
for key, labels in db_labels.items():
|
||||
if labels in auto_segmentation_labels.keys():
|
||||
labels_mapping[auto_segmentation_labels[labels]] = key
|
||||
|
||||
if not len(labels_mapping.values()):
|
||||
raise Exception('No labels found for auto segmentation')
|
||||
|
||||
# Run auto segmentation job
|
||||
queue.enqueue_call(func=create_thread,
|
||||
args=(tid, labels_mapping, request.user),
|
||||
job_id='auto_segmentation.create/{}'.format(tid),
|
||||
timeout=604800) # 7 days
|
||||
|
||||
slogger.task[tid].info('tensorflow segmentation job enqueued with labels {}'.format(labels_mapping))
|
||||
|
||||
except Exception as ex:
|
||||
try:
|
||||
slogger.task[tid].exception("exception was occured during tensorflow segmentation request", exc_info=True)
|
||||
except Exception:
|
||||
pass
|
||||
return HttpResponseBadRequest(str(ex))
|
||||
|
||||
return HttpResponse()
|
||||
|
||||
@login_required
|
||||
@permission_required(perm=['engine.task.access'],
|
||||
fn=objectgetter(TaskModel, 'tid'), raise_exception=True)
|
||||
def check(request, tid):
|
||||
try:
|
||||
queue = django_rq.get_queue('low')
|
||||
job = queue.fetch_job('auto_segmentation.create/{}'.format(tid))
|
||||
if job is not None and 'cancel' in job.meta:
|
||||
return JsonResponse({'status': 'finished'})
|
||||
data = {}
|
||||
if job is None:
|
||||
data['status'] = 'unknown'
|
||||
elif job.is_queued:
|
||||
data['status'] = 'queued'
|
||||
elif job.is_started:
|
||||
data['status'] = 'started'
|
||||
data['progress'] = job.meta['progress']
|
||||
elif job.is_finished:
|
||||
data['status'] = 'finished'
|
||||
job.delete()
|
||||
else:
|
||||
data['status'] = 'failed'
|
||||
data['stderr'] = job.exc_info
|
||||
job.delete()
|
||||
|
||||
except Exception:
|
||||
data['status'] = 'unknown'
|
||||
|
||||
return JsonResponse(data)
|
||||
|
||||
|
||||
@login_required
|
||||
@permission_required(perm=['engine.task.change'],
|
||||
fn=objectgetter(TaskModel, 'tid'), raise_exception=True)
|
||||
def cancel(request, tid):
|
||||
try:
|
||||
queue = django_rq.get_queue('low')
|
||||
job = queue.fetch_job('auto_segmentation.create/{}'.format(tid))
|
||||
if job is None or job.is_finished or job.is_failed:
|
||||
raise Exception('Task is not being segmented currently')
|
||||
elif 'cancel' not in job.meta:
|
||||
job.meta['cancel'] = True
|
||||
job.save()
|
||||
|
||||
except Exception as ex:
|
||||
try:
|
||||
slogger.task[tid].exception("cannot cancel tensorflow segmentation for task #{}".format(tid), exc_info=True)
|
||||
except Exception:
|
||||
pass
|
||||
return HttpResponseBadRequest(str(ex))
|
||||
|
||||
return HttpResponse()
|
||||
Loading…
Reference in New Issue