HuangMason320
2fea1eceec
- Fix mflow_converter to properly handle multi-series configuration creation - Update InferencePipeline to correctly initialize MultiDongle with multi-series config - Add comprehensive multi-series configuration validation in mflow_converter - Enhance deployment dialog to display multi-series configuration details - Improve analysis and configuration tabs to show proper multi-series info This resolves the issue where multi-series mode was falling back to single-series during inference initialization, ensuring proper multi-series dongle support. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
1409 lines
60 KiB
Python
1409 lines
60 KiB
Python
from typing import Union, Tuple
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import os
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import sys
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import argparse
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import time
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import threading
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import queue
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import numpy as np
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import kp
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import cv2
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import time
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from abc import ABC, abstractmethod
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from typing import Callable, Optional, Any, Dict, List
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from dataclasses import dataclass
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from collections import defaultdict
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@dataclass
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class InferenceTask:
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sequence_id: int
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image_data: np.ndarray
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image_format: Any # kp.ImageFormat
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timestamp: float
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@dataclass
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class InferenceResult:
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sequence_id: int
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result: Any
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series_name: str
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timestamp: float
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class DongleSeriesSpec:
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"""Dongle series specifications with GOPS capacity for load balancing"""
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KL520_GOPS = 3
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KL720_GOPS = 28
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SERIES_SPECS = {
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"KL520": {"product_id": 0x100, "gops": KL520_GOPS},
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"KL720": {"product_id": 0x720, "gops": KL720_GOPS},
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"KL630": {"product_id": 0x630, "gops": 400},
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"KL730": {"product_id": 0x730, "gops": 1600},
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"KL540": {"product_id": 0x540, "gops": 800}
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}
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class DataProcessor(ABC):
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"""Abstract base class for data processors in the pipeline"""
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@abstractmethod
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def process(self, data: Any, *args, **kwargs) -> Any:
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"""Process data and return result"""
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pass
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class PreProcessor(DataProcessor):
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def __init__(self, resize_fn: Optional[Callable] = None,
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format_convert_fn: Optional[Callable] = None):
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self.resize_fn = resize_fn or self._default_resize
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self.format_convert_fn = format_convert_fn or self._default_format_convert
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def process(self, frame: np.ndarray, target_size: tuple, target_format: str) -> np.ndarray:
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"""Main processing pipeline"""
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resized = self.resize_fn(frame, target_size)
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return self.format_convert_fn(resized, target_format)
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def _default_resize(self, frame: np.ndarray, target_size: tuple) -> np.ndarray:
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"""Default resize implementation"""
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return cv2.resize(frame, target_size)
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def _default_format_convert(self, frame: np.ndarray, target_format: str) -> np.ndarray:
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"""Default format conversion"""
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if target_format == 'BGR565':
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return cv2.cvtColor(frame, cv2.COLOR_BGR2BGR565)
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elif target_format == 'RGB8888':
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return cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
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return frame
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class PostProcessor(DataProcessor):
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"""Post-processor for handling output data from inference stages"""
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def __init__(self, process_fn: Optional[Callable] = None):
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self.process_fn = process_fn or self._default_process
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def process(self, data: Any, *args, **kwargs) -> Any:
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"""Process inference output data"""
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return self.process_fn(data, *args, **kwargs)
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def _default_process(self, data: Any, *args, **kwargs) -> Any:
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"""Default post-processing - returns data unchanged"""
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return data
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class MultiDongle:
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# Curently, only BGR565, RGB8888, YUYV, and RAW8 formats are supported
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_FORMAT_MAPPING = {
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'BGR565': kp.ImageFormat.KP_IMAGE_FORMAT_RGB565,
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'RGB8888': kp.ImageFormat.KP_IMAGE_FORMAT_RGBA8888,
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'YUYV': kp.ImageFormat.KP_IMAGE_FORMAT_YUYV,
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'RAW8': kp.ImageFormat.KP_IMAGE_FORMAT_RAW8,
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# 'YCBCR422_CRY1CBY0': kp.ImageFormat.KP_IMAGE_FORMAT_YCBCR422_CRY1CBY0,
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# 'YCBCR422_CBY1CRY0': kp.ImageFormat.KP_IMAGE_FORMAT_CBY1CRY0,
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# 'YCBCR422_Y1CRY0CB': kp.ImageFormat.KP_IMAGE_FORMAT_Y1CRY0CB,
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# 'YCBCR422_Y1CBY0CR': kp.ImageFormat.KP_IMAGE_FORMAT_Y1CBY0CR,
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# 'YCBCR422_CRY0CBY1': kp.ImageFormat.KP_IMAGE_FORMAT_CRY0CBY1,
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# 'YCBCR422_CBY0CRY1': kp.ImageFormat.KP_IMAGE_FORMAT_CBY0CRY1,
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# 'YCBCR422_Y0CRY1CB': kp.ImageFormat.KP_IMAGE_FORMAT_Y0CRY1CB,
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# 'YCBCR422_Y0CBY1CR': kp.ImageFormat.KP_IMAGE_FORMAT_Y0CBY1CR,
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}
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DongleModelMap = {
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"0x100": "KL520",
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"0x720": "KL720",
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"0x630": "KL630",
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"0x730": "KL730",
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"0x540": "KL540",
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}
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@staticmethod
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def scan_devices():
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"""
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Scan for available Kneron devices and return their information.
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Returns:
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List[Dict]: List of device information containing port_id, series, and device_descriptor
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"""
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try:
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print('[Scanning Devices]')
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device_descriptors = kp.core.scan_devices()
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print(device_descriptors)
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if not device_descriptors or device_descriptors.device_descriptor_number == 0:
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print(' - No devices found')
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return []
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devices_info = []
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# Access the actual device list from the DeviceDescriptorList object
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devices = device_descriptors.device_descriptor_list
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print(f' - Found {len(devices)} device(s):')
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for i, device_desc in enumerate(devices):
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try:
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product_id_hex = hex(device_desc.product_id).strip().lower()
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dongle_model = MultiDongle.DongleModelMap.get(product_id_hex, "Unknown")
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device_info = {
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'port_id': device_desc.usb_port_id,
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'product_id': product_id_hex,
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'kn_number': device_desc.kn_number,
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'dongle': dongle_model,
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'series': dongle_model, # Assuming series is the same as dongle model
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'device_descriptor': device_desc
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}
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devices_info.append(device_info)
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print(f' [{i+1}] Port ID: {device_info["port_id"]}, Series: {device_info["series"]}, Product ID: {device_info["product_id"]}, KN Number: {device_info["kn_number"]}')
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except Exception as e:
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print(f"Error processing device: {e}")
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return devices_info
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except kp.ApiKPException as exception:
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print(f'Error: scan devices fail, error msg: [{str(exception)}]')
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return []
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@staticmethod
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def _get_device_series(device_descriptor):
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"""
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Extract device series from device descriptor using product_id.
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Args:
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device_descriptor: Device descriptor from scan_devices() - can be dict or object
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Returns:
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str: Device series (e.g., 'KL520', 'KL720', etc.)
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"""
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try:
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# Handle dict format (from JSON)
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if isinstance(device_descriptor, dict):
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product_id = device_descriptor.get('product_id', '')
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if product_id in MultiDongle.DongleModelMap:
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return MultiDongle.DongleModelMap[product_id]
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return f'Unknown ({product_id})'
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# Handle object format (from SDK)
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if hasattr(device_descriptor, 'product_id'):
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product_id = device_descriptor.product_id
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if isinstance(product_id, int):
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product_id = hex(product_id)
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if product_id in MultiDongle.DongleModelMap:
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return MultiDongle.DongleModelMap[product_id]
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return f'Unknown ({product_id})'
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# Legacy chip-based detection (fallback)
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if hasattr(device_descriptor, 'chip'):
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chip = device_descriptor.chip
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if chip == kp.ModelNefDescriptor.KP_CHIP_KL520:
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return 'KL520'
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elif chip == kp.ModelNefDescriptor.KP_CHIP_KL720:
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return 'KL720'
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elif chip == kp.ModelNefDescriptor.KP_CHIP_KL630:
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return 'KL630'
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elif chip == kp.ModelNefDescriptor.KP_CHIP_KL730:
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return 'KL730'
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elif chip == kp.ModelNefDescriptor.KP_CHIP_KL540:
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return 'KL540'
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# Final fallback
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return 'Unknown'
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except Exception as e:
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print(f'Warning: Unable to determine device series: {str(e)}')
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return 'Unknown'
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@staticmethod
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def connect_auto_detected_devices(device_count: int = None):
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"""
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Auto-detect and connect to available Kneron devices.
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Args:
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device_count: Number of devices to connect. If None, connect to all available devices.
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Returns:
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Tuple[kp.DeviceGroup, List[Dict]]: Device group and list of connected device info
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"""
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devices_info = MultiDongle.scan_devices()
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if not devices_info:
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raise Exception("No Kneron devices found")
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# Determine how many devices to connect
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if device_count is None:
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device_count = len(devices_info)
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else:
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device_count = min(device_count, len(devices_info))
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# Get port IDs for connection
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port_ids = [devices_info[i]['port_id'] for i in range(device_count)]
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try:
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print(f'[Connecting to {device_count} device(s)]')
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device_group = kp.core.connect_devices(usb_port_ids=port_ids)
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print(' - Success')
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connected_devices = devices_info[:device_count]
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return device_group, connected_devices
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except kp.ApiKPException as exception:
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raise Exception(f'Failed to connect devices: {str(exception)}')
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def __init__(self, port_id: list = None, scpu_fw_path: str = None, ncpu_fw_path: str = None,
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model_path: str = None, upload_fw: bool = False, auto_detect: bool = False,
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max_queue_size: int = 0, multi_series_config: dict = None):
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"""
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Initialize the MultiDongle class with support for both single and multi-series configurations.
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:param port_id: List of USB port IDs for single-series (legacy). If None and auto_detect=True, will auto-detect.
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:param scpu_fw_path: Path to the SCPU firmware file for single-series (legacy).
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:param ncpu_fw_path: Path to the NCPU firmware file for single-series (legacy).
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:param model_path: Path to the model file for single-series (legacy).
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:param upload_fw: Flag to indicate whether to upload firmware for single-series (legacy).
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:param auto_detect: Flag to auto-detect and connect to available devices for single-series (legacy).
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:param max_queue_size: Maximum size for internal queues. If 0, unlimited queues are used.
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:param multi_series_config: Multi-series configuration dict. Format:
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{
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"KL520": {
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"port_ids": [28, 32],
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"model_path": "path/to/kl520_model.nef",
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"firmware_paths": { # Optional
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"scpu": "path/to/kl520_scpu.bin",
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"ncpu": "path/to/kl520_ncpu.bin"
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}
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}
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}
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"""
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# Determine if we're using multi-series mode
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self.multi_series_mode = multi_series_config is not None
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if self.multi_series_mode:
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# Multi-series initialization
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self._init_multi_series(multi_series_config, max_queue_size)
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else:
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# Legacy single-series initialization
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self._init_single_series(port_id, scpu_fw_path, ncpu_fw_path, model_path,
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upload_fw, auto_detect, max_queue_size)
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def _init_multi_series(self, multi_series_config: dict, max_queue_size: int):
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"""Initialize multi-series configuration"""
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self.series_config = multi_series_config
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self.series_groups = {} # series_name -> config
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self.device_groups = {} # series_name -> device_group
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self.model_descriptors = {} # series_name -> model descriptor
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self.gops_weights = {} # series_name -> normalized weight
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self.current_loads = {} # series_name -> current queue size
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# Set up series groups and calculate weights
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total_gops = 0
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for series_name, config in multi_series_config.items():
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if series_name not in DongleSeriesSpec.SERIES_SPECS:
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raise ValueError(f"Unknown series: {series_name}")
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self.series_groups[series_name] = config
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self.current_loads[series_name] = 0
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# Calculate effective GOPS (series GOPS * number of devices)
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port_count = len(config.get("port_ids", []))
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series_gops = DongleSeriesSpec.SERIES_SPECS[series_name]["gops"]
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effective_gops = series_gops * port_count
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total_gops += effective_gops
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# Calculate normalized weights
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for series_name, config in multi_series_config.items():
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port_count = len(config.get("port_ids", []))
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series_gops = DongleSeriesSpec.SERIES_SPECS[series_name]["gops"]
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effective_gops = series_gops * port_count
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self.gops_weights[series_name] = effective_gops / total_gops if total_gops > 0 else 0
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# Multi-series threading and queues
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if max_queue_size > 0:
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self._input_queue = queue.Queue(maxsize=max_queue_size)
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self._ordered_output_queue = queue.Queue(maxsize=max_queue_size)
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else:
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self._input_queue = queue.Queue()
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self._ordered_output_queue = queue.Queue()
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# Create output queue for legacy compatibility
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self._output_queue = self._ordered_output_queue # Point to the same queue
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self.result_queues = {} # series_name -> queue
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for series_name in multi_series_config.keys():
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self.result_queues[series_name] = queue.Queue()
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# Sequence management for ordered results
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self.sequence_counter = 0
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self.sequence_lock = threading.Lock()
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self.pending_results = {} # sequence_id -> InferenceResult
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self.next_output_sequence = 0
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# Threading
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self._stop_event = threading.Event()
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self.dispatcher_thread = None
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self.send_threads = {} # series_name -> thread
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self.receive_threads = {} # series_name -> thread
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self.result_ordering_thread = None
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# Legacy attributes for compatibility
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self.port_id = []
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self.device_group = None
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self.model_nef_descriptor = None
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self.generic_inference_input_descriptor = None
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self._inference_counter = 0
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def _init_single_series(self, port_id: list, scpu_fw_path: str, ncpu_fw_path: str,
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model_path: str, upload_fw: bool, auto_detect: bool, max_queue_size: int):
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"""Initialize legacy single-series configuration"""
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self.auto_detect = auto_detect
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self.connected_devices_info = []
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if auto_detect:
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# Auto-detect devices
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devices_info = self.scan_devices()
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if devices_info:
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self.port_id = [device['port_id'] for device in devices_info]
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self.connected_devices_info = devices_info
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else:
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raise Exception("No Kneron devices found for auto-detection")
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else:
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self.port_id = port_id or []
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self.upload_fw = upload_fw
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# Always store firmware paths when provided
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self.scpu_fw_path = scpu_fw_path
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self.ncpu_fw_path = ncpu_fw_path
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self.model_path = model_path
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self.device_group = None
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# generic_inference_input_descriptor will be prepared in initialize
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self.model_nef_descriptor = None
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self.generic_inference_input_descriptor = None
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# Queues for data
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if max_queue_size > 0:
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self._input_queue = queue.Queue(maxsize=max_queue_size)
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self._output_queue = queue.Queue(maxsize=max_queue_size)
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else:
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self._input_queue = queue.Queue()
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self._output_queue = queue.Queue()
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# Threading attributes
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self._send_thread = None
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self._receive_thread = None
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self._stop_event = threading.Event()
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self._inference_counter = 0
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# Convert single-series to multi-series format internally for unified processing
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self._convert_single_to_multi_series()
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def _convert_single_to_multi_series(self):
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"""
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Convert single-series configuration to multi-series format internally
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This allows unified processing regardless of initialization mode
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"""
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if not self.port_id:
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# No ports specified, create empty structure
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self.series_groups = {}
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self.gops_weights = {}
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self.current_loads = {}
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return
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# Detect series from connected devices or use default
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detected_series = self._detect_series_from_ports(self.port_id)
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# Create multi-series config format
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self.series_groups = {
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detected_series: {
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"port_ids": self.port_id.copy(),
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"model_path": self.model_path,
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"firmware_paths": {
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"scpu": self.scpu_fw_path,
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"ncpu": self.ncpu_fw_path
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} if self.scpu_fw_path and self.ncpu_fw_path else {}
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}
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}
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# Calculate GOPS weights (100% since it's single series)
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self.gops_weights = {detected_series: 1.0}
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# Initialize load tracking
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self.current_loads = {detected_series: 0}
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print(f"Single-series config converted to multi-series format: {detected_series}")
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def _detect_series_from_ports(self, port_ids: List[int]) -> str:
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"""
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Detect series from port IDs by scanning connected devices
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Falls back to KL520 if unable to detect
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"""
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try:
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# Try to scan devices and match port IDs
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devices_info = self.scan_devices()
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for device_info in devices_info:
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if device_info['port_id'] in port_ids:
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series = device_info.get('series', 'Unknown')
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if series != 'Unknown':
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return series
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|
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# If scanning didn't work, try to auto-detect from the first available device
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if self.auto_detect and self.connected_devices_info:
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for device_info in self.connected_devices_info:
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series = device_info.get('series', 'Unknown')
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if series != 'Unknown':
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return series
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except Exception as e:
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print(f"Warning: Could not detect series from devices: {e}")
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|
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# Fallback to KL520 (most common series)
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print("Warning: Could not detect device series, defaulting to KL520")
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return "KL520"
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|
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def _select_optimal_series(self) -> Optional[str]:
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"""
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Select optimal series based on current load and GOPS capacity
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Returns the series name with the best load/capacity ratio
|
|
"""
|
|
if not self.multi_series_mode or not self.series_groups:
|
|
return None
|
|
|
|
best_ratio = float('inf')
|
|
selected_series = None
|
|
|
|
for series_name in self.series_groups.keys():
|
|
current_load = self.current_loads.get(series_name, 0)
|
|
weight = self.gops_weights.get(series_name, 0)
|
|
|
|
# Calculate load ratio (lower is better)
|
|
load_ratio = current_load / weight if weight > 0 else float('inf')
|
|
|
|
if load_ratio < best_ratio:
|
|
best_ratio = load_ratio
|
|
selected_series = series_name
|
|
|
|
return selected_series
|
|
|
|
def initialize(self):
|
|
"""
|
|
Connect devices, upload firmware (if upload_fw is True), and upload model.
|
|
Must be called before start().
|
|
"""
|
|
if self.multi_series_mode:
|
|
# Multi-series initialization
|
|
self._initialize_multi_series()
|
|
else:
|
|
# Legacy single-series initialization
|
|
self._initialize_single_series()
|
|
|
|
def _initialize_single_series(self):
|
|
"""Initialize single-series (legacy) mode"""
|
|
# Connect device and assign to self.device_group
|
|
try:
|
|
print('[Connect Device]')
|
|
self.device_group = kp.core.connect_devices(usb_port_ids=self.port_id)
|
|
print(' - Success')
|
|
except kp.ApiKPException as exception:
|
|
print('Error: connect device fail, port ID = \'{}\', error msg: [{}]'.format(self.port_id, str(exception)))
|
|
sys.exit(1)
|
|
|
|
# setting timeout of the usb communication with the device
|
|
# Note: Timeout setting removed as it causes crashes when camera is connected
|
|
print('[Set Device Timeout]')
|
|
print(' - Skipped (prevents camera connection crashes)')
|
|
|
|
if self.upload_fw:
|
|
try:
|
|
print('[Upload Firmware]')
|
|
kp.core.load_firmware_from_file(device_group=self.device_group,
|
|
scpu_fw_path=self.scpu_fw_path,
|
|
ncpu_fw_path=self.ncpu_fw_path)
|
|
print(' - Success')
|
|
except kp.ApiKPException as exception:
|
|
print('Error: upload firmware failed, error = \'{}\''.format(str(exception)))
|
|
sys.exit(1)
|
|
|
|
# upload model to device
|
|
try:
|
|
print('[Upload Model]')
|
|
self.model_nef_descriptor = kp.core.load_model_from_file(device_group=self.device_group,
|
|
file_path=self.model_path)
|
|
print(' - Success')
|
|
except kp.ApiKPException as exception:
|
|
print('Error: upload model failed, error = \'{}\''.format(str(exception)))
|
|
sys.exit(1)
|
|
|
|
# Extract model input dimensions automatically from model metadata
|
|
if self.model_nef_descriptor and self.model_nef_descriptor.models:
|
|
model = self.model_nef_descriptor.models[0]
|
|
if hasattr(model, 'input_nodes') and model.input_nodes:
|
|
input_node = model.input_nodes[0]
|
|
# From your JSON: "shape_npu": [1, 3, 128, 128] -> (width, height)
|
|
shape = input_node.tensor_shape_info.data.shape_npu
|
|
self.model_input_shape = (shape[3], shape[2]) # (width, height)
|
|
self.model_input_channels = shape[1] # 3 for RGB
|
|
print(f"Model input shape detected: {self.model_input_shape}, channels: {self.model_input_channels}")
|
|
else:
|
|
self.model_input_shape = (128, 128) # fallback
|
|
self.model_input_channels = 3
|
|
print("Using default input shape (128, 128)")
|
|
else:
|
|
self.model_input_shape = (128, 128)
|
|
self.model_input_channels = 3
|
|
print("Model info not available, using default shape")
|
|
|
|
# Prepare generic inference input descriptor after model is loaded
|
|
if self.model_nef_descriptor:
|
|
self.generic_inference_input_descriptor = kp.GenericImageInferenceDescriptor(
|
|
model_id=self.model_nef_descriptor.models[0].id,
|
|
)
|
|
else:
|
|
print("Warning: Could not get generic inference input descriptor from model.")
|
|
self.generic_inference_input_descriptor = None
|
|
|
|
def _initialize_multi_series(self):
|
|
"""Initialize multi-series mode"""
|
|
print('[Multi-Series Initialization]')
|
|
|
|
# Initialize each series separately
|
|
for series_name, config in self.series_config.items():
|
|
print(f'[Initializing {series_name}]')
|
|
|
|
# Get port IDs for this series
|
|
port_ids = config.get('port_ids', [])
|
|
if not port_ids:
|
|
print(f'Warning: No port IDs configured for {series_name}, skipping')
|
|
continue
|
|
|
|
# Connect devices for this series
|
|
try:
|
|
print(f' [Connect Devices] Port IDs: {port_ids}')
|
|
device_group = kp.core.connect_devices(usb_port_ids=port_ids)
|
|
self.device_groups[series_name] = device_group
|
|
print(f' - Success ({len(port_ids)} devices)')
|
|
except kp.ApiKPException as exception:
|
|
print(f'Error: connect devices failed for {series_name}, port IDs = {port_ids}, error = {str(exception)}')
|
|
continue
|
|
|
|
# Upload firmware if available
|
|
firmware_paths = config.get('firmware_paths')
|
|
if firmware_paths and 'scpu' in firmware_paths and 'ncpu' in firmware_paths:
|
|
try:
|
|
print(f' [Upload Firmware]')
|
|
kp.core.load_firmware_from_file(
|
|
device_group=device_group,
|
|
scpu_fw_path=firmware_paths['scpu'],
|
|
ncpu_fw_path=firmware_paths['ncpu']
|
|
)
|
|
print(f' - Success')
|
|
except kp.ApiKPException as exception:
|
|
print(f'Error: upload firmware failed for {series_name}, error = {str(exception)}')
|
|
continue
|
|
else:
|
|
print(f' [Upload Firmware] - Skipped (no firmware paths configured)')
|
|
|
|
# Upload model
|
|
model_path = config.get('model_path')
|
|
if model_path:
|
|
try:
|
|
print(f' [Upload Model]')
|
|
model_descriptor = kp.core.load_model_from_file(
|
|
device_group=device_group,
|
|
file_path=model_path
|
|
)
|
|
self.model_descriptors[series_name] = model_descriptor
|
|
print(f' - Success')
|
|
|
|
# Extract model input dimensions for this series
|
|
if model_descriptor and model_descriptor.models:
|
|
model = model_descriptor.models[0]
|
|
if hasattr(model, 'input_nodes') and model.input_nodes:
|
|
input_node = model.input_nodes[0]
|
|
shape = input_node.tensor_shape_info.data.shape_npu
|
|
model_input_shape = (shape[3], shape[2]) # (width, height)
|
|
model_input_channels = shape[1] # 3 for RGB
|
|
print(f' Model input shape: {model_input_shape}, channels: {model_input_channels}')
|
|
|
|
# Store series-specific model info
|
|
self.series_groups[series_name]['model_input_shape'] = model_input_shape
|
|
self.series_groups[series_name]['model_input_channels'] = model_input_channels
|
|
|
|
except kp.ApiKPException as exception:
|
|
print(f'Error: upload model failed for {series_name}, error = {str(exception)}')
|
|
continue
|
|
else:
|
|
print(f' [Upload Model] - Skipped (no model path configured)')
|
|
|
|
print('[Multi-Series Initialization Complete]')
|
|
|
|
# Set up legacy compatibility attributes using the first series
|
|
if self.device_groups:
|
|
first_series = next(iter(self.device_groups.keys()))
|
|
self.device_group = self.device_groups[first_series]
|
|
self.model_nef_descriptor = self.model_descriptors.get(first_series)
|
|
|
|
# Set up generic inference descriptor from first series
|
|
if self.model_nef_descriptor:
|
|
self.generic_inference_input_descriptor = kp.GenericImageInferenceDescriptor(
|
|
model_id=self.model_nef_descriptor.models[0].id,
|
|
)
|
|
|
|
# Set model input shape from first series
|
|
if first_series in self.series_groups:
|
|
series_info = self.series_groups[first_series]
|
|
self.model_input_shape = series_info.get('model_input_shape', (640, 640))
|
|
self.model_input_channels = series_info.get('model_input_channels', 3)
|
|
else:
|
|
self.model_input_shape = (640, 640)
|
|
self.model_input_channels = 3
|
|
|
|
def preprocess_frame(self, frame: np.ndarray, target_format: str = 'BGR565') -> np.ndarray:
|
|
"""
|
|
Preprocess frame for inference
|
|
"""
|
|
resized_frame = cv2.resize(frame, self.model_input_shape)
|
|
|
|
if target_format == 'BGR565':
|
|
return cv2.cvtColor(resized_frame, cv2.COLOR_BGR2BGR565)
|
|
elif target_format == 'RGB8888':
|
|
return cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGBA)
|
|
elif target_format == 'YUYV':
|
|
return cv2.cvtColor(resized_frame, cv2.COLOR_BGR2YUV_YUYV)
|
|
else:
|
|
return resized_frame # RAW8 or other formats
|
|
|
|
def get_latest_inference_result(self, timeout: float = 0.01) -> Tuple[float, str]:
|
|
"""
|
|
Get the latest inference result
|
|
Returns: (probability, result_string) or (None, None) if no result
|
|
"""
|
|
output_descriptor = self.get_output(timeout=timeout)
|
|
if not output_descriptor:
|
|
return None, None
|
|
|
|
# Process the output descriptor
|
|
if hasattr(output_descriptor, 'header') and \
|
|
hasattr(output_descriptor.header, 'num_output_node') and \
|
|
hasattr(output_descriptor.header, 'inference_number'):
|
|
|
|
inf_node_output_list = []
|
|
retrieval_successful = True
|
|
|
|
for node_idx in range(output_descriptor.header.num_output_node):
|
|
try:
|
|
inference_float_node_output = kp.inference.generic_inference_retrieve_float_node(
|
|
node_idx=node_idx,
|
|
generic_raw_result=output_descriptor,
|
|
channels_ordering=kp.ChannelOrdering.KP_CHANNEL_ORDERING_CHW
|
|
)
|
|
inf_node_output_list.append(inference_float_node_output.ndarray.copy())
|
|
except kp.ApiKPException as e:
|
|
retrieval_successful = False
|
|
break
|
|
except Exception as e:
|
|
retrieval_successful = False
|
|
break
|
|
|
|
if retrieval_successful and len(inf_node_output_list) > 0:
|
|
# Process output nodes
|
|
if output_descriptor.header.num_output_node == 1:
|
|
raw_output_array = inf_node_output_list[0].flatten()
|
|
else:
|
|
concatenated_outputs = [arr.flatten() for arr in inf_node_output_list]
|
|
raw_output_array = np.concatenate(concatenated_outputs) if concatenated_outputs else np.array([])
|
|
|
|
if raw_output_array.size > 0:
|
|
probability = postprocess(raw_output_array)
|
|
result_str = "Fire" if probability > 0.5 else "No Fire"
|
|
return probability, result_str
|
|
|
|
return None, None
|
|
|
|
|
|
# Modified _send_thread_func to get data from input queue
|
|
def _send_thread_func(self):
|
|
"""Internal function run by the send thread, gets images from input queue."""
|
|
print("Send thread started.")
|
|
send_count = 0
|
|
while not self._stop_event.is_set():
|
|
if self.generic_inference_input_descriptor is None:
|
|
# Wait for descriptor to be ready or stop
|
|
self._stop_event.wait(0.1) # Avoid busy waiting
|
|
continue
|
|
|
|
try:
|
|
# Get image and format from the input queue
|
|
# Blocks until an item is available or stop event is set/timeout occurs
|
|
try:
|
|
# Use get with timeout or check stop event in a loop
|
|
# This pattern allows thread to check stop event while waiting on queue
|
|
item = self._input_queue.get(block=True, timeout=0.1)
|
|
# Check if this is our sentinel value
|
|
if item is None:
|
|
continue
|
|
|
|
# Now safely unpack the tuple
|
|
image_data, image_format_enum = item
|
|
except queue.Empty:
|
|
# If queue is empty after timeout, check stop event and continue loop
|
|
continue
|
|
|
|
# Configure and send the image
|
|
self._inference_counter += 1 # Increment counter for each image
|
|
send_count += 1
|
|
|
|
# Debug: Log send activity every 100 images
|
|
if send_count % 100 == 0:
|
|
print(f"[MultiDongle] Sent {send_count} images to inference")
|
|
|
|
self.generic_inference_input_descriptor.inference_number = self._inference_counter
|
|
self.generic_inference_input_descriptor.input_node_image_list = [kp.GenericInputNodeImage(
|
|
image=image_data,
|
|
image_format=image_format_enum, # Use the format from the queue
|
|
resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
|
|
padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
|
|
normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
|
|
)]
|
|
|
|
kp.inference.generic_image_inference_send(device_group=self.device_group,
|
|
generic_inference_input_descriptor=self.generic_inference_input_descriptor)
|
|
# No need for sleep here usually, as queue.get is blocking
|
|
except kp.ApiKPException as exception:
|
|
print(f' - Error in send thread: inference send failed, error = {exception}')
|
|
self._stop_event.set() # Signal other thread to stop
|
|
except Exception as e:
|
|
print(f' - Unexpected error in send thread: {e}')
|
|
self._stop_event.set()
|
|
|
|
print("Send thread stopped.")
|
|
|
|
# _receive_thread_func remains the same
|
|
def _receive_thread_func(self):
|
|
"""Internal function run by the receive thread, puts results into output queue."""
|
|
print("Receive thread started.")
|
|
receive_count = 0
|
|
while not self._stop_event.is_set():
|
|
try:
|
|
generic_inference_output_descriptor = kp.inference.generic_image_inference_receive(device_group=self.device_group)
|
|
self._output_queue.put(generic_inference_output_descriptor)
|
|
receive_count += 1
|
|
|
|
# Debug: Log receive activity every 100 results
|
|
if receive_count % 100 == 0:
|
|
print(f"[MultiDongle] Received {receive_count} inference results")
|
|
except kp.ApiKPException as exception:
|
|
if not self._stop_event.is_set(): # Avoid printing error if we are already stopping
|
|
print(f' - Error in receive thread: inference receive failed, error = {exception}')
|
|
self._stop_event.set()
|
|
except Exception as e:
|
|
print(f' - Unexpected error in receive thread: {e}')
|
|
self._stop_event.set()
|
|
|
|
print("Receive thread stopped.")
|
|
|
|
def start(self):
|
|
"""
|
|
Start the send and receive threads.
|
|
Must be called after initialize().
|
|
"""
|
|
if self.multi_series_mode:
|
|
self._start_multi_series()
|
|
else:
|
|
self._start_single_series()
|
|
|
|
def _start_single_series(self):
|
|
"""Start single-series (legacy) mode"""
|
|
if self.device_group is None:
|
|
raise RuntimeError("MultiDongle not initialized. Call initialize() first.")
|
|
|
|
if self._send_thread is None or not self._send_thread.is_alive():
|
|
self._stop_event.clear() # Clear stop event for a new start
|
|
self._send_thread = threading.Thread(target=self._send_thread_func, daemon=True)
|
|
self._send_thread.start()
|
|
print("Send thread started.")
|
|
|
|
if self._receive_thread is None or not self._receive_thread.is_alive():
|
|
self._receive_thread = threading.Thread(target=self._receive_thread_func, daemon=True)
|
|
self._receive_thread.start()
|
|
print("Receive thread started.")
|
|
|
|
def _start_multi_series(self):
|
|
"""Start multi-series mode"""
|
|
if not self.device_groups:
|
|
raise RuntimeError("MultiDongle not initialized. Call initialize() first.")
|
|
|
|
print("[Starting Multi-Series Threads]")
|
|
self._stop_event.clear()
|
|
|
|
# Start dispatcher thread
|
|
if self.dispatcher_thread is None or not self.dispatcher_thread.is_alive():
|
|
self.dispatcher_thread = threading.Thread(target=self._dispatcher_thread_func, daemon=True)
|
|
self.dispatcher_thread.start()
|
|
print("Dispatcher thread started.")
|
|
|
|
# Start send/receive threads for each series
|
|
for series_name in self.device_groups.keys():
|
|
# Start send thread for this series
|
|
if series_name not in self.send_threads or not self.send_threads[series_name].is_alive():
|
|
send_thread = threading.Thread(
|
|
target=self._multi_series_send_thread_func,
|
|
args=(series_name,),
|
|
daemon=True
|
|
)
|
|
self.send_threads[series_name] = send_thread
|
|
send_thread.start()
|
|
print(f"Send thread started for {series_name}.")
|
|
|
|
# Start receive thread for this series
|
|
if series_name not in self.receive_threads or not self.receive_threads[series_name].is_alive():
|
|
receive_thread = threading.Thread(
|
|
target=self._multi_series_receive_thread_func,
|
|
args=(series_name,),
|
|
daemon=True
|
|
)
|
|
self.receive_threads[series_name] = receive_thread
|
|
receive_thread.start()
|
|
print(f"Receive thread started for {series_name}.")
|
|
|
|
# Start result ordering thread
|
|
if self.result_ordering_thread is None or not self.result_ordering_thread.is_alive():
|
|
self.result_ordering_thread = threading.Thread(target=self._result_ordering_thread_func, daemon=True)
|
|
self.result_ordering_thread.start()
|
|
print("Result ordering thread started.")
|
|
|
|
def stop(self):
|
|
"""Improved stop method with better cleanup"""
|
|
if self._stop_event.is_set():
|
|
return # Already stopping
|
|
|
|
if self.multi_series_mode:
|
|
self._stop_multi_series()
|
|
else:
|
|
self._stop_single_series()
|
|
|
|
def _stop_single_series(self):
|
|
"""Stop single-series (legacy) mode"""
|
|
print("Stopping threads...")
|
|
self._stop_event.set()
|
|
|
|
# Clear queues to unblock threads
|
|
while not self._input_queue.empty():
|
|
try:
|
|
self._input_queue.get_nowait()
|
|
except queue.Empty:
|
|
break
|
|
|
|
# Signal send thread to wake up
|
|
self._input_queue.put(None)
|
|
|
|
# Join threads with timeout
|
|
for thread, name in [(self._send_thread, "Send"), (self._receive_thread, "Receive")]:
|
|
if thread and thread.is_alive():
|
|
thread.join(timeout=2.0)
|
|
if thread.is_alive():
|
|
print(f"Warning: {name} thread didn't stop cleanly")
|
|
|
|
print("Disconnecting device group...")
|
|
if self.device_group:
|
|
try:
|
|
kp.core.disconnect_devices(device_group=self.device_group)
|
|
print("Device group disconnected successfully.")
|
|
except kp.ApiKPException as e:
|
|
print(f"Error disconnecting device group: {e}")
|
|
self.device_group = None
|
|
|
|
def _stop_multi_series(self):
|
|
"""Stop multi-series mode"""
|
|
print("[Stopping Multi-Series Threads]")
|
|
self._stop_event.set()
|
|
|
|
# Clear input queue to unblock dispatcher
|
|
while not self._input_queue.empty():
|
|
try:
|
|
self._input_queue.get_nowait()
|
|
except queue.Empty:
|
|
break
|
|
|
|
# Signal dispatcher thread to wake up
|
|
self._input_queue.put(None)
|
|
|
|
# Clear series result queues
|
|
for series_name, result_queue in self.result_queues.items():
|
|
while not result_queue.empty():
|
|
try:
|
|
result_queue.get_nowait()
|
|
except queue.Empty:
|
|
break
|
|
|
|
# Stop all send threads
|
|
for series_name, send_thread in self.send_threads.items():
|
|
if send_thread and send_thread.is_alive():
|
|
send_thread.join(timeout=2.0)
|
|
if send_thread.is_alive():
|
|
print(f"Warning: Send thread for {series_name} didn't stop cleanly")
|
|
|
|
# Stop all receive threads
|
|
for series_name, receive_thread in self.receive_threads.items():
|
|
if receive_thread and receive_thread.is_alive():
|
|
receive_thread.join(timeout=2.0)
|
|
if receive_thread.is_alive():
|
|
print(f"Warning: Receive thread for {series_name} didn't stop cleanly")
|
|
|
|
# Stop dispatcher thread
|
|
if self.dispatcher_thread and self.dispatcher_thread.is_alive():
|
|
self.dispatcher_thread.join(timeout=2.0)
|
|
if self.dispatcher_thread.is_alive():
|
|
print("Warning: Dispatcher thread didn't stop cleanly")
|
|
|
|
# Stop result ordering thread
|
|
if self.result_ordering_thread and self.result_ordering_thread.is_alive():
|
|
self.result_ordering_thread.join(timeout=2.0)
|
|
if self.result_ordering_thread.is_alive():
|
|
print("Warning: Result ordering thread didn't stop cleanly")
|
|
|
|
# Disconnect all device groups
|
|
print("Disconnecting device groups...")
|
|
for series_name, device_group in self.device_groups.items():
|
|
try:
|
|
kp.core.disconnect_devices(device_group=device_group)
|
|
print(f"Device group for {series_name} disconnected successfully.")
|
|
except kp.ApiKPException as e:
|
|
print(f"Error disconnecting device group for {series_name}: {e}")
|
|
|
|
self.device_groups.clear()
|
|
|
|
def _dispatcher_thread_func(self):
|
|
"""Dispatcher thread: assigns tasks to dongles based on load balancing"""
|
|
print("Dispatcher thread started")
|
|
|
|
while not self._stop_event.is_set():
|
|
try:
|
|
task = self._input_queue.get(timeout=0.1)
|
|
if task is None: # Sentinel value
|
|
continue
|
|
|
|
# Select optimal dongle based on current load and capacity
|
|
selected_series = self._select_optimal_series()
|
|
if selected_series is None:
|
|
print("Warning: No series available for task dispatch")
|
|
continue
|
|
|
|
# Enqueue to selected series
|
|
self.result_queues[selected_series].put(task)
|
|
self.current_loads[selected_series] += 1
|
|
|
|
except queue.Empty:
|
|
continue
|
|
except Exception as e:
|
|
print(f"Error in dispatcher: {e}")
|
|
if not self._stop_event.is_set():
|
|
self._stop_event.set()
|
|
|
|
print("Dispatcher thread stopped")
|
|
|
|
def _multi_series_send_thread_func(self, series_name: str):
|
|
"""Send thread for specific dongle series - with tuple handling fix"""
|
|
print(f"Send worker started for {series_name}")
|
|
|
|
device_group = self.device_groups[series_name]
|
|
result_queue = self.result_queues[series_name]
|
|
model_descriptor = self.model_descriptors[series_name]
|
|
|
|
while not self._stop_event.is_set():
|
|
try:
|
|
task = result_queue.get(timeout=0.1)
|
|
if task is None:
|
|
continue
|
|
|
|
# Handle both tuple and dict formats
|
|
if isinstance(task, tuple):
|
|
# Legacy single-series format: (image_data, image_format)
|
|
image_data, image_format = task
|
|
sequence_id = getattr(self, '_inference_counter', 0)
|
|
self._inference_counter = sequence_id + 1
|
|
elif isinstance(task, dict):
|
|
# Multi-series format: dict with keys
|
|
image_data = task.get('image_data')
|
|
image_format = task.get('image_format', kp.ImageFormat.KP_IMAGE_FORMAT_RGB565)
|
|
sequence_id = task.get('sequence_id', 0)
|
|
else:
|
|
print(f"Error: Unknown task format: {type(task)}")
|
|
continue
|
|
|
|
if image_data is None:
|
|
print(f"Error: No image data in task")
|
|
continue
|
|
|
|
# Create inference descriptor for this task
|
|
inference_descriptor = kp.GenericImageInferenceDescriptor(
|
|
model_id=model_descriptor.models[0].id,
|
|
)
|
|
inference_descriptor.inference_number = sequence_id
|
|
|
|
inference_descriptor.input_node_image_list = [
|
|
kp.GenericInputNodeImage(
|
|
image=image_data,
|
|
image_format=image_format,
|
|
resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
|
|
padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
|
|
normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
|
|
)
|
|
]
|
|
|
|
# Send inference
|
|
kp.inference.generic_image_inference_send(
|
|
device_group=device_group,
|
|
generic_inference_input_descriptor=inference_descriptor
|
|
)
|
|
|
|
except queue.Empty:
|
|
continue
|
|
except kp.ApiKPException as e:
|
|
print(f"Error in {series_name} send worker: {e}")
|
|
if not self._stop_event.is_set():
|
|
self._stop_event.set()
|
|
except Exception as e:
|
|
print(f"Unexpected error in {series_name} send worker: {e}")
|
|
if not self._stop_event.is_set():
|
|
self._stop_event.set()
|
|
|
|
print(f"Send worker stopped for {series_name}")
|
|
|
|
def _multi_series_receive_thread_func(self, series_name: str):
|
|
"""Receive thread for specific dongle series"""
|
|
print(f"Receive worker started for {series_name}")
|
|
|
|
device_group = self.device_groups[series_name]
|
|
|
|
while not self._stop_event.is_set():
|
|
try:
|
|
# Receive inference result
|
|
raw_result = kp.inference.generic_image_inference_receive(device_group=device_group)
|
|
|
|
# Create result object
|
|
result = {
|
|
'sequence_id': raw_result.header.inference_number,
|
|
'result': raw_result,
|
|
'dongle_series': series_name,
|
|
'timestamp': time.time()
|
|
}
|
|
|
|
# Add to pending results for ordering
|
|
self.pending_results[result['sequence_id']] = result
|
|
self.current_loads[series_name] = max(0, self.current_loads[series_name] - 1)
|
|
|
|
except kp.ApiKPException as e:
|
|
if not self._stop_event.is_set():
|
|
print(f"Error in {series_name} receive worker: {e}")
|
|
self._stop_event.set()
|
|
except Exception as e:
|
|
print(f"Unexpected error in {series_name} receive worker: {e}")
|
|
|
|
print(f"Receive worker stopped for {series_name}")
|
|
|
|
def _result_ordering_thread_func(self):
|
|
"""Result ordering thread: ensures results are output in sequence order"""
|
|
print("Result ordering worker started")
|
|
|
|
while not self._stop_event.is_set():
|
|
# Check if next expected result is available
|
|
if self.next_output_sequence in self.pending_results:
|
|
result = self.pending_results.pop(self.next_output_sequence)
|
|
self._ordered_output_queue.put(result)
|
|
self.next_output_sequence += 1
|
|
|
|
# Clean up old pending results to prevent memory bloat
|
|
if len(self.pending_results) > 1000: # result_buffer_size
|
|
oldest_sequences = sorted(self.pending_results.keys())[:500]
|
|
for seq_id in oldest_sequences:
|
|
if seq_id < self.next_output_sequence:
|
|
self.pending_results.pop(seq_id, None)
|
|
else:
|
|
time.sleep(0.001) # Small delay to prevent busy waiting
|
|
|
|
print("Result ordering worker stopped")
|
|
|
|
def put_input(self, image: Union[str, np.ndarray], format: str, target_size: Tuple[int, int] = None):
|
|
"""
|
|
Put an image into the input queue with flexible preprocessing
|
|
"""
|
|
if isinstance(image, str):
|
|
image_data = cv2.imread(image)
|
|
if image_data is None:
|
|
raise FileNotFoundError(f"Image file not found at {image}")
|
|
if target_size:
|
|
image_data = cv2.resize(image_data, target_size)
|
|
elif isinstance(image, np.ndarray):
|
|
# Don't modify original array, make copy if needed
|
|
image_data = image.copy() if target_size is None else cv2.resize(image, target_size)
|
|
else:
|
|
raise ValueError("Image must be a file path (str) or a numpy array (ndarray).")
|
|
|
|
if format in self._FORMAT_MAPPING:
|
|
image_format_enum = self._FORMAT_MAPPING[format]
|
|
else:
|
|
raise ValueError(f"Unsupported format: {format}")
|
|
|
|
if self.multi_series_mode:
|
|
# In multi-series mode, create a task with sequence ID
|
|
with self.sequence_lock:
|
|
sequence_id = self.sequence_counter
|
|
self.sequence_counter += 1
|
|
|
|
task = {
|
|
'sequence_id': sequence_id,
|
|
'image_data': image_data,
|
|
'image_format': image_format_enum,
|
|
'timestamp': time.time()
|
|
}
|
|
self._input_queue.put(task)
|
|
else:
|
|
# In single-series mode, use the original format
|
|
self._input_queue.put((image_data, image_format_enum))
|
|
|
|
def get_output(self, timeout: float = None):
|
|
"""
|
|
Get the next received data from the output queue.
|
|
This method is non-blocking by default unless a timeout is specified.
|
|
:param timeout: Time in seconds to wait for data. If None, it's non-blocking.
|
|
:return: Received data (e.g., kp.GenericInferenceOutputDescriptor) or None if no data available within timeout.
|
|
"""
|
|
try:
|
|
if self.multi_series_mode:
|
|
# In multi-series mode, use the ordered output queue
|
|
result = self._ordered_output_queue.get(block=timeout is not None, timeout=timeout)
|
|
if result and isinstance(result, dict):
|
|
return result.get('result') # Extract the actual inference result
|
|
return result
|
|
else:
|
|
# In single-series mode, use the regular output queue
|
|
return self._output_queue.get(block=timeout is not None, timeout=timeout)
|
|
except queue.Empty:
|
|
return None
|
|
|
|
def get_device_info(self):
|
|
"""
|
|
Get information about connected devices including port IDs and series.
|
|
|
|
Returns:
|
|
List[Dict]: List of device information with port_id and series
|
|
"""
|
|
if self.auto_detect and self.connected_devices_info:
|
|
return self.connected_devices_info
|
|
|
|
# If not auto-detected, try to get info from device group
|
|
if self.device_group:
|
|
try:
|
|
device_info_list = []
|
|
|
|
# Get device group content
|
|
device_group_content = self.device_group.content
|
|
|
|
# Iterate through devices in the group
|
|
for i, port_id in enumerate(self.port_id):
|
|
device_info = {
|
|
'port_id': port_id,
|
|
'series': 'Unknown', # We'll try to determine this
|
|
'device_descriptor': None
|
|
}
|
|
|
|
# Try to get device series from device group
|
|
try:
|
|
# This is a simplified approach - you might need to adjust
|
|
# based on the actual device group structure
|
|
if hasattr(device_group_content, 'devices') and i < len(device_group_content.devices):
|
|
device = device_group_content.devices[i]
|
|
if hasattr(device, 'chip_id'):
|
|
device_info['series'] = self._chip_id_to_series(device.chip_id)
|
|
except:
|
|
# If we can't get series info, keep as 'Unknown'
|
|
pass
|
|
|
|
device_info_list.append(device_info)
|
|
|
|
return device_info_list
|
|
|
|
except Exception as e:
|
|
print(f"Warning: Could not get device info from device group: {str(e)}")
|
|
|
|
# Fallback: return basic info based on port_id
|
|
return [{'port_id': port_id, 'series': 'Unknown', 'device_descriptor': None} for port_id in self.port_id]
|
|
|
|
def _chip_id_to_series(self, chip_id):
|
|
"""
|
|
Convert chip ID to series name.
|
|
|
|
Args:
|
|
chip_id: Chip ID from device
|
|
|
|
Returns:
|
|
str: Device series name
|
|
"""
|
|
chip_mapping = {
|
|
'kl520': 'KL520',
|
|
'kl720': 'KL720',
|
|
'kl630': 'KL630',
|
|
'kl730': 'KL730',
|
|
'kl540': 'KL540',
|
|
}
|
|
|
|
if isinstance(chip_id, str):
|
|
return chip_mapping.get(chip_id.lower(), 'Unknown')
|
|
|
|
return 'Unknown'
|
|
|
|
def print_device_info(self):
|
|
"""
|
|
Print detailed information about connected devices.
|
|
"""
|
|
devices_info = self.get_device_info()
|
|
|
|
if not devices_info:
|
|
print("No device information available")
|
|
return
|
|
|
|
print(f"\n[Connected Devices - {len(devices_info)} device(s)]")
|
|
for i, device_info in enumerate(devices_info):
|
|
print(f" [{i+1}] Port ID: {device_info['port_id']}, Series: {device_info['series']}")
|
|
|
|
def __del__(self):
|
|
"""Ensure resources are released when the object is garbage collected."""
|
|
self.stop()
|
|
if self.device_group:
|
|
try:
|
|
kp.core.disconnect_devices(device_group=self.device_group)
|
|
print("Device group disconnected in destructor.")
|
|
except Exception as e:
|
|
print(f"Error disconnecting device group in destructor: {e}")
|
|
|
|
def postprocess(raw_model_output: list) -> float:
|
|
"""
|
|
Post-processes the raw model output.
|
|
Assumes the model output is a list/array where the first element is the desired probability.
|
|
"""
|
|
if raw_model_output is not None and len(raw_model_output) > 0:
|
|
probability = raw_model_output[0]
|
|
return float(probability)
|
|
return 0.0 # Default or error value
|
|
|
|
class WebcamInferenceRunner:
|
|
def __init__(self, multidongle: MultiDongle, image_format: str = 'BGR565'):
|
|
self.multidongle = multidongle
|
|
self.image_format = image_format
|
|
self.latest_probability = 0.0
|
|
self.result_str = "No Fire"
|
|
|
|
# Statistics tracking
|
|
self.processed_inference_count = 0
|
|
self.inference_fps_start_time = None
|
|
self.display_fps_start_time = None
|
|
self.display_frame_counter = 0
|
|
|
|
def run(self, camera_id: int = 0):
|
|
cap = cv2.VideoCapture(camera_id)
|
|
if not cap.isOpened():
|
|
raise RuntimeError("Cannot open webcam")
|
|
|
|
try:
|
|
while True:
|
|
ret, frame = cap.read()
|
|
if not ret:
|
|
break
|
|
|
|
# Track display FPS
|
|
if self.display_fps_start_time is None:
|
|
self.display_fps_start_time = time.time()
|
|
self.display_frame_counter += 1
|
|
|
|
# Preprocess and send frame
|
|
processed_frame = self.multidongle.preprocess_frame(frame, self.image_format)
|
|
self.multidongle.put_input(processed_frame, self.image_format)
|
|
|
|
# Get inference result
|
|
prob, result = self.multidongle.get_latest_inference_result()
|
|
if prob is not None:
|
|
# Track inference FPS
|
|
if self.inference_fps_start_time is None:
|
|
self.inference_fps_start_time = time.time()
|
|
self.processed_inference_count += 1
|
|
|
|
self.latest_probability = prob
|
|
self.result_str = result
|
|
|
|
# Display frame with results
|
|
self._display_results(frame)
|
|
|
|
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
break
|
|
|
|
finally:
|
|
# self._print_statistics()
|
|
cap.release()
|
|
cv2.destroyAllWindows()
|
|
|
|
def _display_results(self, frame):
|
|
display_frame = frame.copy()
|
|
text_color = (0, 255, 0) if "Fire" in self.result_str else (0, 0, 255)
|
|
|
|
# Display inference result
|
|
cv2.putText(display_frame, f"{self.result_str} (Prob: {self.latest_probability:.2f})",
|
|
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, text_color, 2)
|
|
|
|
# Calculate and display inference FPS
|
|
if self.inference_fps_start_time and self.processed_inference_count > 0:
|
|
elapsed_time = time.time() - self.inference_fps_start_time
|
|
if elapsed_time > 0:
|
|
inference_fps = self.processed_inference_count / elapsed_time
|
|
cv2.putText(display_frame, f"Inference FPS: {inference_fps:.2f}",
|
|
(10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
|
|
|
|
cv2.imshow('Fire Detection', display_frame)
|
|
|
|
# def _print_statistics(self):
|
|
# """Print final statistics"""
|
|
# print(f"\n--- Summary ---")
|
|
# print(f"Total inferences processed: {self.processed_inference_count}")
|
|
|
|
# if self.inference_fps_start_time and self.processed_inference_count > 0:
|
|
# elapsed = time.time() - self.inference_fps_start_time
|
|
# if elapsed > 0:
|
|
# avg_inference_fps = self.processed_inference_count / elapsed
|
|
# print(f"Average Inference FPS: {avg_inference_fps:.2f}")
|
|
|
|
# if self.display_fps_start_time and self.display_frame_counter > 0:
|
|
# elapsed = time.time() - self.display_fps_start_time
|
|
# if elapsed > 0:
|
|
# avg_display_fps = self.display_frame_counter / elapsed
|
|
# print(f"Average Display FPS: {avg_display_fps:.2f}")
|
|
|
|
if __name__ == "__main__":
|
|
PORT_IDS = [28, 32]
|
|
SCPU_FW = r'fw_scpu.bin'
|
|
NCPU_FW = r'fw_ncpu.bin'
|
|
MODEL_PATH = r'fire_detection_520.nef'
|
|
|
|
try:
|
|
# Initialize inference engine
|
|
print("Initializing MultiDongle...")
|
|
multidongle = MultiDongle(PORT_IDS, SCPU_FW, NCPU_FW, MODEL_PATH, upload_fw=True)
|
|
multidongle.initialize()
|
|
multidongle.start()
|
|
|
|
# Run using the new runner class
|
|
print("Starting webcam inference...")
|
|
runner = WebcamInferenceRunner(multidongle, 'BGR565')
|
|
runner.run()
|
|
|
|
except Exception as e:
|
|
print(f"Error: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
finally:
|
|
if 'multidongle' in locals():
|
|
multidongle.stop() |