Semi Automatic Segmentation (#332)
Boris Sekachev
7 years ago
committed by
Nikita Manovich
parent
b1be97755c
commit
1257a1b245
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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 openvino.inference_engine import IENetwork, IEPlugin
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import subprocess
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import os
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_IE_PLUGINS_PATH = os.getenv("IE_PLUGINS_PATH", None)
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def _check_instruction(instruction):
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return instruction == str.strip(
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subprocess.check_output(
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'lscpu | grep -o "{}" | head -1'.format(instruction), shell=True
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).decode('utf-8')
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)
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def make_plugin():
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if _IE_PLUGINS_PATH is None:
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raise OSError('Inference engine plugin path env not found in the system.')
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plugin = IEPlugin(device='CPU', plugin_dirs=[_IE_PLUGINS_PATH])
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if (_check_instruction('avx2')):
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plugin.add_cpu_extension(os.path.join(_IE_PLUGINS_PATH, 'libcpu_extension_avx2.so'))
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elif (_check_instruction('sse4')):
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plugin.add_cpu_extension(os.path.join(_IE_PLUGINS_PATH, 'libcpu_extension_sse4.so'))
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else:
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raise Exception('Inference engine requires a support of avx2 or sse4.')
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return plugin
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def make_network(model, weights):
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return IENetwork.from_ir(model = model, weights = weights)
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# Semi-Automatic Segmentation with [Deep Extreme Cut](http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/)
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## About the application
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The application allows to use deep learning models for semi-automatic semantic and instance segmentation.
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You can get a segmentation polygon from four (or more) extreme points of an object.
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This application uses the pre-trained DEXTR model which has been converted to Inference Engine format.
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We are grateful to K.K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool who permitted using their models in our tool
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## Build docker image
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```bash
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# OpenVINO component is also needed
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docker-compose -f docker-compose.yml -f components/openvino/docker-compose.openvino.yml -f cvat/apps/dextr_segmentation/docker-compose.dextr.yml build
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```
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## Run docker container
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```bash
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docker-compose -f docker-compose.yml -f components/openvino/docker-compose.openvino.yml -f cvat/apps/dextr_segmentation/docker-compose.dextr.yml up -d
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```
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## Using
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1. Open a job
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2. Select "Auto Segmentation" in the list of shapes
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3. Run the draw mode as usually (by press the "Create Shape" button or by "N" shortcut)
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4. Click four-six (or more if it's need) extreme points of an object
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5. Close the draw mode as usually (by shortcut or pressing the button "Stop Creation")
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6. Wait a moment and you will get a class agnostic annotation polygon
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7. You can close an annotation request if it is too long
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(in case if it is queued to rq worker and all workers are busy)
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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['engine'] = JS_3RDPARTY.get('engine', []) + ['dextr_segmentation/js/enginePlugin.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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from django.apps import AppConfig
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class DextrSegmentationConfig(AppConfig):
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name = 'dextr_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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from cvat.apps.auto_annotation.inference_engine import make_plugin, make_network
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import os
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import cv2
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import PIL
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import numpy as np
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_IE_CPU_EXTENSION = os.getenv("IE_CPU_EXTENSION", "libcpu_extension_avx2.so")
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_IE_PLUGINS_PATH = os.getenv("IE_PLUGINS_PATH", None)
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_DEXTR_MODEL_DIR = os.getenv("DEXTR_MODEL_DIR", None)
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_DEXTR_PADDING = 50
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_DEXTR_TRESHOLD = 0.9
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_DEXTR_SIZE = 512
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class DEXTR_HANDLER:
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def __init__(self):
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self._plugin = None
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self._network = None
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self._exec_network = None
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self._input_blob = None
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self._output_blob = None
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if not _DEXTR_MODEL_DIR:
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raise Exception("DEXTR_MODEL_DIR is not defined")
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def handle(self, im_path, points):
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# Lazy initialization
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if not self._plugin:
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self._plugin = make_plugin()
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self._network = make_network(os.path.join(_DEXTR_MODEL_DIR, 'dextr.xml'),
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os.path.join(_DEXTR_MODEL_DIR, 'dextr.bin'))
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self._input_blob = next(iter(self._network.inputs))
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self._output_blob = next(iter(self._network.outputs))
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self._exec_network = self._plugin.load(network=self._network)
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image = PIL.Image.open(im_path)
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numpy_image = np.array(image)
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points = np.asarray([[int(p["x"]), int(p["y"])] for p in points], dtype=int)
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bounding_box = (
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max(min(points[:, 0]) - _DEXTR_PADDING, 0),
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max(min(points[:, 1]) - _DEXTR_PADDING, 0),
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min(max(points[:, 0]) + _DEXTR_PADDING, numpy_image.shape[1] - 1),
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min(max(points[:, 1]) + _DEXTR_PADDING, numpy_image.shape[0] - 1)
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)
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# Prepare an image
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numpy_cropped = np.array(image.crop(bounding_box))
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resized = cv2.resize(numpy_cropped, (_DEXTR_SIZE, _DEXTR_SIZE),
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interpolation = cv2.INTER_CUBIC).astype(np.float32)
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# Make a heatmap
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points = points - [min(points[:, 0]), min(points[:, 1])] + [_DEXTR_PADDING, _DEXTR_PADDING]
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points = (points * [_DEXTR_SIZE / numpy_cropped.shape[1], _DEXTR_SIZE / numpy_cropped.shape[0]]).astype(int)
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heatmap = np.zeros(shape=resized.shape[:2], dtype=np.float64)
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for point in points:
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gaussian_x_axis = np.arange(0, _DEXTR_SIZE, 1, float) - point[0]
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gaussian_y_axis = np.arange(0, _DEXTR_SIZE, 1, float)[:, np.newaxis] - point[1]
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gaussian = np.exp(-4 * np.log(2) * ((gaussian_x_axis ** 2 + gaussian_y_axis ** 2) / 100)).astype(np.float64)
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heatmap = np.maximum(heatmap, gaussian)
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cv2.normalize(heatmap, heatmap, 0, 255, cv2.NORM_MINMAX)
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# Concat an image and a heatmap
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input_dextr = np.concatenate((resized, heatmap[:, :, np.newaxis].astype(resized.dtype)), axis=2)
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input_dextr = input_dextr.transpose((2,0,1))
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np.set_printoptions(threshold=np.nan)
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pred = self._exec_network.infer(inputs={self._input_blob: input_dextr[np.newaxis, ...]})[self._output_blob][0, 0, :, :]
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pred = cv2.resize(pred, tuple(reversed(numpy_cropped.shape[:2])), interpolation = cv2.INTER_CUBIC)
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result = np.zeros(numpy_image.shape[:2])
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result[bounding_box[1]:bounding_box[1] + pred.shape[0], bounding_box[0]:bounding_box[0] + pred.shape[1]] = pred > _DEXTR_TRESHOLD
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# Convert a mask to a polygon
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result = np.array(result, dtype=np.uint8)
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cv2.normalize(result,result,0,255,cv2.NORM_MINMAX)
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contours = None
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if int(cv2.__version__.split('.')[0]) > 3:
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contours = cv2.findContours(result, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)[0]
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else:
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contours = cv2.findContours(result, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)[1]
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contours = max(contours, key=lambda arr: arr.size)
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if contours.shape.count(1):
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contours = np.squeeze(contours)
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if contours.size < 3 * 2:
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raise Exception('Less then three point have been detected. Can not build a polygon.')
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result = ""
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for point in contours:
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result += "{},{} ".format(int(point[0]), int(point[1]))
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result = result[:-1]
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return result
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def __del__(self):
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if self._exec_network:
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del self._exec_network
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if self._network:
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del self._network
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if self._plugin:
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del self._plugin
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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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WITH_DEXTR: "yes"
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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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AREA_TRESHOLD:false
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PolyShapeModel:false
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ShapeCreatorModel:true
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ShapeCreatorView:true
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showMessage:false
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*/
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/* eslint no-underscore-dangle: 0 */
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window.addEventListener('DOMContentLoaded', () => {
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$('<option value="auto_segmentation" class="regular"> Auto Segmentation </option>').appendTo('#shapeTypeSelector');
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const dextrCancelButtonId = 'dextrCancelButton';
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const dextrOverlay = $(`
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<div class="modal hidden force-modal">
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<div class="modal-content" style="width: 300px; height: 70px;">
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<center> <label class="regular h2"> Segmentation request is being processed </label></center>
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<center style="margin-top: 5px;">
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<button id="${dextrCancelButtonId}" class="regular h2" style="width: 250px;"> Cancel </button>
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</center>
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</div>
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</div>`).appendTo('body');
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const dextrCancelButton = $(`#${dextrCancelButtonId}`);
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dextrCancelButton.on('click', () => {
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dextrCancelButton.prop('disabled', true);
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$.ajax({
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url: `/dextr/cancel/${window.cvat.job.id}`,
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type: 'GET',
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error: (errorData) => {
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const message = `Can not cancel 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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complete: () => {
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dextrCancelButton.prop('disabled', false);
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},
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});
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});
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function ShapeCreatorModelWrapper(OriginalClass) {
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// Constructor will patch some properties for a created instance
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function constructorDecorator(...args) {
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const instance = new OriginalClass(...args);
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// Decorator for the defaultType property
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Object.defineProperty(instance, 'defaultType', {
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get: () => instance._defaultType,
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set: (type) => {
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if (!['box', 'points', 'polygon', 'polyline', 'auto_segmentation'].includes(type)) {
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throw Error(`Unknown shape type found ${type}`);
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}
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instance._defaultType = type;
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},
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});
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// Decorator for finish method.
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const decoratedFinish = instance.finish;
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instance.finish = (result) => {
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if (instance._defaultType === 'auto_segmentation') {
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try {
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instance._defaultType = 'polygon';
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decoratedFinish.call(instance, result);
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} finally {
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instance._defaultType = 'auto_segmentation';
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}
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} else {
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decoratedFinish.call(instance, result);
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}
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};
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return instance;
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}
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constructorDecorator.prototype = OriginalClass.prototype;
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constructorDecorator.prototype.constructor = constructorDecorator;
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return constructorDecorator;
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}
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function ShapeCreatorViewWrapper(OriginalClass) {
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// Constructor will patch some properties for each instance
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function constructorDecorator(...args) {
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const instance = new OriginalClass(...args);
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// Decorator for the _create() method.
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// We save the decorated _create() and we will use it if type != 'auto_segmentation'
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const decoratedCreate = instance._create;
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instance._create = () => {
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if (instance._type !== 'auto_segmentation') {
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decoratedCreate.call(instance);
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return;
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}
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instance._drawInstance = instance._frameContent.polyline().draw({ snapToGrid: 0.1 }).addClass('shapeCreation').attr({
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'stroke-width': 0,
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z_order: Number.MAX_SAFE_INTEGER,
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});
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instance._createPolyEvents();
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/* the _createPolyEvents method have added "drawdone"
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* event handler which invalid for this case
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* because of that reason we remove the handler and
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* create the valid handler instead
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*/
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instance._drawInstance.off('drawdone').on('drawdone', (e) => {
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let actualPoints = window.cvat.translate.points.canvasToActual(e.target.getAttribute('points'));
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actualPoints = PolyShapeModel.convertStringToNumberArray(actualPoints);
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if (actualPoints.length < 4) {
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showMessage('It is need to specify minimum four extreme points for an object');
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instance._controller.switchCreateMode(true);
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return;
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}
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const { frameWidth } = window.cvat.player.geometry;
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const { frameHeight } = window.cvat.player.geometry;
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for (let idx = 0; idx < actualPoints.length; idx += 1) {
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const point = actualPoints[idx];
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point.x = Math.clamp(point.x, 0, frameWidth);
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point.y = Math.clamp(point.y, 0, frameHeight);
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}
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e.target.setAttribute('points',
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window.cvat.translate.points.actualToCanvas(
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PolyShapeModel.convertNumberArrayToString(actualPoints),
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));
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const polybox = e.target.getBBox();
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const area = polybox.width * polybox.height;
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if (area > AREA_TRESHOLD) {
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$.ajax({
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url: `/dextr/create/${window.cvat.job.id}`,
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type: 'POST',
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data: JSON.stringify({
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frame: window.cvat.player.frames.current,
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points: actualPoints,
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}),
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contentType: 'application/json',
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success: () => {
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function intervalCallback() {
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$.ajax({
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url: `/dextr/check/${window.cvat.job.id}`,
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type: 'GET',
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success: (jobData) => {
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if (['queued', 'started'].includes(jobData.status)) {
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if (jobData.status === 'queued') {
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dextrCancelButton.prop('disabled', false);
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}
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setTimeout(intervalCallback, 1000);
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} else {
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dextrOverlay.addClass('hidden');
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if (jobData.status === 'finished') {
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if (jobData.result) {
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instance._controller.finish({ points: jobData.result }, 'polygon');
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}
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} else if (jobData.status === 'failed') {
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const message = `Segmentation has fallen. Error: '${jobData.stderr}'`;
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showMessage(message);
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} else {
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let message = `Check segmentation request returned "${jobData.status}" status.`;
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if (jobData.stderr) {
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message += ` Error: ${jobData.stderr}`;
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}
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showMessage(message);
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}
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}
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},
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error: (errorData) => {
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dextrOverlay.addClass('hidden');
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const message = `Can not check 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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}
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dextrCancelButton.prop('disabled', true);
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dextrOverlay.removeClass('hidden');
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setTimeout(intervalCallback, 1000);
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},
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error: (errorData) => {
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const message = `Can not cancel ReID process. 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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}
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instance._controller.switchCreateMode(true);
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}); // end of "drawdone" handler
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}; // end of _create() method
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return instance;
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} // end of constructorDecorator()
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constructorDecorator.prototype = OriginalClass.prototype;
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constructorDecorator.prototype.constructor = constructorDecorator;
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return constructorDecorator;
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} // end of ShapeCreatorViewWrapper
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// Apply patch for classes
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ShapeCreatorModel = ShapeCreatorModelWrapper(ShapeCreatorModel);
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ShapeCreatorView = ShapeCreatorViewWrapper(ShapeCreatorView);
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});
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@ -0,0 +1,12 @@
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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/<int:jid>', views.create),
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path('cancel/<int:jid>', views.cancel),
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path('check/<int:jid>', views.check)
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]
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@ -0,0 +1,128 @@
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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, HttpResponseBadRequest, JsonResponse
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from cvat.apps.authentication.decorators import login_required
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from rules.contrib.views import permission_required, objectgetter
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from cvat.apps.engine.models import Job
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from cvat.apps.engine.log import slogger
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from cvat.apps.engine.task import get_frame_path
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from cvat.apps.dextr_segmentation.dextr import DEXTR_HANDLER
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import django_rq
|
||||
import json
|
||||
import os
|
||||
import rq
|
||||
|
||||
__RQ_QUEUE_NAME = "default"
|
||||
__DEXTR_HANDLER = DEXTR_HANDLER()
|
||||
|
||||
def _dextr_thread(im_path, points):
|
||||
job = rq.get_current_job()
|
||||
job.meta["result"] = __DEXTR_HANDLER.handle(im_path, points)
|
||||
job.save_meta()
|
||||
|
||||
|
||||
@login_required
|
||||
@permission_required(perm=["engine.job.change"],
|
||||
fn=objectgetter(Job, "jid"), raise_exception=True)
|
||||
def create(request, jid):
|
||||
try:
|
||||
data = json.loads(request.body.decode("utf-8"))
|
||||
|
||||
points = data["points"]
|
||||
frame = int(data["frame"])
|
||||
username = request.user.username
|
||||
|
||||
slogger.job[jid].info("create dextr request for the JOB: {} ".format(jid)
|
||||
+ "by the USER: {} on the FRAME: {}".format(username, frame))
|
||||
|
||||
tid = Job.objects.select_related("segment__task").get(id=jid).segment.task.id
|
||||
im_path = os.path.realpath(get_frame_path(tid, frame))
|
||||
|
||||
queue = django_rq.get_queue(__RQ_QUEUE_NAME)
|
||||
rq_id = "dextr.create/{}/{}".format(jid, username)
|
||||
job = queue.fetch_job(rq_id)
|
||||
|
||||
if job is not None and (job.is_started or job.is_queued):
|
||||
if "cancel" not in job.meta:
|
||||
raise Exception("Segmentation process has been already run for the " +
|
||||
"JOB: {} and the USER: {}".format(jid, username))
|
||||
else:
|
||||
job.delete()
|
||||
|
||||
queue.enqueue_call(func=_dextr_thread,
|
||||
args=(im_path, points),
|
||||
job_id=rq_id,
|
||||
timeout=15,
|
||||
ttl=30)
|
||||
|
||||
return HttpResponse()
|
||||
except Exception as ex:
|
||||
slogger.job[jid].error("can't create a dextr request for the job {}".format(jid), exc_info=True)
|
||||
return HttpResponseBadRequest(str(ex))
|
||||
|
||||
|
||||
@login_required
|
||||
@permission_required(perm=["engine.job.change"],
|
||||
fn=objectgetter(Job, "jid"), raise_exception=True)
|
||||
def cancel(request, jid):
|
||||
try:
|
||||
username = request.user.username
|
||||
slogger.job[jid].info("cancel dextr request for the JOB: {} ".format(jid)
|
||||
+ "by the USER: {}".format(username))
|
||||
|
||||
queue = django_rq.get_queue(__RQ_QUEUE_NAME)
|
||||
rq_id = "dextr.create/{}/{}".format(jid, username)
|
||||
job = queue.fetch_job(rq_id)
|
||||
|
||||
if job is None or job.is_finished or job.is_failed:
|
||||
raise Exception("Segmentation isn't running now")
|
||||
elif "cancel" not in job.meta:
|
||||
job.meta["cancel"] = True
|
||||
job.save_meta()
|
||||
|
||||
return HttpResponse()
|
||||
except Exception as ex:
|
||||
slogger.job[jid].error("can't cancel a dextr request for the job {}".format(jid), exc_info=True)
|
||||
return HttpResponseBadRequest(str(ex))
|
||||
|
||||
|
||||
@login_required
|
||||
@permission_required(perm=["engine.job.change"],
|
||||
fn=objectgetter(Job, "jid"), raise_exception=True)
|
||||
def check(request, jid):
|
||||
try:
|
||||
username = request.user.username
|
||||
slogger.job[jid].info("check dextr request for the JOB: {} ".format(jid)
|
||||
+ "by the USER: {}".format(username))
|
||||
|
||||
queue = django_rq.get_queue(__RQ_QUEUE_NAME)
|
||||
rq_id = "dextr.create/{}/{}".format(jid, username)
|
||||
job = queue.fetch_job(rq_id)
|
||||
data = {}
|
||||
|
||||
if job is None:
|
||||
data["status"] = "unknown"
|
||||
else:
|
||||
if "cancel" in job.meta:
|
||||
data["status"] = "finished"
|
||||
elif job.is_queued:
|
||||
data["status"] = "queued"
|
||||
elif job.is_started:
|
||||
data["status"] = "started"
|
||||
elif job.is_finished:
|
||||
data["status"] = "finished"
|
||||
data["result"] = job.meta["result"]
|
||||
job.delete()
|
||||
else:
|
||||
data["status"] = "failed"
|
||||
data["stderr"] = job.exc_info
|
||||
job.delete()
|
||||
|
||||
return JsonResponse(data)
|
||||
except Exception as ex:
|
||||
slogger.job[jid].error("can't check a dextr request for the job {}".format(jid), exc_info=True)
|
||||
return HttpResponseBadRequest(str(ex))
|
||||
Loading…
Reference in New Issue