Initial custom classification model config support (#18362)

* Add basic config for defining a teachable machine model

* Add model type

* Add basic config for teachable machine models

* Adjust config for state and object

* Use config to process

* Correctly check for objects

* Remove debug

* Rename to not be teachable machine specific

* Cleanup

frigate/data_processing/real_time/custom_classification.py

@@ -0,0 +1,178 @@
+"""Real time processor that works with classification tflite models."""
+
+import logging
+from typing import Any
+
+import cv2
+import numpy as np
+
+from frigate.comms.event_metadata_updater import (
+    EventMetadataPublisher,
+    EventMetadataTypeEnum,
+)
+from frigate.config import FrigateConfig
+from frigate.config.classification import CustomClassificationConfig
+from frigate.util.builtin import load_labels
+from frigate.util.object import calculate_region
+
+from ..types import DataProcessorMetrics
+from .api import RealTimeProcessorApi
+
+try:
+    from tflite_runtime.interpreter import Interpreter
+except ModuleNotFoundError:
+    from tensorflow.lite.python.interpreter import Interpreter
+
+logger = logging.getLogger(__name__)
+
+
+class CustomStateClassificationProcessor(RealTimeProcessorApi):
+    def __init__(
+        self,
+        config: FrigateConfig,
+        model_config: CustomClassificationConfig,
+        metrics: DataProcessorMetrics,
+    ):
+        super().__init__(config, metrics)
+        self.model_config = model_config
+        self.interpreter: Interpreter = None
+        self.tensor_input_details: dict[str, Any] = None
+        self.tensor_output_details: dict[str, Any] = None
+        self.labelmap: dict[int, str] = {}
+        self.__build_detector()
+
+    def __build_detector(self) -> None:
+        self.interpreter = Interpreter(
+            model_path=self.model_config.model_path,
+            num_threads=2,
+        )
+        self.interpreter.allocate_tensors()
+        self.tensor_input_details = self.interpreter.get_input_details()
+        self.tensor_output_details = self.interpreter.get_output_details()
+        self.labelmap = load_labels(self.model_config.labelmap_path, prefill=0)
+
+    def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray):
+        camera = frame_data.get("camera")
+        if camera not in self.model_config.state_config.cameras:
+            return
+
+        camera_config = self.model_config.state_config.cameras[camera]
+        x, y, x2, y2 = calculate_region(
+            frame.shape,
+            camera_config.crop[0],
+            camera_config.crop[1],
+            camera_config.crop[2],
+            camera_config.crop[3],
+            224,
+            1.0,
+        )
+
+        rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
+        input = rgb[
+            y:y2,
+            x:x2,
+        ]
+
+        if input.shape != (224, 224):
+            input = cv2.resize(input, (224, 224))
+
+        input = np.expand_dims(input, axis=0)
+        self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)
+        self.interpreter.invoke()
+        res: np.ndarray = self.interpreter.get_tensor(
+            self.tensor_output_details[0]["index"]
+        )[0]
+        print(f"the gate res is {res}")
+        probs = res / res.sum(axis=0)
+        best_id = np.argmax(probs)
+        score = round(probs[best_id], 2)
+
+        print(f"got {self.labelmap[best_id]} with score {score}")
+
+    def handle_request(self, topic, request_data):
+        return None
+
+    def expire_object(self, object_id, camera):
+        pass
+
+
+class CustomObjectClassificationProcessor(RealTimeProcessorApi):
+    def __init__(
+        self,
+        config: FrigateConfig,
+        model_config: CustomClassificationConfig,
+        sub_label_publisher: EventMetadataPublisher,
+        metrics: DataProcessorMetrics,
+    ):
+        super().__init__(config, metrics)
+        self.model_config = model_config
+        self.interpreter: Interpreter = None
+        self.sub_label_publisher = sub_label_publisher
+        self.tensor_input_details: dict[str, Any] = None
+        self.tensor_output_details: dict[str, Any] = None
+        self.detected_objects: dict[str, float] = {}
+        self.labelmap: dict[int, str] = {}
+        self.__build_detector()
+
+    def __build_detector(self) -> None:
+        self.interpreter = Interpreter(
+            model_path=self.model_config.model_path,
+            num_threads=2,
+        )
+        self.interpreter.allocate_tensors()
+        self.tensor_input_details = self.interpreter.get_input_details()
+        self.tensor_output_details = self.interpreter.get_output_details()
+        self.labelmap = load_labels(self.model_config.labelmap_path, prefill=0)
+
+    def process_frame(self, obj_data, frame):
+        if obj_data["label"] not in self.model_config.object_config.objects:
+            return
+
+        x, y, x2, y2 = calculate_region(
+            frame.shape,
+            obj_data["box"][0],
+            obj_data["box"][1],
+            obj_data["box"][2],
+            obj_data["box"][3],
+            224,
+            1.0,
+        )
+
+        rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
+        input = rgb[
+            y:y2,
+            x:x2,
+        ]
+
+        if input.shape != (224, 224):
+            input = cv2.resize(input, (224, 224))
+
+        input = np.expand_dims(input, axis=0)
+        self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)
+        self.interpreter.invoke()
+        res: np.ndarray = self.interpreter.get_tensor(
+            self.tensor_output_details[0]["index"]
+        )[0]
+        probs = res / res.sum(axis=0)
+        best_id = np.argmax(probs)
+
+        score = round(probs[best_id], 2)
+
+        previous_score = self.detected_objects.get(obj_data["id"], 0.0)
+
+        if score <= previous_score:
+            logger.debug(f"Score {score} is worse than previous score {previous_score}")
+            return
+
+        self.sub_label_publisher.publish(
+            EventMetadataTypeEnum.sub_label,
+            (obj_data["id"], self.labelmap[best_id], score),
+        )
+        self.detected_objects[obj_data["id"]] = score
+
+    def handle_request(self, topic, request_data):
+        return None
+
+    def expire_object(self, object_id, camera):
+        if object_id in self.detected_objects:
+            self.detected_objects.pop(object_id)