core/homeassistant/components/tensorflow/image_processing.py

"""Support for performing TensorFlow classification on images."""
import io
import logging
import os
import sys

from PIL import Image, ImageDraw, UnidentifiedImageError
import numpy as np
import voluptuous as vol

from homeassistant.components.image_processing import (
    CONF_CONFIDENCE,
    CONF_ENTITY_ID,
    CONF_NAME,
    CONF_SOURCE,
    PLATFORM_SCHEMA,
    ImageProcessingEntity,
)
from homeassistant.core import split_entity_id
from homeassistant.helpers import template
import homeassistant.helpers.config_validation as cv
from homeassistant.util.pil import draw_box

_LOGGER = logging.getLogger(__name__)

ATTR_MATCHES = "matches"
ATTR_SUMMARY = "summary"
ATTR_TOTAL_MATCHES = "total_matches"

CONF_AREA = "area"
CONF_BOTTOM = "bottom"
CONF_CATEGORIES = "categories"
CONF_CATEGORY = "category"
CONF_FILE_OUT = "file_out"
CONF_GRAPH = "graph"
CONF_LABELS = "labels"
CONF_LEFT = "left"
CONF_MODEL = "model"
CONF_MODEL_DIR = "model_dir"
CONF_RIGHT = "right"
CONF_TOP = "top"

AREA_SCHEMA = vol.Schema(
    {
        vol.Optional(CONF_BOTTOM, default=1): cv.small_float,
        vol.Optional(CONF_LEFT, default=0): cv.small_float,
        vol.Optional(CONF_RIGHT, default=1): cv.small_float,
        vol.Optional(CONF_TOP, default=0): cv.small_float,
    }
)

CATEGORY_SCHEMA = vol.Schema(
    {vol.Required(CONF_CATEGORY): cv.string, vol.Optional(CONF_AREA): AREA_SCHEMA}
)

PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
    {
        vol.Optional(CONF_FILE_OUT, default=[]): vol.All(cv.ensure_list, [cv.template]),
        vol.Required(CONF_MODEL): vol.Schema(
            {
                vol.Required(CONF_GRAPH): cv.isfile,
                vol.Optional(CONF_AREA): AREA_SCHEMA,
                vol.Optional(CONF_CATEGORIES, default=[]): vol.All(
                    cv.ensure_list, [vol.Any(cv.string, CATEGORY_SCHEMA)]
                ),
                vol.Optional(CONF_LABELS): cv.isfile,
                vol.Optional(CONF_MODEL_DIR): cv.isdir,
            }
        ),
    }
)


def setup_platform(hass, config, add_entities, discovery_info=None):
    """Set up the TensorFlow image processing platform."""
    model_config = config.get(CONF_MODEL)
    model_dir = model_config.get(CONF_MODEL_DIR) or hass.config.path("tensorflow")
    labels = model_config.get(CONF_LABELS) or hass.config.path(
        "tensorflow", "object_detection", "data", "mscoco_label_map.pbtxt"
    )

    # Make sure locations exist
    if not os.path.isdir(model_dir) or not os.path.exists(labels):
        _LOGGER.error("Unable to locate tensorflow models or label map")
        return

    # append custom model path to sys.path
    sys.path.append(model_dir)

    try:
        # Verify that the TensorFlow Object Detection API is pre-installed
        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
        # These imports shouldn't be moved to the top, because they depend on code from the model_dir.
        # (The model_dir is created during the manual setup process. See integration docs.)
        import tensorflow as tf  # pylint: disable=import-outside-toplevel

        # pylint: disable=import-outside-toplevel
        from object_detection.utils import label_map_util
    except ImportError:
        _LOGGER.error(
            "No TensorFlow Object Detection library found! Install or compile "
            "for your system following instructions here: "
            "https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md"
        )
        return

    try:
        # Display warning that PIL will be used if no OpenCV is found.
        import cv2  # noqa: F401 pylint: disable=unused-import, import-outside-toplevel
    except ImportError:
        _LOGGER.warning(
            "No OpenCV library found. TensorFlow will process image with "
            "PIL at reduced resolution"
        )

    # Set up Tensorflow graph, session, and label map to pass to processor
    # pylint: disable=no-member
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(model_config.get(CONF_GRAPH), "rb") as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name="")

    session = tf.Session(graph=detection_graph)
    label_map = label_map_util.load_labelmap(labels)
    categories = label_map_util.convert_label_map_to_categories(
        label_map, max_num_classes=90, use_display_name=True
    )
    category_index = label_map_util.create_category_index(categories)

    entities = []

    for camera in config[CONF_SOURCE]:
        entities.append(
            TensorFlowImageProcessor(
                hass,
                camera[CONF_ENTITY_ID],
                camera.get(CONF_NAME),
                session,
                detection_graph,
                category_index,
                config,
            )
        )

    add_entities(entities)


class TensorFlowImageProcessor(ImageProcessingEntity):
    """Representation of an TensorFlow image processor."""

    def __init__(
        self,
        hass,
        camera_entity,
        name,
        session,
        detection_graph,
        category_index,
        config,
    ):
        """Initialize the TensorFlow entity."""
        model_config = config.get(CONF_MODEL)
        self.hass = hass
        self._camera_entity = camera_entity
        if name:
            self._name = name
        else:
            self._name = "TensorFlow {}".format(split_entity_id(camera_entity)[1])
        self._session = session
        self._graph = detection_graph
        self._category_index = category_index
        self._min_confidence = config.get(CONF_CONFIDENCE)
        self._file_out = config.get(CONF_FILE_OUT)

        # handle categories and specific detection areas
        categories = model_config.get(CONF_CATEGORIES)
        self._include_categories = []
        self._category_areas = {}
        for category in categories:
            if isinstance(category, dict):
                category_name = category.get(CONF_CATEGORY)
                category_area = category.get(CONF_AREA)
                self._include_categories.append(category_name)
                self._category_areas[category_name] = [0, 0, 1, 1]
                if category_area:
                    self._category_areas[category_name] = [
                        category_area.get(CONF_TOP),
                        category_area.get(CONF_LEFT),
                        category_area.get(CONF_BOTTOM),
                        category_area.get(CONF_RIGHT),
                    ]
            else:
                self._include_categories.append(category)
                self._category_areas[category] = [0, 0, 1, 1]

        # Handle global detection area
        self._area = [0, 0, 1, 1]
        area_config = model_config.get(CONF_AREA)
        if area_config:
            self._area = [
                area_config.get(CONF_TOP),
                area_config.get(CONF_LEFT),
                area_config.get(CONF_BOTTOM),
                area_config.get(CONF_RIGHT),
            ]

        template.attach(hass, self._file_out)

        self._matches = {}
        self._total_matches = 0
        self._last_image = None

    @property
    def camera_entity(self):
        """Return camera entity id from process pictures."""
        return self._camera_entity

    @property
    def name(self):
        """Return the name of the image processor."""
        return self._name

    @property
    def state(self):
        """Return the state of the entity."""
        return self._total_matches

    @property
    def device_state_attributes(self):
        """Return device specific state attributes."""
        return {
            ATTR_MATCHES: self._matches,
            ATTR_SUMMARY: {
                category: len(values) for category, values in self._matches.items()
            },
            ATTR_TOTAL_MATCHES: self._total_matches,
        }

    def _save_image(self, image, matches, paths):
        img = Image.open(io.BytesIO(bytearray(image))).convert("RGB")
        img_width, img_height = img.size
        draw = ImageDraw.Draw(img)

        # Draw custom global region/area
        if self._area != [0, 0, 1, 1]:
            draw_box(
                draw, self._area, img_width, img_height, "Detection Area", (0, 255, 255)
            )

        for category, values in matches.items():
            # Draw custom category regions/areas
            if category in self._category_areas and self._category_areas[category] != [
                0,
                0,
                1,
                1,
            ]:
                label = f"{category.capitalize()} Detection Area"
                draw_box(
                    draw,
                    self._category_areas[category],
                    img_width,
                    img_height,
                    label,
                    (0, 255, 0),
                )

            # Draw detected objects
            for instance in values:
                label = "{} {:.1f}%".format(category, instance["score"])
                draw_box(
                    draw, instance["box"], img_width, img_height, label, (255, 255, 0)
                )

        for path in paths:
            _LOGGER.info("Saving results image to %s", path)
            img.save(path)

    def process_image(self, image):
        """Process the image."""

        try:
            import cv2  # pylint: disable=import-error, import-outside-toplevel

            img = cv2.imdecode(np.asarray(bytearray(image)), cv2.IMREAD_UNCHANGED)
            inp = img[:, :, [2, 1, 0]]  # BGR->RGB
            inp_expanded = inp.reshape(1, inp.shape[0], inp.shape[1], 3)
        except ImportError:
            try:
                img = Image.open(io.BytesIO(bytearray(image))).convert("RGB")
            except UnidentifiedImageError:
                _LOGGER.warning("Unable to process image, bad data")
                return
            img.thumbnail((460, 460), Image.ANTIALIAS)
            img_width, img_height = img.size
            inp = (
                np.array(img.getdata())
                .reshape((img_height, img_width, 3))
                .astype(np.uint8)
            )
            inp_expanded = np.expand_dims(inp, axis=0)

        image_tensor = self._graph.get_tensor_by_name("image_tensor:0")
        boxes = self._graph.get_tensor_by_name("detection_boxes:0")
        scores = self._graph.get_tensor_by_name("detection_scores:0")
        classes = self._graph.get_tensor_by_name("detection_classes:0")
        boxes, scores, classes = self._session.run(
            [boxes, scores, classes], feed_dict={image_tensor: inp_expanded}
        )
        boxes, scores, classes = map(np.squeeze, [boxes, scores, classes])
        classes = classes.astype(int)

        matches = {}
        total_matches = 0
        for box, score, obj_class in zip(boxes, scores, classes):
            score = score * 100
            boxes = box.tolist()

            # Exclude matches below min confidence value
            if score < self._min_confidence:
                continue

            # Exclude matches outside global area definition
            if (
                boxes[0] < self._area[0]
                or boxes[1] < self._area[1]
                or boxes[2] > self._area[2]
                or boxes[3] > self._area[3]
            ):
                continue

            category = self._category_index[obj_class]["name"]

            # Exclude unlisted categories
            if self._include_categories and category not in self._include_categories:
                continue

            # Exclude matches outside category specific area definition
            if self._category_areas and (
                boxes[0] < self._category_areas[category][0]
                or boxes[1] < self._category_areas[category][1]
                or boxes[2] > self._category_areas[category][2]
                or boxes[3] > self._category_areas[category][3]
            ):
                continue

            # If we got here, we should include it
            if category not in matches.keys():
                matches[category] = []
            matches[category].append({"score": float(score), "box": boxes})
            total_matches += 1

        # Save Images
        if total_matches and self._file_out:
            paths = []
            for path_template in self._file_out:
                if isinstance(path_template, template.Template):
                    paths.append(
                        path_template.render(camera_entity=self._camera_entity)
                    )
                else:
                    paths.append(path_template)
            self._save_image(image, matches, paths)

        self._matches = matches
        self._total_matches = total_matches