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 from frigate.detectors.util import preprocess # Assuming this function is available # 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_vstreams_params = InputVStreamParams.make( self.network_group, format_type=self.hef.get_input_vstream_infos()[0].format.type, ) self.output_vstreams_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}" ) # Preprocess the tensor input using Frigate's preprocess function processed_tensor = preprocess( tensor_input, (1, self.h8l_model_height, self.h8l_model_width, 3), np.uint8 ) logger.debug( f"[detect_raw] Tensor data and shape after preprocessing: {processed_tensor} {processed_tensor.shape}" ) input_data = processed_tensor logger.debug( f"[detect_raw] Input data for inference shape: {processed_tensor.shape}, dtype: {processed_tensor.dtype}" ) try: with InferVStreams( self.network_group, self.input_vstreams_params, self.output_vstreams_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]