347 lines
13 KiB
Cython
347 lines
13 KiB
Cython
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# """
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# Component that performs TensorFlow classification on images.
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# For a quick start, pick a pre-trained COCO model from:
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# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
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# For more details about this platform, please refer to the documentation at
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# https://home-assistant.io/components/image_processing.tensorflow/
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# """
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# import logging
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# import sys
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# import os
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# import voluptuous as vol
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# from homeassistant.components.image_processing import (
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# CONF_CONFIDENCE, CONF_ENTITY_ID, CONF_NAME, CONF_SOURCE, PLATFORM_SCHEMA,
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# ImageProcessingEntity)
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# from homeassistant.core import split_entity_id
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# from homeassistant.helpers import template
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# import homeassistant.helpers.config_validation as cv
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# REQUIREMENTS = ['numpy==1.15.3', 'pillow==5.2.0', 'protobuf==3.6.1']
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# _LOGGER = logging.getLogger(__name__)
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# ATTR_MATCHES = 'matches'
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# ATTR_SUMMARY = 'summary'
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# ATTR_TOTAL_MATCHES = 'total_matches'
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# CONF_FILE_OUT = 'file_out'
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# CONF_MODEL = 'model'
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# CONF_GRAPH = 'graph'
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# CONF_LABELS = 'labels'
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# CONF_MODEL_DIR = 'model_dir'
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# CONF_CATEGORIES = 'categories'
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# CONF_CATEGORY = 'category'
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# CONF_AREA = 'area'
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# CONF_TOP = 'top'
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# CONF_LEFT = 'left'
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# CONF_BOTTOM = 'bottom'
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# CONF_RIGHT = 'right'
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# AREA_SCHEMA = vol.Schema({
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# vol.Optional(CONF_TOP, default=0): cv.small_float,
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# vol.Optional(CONF_LEFT, default=0): cv.small_float,
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# vol.Optional(CONF_BOTTOM, default=1): cv.small_float,
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# vol.Optional(CONF_RIGHT, default=1): cv.small_float
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# })
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# CATEGORY_SCHEMA = vol.Schema({
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# vol.Required(CONF_CATEGORY): cv.string,
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# vol.Optional(CONF_AREA): AREA_SCHEMA
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# })
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# PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({
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# vol.Optional(CONF_FILE_OUT, default=[]):
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# vol.All(cv.ensure_list, [cv.template]),
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# vol.Required(CONF_MODEL): vol.Schema({
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# vol.Required(CONF_GRAPH): cv.isfile,
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# vol.Optional(CONF_LABELS): cv.isfile,
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# vol.Optional(CONF_MODEL_DIR): cv.isdir,
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# vol.Optional(CONF_AREA): AREA_SCHEMA,
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# vol.Optional(CONF_CATEGORIES, default=[]):
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# vol.All(cv.ensure_list, [vol.Any(
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# cv.string,
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# CATEGORY_SCHEMA
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# )])
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# })
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# })
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# def draw_box(draw, box, img_width,
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# img_height, text='', color=(255, 255, 0)):
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# """Draw bounding box on image."""
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# ymin, xmin, ymax, xmax = box
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# (left, right, top, bottom) = (xmin * img_width, xmax * img_width,
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# ymin * img_height, ymax * img_height)
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# draw.line([(left, top), (left, bottom), (right, bottom),
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# (right, top), (left, top)], width=5, fill=color)
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# if text:
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# draw.text((left, abs(top-15)), text, fill=color)
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# def setup_platform(hass, config, add_entities, discovery_info=None):
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# """Set up the TensorFlow image processing platform."""
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# model_config = config.get(CONF_MODEL)
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# model_dir = model_config.get(CONF_MODEL_DIR) \
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# or hass.config.path('tensorflow')
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# labels = model_config.get(CONF_LABELS) \
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# or hass.config.path('tensorflow', 'object_detection',
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# 'data', 'mscoco_label_map.pbtxt')
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# # Make sure locations exist
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# if not os.path.isdir(model_dir) or not os.path.exists(labels):
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# _LOGGER.error("Unable to locate tensorflow models or label map.")
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# return
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# # append custom model path to sys.path
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# sys.path.append(model_dir)
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# try:
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# # Verify that the TensorFlow Object Detection API is pre-installed
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# # pylint: disable=unused-import,unused-variable
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# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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# import tensorflow as tf # noqa
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# from object_detection.utils import label_map_util # noqa
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# except ImportError:
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# # pylint: disable=line-too-long
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# _LOGGER.error(
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# "No TensorFlow Object Detection library found! Install or compile "
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# "for your system following instructions here: "
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# "https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md") # noqa
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# return
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# try:
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# # Display warning that PIL will be used if no OpenCV is found.
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# # pylint: disable=unused-import,unused-variable
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# import cv2 # noqa
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# except ImportError:
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# _LOGGER.warning("No OpenCV library found. "
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# "TensorFlow will process image with "
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# "PIL at reduced resolution.")
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# # setup tensorflow graph, session, and label map to pass to processor
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# # pylint: disable=no-member
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# detection_graph = tf.Graph()
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# with detection_graph.as_default():
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# od_graph_def = tf.GraphDef()
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# with tf.gfile.GFile(model_config.get(CONF_GRAPH), 'rb') as fid:
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# serialized_graph = fid.read()
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# od_graph_def.ParseFromString(serialized_graph)
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# tf.import_graph_def(od_graph_def, name='')
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# session = tf.Session(graph=detection_graph)
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# label_map = label_map_util.load_labelmap(labels)
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# categories = label_map_util.convert_label_map_to_categories(
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# label_map, max_num_classes=90, use_display_name=True)
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# category_index = label_map_util.create_category_index(categories)
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# entities = []
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# for camera in config[CONF_SOURCE]:
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# entities.append(TensorFlowImageProcessor(
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# hass, camera[CONF_ENTITY_ID], camera.get(CONF_NAME),
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# session, detection_graph, category_index, config))
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# add_entities(entities)
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# class TensorFlowImageProcessor(ImageProcessingEntity):
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# """Representation of an TensorFlow image processor."""
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# def __init__(self, hass, camera_entity, name, session, detection_graph,
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# category_index, config):
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# """Initialize the TensorFlow entity."""
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# model_config = config.get(CONF_MODEL)
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# self.hass = hass
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# self._camera_entity = camera_entity
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# if name:
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# self._name = name
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# else:
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# self._name = "TensorFlow {0}".format(
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# split_entity_id(camera_entity)[1])
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# self._session = session
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# self._graph = detection_graph
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# self._category_index = category_index
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# self._min_confidence = config.get(CONF_CONFIDENCE)
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# self._file_out = config.get(CONF_FILE_OUT)
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# # handle categories and specific detection areas
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# categories = model_config.get(CONF_CATEGORIES)
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# self._include_categories = []
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# self._category_areas = {}
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# for category in categories:
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# if isinstance(category, dict):
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# category_name = category.get(CONF_CATEGORY)
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# category_area = category.get(CONF_AREA)
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# self._include_categories.append(category_name)
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# self._category_areas[category_name] = [0, 0, 1, 1]
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# if category_area:
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# self._category_areas[category_name] = [
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# category_area.get(CONF_TOP),
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# category_area.get(CONF_LEFT),
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# category_area.get(CONF_BOTTOM),
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# category_area.get(CONF_RIGHT)
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# ]
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# else:
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# self._include_categories.append(category)
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# self._category_areas[category] = [0, 0, 1, 1]
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# # Handle global detection area
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# self._area = [0, 0, 1, 1]
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# area_config = model_config.get(CONF_AREA)
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# if area_config:
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# self._area = [
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# area_config.get(CONF_TOP),
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# area_config.get(CONF_LEFT),
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# area_config.get(CONF_BOTTOM),
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# area_config.get(CONF_RIGHT)
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# ]
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# template.attach(hass, self._file_out)
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# self._matches = {}
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# self._total_matches = 0
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# self._last_image = None
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# @property
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# def camera_entity(self):
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# """Return camera entity id from process pictures."""
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# return self._camera_entity
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# @property
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# def name(self):
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# """Return the name of the image processor."""
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# return self._name
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# @property
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# def state(self):
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# """Return the state of the entity."""
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# return self._total_matches
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# @property
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# def device_state_attributes(self):
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# """Return device specific state attributes."""
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# return {
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# ATTR_MATCHES: self._matches,
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# ATTR_SUMMARY: {category: len(values)
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# for category, values in self._matches.items()},
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# ATTR_TOTAL_MATCHES: self._total_matches
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# }
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# def _save_image(self, image, matches, paths):
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# from PIL import Image, ImageDraw
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# import io
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# img = Image.open(io.BytesIO(bytearray(image))).convert('RGB')
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# img_width, img_height = img.size
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# draw = ImageDraw.Draw(img)
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# # Draw custom global region/area
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# if self._area != [0, 0, 1, 1]:
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# draw_box(draw, self._area,
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# img_width, img_height,
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# "Detection Area", (0, 255, 255))
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# for category, values in matches.items():
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# # Draw custom category regions/areas
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# if (category in self._category_areas
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# and self._category_areas[category] != [0, 0, 1, 1]):
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# label = "{} Detection Area".format(category.capitalize())
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# draw_box(draw, self._category_areas[category], img_width,
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# img_height, label, (0, 255, 0))
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# # Draw detected objects
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# for instance in values:
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# label = "{0} {1:.1f}%".format(category, instance['score'])
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# draw_box(draw, instance['box'],
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# img_width, img_height,
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# label, (255, 255, 0))
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# for path in paths:
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# _LOGGER.info("Saving results image to %s", path)
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# img.save(path)
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# def process_image(self, image):
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# """Process the image."""
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# import numpy as np
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# try:
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# import cv2 # pylint: disable=import-error
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# img = cv2.imdecode(
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# np.asarray(bytearray(image)), cv2.IMREAD_UNCHANGED)
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# inp = img[:, :, [2, 1, 0]] # BGR->RGB
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# inp_expanded = inp.reshape(1, inp.shape[0], inp.shape[1], 3)
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# except ImportError:
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# from PIL import Image
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# import io
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# img = Image.open(io.BytesIO(bytearray(image))).convert('RGB')
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# img.thumbnail((460, 460), Image.ANTIALIAS)
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# img_width, img_height = img.size
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# inp = np.array(img.getdata()).reshape(
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# (img_height, img_width, 3)).astype(np.uint8)
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# inp_expanded = np.expand_dims(inp, axis=0)
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# image_tensor = self._graph.get_tensor_by_name('image_tensor:0')
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# boxes = self._graph.get_tensor_by_name('detection_boxes:0')
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# scores = self._graph.get_tensor_by_name('detection_scores:0')
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# classes = self._graph.get_tensor_by_name('detection_classes:0')
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# boxes, scores, classes = self._session.run(
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# [boxes, scores, classes],
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# feed_dict={image_tensor: inp_expanded})
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# boxes, scores, classes = map(np.squeeze, [boxes, scores, classes])
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# classes = classes.astype(int)
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# matches = {}
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# total_matches = 0
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# for box, score, obj_class in zip(boxes, scores, classes):
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# score = score * 100
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# boxes = box.tolist()
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# # Exclude matches below min confidence value
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# if score < self._min_confidence:
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# continue
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# # Exclude matches outside global area definition
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# if (boxes[0] < self._area[0] or boxes[1] < self._area[1]
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# or boxes[2] > self._area[2] or boxes[3] > self._area[3]):
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# continue
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# category = self._category_index[obj_class]['name']
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# # Exclude unlisted categories
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# if (self._include_categories
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# and category not in self._include_categories):
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# continue
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# # Exclude matches outside category specific area definition
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# if (self._category_areas
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# and (boxes[0] < self._category_areas[category][0]
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# or boxes[1] < self._category_areas[category][1]
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# or boxes[2] > self._category_areas[category][2]
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# or boxes[3] > self._category_areas[category][3])):
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# continue
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# # If we got here, we should include it
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# if category not in matches.keys():
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# matches[category] = []
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# matches[category].append({
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# 'score': float(score),
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# 'box': boxes
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# })
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# total_matches += 1
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# # Save Images
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# if total_matches and self._file_out:
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# paths = []
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# for path_template in self._file_out:
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# if isinstance(path_template, template.Template):
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# paths.append(path_template.render(
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# camera_entity=self._camera_entity))
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# else:
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# paths.append(path_template)
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# self._save_image(image, matches, paths)
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||
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# self._matches = matches
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||
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# self._total_matches = total_matches
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