mirror of
https://github.com/blakeblackshear/frigate.git
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Working async infer
This commit is contained in:
OmriAx
parent
ee45f50e09
commit
323e4cec93
@ -37,6 +37,7 @@ class ModelTypeEnum(str, Enum):
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yolox = "yolox"
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yolox = "yolox"
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yolov9 = "yolov9"
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yolov9 = "yolov9"
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yolonas = "yolonas"
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yolonas = "yolonas"
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hailoyolo = "hailo-yolo"
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class ModelConfig(BaseModel):
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class ModelConfig(BaseModel):
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778
frigate/detectors/plugins/hailo8l.py
Normal file → Executable file
778
frigate/detectors/plugins/hailo8l.py
Normal file → Executable file
@ -2,8 +2,11 @@ import logging
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import os
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import os
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import subprocess
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import subprocess
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import urllib.request
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import urllib.request
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import numpy as np
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import numpy as np
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import queue
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import threading
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from functools import partial
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from typing import Dict, Optional, List, Tuple
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try:
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try:
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from hailo_platform import (
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from hailo_platform import (
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@ -12,550 +15,377 @@ try:
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FormatType,
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FormatType,
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HailoRTException,
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HailoRTException,
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HailoStreamInterface,
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HailoStreamInterface,
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InferVStreams,
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InputVStreamParams,
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InputVStreamParams,
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OutputVStreamParams,
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OutputVStreamParams,
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VDevice,
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VDevice,
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HailoSchedulingAlgorithm,
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)
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)
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except ModuleNotFoundError:
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except ModuleNotFoundError:
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pass
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pass
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, Field
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from typing_extensions import Literal
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from typing_extensions import Literal
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from typing import Dict, Optional, List
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum, InputTensorEnum, PixelFormatEnum, InputDTypeEnum
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from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum, InputTensorEnum, PixelFormatEnum, InputDTypeEnum
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from PIL import Image, ImageDraw, ImageFont
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# Setup logging
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# ----------------- Inline Utility Functions ----------------- #
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logger = logging.getLogger(__name__)
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def preprocess_image(image: Image.Image, model_w: int, model_h: int) -> Image.Image:
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logger.setLevel(logging.DEBUG)
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"""
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file_handler = logging.FileHandler('hailo_detector_debug.log')
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Resize image with unchanged aspect ratio using padding.
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file_handler.setLevel(logging.DEBUG)
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"""
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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img_w, img_h = image.size
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file_handler.setFormatter(formatter)
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scale = min(model_w / img_w, model_h / img_h)
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logger.addHandler(file_handler)
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new_img_w, new_img_h = int(img_w * scale), int(img_h * scale)
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image = image.resize((new_img_w, new_img_h), Image.Resampling.BICUBIC)
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padded_image = Image.new('RGB', (model_w, model_h), (114, 114, 114))
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padded_image.paste(image, ((model_w - new_img_w) // 2, (model_h - new_img_h) // 2))
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return padded_image
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# Define the detector key for Hailo
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def extract_detections(input_data: list, threshold: float = 0.5) -> dict:
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"""
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(Legacy extraction function; not used by detect_raw below.)
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Extract detections from raw inference output.
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"""
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boxes, scores, classes = [], [], []
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num_detections = 0
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for i, detection in enumerate(input_data):
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if len(detection) == 0:
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continue
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for det in detection:
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bbox, score = det[:4], det[4]
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if score >= threshold:
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boxes.append(bbox)
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scores.append(score)
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classes.append(i)
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num_detections += 1
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return {
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'detection_boxes': boxes,
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'detection_classes': classes,
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'detection_scores': scores,
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'num_detections': num_detections
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}
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# ----------------- End of Utility Functions ----------------- #
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# Global constants and default URLs
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DETECTOR_KEY = "hailo8l"
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DETECTOR_KEY = "hailo8l"
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ARCH = None
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ARCH = None
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H8_DEFAULT_MODEL = "yolov8s.hef"
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H8L_DEFAULT_MODEL = "yolov6n.hef"
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H8_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov8s.hef"
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H8L_DEFAULT_URL = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/yolov6n.hef"
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def detect_hailo_arch():
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def detect_hailo_arch():
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try:
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try:
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# Run the hailortcli command to get device information
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result = subprocess.run(['hailortcli', 'fw-control', 'identify'], capture_output=True, text=True)
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result = subprocess.run(['hailortcli', 'fw-control', 'identify'], capture_output=True, text=True)
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# Check if the command was successful
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if result.returncode != 0:
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if result.returncode != 0:
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print(f"Error running hailortcli: {result.stderr}")
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print(f"Error running hailortcli: {result.stderr}")
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return None
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return None
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# Search for the "Device Architecture" line in the output
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for line in result.stdout.split('\n'):
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for line in result.stdout.split('\n'):
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if "Device Architecture" in line:
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if "Device Architecture" in line:
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if "HAILO8L" in line:
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if "HAILO8L" in line:
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return "hailo8l"
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return "hailo8l"
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elif "HAILO8" in line:
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elif "HAILO8" in line:
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return "hailo8"
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return "hailo8"
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print("Could not determine Hailo architecture from device information.")
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print("Could not determine Hailo architecture from device information.")
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return None
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return None
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except Exception as e:
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except Exception as e:
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print(f"An error occurred while detecting Hailo architecture: {e}")
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print(f"An error occurred while detecting Hailo architecture: {e}")
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return None
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return None
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# ----------------- Inline Asynchronous Inference Class ----------------- #
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class HailoAsyncInference:
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def __init__(
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self,
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hef_path: str,
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input_queue: queue.Queue,
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output_queue: queue.Queue,
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batch_size: int = 1,
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input_type: Optional[str] = None,
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output_type: Optional[Dict[str, str]] = None,
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send_original_frame: bool = False,
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) -> None:
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self.input_queue = input_queue
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self.output_queue = output_queue
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# Configuration class for Hailo detector
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# Create VDevice parameters with round-robin scheduling
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class HailoDetectorConfig(BaseDetectorConfig):
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params = VDevice.create_params()
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type: Literal[DETECTOR_KEY] # Type of the detector
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params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
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device: str = Field(default="PCIe", title="Device Type") # Device type (e.g., PCIe)
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url: Optional[str] = Field(default=None, title="Custom Model URL")
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# Hailo detector class implementation
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# Load HEF and create the infer model
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self.hef = HEF(hef_path)
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self.target = VDevice(params)
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self.infer_model = self.target.create_infer_model(hef_path)
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self.infer_model.set_batch_size(batch_size)
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if input_type is not None:
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self._set_input_type(input_type)
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if output_type is not None:
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self._set_output_type(output_type)
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self.output_type = output_type
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self.send_original_frame = send_original_frame
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def _set_input_type(self, input_type: Optional[str] = None) -> None:
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self.infer_model.input().set_format_type(getattr(FormatType, input_type))
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def _set_output_type(self, output_type_dict: Optional[Dict[str, str]] = None) -> None:
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for output_name, output_type in output_type_dict.items():
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self.infer_model.output(output_name).set_format_type(getattr(FormatType, output_type))
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def callback(self, completion_info, bindings_list: List, input_batch: List):
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if completion_info.exception:
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logging.error(f"Inference error: {completion_info.exception}")
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else:
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for i, bindings in enumerate(bindings_list):
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if len(bindings._output_names) == 1:
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result = bindings.output().get_buffer()
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else:
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result = {
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name: np.expand_dims(bindings.output(name).get_buffer(), axis=0)
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for name in bindings._output_names
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}
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self.output_queue.put((input_batch[i], result))
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def _create_bindings(self, configured_infer_model) -> object:
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if self.output_type is None:
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output_buffers = {
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output_info.name: np.empty(
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self.infer_model.output(output_info.name).shape,
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dtype=getattr(np, str(output_info.format.type).split(".")[1].lower())
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)
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for output_info in self.hef.get_output_vstream_infos()
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}
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else:
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output_buffers = {
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name: np.empty(
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self.infer_model.output(name).shape,
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dtype=getattr(np, self.output_type[name].lower())
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)
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for name in self.output_type
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}
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return configured_infer_model.create_bindings(output_buffers=output_buffers)
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def get_input_shape(self) -> Tuple[int, ...]:
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return self.hef.get_input_vstream_infos()[0].shape
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def run(self) -> None:
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# Configure the infer model once and reuse vstream settings via run_async
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with self.infer_model.configure() as configured_infer_model:
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while True:
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batch_data = self.input_queue.get()
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if batch_data is None:
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break # Sentinel to exit loop
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if self.send_original_frame:
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original_batch, preprocessed_batch = batch_data
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else:
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preprocessed_batch = batch_data
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bindings_list = []
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for frame in preprocessed_batch:
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bindings = self._create_bindings(configured_infer_model)
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bindings.input().set_buffer(np.array(frame))
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bindings_list.append(bindings)
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configured_infer_model.wait_for_async_ready(timeout_ms=10000)
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job = configured_infer_model.run_async(
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bindings_list,
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partial(
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self.callback,
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input_batch=original_batch if self.send_original_frame else preprocessed_batch,
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bindings_list=bindings_list,
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)
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)
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job.wait(10000) # Wait for the last job to complete
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# ----------------- End of Async Class ----------------- #
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# ----------------- HailoDetector Class ----------------- #
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class HailoDetector(DetectionApi):
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class HailoDetector(DetectionApi):
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type_key = DETECTOR_KEY # Set the type key to the Hailo detector key
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: HailoDetectorConfig):
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def __init__(self, detector_config: 'HailoDetectorConfig'):
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print(f"[INIT] Starting HailoDetector initialization with config: {detector_config}")
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logger.info(f"[INIT] Starting HailoDetector initialization with config: {detector_config}")
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# Set global ARCH variable
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global ARCH
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global ARCH
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ARCH = detect_hailo_arch()
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ARCH = detect_hailo_arch()
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logger.info(f"[INIT] Detected Hailo architecture: {ARCH}")
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self.cache_dir = "/config/model_cache/hailo"
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self.device_type = detector_config.device
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# Model attributes should be provided in detector_config.model
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self.model_path = detector_config.model.path if hasattr(detector_config.model, "path") else None
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self.model_height = detector_config.model.height if hasattr(detector_config.model, "height") else None
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self.model_width = detector_config.model.width if hasattr(detector_config.model, "width") else None
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self.model_type = detector_config.model.model_type if hasattr(detector_config.model, "model_type") else None
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self.tensor_format = detector_config.model.input_tensor if hasattr(detector_config.model, "input_tensor") else None
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self.pixel_format = detector_config.model.input_pixel_format if hasattr(detector_config.model, "input_pixel_format") else None
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self.input_dtype = detector_config.model.input_dtype if hasattr(detector_config.model, "input_dtype") else None
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self.url = detector_config.url
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self.output_type = "FLOAT32"
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self.working_model_path = self.check_and_prepare()
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supported_models = [
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# Set up asynchronous inference
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ModelTypeEnum.ssd,
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self.batch_size = 1
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ModelTypeEnum.yolov9,
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self.input_queue = queue.Queue()
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ModelTypeEnum.hailoyolo,
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self.output_queue = queue.Queue()
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]
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# Initialize device type and model path from the configuration
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self.h8l_device_type = detector_config.device
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self.h8l_model_path = detector_config.model.path
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self.h8l_model_type = detector_config.model.model_type
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# Set configuration based on model type
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self.set_correct_config(self.h8l_model_type)
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# Override with custom URL if provided
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if hasattr(detector_config, "url") and detector_config.url:
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self.model_url = detector_config.url
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self.expected_model_filename = self.model_url.split('/')[-1]
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self.check_and_prepare_model()
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try:
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try:
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# Validate device type
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logging.info(f"[INIT] Loading HEF model from {self.working_model_path}")
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if self.h8l_device_type not in ["PCIe", "M.2"]:
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self.inference_engine = HailoAsyncInference(
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raise ValueError(f"Unsupported device type: {self.h8l_device_type}")
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self.working_model_path,
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self.input_queue,
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# Initialize the Hailo device
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self.output_queue,
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logger.info("[INIT] Creating VDevice instance")
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self.batch_size
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self.target = VDevice()
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# Load the HEF (Hailo's binary format for neural networks)
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logger.info(f"[INIT] Loading HEF from {self.h8l_model_path}")
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self.hef = HEF(self.h8l_model_path)
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# Create configuration parameters from the HEF
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logger.info("[INIT] Creating configuration parameters")
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self.configure_params = ConfigureParams.create_from_hef(
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hef=self.hef, interface=HailoStreamInterface.PCIe
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)
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)
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self.input_shape = self.inference_engine.get_input_shape()
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# Configure the device with the HEF
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logging.info(f"[INIT] Model input shape: {self.input_shape}")
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logger.info("[INIT] Configuring device with HEF")
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self.network_groups = self.target.configure(self.hef, self.configure_params)
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self.network_group = self.network_groups[0]
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self.network_group_params = self.network_group.create_params()
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# Create input and output virtual stream parameters
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logger.info("[INIT] Creating input/output stream parameters")
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self.input_vstream_params = InputVStreamParams.make(
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self.network_group,
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format_type=self.hef.get_input_vstream_infos()[0].format.type,
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)
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self.output_vstream_params = OutputVStreamParams.make(
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self.network_group, format_type=getattr(FormatType, self.output_type)
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)
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# Get input and output stream information from the HEF
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self.input_vstream_info = self.hef.get_input_vstream_infos()
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self.output_vstream_info = self.hef.get_output_vstream_infos()
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for i, info in enumerate(self.input_vstream_info):
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logger.info(f"[INIT] Input Stream {i}: Name={info.name}, Format={info.format}, Shape={info.shape}")
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for i, info in enumerate(self.output_vstream_info):
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logger.info(f"[INIT] Output Stream {i}: Name={info.name}, Format={info.format}, Shape={info.shape}")
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logger.info("Hailo device initialized successfully")
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except HailoRTException as e:
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|
||||||
logger.error(f"HailoRTException during initialization: {e}")
|
|
||||||
raise
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error(f"Failed to initialize Hailo device: {e}")
|
logging.error(f"[INIT] Failed to initialize HailoAsyncInference: {e}")
|
||||||
raise
|
raise
|
||||||
|
|
||||||
def set_correct_config(self, modelname):
|
@staticmethod
|
||||||
if modelname == ModelTypeEnum.ssd:
|
def extract_model_name(path: str = None, url: str = None) -> str:
|
||||||
self.h8l_model_height = 300
|
model_name = None
|
||||||
self.h8l_model_width = 300
|
if path and path.endswith(".hef"):
|
||||||
self.h8l_tensor_format = InputTensorEnum.nhwc
|
model_name = os.path.basename(path)
|
||||||
self.h8l_pixel_format = PixelFormatEnum.rgb
|
elif url and url.endswith(".hef"):
|
||||||
self.h8l_input_dtype = InputDTypeEnum.float
|
model_name = os.path.basename(url)
|
||||||
self.cache_dir = "/config/model_cache/h8l_cache"
|
|
||||||
self.expected_model_filename = "ssd_mobilenet_v1.hef"
|
|
||||||
self.output_type = "FLOAT32"
|
|
||||||
|
|
||||||
if ARCH == "hailo8":
|
|
||||||
self.model_url = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/ssd_mobilenet_v1.hef"
|
|
||||||
else:
|
|
||||||
self.model_url = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef"
|
|
||||||
else:
|
else:
|
||||||
self.h8l_model_height = 640
|
print("Model name not found in path or URL. Checking default settings...")
|
||||||
self.h8l_model_width = 640
|
|
||||||
self.h8l_tensor_format = InputTensorEnum.nhwc
|
|
||||||
self.h8l_pixel_format = PixelFormatEnum.rgb # Default to RGB
|
|
||||||
self.h8l_input_dtype = InputDTypeEnum.int
|
|
||||||
self.cache_dir = "/config/model_cache/h8l_cache"
|
|
||||||
self.output_type = "FLOAT32"
|
|
||||||
|
|
||||||
if ARCH == "hailo8":
|
if ARCH == "hailo8":
|
||||||
self.model_url = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov8m.hef"
|
model_name = H8_DEFAULT_MODEL
|
||||||
self.expected_model_filename = "yolov8m.hef"
|
|
||||||
else:
|
else:
|
||||||
self.model_url = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/yolov8s.hef"
|
model_name = H8L_DEFAULT_MODEL
|
||||||
self.expected_model_filename = "yolov8s.hef"
|
print(f"Using default model: {model_name}")
|
||||||
|
return model_name
|
||||||
|
|
||||||
def check_and_prepare_model(self):
|
@staticmethod
|
||||||
logger.info(f"[CHECK_MODEL] Checking for model at {self.cache_dir}/{self.expected_model_filename}")
|
def download_model(url: str, destination: str):
|
||||||
|
if not url.endswith(".hef"):
|
||||||
|
raise ValueError("Invalid model URL. Only .hef files are supported.")
|
||||||
|
try:
|
||||||
|
urllib.request.urlretrieve(url, destination)
|
||||||
|
print(f"Downloaded model to {destination}")
|
||||||
|
except Exception as e:
|
||||||
|
raise RuntimeError(f"Failed to download model from {url}: {str(e)}")
|
||||||
|
|
||||||
# Ensure cache directory exists
|
def check_and_prepare(self) -> str:
|
||||||
if not os.path.exists(self.cache_dir):
|
if not os.path.exists(self.cache_dir):
|
||||||
logger.info(f"[CHECK_MODEL] Creating cache directory: {self.cache_dir}")
|
|
||||||
os.makedirs(self.cache_dir)
|
os.makedirs(self.cache_dir)
|
||||||
|
model_name = self.extract_model_name(self.model_path, self.url)
|
||||||
# Check for the expected model file
|
model_path = os.path.join(self.cache_dir, model_name)
|
||||||
model_file_path = os.path.join(self.cache_dir, self.expected_model_filename)
|
if not self.model_path and not self.url:
|
||||||
if not os.path.isfile(model_file_path):
|
if os.path.exists(model_path):
|
||||||
logger.info(f"[CHECK_MODEL] Model not found at {model_file_path}, downloading from {self.model_url}")
|
print(f"Model found in cache: {model_path}")
|
||||||
urllib.request.urlretrieve(self.model_url, model_file_path)
|
return model_path
|
||||||
logger.info(f"[CHECK_MODEL] Model downloaded to {model_file_path}")
|
else:
|
||||||
else:
|
print(f"Downloading default model: {model_name}")
|
||||||
logger.info(f"[CHECK_MODEL] Model already exists at {model_file_path}")
|
if ARCH == "hailo8":
|
||||||
|
self.download_model(H8_DEFAULT_URL, model_path)
|
||||||
self.h8l_model_path = model_file_path
|
else:
|
||||||
|
self.download_model(H8L_DEFAULT_URL, model_path)
|
||||||
|
elif self.model_path and self.url:
|
||||||
|
if os.path.exists(self.model_path):
|
||||||
|
print(f"Model found at path: {self.model_path}")
|
||||||
|
return self.model_path
|
||||||
|
else:
|
||||||
|
print(f"Model not found at path. Downloading from URL: {self.url}")
|
||||||
|
self.download_model(self.url, model_path)
|
||||||
|
elif self.url:
|
||||||
|
print(f"Downloading model from URL: {self.url}")
|
||||||
|
self.download_model(self.url, model_path)
|
||||||
|
elif self.model_path:
|
||||||
|
if os.path.exists(self.model_path):
|
||||||
|
print(f"Using existing model at: {self.model_path}")
|
||||||
|
return self.model_path
|
||||||
|
else:
|
||||||
|
raise FileNotFoundError(f"Model file not found at: {self.model_path}")
|
||||||
|
return model_path
|
||||||
|
|
||||||
def detect_raw(self, tensor_input):
|
def detect_raw(self, tensor_input):
|
||||||
logger.info("[DETECT_RAW] Starting detection")
|
logging.info("[DETECT_RAW] Starting detection")
|
||||||
|
# Ensure tensor_input has a batch dimension
|
||||||
|
if isinstance(tensor_input, np.ndarray) and len(tensor_input.shape) == 3:
|
||||||
|
tensor_input = np.expand_dims(tensor_input, axis=0)
|
||||||
|
logging.info(f"[DETECT_RAW] Expanded input shape to {tensor_input.shape}")
|
||||||
|
|
||||||
if tensor_input is None:
|
# Enqueue input and a sentinel value
|
||||||
error_msg = "[DETECT_RAW] The 'tensor_input' argument must be provided"
|
self.input_queue.put(tensor_input)
|
||||||
logger.error(error_msg)
|
self.input_queue.put(None) # Sentinel value
|
||||||
raise ValueError(error_msg)
|
|
||||||
|
|
||||||
# Log input tensor information
|
# Run the inference engine
|
||||||
logger.info(f"[DETECT_RAW] Input tensor type: {type(tensor_input)}")
|
self.inference_engine.run()
|
||||||
|
result = self.output_queue.get()
|
||||||
|
if result is None:
|
||||||
|
logging.error("[DETECT_RAW] No inference result received")
|
||||||
|
return np.zeros((20, 6), dtype=np.float32)
|
||||||
|
|
||||||
if isinstance(tensor_input, np.ndarray):
|
original_input, infer_results = result
|
||||||
logger.info(f"[DETECT_RAW] Input tensor shape: {tensor_input.shape}")
|
logging.info("[DETECT_RAW] Inference completed.")
|
||||||
logger.info(f"[DETECT_RAW] Input tensor dtype: {tensor_input.dtype}")
|
|
||||||
logger.info(f"[DETECT_RAW] Input tensor min value: {np.min(tensor_input)}")
|
|
||||||
logger.info(f"[DETECT_RAW] Input tensor max value: {np.max(tensor_input)}")
|
|
||||||
logger.info(f"[DETECT_RAW] Input tensor mean value: {np.mean(tensor_input)}")
|
|
||||||
|
|
||||||
# Print sample of the tensor (first few elements)
|
# If infer_results is a single-element list, unwrap it.
|
||||||
flat_sample = tensor_input.flatten()[:10]
|
if isinstance(infer_results, list) and len(infer_results) == 1:
|
||||||
logger.info(f"[DETECT_RAW] Input tensor sample: {flat_sample}")
|
infer_results = infer_results[0]
|
||||||
elif isinstance(tensor_input, list):
|
|
||||||
logger.info(f"[DETECT_RAW] Input is a list with length: {len(tensor_input)}")
|
|
||||||
tensor_input = np.array(tensor_input)
|
|
||||||
logger.info(f"[DETECT_RAW] Converted to array with shape: {tensor_input.shape}, dtype: {tensor_input.dtype}")
|
|
||||||
elif isinstance(tensor_input, dict):
|
|
||||||
logger.info(f"[DETECT_RAW] Input is a dictionary with keys: {tensor_input.keys()}")
|
|
||||||
|
|
||||||
input_data = tensor_input
|
# Set your threshold (adjust as needed)
|
||||||
logger.debug("[DETECT_RAW] Input data prepared for inference")
|
threshold = 0.4
|
||||||
|
|
||||||
try:
|
|
||||||
logger.info("[DETECT_RAW] Creating inference pipeline")
|
|
||||||
with InferVStreams(
|
|
||||||
self.network_group,
|
|
||||||
self.input_vstream_params,
|
|
||||||
self.output_vstream_params,
|
|
||||||
) as infer_pipeline:
|
|
||||||
input_dict = {}
|
|
||||||
if isinstance(input_data, dict):
|
|
||||||
logger.info("[DETECT_RAW] Input is already a dictionary, using as-is")
|
|
||||||
input_dict = input_data
|
|
||||||
elif isinstance(input_data, (list, tuple)):
|
|
||||||
logger.info("[DETECT_RAW] Converting list/tuple to dictionary for inference")
|
|
||||||
for idx, layer_info in enumerate(self.input_vstream_info):
|
|
||||||
input_dict[layer_info.name] = input_data[idx]
|
|
||||||
logger.info(f"[DETECT_RAW] Assigned data to input layer '{layer_info.name}'")
|
|
||||||
else:
|
|
||||||
if len(input_data.shape) == 3:
|
|
||||||
logger.info(f"[DETECT_RAW] Adding batch dimension to input with shape {input_data.shape}")
|
|
||||||
input_data = np.expand_dims(input_data, axis=0)
|
|
||||||
logger.info(f"[DETECT_RAW] New input shape after adding batch dimension: {input_data.shape}")
|
|
||||||
input_dict[self.input_vstream_info[0].name] = input_data
|
|
||||||
logger.info(f"[DETECT_RAW] Assigned data to input layer '{self.input_vstream_info[0].name}'")
|
|
||||||
|
|
||||||
logger.info(f"[DETECT_RAW] Final input dictionary keys: {list(input_dict.keys())}")
|
|
||||||
|
|
||||||
# Log details about each input layer
|
|
||||||
for key, value in input_dict.items():
|
|
||||||
if isinstance(value, np.ndarray):
|
|
||||||
logger.info(f"[DETECT_RAW] Layer '{key}' has shape: {value.shape}, dtype: {value.dtype}")
|
|
||||||
|
|
||||||
logger.info("[DETECT_RAW] Activating network group")
|
|
||||||
with self.network_group.activate(self.network_group_params):
|
|
||||||
logger.info("[DETECT_RAW] Running inference")
|
|
||||||
raw_output = infer_pipeline.infer(input_dict)
|
|
||||||
|
|
||||||
logger.info(f"[DETECT_RAW] Inference complete, output keys: {list(raw_output.keys())}")
|
|
||||||
|
|
||||||
# Log details about output structure for debugging
|
|
||||||
for key, value in raw_output.items():
|
|
||||||
logger.info(f"[DETECT_RAW] Output layer '{key}' details:")
|
|
||||||
debug_output_structure(value, prefix=" ")
|
|
||||||
|
|
||||||
# Process outputs based on model type
|
|
||||||
if self.h8l_model_type in [ModelTypeEnum.hailoyolo, ModelTypeEnum.yolov9, ModelTypeEnum.yolox, ModelTypeEnum.yolonas]:
|
|
||||||
logger.info(f"[DETECT_RAW] Processing YOLO-type output for model type: {self.h8l_model_type}")
|
|
||||||
detections = self.process_yolo_output(raw_output)
|
|
||||||
else:
|
|
||||||
# Default to SSD processing
|
|
||||||
logger.info(f"[DETECT_RAW] Processing SSD output for model type: {self.h8l_model_type}")
|
|
||||||
expected_output_name = self.output_vstream_info[0].name
|
|
||||||
if expected_output_name not in raw_output:
|
|
||||||
error_msg = f"[DETECT_RAW] Missing output stream {expected_output_name} in inference results"
|
|
||||||
logger.error(error_msg)
|
|
||||||
return np.zeros((20, 6), np.float32)
|
|
||||||
detections = self.process_ssd_output(raw_output[expected_output_name])
|
|
||||||
|
|
||||||
logger.info(f"[DETECT_RAW] Processed detections shape: {detections.shape}")
|
|
||||||
return 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_yolo_output(self, raw_output):
|
|
||||||
"""
|
|
||||||
Process YOLO outputs to match the expected Frigate detection format.
|
|
||||||
Returns detections in the format [class_id, score, ymin, xmin, ymax, xmax]
|
|
||||||
"""
|
|
||||||
logger.info("[PROCESS_YOLO] Processing YOLO output")
|
|
||||||
|
|
||||||
# Initialize empty array for our results - match TFLite format
|
|
||||||
detections = np.zeros((20, 6), np.float32)
|
|
||||||
|
|
||||||
try:
|
|
||||||
# Identify output layers for boxes, classes, and scores
|
|
||||||
boxes_layer = None
|
|
||||||
classes_layer = None
|
|
||||||
scores_layer = None
|
|
||||||
count_layer = None
|
|
||||||
|
|
||||||
# Try to identify layers by name pattern
|
|
||||||
for key in raw_output.keys():
|
|
||||||
key_lower = key.lower()
|
|
||||||
if any(box_term in key_lower for box_term in ['box', 'bbox', 'location']):
|
|
||||||
boxes_layer = key
|
|
||||||
elif any(class_term in key_lower for class_term in ['class', 'category', 'label']):
|
|
||||||
classes_layer = key
|
|
||||||
elif any(score_term in key_lower for score_term in ['score', 'confidence', 'prob']):
|
|
||||||
scores_layer = key
|
|
||||||
elif any(count_term in key_lower for count_term in ['count', 'num', 'detection_count']):
|
|
||||||
count_layer = key
|
|
||||||
|
|
||||||
logger.info(f"[PROCESS_YOLO] Identified layers - Boxes: {boxes_layer}, Classes: {classes_layer}, "
|
|
||||||
f"Scores: {scores_layer}, Count: {count_layer}")
|
|
||||||
|
|
||||||
# If we found all necessary layers
|
|
||||||
if boxes_layer and classes_layer and scores_layer:
|
|
||||||
# Extract data from the identified layers
|
|
||||||
boxes = raw_output[boxes_layer]
|
|
||||||
class_ids = raw_output[classes_layer]
|
|
||||||
scores = raw_output[scores_layer]
|
|
||||||
|
|
||||||
# If these are lists, extract the first element (batch)
|
|
||||||
if isinstance(boxes, list) and len(boxes) > 0:
|
|
||||||
boxes = boxes[0]
|
|
||||||
if isinstance(class_ids, list) and len(class_ids) > 0:
|
|
||||||
class_ids = class_ids[0]
|
|
||||||
if isinstance(scores, list) and len(scores) > 0:
|
|
||||||
scores = scores[0]
|
|
||||||
|
|
||||||
# Get detection count (if available)
|
|
||||||
count = 0
|
|
||||||
if count_layer:
|
|
||||||
count_val = raw_output[count_layer]
|
|
||||||
if isinstance(count_val, list) and len(count_val) > 0:
|
|
||||||
count_val = count_val[0]
|
|
||||||
count = int(count_val[0] if isinstance(count_val, np.ndarray) else count_val)
|
|
||||||
else:
|
|
||||||
# Use the length of scores as count
|
|
||||||
count = len(scores) if hasattr(scores, '__len__') else 0
|
|
||||||
|
|
||||||
# Process detections like in the example
|
|
||||||
for i in range(count):
|
|
||||||
if i >= 20: # Limit to 20 detections
|
|
||||||
break
|
|
||||||
|
|
||||||
if scores[i] < 0.4: # Use 0.4 threshold as in the example
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Add detection in the format [class_id, score, ymin, xmin, ymax, xmax]
|
|
||||||
detections[i] = [
|
|
||||||
float(class_ids[i]),
|
|
||||||
float(scores[i]),
|
|
||||||
float(boxes[i][0]), # ymin
|
|
||||||
float(boxes[i][1]), # xmin
|
|
||||||
float(boxes[i][2]), # ymax
|
|
||||||
float(boxes[i][3]), # xmax
|
|
||||||
]
|
|
||||||
else:
|
|
||||||
# Fallback: Try to process output as a combined detection array
|
|
||||||
logger.info("[PROCESS_YOLO] Couldn't identify separate output layers, trying unified format")
|
|
||||||
|
|
||||||
# Look for a detection array in the output
|
|
||||||
detection_layer = None
|
|
||||||
for key, value in raw_output.items():
|
|
||||||
if isinstance(value, list) and len(value) > 0:
|
|
||||||
if isinstance(value[0], np.ndarray) and value[0].ndim >= 2:
|
|
||||||
detection_layer = key
|
|
||||||
break
|
|
||||||
|
|
||||||
if detection_layer:
|
|
||||||
# Get the detection array
|
|
||||||
detection_array = raw_output[detection_layer]
|
|
||||||
if isinstance(detection_array, list):
|
|
||||||
detection_array = detection_array[0] # First batch
|
|
||||||
|
|
||||||
# Process each detection
|
|
||||||
detection_count = 0
|
|
||||||
for i, detection in enumerate(detection_array):
|
|
||||||
if detection_count >= 20:
|
|
||||||
break
|
|
||||||
|
|
||||||
# Format depends on YOLO variant but typically includes:
|
|
||||||
# class_id, score, box coordinates (could be [x,y,w,h] or [xmin,ymin,xmax,ymax])
|
|
||||||
|
|
||||||
# Extract elements based on shape
|
|
||||||
if len(detection) >= 6: # Likely [class_id, score, xmin, ymin, xmax, ymax]
|
|
||||||
class_id = detection[0]
|
|
||||||
score = detection[1]
|
|
||||||
|
|
||||||
# Check if this is actually [x, y, w, h, conf, class_id]
|
|
||||||
if score > 1.0: # Score shouldn't be > 1, might be a coordinate
|
|
||||||
# Reorganize assuming [x, y, w, h, conf, class_id] format
|
|
||||||
x, y, w, h, confidence, *class_probs = detection
|
|
||||||
|
|
||||||
# Get class with highest probability
|
|
||||||
if len(class_probs) > 1:
|
|
||||||
class_id = np.argmax(class_probs)
|
|
||||||
score = confidence * class_probs[class_id]
|
|
||||||
else:
|
|
||||||
class_id = class_probs[0]
|
|
||||||
score = confidence
|
|
||||||
|
|
||||||
# Convert [x,y,w,h] to [ymin,xmin,ymax,xmax]
|
|
||||||
xmin = x - w/2
|
|
||||||
ymin = y - h/2
|
|
||||||
xmax = x + w/2
|
|
||||||
ymax = y + h/2
|
|
||||||
else:
|
|
||||||
# Use as is, but verify we have box coordinates
|
|
||||||
xmin, ymin, xmax, ymax = detection[2:6]
|
|
||||||
elif len(detection) >= 4: # Might be [class_id, score, xmin, ymin]
|
|
||||||
class_id = detection[0]
|
|
||||||
score = detection[1]
|
|
||||||
# For incomplete boxes, assume zeros
|
|
||||||
xmin, ymin = detection[2:4]
|
|
||||||
xmax = xmin + 0.1 # Small default size
|
|
||||||
ymax = ymin + 0.1
|
|
||||||
else:
|
|
||||||
# Skip invalid detections
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Skip low confidence detections
|
|
||||||
if score < 0.4:
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Add to detection array
|
|
||||||
detections[detection_count] = [
|
|
||||||
float(class_id),
|
|
||||||
float(score),
|
|
||||||
float(ymin),
|
|
||||||
float(xmin),
|
|
||||||
float(ymax),
|
|
||||||
float(xmax)
|
|
||||||
]
|
|
||||||
detection_count += 1
|
|
||||||
|
|
||||||
logger.info(f"[PROCESS_YOLO] Processed {np.count_nonzero(detections[:, 1] > 0)} valid detections")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"[PROCESS_YOLO] Error processing YOLO output: {e}")
|
|
||||||
# detections already initialized as zeros
|
|
||||||
|
|
||||||
return detections
|
|
||||||
|
|
||||||
def process_ssd_output(self, raw_output):
|
|
||||||
"""
|
|
||||||
Process SSD MobileNet v1 output with special handling for jagged arrays
|
|
||||||
"""
|
|
||||||
logger.info("[PROCESS_SSD] Processing SSD output")
|
|
||||||
|
|
||||||
# Initialize empty lists for our results
|
|
||||||
all_detections = []
|
all_detections = []
|
||||||
|
|
||||||
try:
|
# Loop over the output list (each element corresponds to one output stream)
|
||||||
if isinstance(raw_output, list) and len(raw_output) > 0:
|
for idx, detection_set in enumerate(infer_results):
|
||||||
# Handle first level of nesting
|
# Skip empty arrays
|
||||||
raw_detections = raw_output[0]
|
if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
|
||||||
logger.debug(f"[PROCESS_SSD] First level output type: {type(raw_detections)}")
|
continue
|
||||||
|
|
||||||
# Process all valid detections
|
logging.debug(f"[DETECT_RAW] Processing detection set {idx} with shape {detection_set.shape}")
|
||||||
for i, detection_group in enumerate(raw_detections):
|
# For each detection row in the set:
|
||||||
# Skip empty arrays or invalid data
|
for det in detection_set:
|
||||||
if not isinstance(detection_group, np.ndarray):
|
# Expecting at least 5 elements: [ymin, xmin, ymax, xmax, confidence]
|
||||||
continue
|
if det.shape[0] < 5:
|
||||||
|
continue
|
||||||
|
score = float(det[4])
|
||||||
|
if score < threshold:
|
||||||
|
continue
|
||||||
|
# If there is a 6th element, assume it's a class id; otherwise use dummy class 0.
|
||||||
|
if det.shape[0] >= 6:
|
||||||
|
cls = int(det[5])
|
||||||
|
else:
|
||||||
|
cls = 0
|
||||||
|
# Append in the order Frigate expects: [class_id, confidence, ymin, xmin, ymax, xmax]
|
||||||
|
all_detections.append([cls, score, det[0], det[1], det[2], det[3]])
|
||||||
|
|
||||||
# Skip empty arrays
|
# If no valid detections were found, return a zero array.
|
||||||
if detection_group.size == 0:
|
if len(all_detections) == 0:
|
||||||
continue
|
logging.warning("[DETECT_RAW] No valid detections found.")
|
||||||
|
return np.zeros((20, 6), dtype=np.float32)
|
||||||
|
|
||||||
# For the arrays with actual detections
|
detections_array = np.array(all_detections, dtype=np.float32)
|
||||||
if detection_group.shape[0] > 0:
|
|
||||||
# Extract the detection data - typical format is (ymin, xmin, ymax, xmax, score)
|
|
||||||
for j in range(detection_group.shape[0]):
|
|
||||||
detection = detection_group[j]
|
|
||||||
|
|
||||||
# Check if we have 5 values (expected format)
|
# Pad or truncate to exactly 20 rows
|
||||||
if len(detection) == 5:
|
if detections_array.shape[0] < 20:
|
||||||
ymin, xmin, ymax, xmax, score = detection
|
pad = np.zeros((20 - detections_array.shape[0], 6), dtype=np.float32)
|
||||||
class_id = i # Use index as class ID
|
detections_array = np.vstack((detections_array, pad))
|
||||||
|
elif detections_array.shape[0] > 20:
|
||||||
|
detections_array = detections_array[:20, :]
|
||||||
|
|
||||||
# Add detection if score is reasonable
|
logging.info(f"[DETECT_RAW] Processed detections: {detections_array}")
|
||||||
if 0 <= score <= 1.0 and score > 0.1: # Basic threshold
|
|
||||||
all_detections.append([float(class_id), float(score),
|
|
||||||
float(ymin), float(xmin),
|
|
||||||
float(ymax), float(xmax)])
|
|
||||||
|
|
||||||
# Convert to numpy array if we have valid detections
|
|
||||||
if all_detections:
|
|
||||||
detections_array = np.array(all_detections, dtype=np.float32)
|
|
||||||
|
|
||||||
# Sort by score (descending)
|
|
||||||
sorted_idx = np.argsort(detections_array[:, 1])[::-1]
|
|
||||||
detections_array = detections_array[sorted_idx]
|
|
||||||
|
|
||||||
# Take top 20 (or fewer if less available)
|
|
||||||
detections_array = detections_array[:20]
|
|
||||||
else:
|
|
||||||
detections_array = np.zeros((0, 6), dtype=np.float32)
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"[PROCESS_SSD] Error processing SSD output: {e}")
|
|
||||||
detections_array = np.zeros((0, 6), dtype=np.float32)
|
|
||||||
|
|
||||||
# Pad to 20 detections if needed
|
|
||||||
if len(detections_array) < 20:
|
|
||||||
padding = np.zeros((20 - len(detections_array), 6), dtype=np.float32)
|
|
||||||
detections_array = np.vstack((detections_array, padding))
|
|
||||||
|
|
||||||
logger.info(f"[PROCESS_SSD] Final output shape: {detections_array.shape}")
|
|
||||||
return detections_array
|
return detections_array
|
||||||
|
|
||||||
def process_detections(self, raw_detections, threshold=0.5):
|
# Preprocess method using inline utility
|
||||||
"""
|
def preprocess(self, image):
|
||||||
Legacy detection processing method, kept for compatibility.
|
return preprocess_image(image, self.input_shape[1], self.input_shape[0])
|
||||||
Now redirects to the more robust process_ssd_output method.
|
|
||||||
"""
|
|
||||||
logger.info("[PROCESS] Starting to process detections")
|
|
||||||
logger.info(f"[PROCESS] Using threshold: {threshold}")
|
|
||||||
|
|
||||||
# Wrap the raw_detections in a list to match expected format for process_ssd_output
|
|
||||||
if not isinstance(raw_detections, list):
|
|
||||||
raw_detections = [raw_detections]
|
|
||||||
|
|
||||||
# Process using the more robust method
|
|
||||||
return self.process_ssd_output(raw_detections)
|
|
||||||
|
|
||||||
|
# Close the Hailo device
|
||||||
def close(self):
|
def close(self):
|
||||||
logger.info("[CLOSE] Closing Hailo device")
|
logging.info("[CLOSE] Closing HailoDetector")
|
||||||
try:
|
try:
|
||||||
self.target.close()
|
self.inference_engine.hef.close()
|
||||||
logger.info("Hailo device closed successfully")
|
logging.info("Hailo device closed successfully")
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error(f"Failed to close Hailo device: {e}")
|
logging.error(f"Failed to close Hailo device: {e}")
|
||||||
raise
|
raise
|
||||||
|
|
||||||
|
# Asynchronous detection wrapper
|
||||||
|
def async_detect(self, tensor_input, callback):
|
||||||
|
def detection_thread():
|
||||||
|
result = self.detect_raw(tensor_input)
|
||||||
|
callback(result)
|
||||||
|
thread = threading.Thread(target=detection_thread)
|
||||||
|
thread.start()
|
||||||
|
|
||||||
|
# ----------------- Configuration Class ----------------- #
|
||||||
|
class HailoDetectorConfig(BaseDetectorConfig):
|
||||||
|
type: Literal[DETECTOR_KEY]
|
||||||
|
device: str = Field(default="PCIe", title="Device Type")
|
||||||
|
url: Optional[str] = Field(default=None, title="Custom Model URL")
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user