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OmriAx 2025-03-10 10:55:59 +02:00
parent 469f9c0a83
commit c674d142dc
1 changed files with 93 additions and 24 deletions
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117
frigate/detectors/plugins/hailo8l.py
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@ -29,11 +29,18 @@ from typing_extensions import Literal
from frigate.const import MODEL_CACHE_DIR from frigate.const import MODEL_CACHE_DIR
from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum, InputTensorEnum, PixelFormatEnum, InputDTypeEnum from frigate.detectors.detector_config import (
BaseDetectorConfig,
ModelTypeEnum,
InputTensorEnum,
PixelFormatEnum,
InputDTypeEnum,
)
from PIL import Image, ImageDraw, ImageFont from PIL import Image, ImageDraw, ImageFont
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# ----------------- ResponseStore Class ----------------- # # ----------------- ResponseStore Class ----------------- #
class ResponseStore: class ResponseStore:
""" """
@ -41,6 +48,7 @@ class ResponseStore:
to their results. Threads can wait on the condition variable until to their results. Threads can wait on the condition variable until
their request's result appears. their request's result appears.
""" """
def __init__(self): def __init__(self):
self.responses = {} # Maps request_id -> (original_input, infer_results) self.responses = {} # Maps request_id -> (original_input, infer_results)
self.lock = threading.Lock() self.lock = threading.Lock()
@ -53,12 +61,16 @@ class ResponseStore:
def get(self, request_id, timeout=None): def get(self, request_id, timeout=None):
with self.cond: with self.cond:
if not self.cond.wait_for(lambda: request_id in self.responses, timeout=timeout): if not self.cond.wait_for(
lambda: request_id in self.responses, timeout=timeout
):
raise TimeoutError(f"Timeout waiting for response {request_id}") raise TimeoutError(f"Timeout waiting for response {request_id}")
return self.responses.pop(request_id) return self.responses.pop(request_id)
# ----------------- Utility Functions ----------------- # # ----------------- Utility Functions ----------------- #
def preprocess_tensor(image: np.ndarray, model_w: int, model_h: int) -> np.ndarray: def preprocess_tensor(image: np.ndarray, model_w: int, model_h: int) -> np.ndarray:
""" """
Resize an image with unchanged aspect ratio using padding. Resize an image with unchanged aspect ratio using padding.
@ -78,9 +90,12 @@ def preprocess_tensor(image: np.ndarray, model_w: int, model_h: int) -> np.ndarr
padded_image = np.full((model_h, model_w, 3), 114, dtype=image.dtype) padded_image = np.full((model_h, model_w, 3), 114, dtype=image.dtype)
x_offset = (model_w - new_w) // 2 x_offset = (model_w - new_w) // 2
y_offset = (model_h - new_h) // 2 y_offset = (model_h - new_h) // 2
padded_image[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_image padded_image[y_offset : y_offset + new_h, x_offset : x_offset + new_w] = (
resized_image
)
return padded_image return padded_image
# ----------------- Global Constants ----------------- # # ----------------- Global Constants ----------------- #
DETECTOR_KEY = "hailo8l" DETECTOR_KEY = "hailo8l"
ARCH = None ARCH = None
@ -89,13 +104,16 @@ H8L_DEFAULT_MODEL = "yolov6n.hef"
H8_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef" H8_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef"
H8L_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/yolov6n.hef" H8L_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/yolov6n.hef"
def detect_hailo_arch(): def detect_hailo_arch():
try: try:
result = subprocess.run(['hailortcli', 'fw-control', 'identify'], capture_output=True, text=True) result = subprocess.run(
["hailortcli", "fw-control", "identify"], capture_output=True, text=True
)
if result.returncode != 0: if result.returncode != 0:
logger.error(f"Inference error: {result.stderr}") logger.error(f"Inference error: {result.stderr}")
return None return None
for line in result.stdout.split('\n'): for line in result.stdout.split("\n"):
if "Device Architecture" in line: if "Device Architecture" in line:
if "HAILO8L" in line: if "HAILO8L" in line:
return "hailo8l" return "hailo8l"
@ -107,6 +125,7 @@ def detect_hailo_arch():
logger.error(f"Inference error: {e}") logger.error(f"Inference error: {e}")
return None return None
# ----------------- HailoAsyncInference Class ----------------- # # ----------------- HailoAsyncInference Class ----------------- #
class HailoAsyncInference: class HailoAsyncInference:
def __init__( def __init__(
@ -139,11 +158,21 @@ class HailoAsyncInference:
def _set_input_type(self, input_type: Optional[str] = None) -> None: def _set_input_type(self, input_type: Optional[str] = None) -> None:
self.infer_model.input().set_format_type(getattr(FormatType, input_type)) self.infer_model.input().set_format_type(getattr(FormatType, input_type))
def _set_output_type(self, output_type_dict: Optional[Dict[str, str]] = None) -> None: def _set_output_type(
self, output_type_dict: Optional[Dict[str, str]] = None
) -> None:
for output_name, output_type in output_type_dict.items(): for output_name, output_type in output_type_dict.items():
self.infer_model.output(output_name).set_format_type(getattr(FormatType, output_type)) self.infer_model.output(output_name).set_format_type(
getattr(FormatType, output_type)
)
def callback(self, completion_info, bindings_list: List, input_batch: List, request_ids: List[int]): def callback(
self,
completion_info,
bindings_list: List,
input_batch: List,
request_ids: List[int],
):
if completion_info.exception: if completion_info.exception:
logger.error(f"Inference error: {completion_info.exception}") logger.error(f"Inference error: {completion_info.exception}")
else: else:
@ -162,7 +191,9 @@ class HailoAsyncInference:
output_buffers = { output_buffers = {
output_info.name: np.empty( output_info.name: np.empty(
self.infer_model.output(output_info.name).shape, self.infer_model.output(output_info.name).shape,
dtype=getattr(np, str(output_info.format.type).split(".")[1].lower()) dtype=getattr(
np, str(output_info.format.type).split(".")[1].lower()
),
) )
for output_info in self.hef.get_output_vstream_infos() for output_info in self.hef.get_output_vstream_infos()
} }
@ -170,7 +201,7 @@ class HailoAsyncInference:
output_buffers = { output_buffers = {
name: np.empty( name: np.empty(
self.infer_model.output(name).shape, self.infer_model.output(name).shape,
dtype=getattr(np, self.output_type[name].lower()) dtype=getattr(np, self.output_type[name].lower()),
) )
for name in self.output_type for name in self.output_type
} }
@ -203,25 +234,50 @@ class HailoAsyncInference:
input_batch=input_batch, input_batch=input_batch,
request_ids=request_ids, request_ids=request_ids,
bindings_list=bindings_list, bindings_list=bindings_list,
) ),
) )
job.wait(100) job.wait(100)
# ----------------- HailoDetector Class ----------------- # # ----------------- HailoDetector Class ----------------- #
class HailoDetector(DetectionApi): class HailoDetector(DetectionApi):
type_key = DETECTOR_KEY type_key = DETECTOR_KEY
def __init__(self, detector_config: 'HailoDetectorConfig'): def __init__(self, detector_config: "HailoDetectorConfig"):
global ARCH global ARCH
ARCH = detect_hailo_arch() ARCH = detect_hailo_arch()
self.cache_dir = MODEL_CACHE_DIR self.cache_dir = MODEL_CACHE_DIR
self.device_type = detector_config.device self.device_type = detector_config.device
self.model_height = detector_config.model.height if hasattr(detector_config.model, "height") else None self.model_height = (
self.model_width = detector_config.model.width if hasattr(detector_config.model, "width") else None detector_config.model.height
self.model_type = detector_config.model.model_type if hasattr(detector_config.model, "model_type") else None if hasattr(detector_config.model, "height")
self.tensor_format = detector_config.model.input_tensor if hasattr(detector_config.model, "input_tensor") else None else None
self.pixel_format = detector_config.model.input_pixel_format if hasattr(detector_config.model, "input_pixel_format") else None )
self.input_dtype = detector_config.model.input_dtype if hasattr(detector_config.model, "input_dtype") else None self.model_width = (
detector_config.model.width
if hasattr(detector_config.model, "width")
else None
)
self.model_type = (
detector_config.model.model_type
if hasattr(detector_config.model, "model_type")
else None
)
self.tensor_format = (
detector_config.model.input_tensor
if hasattr(detector_config.model, "input_tensor")
else None
)
self.pixel_format = (
detector_config.model.input_pixel_format
if hasattr(detector_config.model, "input_pixel_format")
else None
)
self.input_dtype = (
detector_config.model.input_dtype
if hasattr(detector_config.model, "input_dtype")
else None
)
self.output_type = "FLOAT32" self.output_type = "FLOAT32"
self.set_path_and_url(detector_config.model.path) self.set_path_and_url(detector_config.model.path)
self.working_model_path = self.check_and_prepare() self.working_model_path = self.check_and_prepare()
@ -238,11 +294,13 @@ class HailoDetector(DetectionApi):
self.working_model_path, self.working_model_path,
self.input_queue, self.input_queue,
self.response_store, self.response_store,
self.batch_size self.batch_size,
) )
self.input_shape = self.inference_engine.get_input_shape() self.input_shape = self.inference_engine.get_input_shape()
logger.debug(f"[INIT] Model input shape: {self.input_shape}") logger.debug(f"[INIT] Model input shape: {self.input_shape}")
self.inference_thread = threading.Thread(target=self.inference_engine.run, daemon=True) self.inference_thread = threading.Thread(
target=self.inference_engine.run, daemon=True
)
self.inference_thread.start() self.inference_thread.start()
except Exception as e: except Exception as e:
logger.error(f"[INIT] Failed to initialize HailoAsyncInference: {e}") logger.error(f"[INIT] Failed to initialize HailoAsyncInference: {e}")
@ -261,7 +319,11 @@ class HailoDetector(DetectionApi):
self.url = None self.url = None
def is_url(self, url: str) -> bool: def is_url(self, url: str) -> bool:
return url.startswith("http://") or url.startswith("https://") or url.startswith("www.") return (
url.startswith("http://")
or url.startswith("https://")
or url.startswith("www.")
)
@staticmethod @staticmethod
def extract_model_name(path: str = None, url: str = None) -> str: def extract_model_name(path: str = None, url: str = None) -> str:
@ -328,9 +390,13 @@ class HailoDetector(DetectionApi):
self.input_queue.put((request_id, tensor_input)) self.input_queue.put((request_id, tensor_input))
try: try:
original_input, infer_results = self.response_store.get(request_id, timeout=10.0) original_input, infer_results = self.response_store.get(
request_id, timeout=10.0
)
except TimeoutError: except TimeoutError:
logger.error(f"Timeout waiting for inference results for request {request_id}") logger.error(
f"Timeout waiting for inference results for request {request_id}"
)
return np.zeros((20, 6), dtype=np.float32) return np.zeros((20, 6), dtype=np.float32)
if isinstance(infer_results, list) and len(infer_results) == 1: if isinstance(infer_results, list) and len(infer_results) == 1:
@ -363,7 +429,9 @@ class HailoDetector(DetectionApi):
def preprocess(self, image): def preprocess(self, image):
if isinstance(image, np.ndarray): if isinstance(image, np.ndarray):
processed = preprocess_tensor(image, self.input_shape[1], self.input_shape[0]) processed = preprocess_tensor(
image, self.input_shape[1], self.input_shape[0]
)
return np.expand_dims(processed, axis=0) return np.expand_dims(processed, axis=0)
else: else:
raise ValueError("Unsupported image format for preprocessing") raise ValueError("Unsupported image format for preprocessing")
@ -384,6 +452,7 @@ class HailoDetector(DetectionApi):
"""Destructor to ensure cleanup when the object is deleted.""" """Destructor to ensure cleanup when the object is deleted."""
self.close() self.close()
# ----------------- HailoDetectorConfig Class ----------------- # # ----------------- HailoDetectorConfig Class ----------------- #
class HailoDetectorConfig(BaseDetectorConfig): class HailoDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY] type: Literal[DETECTOR_KEY]