Bird classification (#15966)

* Start working on bird processor

* Initial setup for bird processing

* Improvements to handling

* Get classification working

* Cleanup classification

* Add classification config

* Update sort

frigate/data_processing/real_time/bird_processor.py

@@ -0,0 +1,154 @@
+"""Handle processing images to classify birds."""
+
+import logging
+import os
+
+import cv2
+import numpy as np
+import requests
+
+from frigate.config import FrigateConfig
+from frigate.const import FRIGATE_LOCALHOST, MODEL_CACHE_DIR
+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 BirdProcessor(RealTimeProcessorApi):
+    def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics):
+        super().__init__(config, metrics)
+        self.interpreter: Interpreter = None
+        self.tensor_input_details: dict[str, any] = None
+        self.tensor_output_details: dict[str, any] = None
+        self.detected_birds: dict[str, float] = {}
+        self.labelmap: dict[int, str] = {}
+
+        download_path = os.path.join(MODEL_CACHE_DIR, "bird")
+        self.model_files = {
+            "bird.tflite": "https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_inat_bird_quant.tflite",
+            "birdmap.txt": "https://raw.githubusercontent.com/google-coral/test_data/master/inat_bird_labels.txt",
+        }
+
+        if not all(
+            os.path.exists(os.path.join(download_path, n))
+            for n in self.model_files.keys()
+        ):
+            # conditionally import ModelDownloader
+            from frigate.util.downloader import ModelDownloader
+
+            self.downloader = ModelDownloader(
+                model_name="bird",
+                download_path=download_path,
+                file_names=self.model_files.keys(),
+                download_func=self.__download_models,
+                complete_func=self.__build_detector,
+            )
+            self.downloader.ensure_model_files()
+        else:
+            self.__build_detector()
+
+    def __download_models(self, path: str) -> None:
+        try:
+            file_name = os.path.basename(path)
+
+            # conditionally import ModelDownloader
+            from frigate.util.downloader import ModelDownloader
+
+            ModelDownloader.download_from_url(self.model_files[file_name], path)
+        except Exception as e:
+            logger.error(f"Failed to download {path}: {e}")
+
+    def __build_detector(self) -> None:
+        self.interpreter = Interpreter(
+            model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
+            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()
+
+        i = 0
+
+        with open(os.path.join(MODEL_CACHE_DIR, "bird/birdmap.txt")) as f:
+            line = f.readline()
+            while line:
+                start = line.find("(")
+                end = line.find(")")
+                self.labelmap[i] = line[start + 1 : end]
+                i += 1
+                line = f.readline()
+
+    def process_frame(self, obj_data, frame):
+        if obj_data["label"] != "bird":
+            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,
+        ]
+
+        cv2.imwrite("/media/frigate/test_class.png", input)
+
+        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)
+
+        if best_id == 964:
+            logger.debug("No bird classification was detected.")
+            return
+
+        score = round(probs[best_id], 2)
+
+        if score < self.config.classification.bird.threshold:
+            logger.debug(f"Score {score} is not above required threshold")
+            return
+
+        previous_score = self.detected_birds.get(obj_data["id"], 0.0)
+
+        if score <= previous_score:
+            logger.debug(f"Score {score} is worse than previous score {previous_score}")
+            return
+
+        resp = requests.post(
+            f"{FRIGATE_LOCALHOST}/api/events/{obj_data['id']}/sub_label",
+            json={
+                "camera": obj_data.get("camera"),
+                "subLabel": self.labelmap[best_id],
+                "subLabelScore": score,
+            },
+        )
+
+        if resp.status_code == 200:
+            self.detected_birds[obj_data["id"]] = score
+
+    def handle_request(self, request_data):
+        return None
+
+    def expire_object(self, object_id):
+        if object_id in self.detected_birds:
+            self.detected_birds.pop(object_id)