5a83a92390
* Refactoring imports for tensorflow * Removing whitespace spaces on blank line 110 * Moving tensorflow to try/except block * Fixed black formatting * Refactoring try/except to if/else
363 lines
12 KiB
Python
363 lines
12 KiB
Python
"""Support for performing TensorFlow classification on images."""
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import logging
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import os
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import sys
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import io
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import voluptuous as vol
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from PIL import Image, ImageDraw
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import numpy as np
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try:
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import cv2
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except ImportError:
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cv2 = None
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try:
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# Verify that the TensorFlow Object Detection API is pre-installed
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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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label_map_util = None
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from homeassistant.components.image_processing import (
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CONF_CONFIDENCE,
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CONF_ENTITY_ID,
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CONF_NAME,
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CONF_SOURCE,
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PLATFORM_SCHEMA,
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ImageProcessingEntity,
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draw_box,
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)
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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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_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_AREA = "area"
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CONF_BOTTOM = "bottom"
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CONF_CATEGORIES = "categories"
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CONF_CATEGORY = "category"
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CONF_FILE_OUT = "file_out"
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CONF_GRAPH = "graph"
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CONF_LABELS = "labels"
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CONF_LEFT = "left"
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CONF_MODEL = "model"
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CONF_MODEL_DIR = "model_dir"
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CONF_RIGHT = "right"
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CONF_TOP = "top"
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AREA_SCHEMA = vol.Schema(
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{
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vol.Optional(CONF_BOTTOM, default=1): cv.small_float,
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vol.Optional(CONF_LEFT, default=0): cv.small_float,
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vol.Optional(CONF_RIGHT, default=1): cv.small_float,
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vol.Optional(CONF_TOP, default=0): cv.small_float,
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}
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)
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CATEGORY_SCHEMA = vol.Schema(
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{vol.Required(CONF_CATEGORY): cv.string, vol.Optional(CONF_AREA): AREA_SCHEMA}
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)
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PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
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{
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vol.Optional(CONF_FILE_OUT, default=[]): vol.All(cv.ensure_list, [cv.template]),
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vol.Required(CONF_MODEL): vol.Schema(
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{
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vol.Required(CONF_GRAPH): cv.isfile,
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vol.Optional(CONF_AREA): AREA_SCHEMA,
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vol.Optional(CONF_CATEGORIES, default=[]): vol.All(
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cv.ensure_list, [vol.Any(cv.string, CATEGORY_SCHEMA)]
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),
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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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}
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),
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}
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)
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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) or hass.config.path("tensorflow")
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labels = model_config.get(CONF_LABELS) or hass.config.path(
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"tensorflow", "object_detection", "data", "mscoco_label_map.pbtxt"
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)
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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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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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if label_map_util is None:
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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"
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) # noqa
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return
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if cv2 is None:
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_LOGGER.warning(
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"No OpenCV library found. TensorFlow will process image with "
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"PIL at reduced resolution"
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)
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# Set up 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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)
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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(
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TensorFlowImageProcessor(
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hass,
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camera[CONF_ENTITY_ID],
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camera.get(CONF_NAME),
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session,
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detection_graph,
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category_index,
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config,
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)
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)
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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__(
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self,
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hass,
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camera_entity,
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name,
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session,
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detection_graph,
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category_index,
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config,
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):
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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(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: {
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category: len(values) for category, values in self._matches.items()
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},
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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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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(
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draw, self._area, img_width, img_height, "Detection Area", (0, 255, 255)
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)
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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 and self._category_areas[category] != [
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0,
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0,
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1,
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1,
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]:
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label = "{} Detection Area".format(category.capitalize())
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draw_box(
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draw,
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self._category_areas[category],
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img_width,
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img_height,
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label,
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(0, 255, 0),
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)
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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(
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draw, instance["box"], img_width, img_height, label, (255, 255, 0)
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)
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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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if cv2 is None:
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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 = (
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np.array(img.getdata())
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.reshape((img_height, img_width, 3))
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.astype(np.uint8)
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)
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inp_expanded = np.expand_dims(inp, axis=0)
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else:
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img = cv2.imdecode(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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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], feed_dict={image_tensor: inp_expanded}
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)
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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 (
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boxes[0] < self._area[0]
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or boxes[1] < self._area[1]
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or boxes[2] > self._area[2]
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or boxes[3] > self._area[3]
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):
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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 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 and (
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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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):
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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({"score": float(score), "box": boxes})
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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(
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path_template.render(camera_entity=self._camera_entity)
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)
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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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self._matches = matches
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self._total_matches = total_matches
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