MaskRCNN.py

'''
Copyright 2018 Esri

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.​
'''

import json
import os
import sys

import numpy as np

sys.path.append(os.path.dirname(__file__))
import importlib
from skimage.measure import find_contours
import keras.backend as K
import tensorflow as tf

class MatterMaskRCNN:
    def initialize(self, model, model_as_file):
        K.clear_session()

        if model_as_file:
            with open(model, 'r') as f:
                self.json_info = json.load(f)
        else:
            self.json_info = json.loads(model)

        model_path = self.json_info['ModelFile']
        if model_as_file and not os.path.isabs(model_path):
            model_path = os.path.abspath(os.path.join(os.path.dirname(model), model_path))

        config_module = self.json_info['ModelConfiguration']['Config']
        if not os.path.isabs(config_module):
            config_module = os.path.abspath(os.path.join(os.path.dirname(model), config_module))

        sys.path.append(os.path.dirname(config_module))
        config_module_name = os.path.basename(config_module)

        if config_module_name in sys.modules:
            del sys.modules[config_module_name]

        self.config = getattr(importlib.import_module(config_module_name), 'config')

        architecture_module = self.json_info['ModelConfiguration']['Architecture']
        if not os.path.isabs(architecture_module):
            architecture_module = os.path.abspath(os.path.join(os.path.dirname(model), architecture_module))

        sys.path.append(os.path.dirname(architecture_module))
        architecture_module_name = os.path.basename(architecture_module)

        if (architecture_module_name != config_module_name) and (architecture_module_name in sys.modules):
            del sys.modules[architecture_module_name]

        self.model = getattr(importlib.import_module(architecture_module_name), 'model')

        self.model.load_weights(model_path, by_name=True)

        self.graph = tf.get_default_graph()

    def getParameterInfo(self, required_parameters):
        return required_parameters

    def getConfiguration(self, **scalars):
        self.padding = int(scalars['padding'])

        return {
            'extractBands': tuple(self.json_info['ExtractBands']),
            'padding': int(scalars['padding']),
            'tx': self.json_info['ImageWidth'] - 2 * self.padding,
            'ty': self.json_info['ImageHeight'] - 2 * self.padding
        }

class ChildImageClassifier(MatterMaskRCNN):
    def updatePixels(self, tlc, shape, props, **pixelBlocks):
        image = pixelBlocks['raster_pixels']
        _, height, width = image.shape
        image = np.transpose(image, [1,2,0])

        with self.graph.as_default():
            results = self.model.detect([image], verbose=1)

        masks = results[0]['masks']
        class_ids = results[0]['class_ids']
        output_raster = np.zeros((masks.shape[0], masks.shape[1], 1), dtype=props['pixelType'])
        mask_count = masks.shape[-1]
        for i in range(mask_count):
            mask = masks[:, :, i]
            output_raster[np.where(mask==True)] = class_ids[i]

        return np.transpose(output_raster, [2,0,1])


class ChildObjectDetector(MatterMaskRCNN):
    def vectorize(self,  **pixelBlocks):
        image = pixelBlocks['raster_pixels']
        _, height, width = image.shape
        image = np.transpose(image, [1,2,0])

        with self.graph.as_default():
            results = self.model.detect([image], verbose=1)

        masks = results[0]['masks']
        mask_count = masks.shape[-1]
        coord_list = []
        for m in range(mask_count):
            mask = masks[:, :, m]
            padded_mask = np.zeros((mask.shape[0]+2, mask.shape[1]+2), dtype=np.uint8)
            padded_mask[1:-1, 1:-1] = mask
            contours = find_contours(padded_mask, 0.5, fully_connected='high')

            if len(contours) != 0:
                verts = contours[0] - 1
                coord_list.append(verts)

        if self.padding != 0:
            coord_list[:] = [item - self.padding for item in coord_list]

        return coord_list, results[0]['scores'], results[0]['class_ids']