mirror of
https://github.com/home-assistant/core.git
synced 2026-05-08 09:38:58 +01:00
Remove tensorflow integration (#155462)
This commit is contained in:
Robert Resch
GitHub
@@ -1,4 +0,0 @@
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"""The tensorflow component."""
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DOMAIN = "tensorflow"
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CONF_GRAPH = "graph"
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@@ -1,438 +0,0 @@
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"""Support for performing TensorFlow classification on images."""
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from __future__ import annotations
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import io
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import logging
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import os
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import sys
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import time
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from typing import Any
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import numpy as np
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from PIL import Image, ImageDraw, UnidentifiedImageError
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import tensorflow as tf
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import voluptuous as vol
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from homeassistant.components.image_processing import (
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CONF_CONFIDENCE,
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PLATFORM_SCHEMA as IMAGE_PROCESSING_PLATFORM_SCHEMA,
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ImageProcessingEntity,
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)
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from homeassistant.const import (
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CONF_ENTITY_ID,
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CONF_MODEL,
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CONF_NAME,
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CONF_SOURCE,
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EVENT_HOMEASSISTANT_START,
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)
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from homeassistant.core import (
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DOMAIN as HOMEASSISTANT_DOMAIN,
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HomeAssistant,
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split_entity_id,
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)
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from homeassistant.helpers import config_validation as cv, template
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from homeassistant.helpers.entity_platform import AddEntitiesCallback
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from homeassistant.helpers.issue_registry import IssueSeverity, create_issue
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from homeassistant.helpers.typing import ConfigType, DiscoveryInfoType
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from homeassistant.util.pil import draw_box
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from . import CONF_GRAPH, DOMAIN
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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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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ATTR_PROCESS_TIME = "process_time"
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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_LABELS = "labels"
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CONF_LABEL_OFFSET = "label_offset"
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CONF_LEFT = "left"
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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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_DEFAULT_AREA = (0.0, 0.0, 1.0, 1.0)
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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 = IMAGE_PROCESSING_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.isdir,
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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_LABEL_OFFSET, default=1): int,
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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 get_model_detection_function(model):
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"""Get a tf.function for detection."""
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@tf.function
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def detect_fn(image):
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"""Detect objects in image."""
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image, shapes = model.preprocess(image)
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prediction_dict = model.predict(image, shapes)
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return model.postprocess(prediction_dict, shapes)
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return detect_fn
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def setup_platform(
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hass: HomeAssistant,
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config: ConfigType,
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add_entities: AddEntitiesCallback,
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discovery_info: DiscoveryInfoType | None = None,
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) -> None:
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"""Set up the TensorFlow image processing platform."""
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create_issue(
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hass,
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HOMEASSISTANT_DOMAIN,
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f"deprecated_system_packages_yaml_integration_{DOMAIN}",
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breaks_in_ha_version="2025.12.0",
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is_fixable=False,
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issue_domain=DOMAIN,
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severity=IssueSeverity.WARNING,
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translation_key="deprecated_system_packages_yaml_integration",
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translation_placeholders={
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"domain": DOMAIN,
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"integration_title": "Tensorflow",
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},
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)
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model_config = config[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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checkpoint = os.path.join(model_config[CONF_GRAPH], "checkpoint")
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pipeline_config = os.path.join(model_config[CONF_GRAPH], "pipeline.config")
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# Make sure locations exist
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if (
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not os.path.isdir(model_dir)
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or not os.path.isdir(checkpoint)
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or not os.path.exists(pipeline_config)
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or not os.path.exists(labels)
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):
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_LOGGER.error("Unable to locate tensorflow model 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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# These imports shouldn't be moved to the top, because they depend on code from the model_dir.
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# (The model_dir is created during the manual setup process. See integration docs.)
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from object_detection.builders import model_builder # noqa: PLC0415
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from object_detection.utils import config_util, label_map_util # noqa: PLC0415
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except ImportError:
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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/tf2.md#installation"
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)
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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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import cv2 # noqa: F401, PLC0415
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except ImportError:
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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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hass.data[DOMAIN] = {CONF_MODEL: None}
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def tensorflow_hass_start(_event):
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"""Set up TensorFlow model on hass start."""
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start = time.perf_counter()
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# Load pipeline config and build a detection model
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pipeline_configs = config_util.get_configs_from_pipeline_file(pipeline_config)
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detection_model = model_builder.build(
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model_config=pipeline_configs["model"], is_training=False
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)
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# Restore checkpoint
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ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
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ckpt.restore(os.path.join(checkpoint, "ckpt-0")).expect_partial()
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_LOGGER.debug(
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"Model checkpoint restore took %d seconds", time.perf_counter() - start
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)
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model = get_model_detection_function(detection_model)
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# Preload model cache with empty image tensor
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inp = np.zeros([2160, 3840, 3], dtype=np.uint8)
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# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
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input_tensor = tf.convert_to_tensor(inp, dtype=tf.float32)
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# The model expects a batch of images, so add an axis with `tf.newaxis`.
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input_tensor = input_tensor[tf.newaxis, ...]
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# Run inference
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model(input_tensor)
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_LOGGER.debug("Model load took %d seconds", time.perf_counter() - start)
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hass.data[DOMAIN][CONF_MODEL] = model
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hass.bus.listen_once(EVENT_HOMEASSISTANT_START, tensorflow_hass_start)
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category_index: dict[int, dict[str, Any]] = (
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label_map_util.create_category_index_from_labelmap(
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labels, use_display_name=True
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)
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)
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source: list[dict[str, str]] = config[CONF_SOURCE]
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add_entities(
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TensorFlowImageProcessor(
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camera[CONF_ENTITY_ID],
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camera.get(CONF_NAME),
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category_index,
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config,
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)
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for camera in source
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)
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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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camera_entity: str,
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name: str | None,
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category_index: dict[int, dict[str, Any]],
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config: ConfigType,
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) -> None:
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"""Initialize the TensorFlow entity."""
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model_config: dict[str, Any] = config[CONF_MODEL]
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self._attr_camera_entity = camera_entity
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if name:
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self._attr_name = name
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else:
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self._attr_name = f"TensorFlow {split_entity_id(camera_entity)[1]}"
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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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self._label_id_offset = model_config.get(CONF_LABEL_OFFSET)
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categories: list[str | dict[str, Any]] = model_config[CONF_CATEGORIES]
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self._include_categories = []
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self._category_areas: dict[str, tuple[float, float, float, float]] = {}
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for category in categories:
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if isinstance(category, dict):
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category_name: str = category[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] = _DEFAULT_AREA
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if category_area:
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self._category_areas[category_name] = (
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category_area[CONF_TOP],
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category_area[CONF_LEFT],
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category_area[CONF_BOTTOM],
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category_area[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] = _DEFAULT_AREA
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# Handle global detection area
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self._area = _DEFAULT_AREA
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if area_config := model_config.get(CONF_AREA):
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self._area = (
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area_config[CONF_TOP],
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area_config[CONF_LEFT],
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area_config[CONF_BOTTOM],
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area_config[CONF_RIGHT],
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)
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self._matches: dict[str, list[dict[str, Any]]] = {}
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self._total_matches = 0
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self._last_image = None
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self._process_time = 0.0
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@property
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def state(self) -> int:
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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 extra_state_attributes(self) -> dict[str, Any]:
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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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ATTR_PROCESS_TIME: self._process_time,
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}
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def _save_image(
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self, image: bytes, matches: dict[str, list[dict[str, Any]]], paths: list[str]
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) -> None:
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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 != _DEFAULT_AREA:
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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 (
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category in self._category_areas
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and self._category_areas[category] != _DEFAULT_AREA
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):
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label = f"{category.capitalize()} Detection Area"
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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 = f"{category} {instance['score']:.1f}%"
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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.debug("Saving results image to %s", path)
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os.makedirs(os.path.dirname(path), exist_ok=True)
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img.save(path)
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def process_image(self, image: bytes) -> None:
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"""Process the image."""
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if not (model := self.hass.data[DOMAIN][CONF_MODEL]):
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_LOGGER.debug("Model not yet ready")
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return
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start = time.perf_counter()
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try:
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import cv2 # noqa: PLC0415
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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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except ImportError:
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try:
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img = Image.open(io.BytesIO(bytearray(image))).convert("RGB")
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except UnidentifiedImageError:
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_LOGGER.warning("Unable to process image, bad data")
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return
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img.thumbnail((460, 460), Image.Resampling.LANCZOS)
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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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# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
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input_tensor = tf.convert_to_tensor(inp_expanded, dtype=tf.float32)
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detections = model(input_tensor)
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boxes = detections["detection_boxes"][0].numpy()
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scores = detections["detection_scores"][0].numpy()
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classes = (
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detections["detection_classes"][0].numpy() + self._label_id_offset
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).astype(int)
|
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|
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matches: dict[str, list[dict[str, Any]]] = {}
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total_matches = 0
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for box, score, obj_class in zip(boxes, scores, classes, strict=False):
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score = score * 100
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boxes = box.tolist()
|
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|
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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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|
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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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|
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category = self._category_index[obj_class]["name"]
|
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|
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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
|
||||
|
||||
# Exclude matches outside category specific area definition
|
||||
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]
|
||||
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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|
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# If we got here, we should include it
|
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if category not in matches:
|
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matches[category] = []
|
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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
|
||||
self._process_time = time.perf_counter() - start
|
||||
@@ -1,16 +0,0 @@
|
||||
{
|
||||
"domain": "tensorflow",
|
||||
"name": "TensorFlow",
|
||||
"codeowners": [],
|
||||
"documentation": "https://www.home-assistant.io/integrations/tensorflow",
|
||||
"iot_class": "local_polling",
|
||||
"loggers": ["tensorflow"],
|
||||
"quality_scale": "legacy",
|
||||
"requirements": [
|
||||
"tensorflow==2.5.0",
|
||||
"tf-models-official==2.5.0",
|
||||
"pycocotools==2.0.6",
|
||||
"numpy==2.3.2",
|
||||
"Pillow==12.0.0"
|
||||
]
|
||||
}
|
||||
@@ -6747,12 +6747,6 @@
|
||||
"config_flow": false,
|
||||
"iot_class": "local_polling"
|
||||
},
|
||||
"tensorflow": {
|
||||
"name": "TensorFlow",
|
||||
"integration_type": "hub",
|
||||
"config_flow": false,
|
||||
"iot_class": "local_polling"
|
||||
},
|
||||
"tesla": {
|
||||
"name": "Tesla",
|
||||
"integrations": {
|
||||
|
||||
Generated
-11
@@ -35,7 +35,6 @@ PSNAWP==3.0.0
|
||||
# homeassistant.components.qrcode
|
||||
# homeassistant.components.seven_segments
|
||||
# homeassistant.components.sighthound
|
||||
# homeassistant.components.tensorflow
|
||||
Pillow==12.0.0
|
||||
|
||||
# homeassistant.components.plex
|
||||
@@ -1595,7 +1594,6 @@ numato-gpio==0.13.0
|
||||
# homeassistant.components.compensation
|
||||
# homeassistant.components.iqvia
|
||||
# homeassistant.components.stream
|
||||
# homeassistant.components.tensorflow
|
||||
# homeassistant.components.trend
|
||||
numpy==2.3.2
|
||||
|
||||
@@ -1929,9 +1927,6 @@ pycketcasts==1.0.1
|
||||
# homeassistant.components.cmus
|
||||
pycmus==0.1.1
|
||||
|
||||
# homeassistant.components.tensorflow
|
||||
# pycocotools==2.0.6
|
||||
|
||||
# homeassistant.components.comfoconnect
|
||||
pycomfoconnect==0.5.1
|
||||
|
||||
@@ -2967,9 +2962,6 @@ temescal==0.5
|
||||
# homeassistant.components.temper
|
||||
temperusb==1.6.1
|
||||
|
||||
# homeassistant.components.tensorflow
|
||||
# tensorflow==2.5.0
|
||||
|
||||
# homeassistant.components.tesla_fleet
|
||||
# homeassistant.components.teslemetry
|
||||
# homeassistant.components.tessie
|
||||
@@ -2987,9 +2979,6 @@ teslemetry-stream==0.7.10
|
||||
# homeassistant.components.tessie
|
||||
tessie-api==0.1.1
|
||||
|
||||
# homeassistant.components.tensorflow
|
||||
# tf-models-official==2.5.0
|
||||
|
||||
# homeassistant.components.thermobeacon
|
||||
thermobeacon-ble==0.10.0
|
||||
|
||||
|
||||
Generated
-11
@@ -35,7 +35,6 @@ PSNAWP==3.0.0
|
||||
# homeassistant.components.qrcode
|
||||
# homeassistant.components.seven_segments
|
||||
# homeassistant.components.sighthound
|
||||
# homeassistant.components.tensorflow
|
||||
Pillow==12.0.0
|
||||
|
||||
# homeassistant.components.plex
|
||||
@@ -1372,7 +1371,6 @@ numato-gpio==0.13.0
|
||||
# homeassistant.components.compensation
|
||||
# homeassistant.components.iqvia
|
||||
# homeassistant.components.stream
|
||||
# homeassistant.components.tensorflow
|
||||
# homeassistant.components.trend
|
||||
numpy==2.3.2
|
||||
|
||||
@@ -1622,9 +1620,6 @@ pybravia==0.3.4
|
||||
# homeassistant.components.cloudflare
|
||||
pycfdns==3.0.0
|
||||
|
||||
# homeassistant.components.tensorflow
|
||||
# pycocotools==2.0.6
|
||||
|
||||
# homeassistant.components.comfoconnect
|
||||
pycomfoconnect==0.5.1
|
||||
|
||||
@@ -2456,9 +2451,6 @@ temescal==0.5
|
||||
# homeassistant.components.temper
|
||||
temperusb==1.6.1
|
||||
|
||||
# homeassistant.components.tensorflow
|
||||
# tensorflow==2.5.0
|
||||
|
||||
# homeassistant.components.tesla_fleet
|
||||
# homeassistant.components.teslemetry
|
||||
# homeassistant.components.tessie
|
||||
@@ -2476,9 +2468,6 @@ teslemetry-stream==0.7.10
|
||||
# homeassistant.components.tessie
|
||||
tessie-api==0.1.1
|
||||
|
||||
# homeassistant.components.tensorflow
|
||||
# tf-models-official==2.5.0
|
||||
|
||||
# homeassistant.components.thermobeacon
|
||||
thermobeacon-ble==0.10.0
|
||||
|
||||
|
||||
@@ -30,13 +30,10 @@ EXCLUDED_REQUIREMENTS_ALL = {
|
||||
"evdev",
|
||||
"face-recognition",
|
||||
"pybluez",
|
||||
"pycocotools",
|
||||
"pycups",
|
||||
"python-gammu",
|
||||
"python-lirc",
|
||||
"pyuserinput",
|
||||
"tensorflow",
|
||||
"tf-models-official",
|
||||
}
|
||||
|
||||
# Requirements excluded by EXCLUDED_REQUIREMENTS_ALL which should be included when
|
||||
|
||||
@@ -971,7 +971,6 @@ INTEGRATIONS_WITHOUT_QUALITY_SCALE_FILE = [
|
||||
"telnet",
|
||||
"temper",
|
||||
"template",
|
||||
"tensorflow",
|
||||
"tesla_fleet",
|
||||
"tesla_wall_connector",
|
||||
"teslemetry",
|
||||
@@ -2014,7 +2013,6 @@ INTEGRATIONS_WITHOUT_SCALE = [
|
||||
"telnet",
|
||||
"temper",
|
||||
"template",
|
||||
"tensorflow",
|
||||
"tesla_fleet",
|
||||
"tesla_wall_connector",
|
||||
"teslemetry",
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
"""TensorFlow component tests."""
|
||||
@@ -1,40 +0,0 @@
|
||||
"""Tensorflow test."""
|
||||
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
from homeassistant.components.image_processing import DOMAIN as IMAGE_PROCESSING_DOMAINN
|
||||
from homeassistant.components.tensorflow import CONF_GRAPH, DOMAIN
|
||||
from homeassistant.const import CONF_ENTITY_ID, CONF_MODEL, CONF_PLATFORM, CONF_SOURCE
|
||||
from homeassistant.core import DOMAIN as HOMEASSISTANT_DOMAIN, HomeAssistant
|
||||
from homeassistant.helpers import issue_registry as ir
|
||||
from homeassistant.setup import async_setup_component
|
||||
|
||||
|
||||
@patch.dict("sys.modules", tensorflow=Mock())
|
||||
async def test_repair_issue_is_created(
|
||||
hass: HomeAssistant,
|
||||
issue_registry: ir.IssueRegistry,
|
||||
) -> None:
|
||||
"""Test repair issue is created."""
|
||||
assert await async_setup_component(
|
||||
hass,
|
||||
IMAGE_PROCESSING_DOMAINN,
|
||||
{
|
||||
IMAGE_PROCESSING_DOMAINN: [
|
||||
{
|
||||
CONF_PLATFORM: DOMAIN,
|
||||
CONF_SOURCE: [
|
||||
{CONF_ENTITY_ID: "camera.test_camera"},
|
||||
],
|
||||
CONF_MODEL: {
|
||||
CONF_GRAPH: ".",
|
||||
},
|
||||
}
|
||||
],
|
||||
},
|
||||
)
|
||||
await hass.async_block_till_done()
|
||||
assert (
|
||||
HOMEASSISTANT_DOMAIN,
|
||||
f"deprecated_system_packages_yaml_integration_{DOMAIN}",
|
||||
) in issue_registry.issues
|
||||
Reference in New Issue
Block a user