mirror of
https://github.com/blakeblackshear/frigate.git
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5c6f169975
* Always initialize face model on startup * Add ability to save face images for debugging * Implement better face recognition reasonability
270 lines
9.0 KiB
Python
270 lines
9.0 KiB
Python
"""Model Utils"""
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import logging
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import os
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from typing import Any, Optional
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import cv2
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import numpy as np
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import onnxruntime as ort
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from playhouse.sqliteq import SqliteQueueDatabase
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from frigate.config.semantic_search import FaceRecognitionConfig
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try:
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import openvino as ov
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except ImportError:
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# openvino is not included
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pass
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logger = logging.getLogger(__name__)
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MIN_MATCHING_FACES = 2
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def get_ort_providers(
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force_cpu: bool = False, device: str = "AUTO", requires_fp16: bool = False
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) -> tuple[list[str], list[dict[str, any]]]:
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if force_cpu:
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return (
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["CPUExecutionProvider"],
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[
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{
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"enable_cpu_mem_arena": False,
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}
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],
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)
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providers = []
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options = []
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for provider in ort.get_available_providers():
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if provider == "CUDAExecutionProvider":
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device_id = 0 if not device.isdigit() else int(device)
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providers.append(provider)
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options.append(
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{
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"arena_extend_strategy": "kSameAsRequested",
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"device_id": device_id,
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}
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)
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elif provider == "TensorrtExecutionProvider":
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# TensorrtExecutionProvider uses too much memory without options to control it
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# so it is not enabled by default
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if device == "Tensorrt":
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os.makedirs(
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"/config/model_cache/tensorrt/ort/trt-engines", exist_ok=True
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)
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device_id = 0 if not device.isdigit() else int(device)
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providers.append(provider)
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options.append(
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{
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"device_id": device_id,
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"trt_fp16_enable": requires_fp16
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and os.environ.get("USE_FP_16", "True") != "False",
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"trt_timing_cache_enable": True,
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"trt_engine_cache_enable": True,
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"trt_timing_cache_path": "/config/model_cache/tensorrt/ort",
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"trt_engine_cache_path": "/config/model_cache/tensorrt/ort/trt-engines",
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}
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)
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else:
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continue
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elif provider == "OpenVINOExecutionProvider":
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os.makedirs("/config/model_cache/openvino/ort", exist_ok=True)
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providers.append(provider)
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options.append(
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{
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"arena_extend_strategy": "kSameAsRequested",
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"cache_dir": "/config/model_cache/openvino/ort",
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"device_type": device,
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}
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)
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elif provider == "CPUExecutionProvider":
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providers.append(provider)
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options.append(
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{
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"enable_cpu_mem_arena": False,
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}
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)
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else:
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providers.append(provider)
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options.append({})
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return (providers, options)
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class ONNXModelRunner:
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"""Run onnx models optimally based on available hardware."""
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def __init__(self, model_path: str, device: str, requires_fp16: bool = False):
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self.model_path = model_path
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self.ort: ort.InferenceSession = None
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self.ov: ov.Core = None
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providers, options = get_ort_providers(device == "CPU", device, requires_fp16)
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self.interpreter = None
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if "OpenVINOExecutionProvider" in providers:
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try:
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# use OpenVINO directly
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self.type = "ov"
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self.ov = ov.Core()
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self.ov.set_property(
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{ov.properties.cache_dir: "/config/model_cache/openvino"}
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)
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self.interpreter = self.ov.compile_model(
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model=model_path, device_name=device
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)
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except Exception as e:
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logger.warning(
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f"OpenVINO failed to build model, using CPU instead: {e}"
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)
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self.interpreter = None
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# Use ONNXRuntime
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if self.interpreter is None:
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self.type = "ort"
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self.ort = ort.InferenceSession(
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model_path,
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providers=providers,
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provider_options=options,
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)
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def get_input_names(self) -> list[str]:
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if self.type == "ov":
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input_names = []
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for input in self.interpreter.inputs:
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input_names.extend(input.names)
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return input_names
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elif self.type == "ort":
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return [input.name for input in self.ort.get_inputs()]
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def run(self, input: dict[str, Any]) -> Any:
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if self.type == "ov":
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infer_request = self.interpreter.create_infer_request()
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input_tensor = list(input.values())
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if len(input_tensor) == 1:
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input_tensor = ov.Tensor(array=input_tensor[0])
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else:
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input_tensor = ov.Tensor(array=input_tensor)
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infer_request.infer(input_tensor)
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return [infer_request.get_output_tensor().data]
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elif self.type == "ort":
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return self.ort.run(None, input)
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class FaceClassificationModel:
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def __init__(self, config: FaceRecognitionConfig, db: SqliteQueueDatabase):
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self.config = config
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self.db = db
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self.landmark_detector = cv2.face.createFacemarkLBF()
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self.landmark_detector.loadModel("/config/model_cache/facedet/landmarkdet.yaml")
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self.recognizer: cv2.face.LBPHFaceRecognizer = (
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cv2.face.LBPHFaceRecognizer_create(
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radius=2, threshold=(1 - config.threshold) * 1000
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)
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)
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self.label_map: dict[int, str] = {}
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self.__build_classifier()
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def __build_classifier(self) -> None:
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labels = []
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faces = []
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dir = "/media/frigate/clips/faces"
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for idx, name in enumerate(os.listdir(dir)):
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if name == "debug":
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continue
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self.label_map[idx] = name
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face_folder = os.path.join(dir, name)
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for image in os.listdir(face_folder):
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img = cv2.imread(os.path.join(face_folder, image))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = self.__align_face(img, img.shape[1], img.shape[0])
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faces.append(img)
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labels.append(idx)
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self.recognizer.train(faces, np.array(labels))
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def __align_face(
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self,
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image: np.ndarray,
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output_width: int,
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output_height: int,
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) -> np.ndarray:
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_, lands = self.landmark_detector.fit(
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image, np.array([(0, 0, image.shape[1], image.shape[0])])
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)
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landmarks = lands[0][0]
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# get landmarks for eyes
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leftEyePts = landmarks[42:48]
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rightEyePts = landmarks[36:42]
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# compute the center of mass for each eye
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leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
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rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
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# compute the angle between the eye centroids
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dY = rightEyeCenter[1] - leftEyeCenter[1]
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dX = rightEyeCenter[0] - leftEyeCenter[0]
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angle = np.degrees(np.arctan2(dY, dX)) - 180
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# compute the desired right eye x-coordinate based on the
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# desired x-coordinate of the left eye
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desiredRightEyeX = 1.0 - 0.35
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# determine the scale of the new resulting image by taking
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# the ratio of the distance between eyes in the *current*
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# image to the ratio of distance between eyes in the
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# *desired* image
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dist = np.sqrt((dX**2) + (dY**2))
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desiredDist = desiredRightEyeX - 0.35
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desiredDist *= output_width
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scale = desiredDist / dist
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# compute center (x, y)-coordinates (i.e., the median point)
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# between the two eyes in the input image
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# grab the rotation matrix for rotating and scaling the face
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eyesCenter = (
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int((leftEyeCenter[0] + rightEyeCenter[0]) // 2),
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int((leftEyeCenter[1] + rightEyeCenter[1]) // 2),
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)
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M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
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# update the translation component of the matrix
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tX = output_width * 0.5
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tY = output_height * 0.35
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M[0, 2] += tX - eyesCenter[0]
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M[1, 2] += tY - eyesCenter[1]
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# apply the affine transformation
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return cv2.warpAffine(
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image, M, (output_width, output_height), flags=cv2.INTER_CUBIC
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)
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def clear_classifier(self) -> None:
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self.classifier = None
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self.labeler = None
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self.label_map = {}
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def classify_face(self, face_image: np.ndarray) -> Optional[tuple[str, float]]:
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if not self.label_map:
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self.__build_classifier()
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img = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
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img = self.__align_face(img, img.shape[1], img.shape[0])
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index, distance = self.recognizer.predict(img)
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if index == -1:
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return None
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score = 1.0 - (distance / 1000)
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return self.label_map[index], round(score, 2)
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