import logging
import os
import urllib.request
import numpy as np
try:
from hailo_platform import (
HEF,
ConfigureParams,
FormatType,
HailoRTException,
HailoStreamInterface,
InferVStreams,
InputVStreamParams,
OutputVStreamParams,
VDevice,
)
except ModuleNotFoundError:
pass
from pydantic import BaseModel, Field
from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig
# Set up logging
logger = logging.getLogger(__name__)
# Define the detector key for Hailo
DETECTOR_KEY = "hailo8l"
# Configuration class for model settings
class ModelConfig(BaseModel):
path: str = Field(default=None, title="Model Path") # Path to the HEF file
# Configuration class for Hailo detector
class HailoDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY] # Type of the detector
device: str = Field(default="PCIe", title="Device Type") # Device type (e.g., PCIe)
# Hailo detector class implementation
class HailoDetector(DetectionApi):
type_key = DETECTOR_KEY # Set the type key to the Hailo detector key
def __init__(self, detector_config: HailoDetectorConfig):
# Initialize device type and model path from the configuration
self.h8l_device_type = detector_config.device
self.h8l_model_path = detector_config.model.path
self.h8l_model_height = detector_config.model.height
self.h8l_model_width = detector_config.model.width
self.h8l_model_type = detector_config.model.model_type
self.h8l_tensor_format = detector_config.model.input_tensor
self.h8l_pixel_format = detector_config.model.input_pixel_format
self.model_url = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.11.0/hailo8l/ssd_mobilenet_v1.hef"
self.cache_dir = "/config/model_cache/h8l_cache"
self.expected_model_filename = "ssd_mobilenet_v1.hef"
output_type = "FLOAT32"
logger.info(f"Initializing Hailo device as {self.h8l_device_type}")
self.check_and_prepare_model()
try:
# Validate device type
if self.h8l_device_type not in ["PCIe", "M.2"]:
raise ValueError(f"Unsupported device type: {self.h8l_device_type}")
# Initialize the Hailo device
self.target = VDevice()
# Load the HEF (Hailo's binary format for neural networks)
self.hef = HEF(self.h8l_model_path)
# Create configuration parameters from the HEF
self.configure_params = ConfigureParams.create_from_hef(
hef=self.hef, interface=HailoStreamInterface.PCIe
)
# Configure the device with the HEF
self.network_groups = self.target.configure(self.hef, self.configure_params)
self.network_group = self.network_groups[0]
self.network_group_params = self.network_group.create_params()
# Create input and output virtual stream parameters
self.input_vstream_params = InputVStreamParams.make(
self.network_group,
format_type=self.hef.get_input_vstream_infos()[0].format.type,
)
self.output_vstream_params = OutputVStreamParams.make(
self.network_group, format_type=getattr(FormatType, output_type)
)
# Get input and output stream information from the HEF
self.input_vstream_info = self.hef.get_input_vstream_infos()
self.output_vstream_info = self.hef.get_output_vstream_infos()
logger.info("Hailo device initialized successfully")
logger.debug(f"[__init__] Model Path: {self.h8l_model_path}")
logger.debug(f"[__init__] Input Tensor Format: {self.h8l_tensor_format}")
logger.debug(f"[__init__] Input Pixel Format: {self.h8l_pixel_format}")
logger.debug(f"[__init__] Input VStream Info: {self.input_vstream_info[0]}")
logger.debug(
f"[__init__] Output VStream Info: {self.output_vstream_info[0]}"
)
except HailoRTException as e:
logger.error(f"HailoRTException during initialization: {e}")
raise
except Exception as e:
logger.error(f"Failed to initialize Hailo device: {e}")
raise
def check_and_prepare_model(self):
# Ensure cache directory exists
if not os.path.exists(self.cache_dir):
os.makedirs(self.cache_dir)
# Check for the expected model file
model_file_path = os.path.join(self.cache_dir, self.expected_model_filename)
if not os.path.isfile(model_file_path):
logger.info(
f"A model file was not found at {model_file_path}, Downloading one from {self.model_url}."
)
urllib.request.urlretrieve(self.model_url, model_file_path)
logger.info(f"A model file was downloaded to {model_file_path}.")
else:
logger.info(
f"A model file already exists at {model_file_path} not downloading one."
)
def detect_raw(self, tensor_input):
logger.debug("[detect_raw] Entering function")
logger.debug(
f"[detect_raw] The `tensor_input` = {tensor_input} tensor_input shape = {tensor_input.shape}"
)
if tensor_input is None:
raise ValueError(
"[detect_raw] The 'tensor_input' argument must be provided"
)
# Ensure tensor_input is a numpy array
if isinstance(tensor_input, list):
tensor_input = np.array(tensor_input)
logger.debug(
f"[detect_raw] Converted tensor_input to numpy array: shape {tensor_input.shape}"
)
input_data = tensor_input
logger.debug(
f"[detect_raw] Input data for inference shape: {tensor_input.shape}, dtype: {tensor_input.dtype}"
)
try:
with InferVStreams(
self.network_group,
self.input_vstream_params,
self.output_vstream_params,
) as infer_pipeline:
input_dict = {}
if isinstance(input_data, dict):
input_dict = input_data
logger.debug("[detect_raw] it a dictionary.")
elif isinstance(input_data, (list, tuple)):
for idx, layer_info in enumerate(self.input_vstream_info):
input_dict[layer_info.name] = input_data[idx]
logger.debug("[detect_raw] converted from list/tuple.")
else:
if len(input_data.shape) == 3:
input_data = np.expand_dims(input_data, axis=0)
logger.debug("[detect_raw] converted from an array.")
input_dict[self.input_vstream_info[0].name] = input_data
logger.debug(
f"[detect_raw] Input dictionary for inference keys: {input_dict.keys()}"
)
with self.network_group.activate(self.network_group_params):
raw_output = infer_pipeline.infer(input_dict)
logger.debug(f"[detect_raw] Raw inference output: {raw_output}")
if self.output_vstream_info[0].name not in raw_output:
logger.error(
f"[detect_raw] Missing output stream {self.output_vstream_info[0].name} in inference results"
)
return np.zeros((20, 6), np.float32)
raw_output = raw_output[self.output_vstream_info[0].name][0]
logger.debug(
f"[detect_raw] Raw output for stream {self.output_vstream_info[0].name}: {raw_output}"
)
# Process the raw output
detections = self.process_detections(raw_output)
if len(detections) == 0:
logger.debug(
"[detect_raw] No detections found after processing. Setting default values."
)
return np.zeros((20, 6), np.float32)
else:
formatted_detections = detections
if (
formatted_detections.shape[1] != 6
): # Ensure the formatted detections have 6 columns
logger.error(
f"[detect_raw] Unexpected shape for formatted detections: {formatted_detections.shape}. Expected (20, 6)."
)
return np.zeros((20, 6), np.float32)
return formatted_detections
except HailoRTException as e:
logger.error(f"[detect_raw] HailoRTException during inference: {e}")
return np.zeros((20, 6), np.float32)
except Exception as e:
logger.error(f"[detect_raw] Exception during inference: {e}")
return np.zeros((20, 6), np.float32)
finally:
logger.debug("[detect_raw] Exiting function")
def process_detections(self, raw_detections, threshold=0.5):
boxes, scores, classes = [], [], []
num_detections = 0
logger.debug(f"[process_detections] Raw detections: {raw_detections}")
for i, detection_set in enumerate(raw_detections):
if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
logger.debug(
f"[process_detections] Detection set {i} is empty or not an array, skipping."
)
continue
logger.debug(
f"[process_detections] Detection set {i} shape: {detection_set.shape}"
)
for detection in detection_set:
if detection.shape[0] == 0:
logger.debug(
f"[process_detections] Detection in set {i} is empty, skipping."
)
continue
ymin, xmin, ymax, xmax = detection[:4]
score = np.clip(detection[4], 0, 1) # Use np.clip for clarity
if score < threshold:
logger.debug(
f"[process_detections] Detection in set {i} has a score {score} below threshold {threshold}. Skipping."
)
continue
logger.debug(
f"[process_detections] Adding detection with coordinates: ({xmin}, {ymin}), ({xmax}, {ymax}) and score: {score}"
)
boxes.append([ymin, xmin, ymax, xmax])
scores.append(score)
classes.append(i)
num_detections += 1
logger.debug(
f"[process_detections] Boxes: {boxes}, Scores: {scores}, Classes: {classes}, Num detections: {num_detections}"
)
if num_detections == 0:
logger.debug("[process_detections] No valid detections found.")
return np.zeros((20, 6), np.float32)
combined = np.hstack(
(
np.array(classes)[:, np.newaxis],
np.array(scores)[:, np.newaxis],
np.array(boxes),
)
)
if combined.shape[0] < 20:
padding = np.zeros(
(20 - combined.shape[0], combined.shape[1]), dtype=combined.dtype
)
combined = np.vstack((combined, padding))
logger.debug(
f"[process_detections] Combined detections (padded to 20 if necessary): {np.array_str(combined, precision=4, suppress_small=True)}"
)
return combined[:20, :6]