mirror of
https://github.com/firestar5683/StarPilot.git
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firestarsdog
1027 lines
39 KiB
Python
1027 lines
39 KiB
Python
"""Frozen active US sign inference, isolated from camera/Params and debug collection."""
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from __future__ import annotations
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from collections import Counter, deque
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from dataclasses import dataclass
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from pathlib import Path
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import hashlib
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import math
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import time
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import numpy as np
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try:
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import cv2
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except ImportError: # Keep inference unavailable when OpenCV is absent.
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cv2 = None
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MIN_DETECTION_CONFIDENCE = 0.2
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PUBLISHED_CHANGE_COOLDOWN_SECONDS = 1.4
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PUBLISHED_REVERT_CONFIDENCE = 0.97
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TEMPORAL_TRACKING_ENABLED = False
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TRACK_MIN_PROPOSAL_CONFIDENCE = 0.10
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TRACK_UNREADABLE_MIN_PROPOSAL_CONFIDENCE = 0.22
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DETECTOR_CLASSIFIER_REGION_MODE = "right_roi" # full, right_roi, full_and_right_roi
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STRONG_DETECTION_CONFIDENCE = 0.72
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HISTORY_SECONDS = 2.0
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CONSISTENT_DETECTIONS = 2
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CHANGE_CONSISTENT_DETECTIONS = 2
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CHANGE_SINGLE_READ_MIN_CONFIDENCE = 0.83
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CHANGE_REPEAT_MIN_CONFIDENCE = 0.70
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LOW_SPEED_CHANGE_CONSISTENT_DETECTIONS = 2
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LOW_SPEED_CHANGE_MIN_CONFIDENCE = 0.90
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LOW_SPEED_CHANGE_ALLOW_STRONG_CONSENSUS = True
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MODEL_DETECTION_SHORT_CIRCUIT_CONFIDENCE = 0.65
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MODEL_PROPOSAL_MAX_COUNT = 4
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MODEL_PROPOSAL_MAX_AREA_RATIO = 0.18
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MODEL_PROPOSAL_MIN_WIDTH = 10
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MODEL_PROPOSAL_MIN_HEIGHT = 18
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MODEL_PROPOSAL_MIN_X_RATIO = 0.35
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MODEL_PROPOSAL_MAX_Y_RATIO = 0.82
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ROI_WINDOWS = (
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{"bounds": (0.48, 0.00, 0.98, 0.42), "min_confidence": MIN_DETECTION_CONFIDENCE},
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{"bounds": (0.52, 0.02, 0.97, 0.58), "min_confidence": 0.22},
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{"bounds": (0.62, 0.02, 0.99, 0.68), "min_confidence": 0.18},
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{"bounds": (0.45, 0.00, 1.00, 0.82), "min_confidence": 0.06},
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)
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REGULATORY_WHITE_VALUE_MIN = 135
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REGULATORY_WHITE_SAT_MAX = 70
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REGULATORY_DARK_VALUE_MAX = 115
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REGULATORY_DARK_SAT_MAX = 110
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REGULATORY_YELLOW_HUE_MIN = 12
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REGULATORY_YELLOW_HUE_MAX = 45
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REGULATORY_YELLOW_SAT_MIN = 70
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REGULATORY_YELLOW_VALUE_MIN = 85
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REGULATORY_RED_LOW_HUE_MAX = 12
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REGULATORY_RED_HIGH_HUE_MIN = 168
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REGULATORY_RED_SAT_MIN = 80
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REGULATORY_RED_VALUE_MIN = 60
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REGULATORY_GREEN_HUE_MIN = 45
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REGULATORY_GREEN_HUE_MAX = 90
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REGULATORY_BLUE_HUE_MIN = 90
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REGULATORY_BLUE_HUE_MAX = 135
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REGULATORY_COLORED_SAT_MIN = 70
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REGULATORY_COLORED_VALUE_MIN = 70
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REGULATORY_MIN_WHITE_RATIO = 0.08
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REGULATORY_MIN_DARK_RATIO = 0.01
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REGULATORY_MAX_YELLOW_RATIO = 0.12
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REGULATORY_MAX_RED_RATIO = 0.10
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REGULATORY_MAX_GREEN_RATIO = 0.35
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REGULATORY_MAX_BLUE_RATIO = 0.35
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REGULATORY_MIN_WHITE_COMPONENT_RATIO = 0.012
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REGULATORY_MIN_COMPONENT_FILL = 0.36
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REGULATORY_MIN_COMPONENT_HEIGHT_RATIO = 0.2
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REGULATORY_MIN_COMPONENT_WIDTH_RATIO = 0.12
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REGULATORY_MIN_ASPECT_RATIO = 0.28
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REGULATORY_MAX_ASPECT_RATIO = 1.25
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MIN_PUBLISHABLE_SPEED_LIMIT_MPH = 5
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MAX_IMPERIAL_PUBLISHABLE_SPEED_LIMIT_MPH = 80
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US_DETECTOR_CLASSES = {
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0: "regulatory_speed_limit",
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1: "advisory_speed_limit",
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2: "school_zone_speed_limit",
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}
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US_CLASSIFIER_SPEED_VALUES = (10, 100, 15, 20, 25, 30, 35, 40, 45, 5, 50, 55, 60, 65, 70, 75, 80, 90)
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EXTENDED_CLASSIFIER_SPEED_VALUES = frozenset((5, 10, 80, 90, 100))
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SCHOOL_ZONE_SPEED_VALUES = frozenset((15, 20, 25))
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US_DETECTOR_MIN_CONFIDENCE = 0.06
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US_CLASSIFIER_MIN_CONFIDENCE = 0.60
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EXTENDED_CLASSIFIER_MIN_CONFIDENCE = 0.90
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US_CLASSIFIER_REJECT_MIN_CONFIDENCE = 0.85
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DETECTOR_CLASSIFIER_EXPANSIONS = (
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(0.00, 0.00, 0.00, 0.00, 1.10),
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(0.10, 0.06, 0.10, 0.12, 1.00),
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(0.00, 0.00, 0.18, 0.18, 0.55),
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)
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SCHOOL_ZONE_DIRECT_EXPANSIONS = (
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(0.00, 0.00, 0.18, 0.18),
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(0.00, 0.00, 0.22, 0.18),
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)
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SCHOOL_ZONE_READ_VARIANTS = (
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(0.00, 0.00, 1.00, 1.00, 0.80),
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(0.00, 0.35, 1.00, 1.00, 0.88),
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(0.00, 0.45, 1.00, 1.00, 0.96),
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(0.12, 0.38, 0.88, 1.00, 1.00),
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)
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DETECTOR_CLASSIFIER_SUPPORT_BONUS = 0.06
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DETECTOR_CLASSIFIER_REGULATORY_BONUS = 0.05
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DETECTOR_CLASSIFIER_NON_REGULATORY_PENALTY = 0.03
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DETECTOR_CLASSIFIER_SMALL_BOX_AREA_RATIO = 0.004
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DETECTOR_CLASSIFIER_TINY_LOW_CONF_AREA_RATIO = 0.002
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DETECTOR_CLASSIFIER_TINY_LOW_CONF_MIN_CONFIDENCE = 0.16
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DETECTOR_CLASSIFIER_MIN_ACCEPT_WIDTH = 28
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DETECTOR_CLASSIFIER_MIN_ACCEPT_HEIGHT = 40
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DETECTOR_CLASSIFIER_RESCUE_MIN_WIDTH = 14
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DETECTOR_CLASSIFIER_RESCUE_MIN_HEIGHT = 18
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DETECTOR_CLASSIFIER_RESCUE_MIN_X_RATIO = 0.52
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DETECTOR_CLASSIFIER_RESCUE_MIN_SUPPORT = 2
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DETECTOR_CLASSIFIER_RESCUE_MIN_CONFIDENCE = 0.90
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DETECTOR_CLASSIFIER_RESCUE_MAX_SCORE = 0.64
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DETECTOR_CLASSIFIER_STRONG_RESCUE_MIN_SUPPORT = 3
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DETECTOR_CLASSIFIER_STRONG_RESCUE_MIN_PROPOSAL_CONFIDENCE = 0.60
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DETECTOR_CLASSIFIER_STRONG_RESCUE_MIN_READ_CONFIDENCE = 0.995
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DETECTOR_CLASSIFIER_STRONG_RESCUE_MAX_SCORE = 0.74
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DETECTOR_CLASSIFIER_TRUSTED_MODEL_MAX_HEIGHT = 55
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DETECTOR_CLASSIFIER_TRUSTED_MODEL_MAX_AREA_RATIO = 0.002
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DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_PROPOSAL_CONFIDENCE = 0.18
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DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_X_RATIO = 0.52
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DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_READ_CONFIDENCE = 0.65
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DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_SUPPORT = 2
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DETECTOR_CLASSIFIER_STRONG_MODEL_MIN_PROPOSAL_CONFIDENCE = 0.60
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DETECTOR_CLASSIFIER_STRONG_MODEL_MIN_READ_CONFIDENCE = 0.995
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DETECTOR_CLASSIFIER_STRONG_MODEL_CONSENSUS_MIN_READ_CONFIDENCE = 0.95
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DETECTOR_CLASSIFIER_STRONG_MODEL_CONSENSUS_ENABLED = True
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DETECTOR_CLASSIFIER_STRONG_MODEL_CONSENSUS_MIN_SUPPORT = 2
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DETECTOR_CLASSIFIER_MODEL_ONLY_CONSENSUS_MIN_CONFIDENCE = 0.90
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DETECTOR_CLASSIFIER_MODEL_ONLY_CONSENSUS_MIN_SUPPORT = 2
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SCHOOL_ZONE_SPEED_PRIOR = 0.12
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SCHOOL_ZONE_SUPPORT_BONUS = 0.08
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SCHOOL_ZONE_MIN_SUPPORT = 2
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SCHOOL_ZONE_MIN_CONFIDENCE = 0.70
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SCHOOL_ZONE_SINGLE_READ_CONFIDENCE = 0.975
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SCHOOL_ZONE_SHORT_CIRCUIT_CONFIDENCE = 0.78
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SCHOOL_ZONE_FALLBACK_MIN_CONFIDENCE = 0.35
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NON_SCHOOL_LOW_SPEED_COMPETING_MIN_CONFIDENCE = 0.95
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@dataclass(frozen=True)
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class Detection:
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speed_limit_mph: int
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confidence: float
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strong_consensus: bool = False
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@dataclass(frozen=True)
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class DetectorProposal:
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confidence: float
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class_id: int
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bbox: tuple[int, int, int, int]
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speed_limit_mph: int = 0
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@dataclass(frozen=True)
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class HistoryEntry:
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speed_limit_mph: int
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confidence: float
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created_at: float
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strong_consensus: bool = False
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class VisionModelCore:
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"""The frozen active detector/classifier path, with no camera or Params ownership."""
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ASSET_DIR = Path(__file__).resolve().parent / "assets"
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DETECTOR_SHA256 = "82408b68c79c269296f0af942130c5383cace4ee06c78e2a4690e8488720116a"
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CLASSIFIER_SHA256 = "07c6696e530eb940d2757d5849b4bc0f1d785cda704e5296e18c0a94959f30a5"
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DETECTOR_SIZE = 256
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CLASSIFIER_SIZE = 128
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def __init__(self, *, is_metric: bool):
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if cv2 is None:
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raise RuntimeError("OpenCV DNN is unavailable")
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self.is_metric = is_metric
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self.net = self._load_net("speed_limit_us_detector.onnx", self.DETECTOR_SHA256)
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self.classifier_net = self._load_net("speed_limit_us_value_classifier.onnx", self.CLASSIFIER_SHA256)
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self._validate_model_abi()
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self.detector_input_size = self.DETECTOR_SIZE
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self.classifier_input_size = self.CLASSIFIER_SIZE
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self.last_detector_forward_count = 0
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self.last_detector_forward_duration_s = 0.0
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self.last_classifier_forward_count = 0
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self.last_classifier_forward_duration_s = 0.0
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self.temporal_tracking_enabled = False
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self.latest_detector_proposal = None
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self.history: deque[HistoryEntry] = deque()
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self.published_speed_limit_mph = 0
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self.published_confidence = 0.0
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self.previous_published_speed_limit_mph = 0
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self.last_publish_change_at = 0.0
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self.last_detection_at = 0.0
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self.last_published_support_at = 0.0
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self.episode = 0
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self.published_support_count = 0
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def reset(self) -> None:
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"""Discard evidence from a previous camera, stream or sign-unit setting."""
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self.history.clear()
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self.latest_detector_proposal = None
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self.published_speed_limit_mph = 0
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self.published_confidence = 0.0
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self.previous_published_speed_limit_mph = 0
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self.last_publish_change_at = 0.0
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self.last_detection_at = 0.0
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self.last_published_support_at = 0.0
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self.published_support_count = 0
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self.episode = 0
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@classmethod
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def _load_net(cls, name: str, expected_sha256: str):
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path = cls.ASSET_DIR / name
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with path.open("rb") as stream:
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actual = hashlib.file_digest(stream, "sha256").hexdigest()
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if actual != expected_sha256:
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raise ValueError(f"Vision model identity mismatch: {name}")
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net = cv2.dnn.readNetFromONNX(str(path))
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net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
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net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
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return net
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def _validate_model_abi(self) -> None:
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self.net.setInput(np.zeros((1, 3, self.DETECTOR_SIZE, self.DETECTOR_SIZE), dtype=np.float32))
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detector_output = self.net.forward()
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self.classifier_net.setInput(np.zeros((1, 3, self.CLASSIFIER_SIZE, self.CLASSIFIER_SIZE), dtype=np.float32))
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classifier_output = self.classifier_net.forward()
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if (detector_output.shape != (1, 5, 1344) or classifier_output.shape != (1, 19) or
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not np.isfinite(detector_output).all() or not np.isfinite(classifier_output).all()):
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raise ValueError("Unsupported Vision model output shape or nonfinite output")
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def infer(self, frame_bgr: np.ndarray) -> Detection | None:
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if frame_bgr.ndim != 3 or frame_bgr.shape[2] != 3 or frame_bgr.size == 0:
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raise ValueError("Expected a nonempty BGR frame")
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return self._publishable_detection(self._detect_sign_from_detector_classifier(frame_bgr))
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def observe(self, frame_bgr: np.ndarray, *, now: float) -> tuple[int, float, int, int] | None:
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"""Return the confirmed sign numeral in the configured units until reset or replacement."""
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if not math.isfinite(now) or now <= 0:
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raise ValueError("Invalid observation time")
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detection = self.infer(frame_bgr)
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if detection is not None and math.isfinite(detection.confidence) and 0 <= detection.confidence <= 1:
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self.last_detection_at = now
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self.history.append(HistoryEntry(detection.speed_limit_mph, detection.confidence, now, detection.strong_consensus))
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self._prune_history(now)
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confirmed = self._confirm_detection()
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if confirmed is not None:
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speed, confidence = confirmed
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reverting_early = (speed != self.published_speed_limit_mph and
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speed == self.previous_published_speed_limit_mph and
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now - self.last_publish_change_at < PUBLISHED_CHANGE_COOLDOWN_SECONDS and
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confidence < PUBLISHED_REVERT_CONFIDENCE)
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if reverting_early:
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return (
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self.published_speed_limit_mph, self.published_confidence, self.published_support_count, self.episode)
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support = sum(entry.speed_limit_mph == speed for entry in self.history)
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if speed != self.published_speed_limit_mph:
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self.previous_published_speed_limit_mph = self.published_speed_limit_mph
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self.published_speed_limit_mph = speed
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self.last_publish_change_at = now
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self.episode += 1
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self.history.clear()
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self.history.append(HistoryEntry(speed, confidence, now, detection.strong_consensus))
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self.published_confidence = confidence
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self.published_support_count = support
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if detection.speed_limit_mph == speed:
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self.last_published_support_at = now
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if self.published_speed_limit_mph:
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return self.published_speed_limit_mph, self.published_confidence, self.published_support_count, self.episode
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return None
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@staticmethod
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def _letterbox(image, shape=(640, 640), color=(114, 114, 114)):
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image_height, image_width = image.shape[:2]
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ratio = min(shape[0] / image_height, shape[1] / image_width)
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resized_width = int(round(image_width * ratio))
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resized_height = int(round(image_height * ratio))
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pad_width = (shape[1] - resized_width) / 2
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pad_height = (shape[0] - resized_height) / 2
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if (image_width, image_height) != (resized_width, resized_height):
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image = cv2.resize(image, (resized_width, resized_height), interpolation=cv2.INTER_LINEAR)
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top = int(round(pad_height - 0.1))
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bottom = int(round(pad_height + 0.1))
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left = int(round(pad_width - 0.1))
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right = int(round(pad_width + 0.1))
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image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
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return image, ratio, pad_width, pad_height
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def _remember_detector_proposal(self, confidence, class_id, bbox, speed_limit_mph=0, preferred=False):
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min_confidence = TRACK_MIN_PROPOSAL_CONFIDENCE if speed_limit_mph else TRACK_UNREADABLE_MIN_PROPOSAL_CONFIDENCE
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if not getattr(self, "temporal_tracking_enabled", TEMPORAL_TRACKING_ENABLED) or class_id == 1 or confidence < min_confidence:
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return
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proposal = DetectorProposal(float(confidence), int(class_id), bbox, int(speed_limit_mph))
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latest_proposal = getattr(self, "latest_detector_proposal", None)
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if preferred or latest_proposal is None or proposal.confidence > latest_proposal.confidence:
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self.latest_detector_proposal = proposal
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def _publishable_detection(self, detection):
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if detection is None:
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return None
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if detection.speed_limit_mph < MIN_PUBLISHABLE_SPEED_LIMIT_MPH:
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return None
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if not getattr(self, "is_metric", False) and detection.speed_limit_mph > MAX_IMPERIAL_PUBLISHABLE_SPEED_LIMIT_MPH:
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return None
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return detection
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def _is_regulatory_speed_sign(self, sign_crop):
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if sign_crop.size == 0:
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return False
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crop_height, crop_width = sign_crop.shape[:2]
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crop_area = crop_height * crop_width
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if crop_area <= 0:
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return False
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hsv = cv2.cvtColor(sign_crop, cv2.COLOR_BGR2HSV)
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hue = hsv[:, :, 0]
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saturation = hsv[:, :, 1]
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value = hsv[:, :, 2]
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white_mask = ((value >= REGULATORY_WHITE_VALUE_MIN) & (saturation <= REGULATORY_WHITE_SAT_MAX)).astype(np.uint8)
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dark_mask = ((value <= REGULATORY_DARK_VALUE_MAX) & (saturation <= REGULATORY_DARK_SAT_MAX)).astype(np.uint8)
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yellow_mask = (
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(hue >= REGULATORY_YELLOW_HUE_MIN) &
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(hue <= REGULATORY_YELLOW_HUE_MAX) &
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(saturation >= REGULATORY_YELLOW_SAT_MIN) &
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(value >= REGULATORY_YELLOW_VALUE_MIN)
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).astype(np.uint8)
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red_mask = (
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(((hue <= REGULATORY_RED_LOW_HUE_MAX) | (hue >= REGULATORY_RED_HIGH_HUE_MIN))) &
|
|
(saturation >= REGULATORY_RED_SAT_MIN) &
|
|
(value >= REGULATORY_RED_VALUE_MIN)
|
|
).astype(np.uint8)
|
|
green_mask = (
|
|
(hue >= REGULATORY_GREEN_HUE_MIN) &
|
|
(hue <= REGULATORY_GREEN_HUE_MAX) &
|
|
(saturation >= REGULATORY_COLORED_SAT_MIN) &
|
|
(value >= REGULATORY_COLORED_VALUE_MIN)
|
|
).astype(np.uint8)
|
|
blue_mask = (
|
|
(hue >= REGULATORY_BLUE_HUE_MIN) &
|
|
(hue <= REGULATORY_BLUE_HUE_MAX) &
|
|
(saturation >= REGULATORY_COLORED_SAT_MIN) &
|
|
(value >= REGULATORY_COLORED_VALUE_MIN)
|
|
).astype(np.uint8)
|
|
|
|
white_ratio = float(white_mask.mean())
|
|
dark_ratio = float(dark_mask.mean())
|
|
yellow_ratio = float(yellow_mask.mean())
|
|
red_ratio = float(red_mask.mean())
|
|
green_ratio = float(green_mask.mean())
|
|
blue_ratio = float(blue_mask.mean())
|
|
|
|
if white_ratio < REGULATORY_MIN_WHITE_RATIO or dark_ratio < REGULATORY_MIN_DARK_RATIO:
|
|
return False
|
|
if yellow_ratio > REGULATORY_MAX_YELLOW_RATIO and yellow_ratio > white_ratio * 0.45:
|
|
return False
|
|
if red_ratio > REGULATORY_MAX_RED_RATIO and red_ratio > white_ratio * 0.35:
|
|
return False
|
|
if green_ratio > REGULATORY_MAX_GREEN_RATIO and green_ratio > white_ratio * 0.60:
|
|
return False
|
|
if blue_ratio > REGULATORY_MAX_BLUE_RATIO and blue_ratio > white_ratio * 0.60:
|
|
return False
|
|
|
|
white_binary = (white_mask * 255).astype(np.uint8)
|
|
contours, _ = cv2.findContours(white_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
min_component_area = crop_area * REGULATORY_MIN_WHITE_COMPONENT_RATIO
|
|
|
|
for contour in contours:
|
|
area = cv2.contourArea(contour)
|
|
if area < min_component_area:
|
|
continue
|
|
|
|
x, y, width, height = cv2.boundingRect(contour)
|
|
if height < crop_height * REGULATORY_MIN_COMPONENT_HEIGHT_RATIO:
|
|
continue
|
|
if width < crop_width * REGULATORY_MIN_COMPONENT_WIDTH_RATIO:
|
|
continue
|
|
|
|
aspect_ratio = width / max(height, 1)
|
|
if aspect_ratio < REGULATORY_MIN_ASPECT_RATIO or aspect_ratio > REGULATORY_MAX_ASPECT_RATIO:
|
|
continue
|
|
|
|
fill_ratio = area / max(width * height, 1)
|
|
if fill_ratio < REGULATORY_MIN_COMPONENT_FILL:
|
|
continue
|
|
|
|
return True
|
|
|
|
return False
|
|
|
|
|
|
@staticmethod
|
|
def _softmax(scores):
|
|
scores = scores.astype(np.float32)
|
|
scores = scores - np.max(scores)
|
|
exp_scores = np.exp(scores)
|
|
denominator = np.sum(exp_scores)
|
|
if denominator <= 0:
|
|
return exp_scores
|
|
return exp_scores / denominator
|
|
|
|
|
|
@staticmethod
|
|
def _normalize_classifier_output(scores):
|
|
scores = scores.astype(np.float32)
|
|
if scores.size == 0:
|
|
return scores
|
|
|
|
# Ultralytics classifier ONNX exports may already emit normalized probabilities.
|
|
if np.all(scores >= 0.0) and np.all(scores <= 1.0):
|
|
total = float(np.sum(scores))
|
|
if 0.99 <= total <= 1.01:
|
|
return scores
|
|
|
|
return VisionModelCore._softmax(scores)
|
|
|
|
|
|
@staticmethod
|
|
def _square_resize(image, size=128, color=(114, 114, 114)):
|
|
image_height, image_width = image.shape[:2]
|
|
ratio = min(size / max(image_height, 1), size / max(image_width, 1))
|
|
resized_width = max(int(round(image_width * ratio)), 1)
|
|
resized_height = max(int(round(image_height * ratio)), 1)
|
|
image = cv2.resize(image, (resized_width, resized_height), interpolation=cv2.INTER_LINEAR)
|
|
|
|
canvas = np.full((size, size, image.shape[2]), color, dtype=image.dtype)
|
|
offset_x = (size - resized_width) // 2
|
|
offset_y = (size - resized_height) // 2
|
|
canvas[offset_y:offset_y + resized_height, offset_x:offset_x + resized_width] = image
|
|
return canvas
|
|
|
|
|
|
@staticmethod
|
|
def _crop_by_ratio(image, left_ratio, top_ratio, right_ratio, bottom_ratio):
|
|
image_height, image_width = image.shape[:2]
|
|
x1 = max(int(image_width * left_ratio), 0)
|
|
y1 = max(int(image_height * top_ratio), 0)
|
|
x2 = min(int(image_width * right_ratio), image_width)
|
|
y2 = min(int(image_height * bottom_ratio), image_height)
|
|
if x2 <= x1 or y2 <= y1:
|
|
return None
|
|
crop = image[y1:y2, x1:x2]
|
|
return crop if crop.size > 0 else None
|
|
|
|
|
|
def _iter_school_zone_read_crops(self, sign_crop):
|
|
yielded = set()
|
|
for left_ratio, top_ratio, right_ratio, bottom_ratio, weight in SCHOOL_ZONE_READ_VARIANTS:
|
|
crop = self._crop_by_ratio(sign_crop, left_ratio, top_ratio, right_ratio, bottom_ratio)
|
|
if crop is None:
|
|
continue
|
|
key = (crop.shape[1], crop.shape[0], left_ratio, top_ratio, right_ratio, bottom_ratio)
|
|
if key in yielded:
|
|
continue
|
|
yielded.add(key)
|
|
yield crop, weight
|
|
|
|
|
|
def _collect_detector_classifier_proposals_from_region(self, frame_bgr, origin_x, origin_y, full_frame_width, full_frame_height, min_confidence):
|
|
if self.net is None:
|
|
return []
|
|
|
|
region_height, region_width = frame_bgr.shape[:2]
|
|
detector_shape = (self.detector_input_size, self.detector_input_size)
|
|
letterboxed, ratio, pad_width, pad_height = self._letterbox(frame_bgr, shape=detector_shape)
|
|
blob = cv2.dnn.blobFromImage(letterboxed, scalefactor=1 / 255.0, size=detector_shape, swapRB=True, crop=False)
|
|
self.net.setInput(blob)
|
|
|
|
forward_started_at = time.monotonic()
|
|
predictions = np.squeeze(self.net.forward())
|
|
self.last_detector_forward_count += 1
|
|
self.last_detector_forward_duration_s += time.monotonic() - forward_started_at
|
|
if predictions.ndim != 2:
|
|
return []
|
|
if predictions.shape[0] < predictions.shape[1]:
|
|
predictions = predictions.T
|
|
|
|
candidates = []
|
|
class_scores = predictions[:, 4:]
|
|
class_ids = np.argmax(class_scores, axis=1)
|
|
confidences = class_scores[np.arange(len(predictions)), class_ids].astype(np.float64)
|
|
selected = np.flatnonzero(np.isin(class_ids, tuple(US_DETECTOR_CLASSES)) & ~(confidences < min_confidence))
|
|
for index in selected:
|
|
prediction = predictions[index]
|
|
class_id = int(class_ids[index])
|
|
confidence = float(confidences[index])
|
|
|
|
center_x, center_y, width, height = prediction[:4]
|
|
x1 = max(int((center_x - width / 2 - pad_width) / ratio), 0)
|
|
y1 = max(int((center_y - height / 2 - pad_height) / ratio), 0)
|
|
x2 = min(int((center_x + width / 2 - pad_width) / ratio), region_width)
|
|
y2 = min(int((center_y + height / 2 - pad_height) / ratio), region_height)
|
|
if x2 <= x1 or y2 <= y1:
|
|
continue
|
|
|
|
x1 += origin_x
|
|
y1 += origin_y
|
|
x2 += origin_x
|
|
y2 += origin_y
|
|
|
|
box_width = x2 - x1
|
|
box_height = y2 - y1
|
|
if box_width < MODEL_PROPOSAL_MIN_WIDTH or box_height < MODEL_PROPOSAL_MIN_HEIGHT:
|
|
continue
|
|
if box_width * box_height > full_frame_width * full_frame_height * MODEL_PROPOSAL_MAX_AREA_RATIO:
|
|
continue
|
|
if (x1 + x2) / 2 < full_frame_width * MODEL_PROPOSAL_MIN_X_RATIO:
|
|
continue
|
|
if y1 > full_frame_height * MODEL_PROPOSAL_MAX_Y_RATIO:
|
|
continue
|
|
|
|
candidates.append((confidence, class_id, (x1, y1, x2, y2)))
|
|
|
|
return candidates
|
|
|
|
|
|
def _collect_detector_classifier_proposals(self, frame_bgr):
|
|
if self.net is None:
|
|
return []
|
|
|
|
frame_height, frame_width = frame_bgr.shape[:2]
|
|
candidates = []
|
|
if DETECTOR_CLASSIFIER_REGION_MODE in ("full", "full_and_right_roi"):
|
|
candidates.extend(self._collect_detector_classifier_proposals_from_region(
|
|
frame_bgr,
|
|
0,
|
|
0,
|
|
frame_width,
|
|
frame_height,
|
|
US_DETECTOR_MIN_CONFIDENCE,
|
|
))
|
|
|
|
# A second pass on a focused right-side ROI materially improves small U.S. sign reads.
|
|
if DETECTOR_CLASSIFIER_REGION_MODE in ("right_roi", "full_and_right_roi"):
|
|
bounds = ROI_WINDOWS[-1]["bounds"]
|
|
minimum = ROI_WINDOWS[-1]["min_confidence"]
|
|
assert isinstance(bounds, tuple) and isinstance(minimum, (int, float))
|
|
left_ratio, top_ratio, right_ratio, bottom_ratio = bounds
|
|
left = int(frame_width * left_ratio)
|
|
top = int(frame_height * top_ratio)
|
|
right = int(frame_width * right_ratio)
|
|
bottom = int(frame_height * bottom_ratio)
|
|
roi = frame_bgr[top:bottom, left:right]
|
|
if roi.size > 0:
|
|
candidates.extend(self._collect_detector_classifier_proposals_from_region(
|
|
roi,
|
|
left,
|
|
top,
|
|
frame_width,
|
|
frame_height,
|
|
max(float(minimum), US_DETECTOR_MIN_CONFIDENCE),
|
|
))
|
|
|
|
return sorted(candidates, reverse=True)[:MODEL_PROPOSAL_MAX_COUNT]
|
|
|
|
|
|
def _classify_speed_limit_from_model(self, sign_crop):
|
|
if self.classifier_net is None or sign_crop.size == 0:
|
|
return None
|
|
|
|
speed_class_count = len(US_CLASSIFIER_SPEED_VALUES)
|
|
|
|
input_size = self.classifier_input_size
|
|
padded_crop = self._square_resize(sign_crop, size=input_size)
|
|
blob = cv2.dnn.blobFromImage(padded_crop, scalefactor=1 / 255.0, size=(input_size, input_size), swapRB=True, crop=False)
|
|
self.classifier_net.setInput(blob)
|
|
|
|
forward_started_at = time.monotonic()
|
|
scores = np.array(self.classifier_net.forward()).reshape(-1)
|
|
self.last_classifier_forward_count += 1
|
|
self.last_classifier_forward_duration_s += time.monotonic() - forward_started_at
|
|
has_reject_class = scores.size == speed_class_count + 1
|
|
if scores.size != speed_class_count and not has_reject_class:
|
|
return None
|
|
|
|
probabilities = self._normalize_classifier_output(scores)
|
|
speed_probabilities = probabilities[:speed_class_count]
|
|
class_index = int(np.argmax(speed_probabilities))
|
|
confidence = float(speed_probabilities[class_index])
|
|
speed_limit = US_CLASSIFIER_SPEED_VALUES[class_index]
|
|
if has_reject_class and float(probabilities[speed_class_count]) >= max(confidence, US_CLASSIFIER_REJECT_MIN_CONFIDENCE):
|
|
return None
|
|
minimum_confidence = (
|
|
EXTENDED_CLASSIFIER_MIN_CONFIDENCE if speed_limit in EXTENDED_CLASSIFIER_SPEED_VALUES else US_CLASSIFIER_MIN_CONFIDENCE
|
|
)
|
|
if confidence < minimum_confidence:
|
|
return None
|
|
|
|
return speed_limit, confidence
|
|
|
|
|
|
def _detect_sign_from_detector_classifier(self, frame_bgr):
|
|
frame_height, frame_width = frame_bgr.shape[:2]
|
|
best_detection = None
|
|
best_score = 0.0
|
|
|
|
for proposal_confidence, class_id, (x1, y1, x2, y2) in self._collect_detector_classifier_proposals(frame_bgr):
|
|
if class_id == 1:
|
|
continue
|
|
|
|
box_width = x2 - x1
|
|
box_height = y2 - y1
|
|
if box_width <= 0 or box_height <= 0:
|
|
continue
|
|
is_small_box = box_width < DETECTOR_CLASSIFIER_MIN_ACCEPT_WIDTH or box_height < DETECTOR_CLASSIFIER_MIN_ACCEPT_HEIGHT
|
|
# Tiny far-away proposals are the main nighttime false-positive source.
|
|
# Keep a narrow rescue path for right-side high-confidence reads,
|
|
# then let temporal confirmation decide whether they are real.
|
|
if is_small_box and (
|
|
box_width < DETECTOR_CLASSIFIER_RESCUE_MIN_WIDTH or
|
|
box_height < DETECTOR_CLASSIFIER_RESCUE_MIN_HEIGHT or
|
|
x1 < frame_width * DETECTOR_CLASSIFIER_RESCUE_MIN_X_RATIO
|
|
):
|
|
continue
|
|
|
|
proposal_area_ratio = (box_width * box_height) / max(frame_width * frame_height, 1)
|
|
is_tiny_low_conf_box = (
|
|
class_id != 2 and
|
|
proposal_area_ratio < DETECTOR_CLASSIFIER_TINY_LOW_CONF_AREA_RATIO and
|
|
proposal_confidence < DETECTOR_CLASSIFIER_TINY_LOW_CONF_MIN_CONFIDENCE
|
|
)
|
|
self._remember_detector_proposal(proposal_confidence, class_id, (x1, y1, x2, y2))
|
|
|
|
if class_id == 2:
|
|
school_scores: dict[int, float] = {}
|
|
competing_scores: dict[int, float] = {}
|
|
school_best_confidences: dict[int, float] = {}
|
|
school_support_counts: dict[int, int] = {}
|
|
for expand_left, expand_top, expand_right, expand_bottom in SCHOOL_ZONE_DIRECT_EXPANSIONS:
|
|
expanded_x1 = max(int(x1 - box_width * expand_left), 0)
|
|
expanded_y1 = max(int(y1 - box_height * expand_top), 0)
|
|
expanded_x2 = min(int(x2 + box_width * expand_right), frame_width)
|
|
expanded_y2 = min(int(y2 + box_height * expand_bottom), frame_height)
|
|
sign_crop = frame_bgr[expanded_y1:expanded_y2, expanded_x1:expanded_x2]
|
|
if sign_crop.size == 0:
|
|
continue
|
|
|
|
for school_crop, crop_weight in self._iter_school_zone_read_crops(sign_crop):
|
|
read_result = self._classify_speed_limit_from_model(school_crop)
|
|
if read_result is None:
|
|
continue
|
|
|
|
speed_limit_mph, read_confidence = read_result
|
|
if speed_limit_mph not in SCHOOL_ZONE_SPEED_VALUES:
|
|
competing_scores[speed_limit_mph] = competing_scores.get(speed_limit_mph, 0.0) + read_confidence * crop_weight
|
|
continue
|
|
|
|
school_scores[speed_limit_mph] = school_scores.get(speed_limit_mph, 0.0) + read_confidence * crop_weight
|
|
school_best_confidences[speed_limit_mph] = max(school_best_confidences.get(speed_limit_mph, 0.0), read_confidence)
|
|
school_support_counts[speed_limit_mph] = school_support_counts.get(speed_limit_mph, 0) + 1
|
|
|
|
if school_scores:
|
|
speed_limit_mph = max(
|
|
school_scores,
|
|
key=lambda speed: (
|
|
school_scores[speed] + max(school_support_counts[speed] - 1, 0) * SCHOOL_ZONE_SUPPORT_BONUS,
|
|
school_best_confidences[speed],
|
|
),
|
|
)
|
|
read_confidence = school_best_confidences[speed_limit_mph]
|
|
support_count = school_support_counts[speed_limit_mph]
|
|
if school_scores[speed_limit_mph] > max(competing_scores.values(), default=0.0):
|
|
if (
|
|
(support_count >= SCHOOL_ZONE_MIN_SUPPORT and read_confidence >= SCHOOL_ZONE_MIN_CONFIDENCE) or
|
|
read_confidence >= SCHOOL_ZONE_SINGLE_READ_CONFIDENCE
|
|
):
|
|
score = min(
|
|
read_confidence * 0.72 +
|
|
proposal_confidence * 0.22 +
|
|
max(support_count - 1, 0) * SCHOOL_ZONE_SUPPORT_BONUS +
|
|
0.04,
|
|
0.95,
|
|
)
|
|
if score >= SCHOOL_ZONE_SHORT_CIRCUIT_CONFIDENCE:
|
|
self._remember_detector_proposal(
|
|
proposal_confidence, class_id, (x1, y1, x2, y2), speed_limit_mph, preferred=True,
|
|
)
|
|
return Detection(speed_limit_mph, score)
|
|
|
|
speed_scores: dict[int, float] = {}
|
|
speed_best_confidences: dict[int, float] = {}
|
|
speed_support_counts: dict[int, int] = {}
|
|
speed_regulatory_support: dict[int, int] = {}
|
|
speed_trusted_model_support: dict[int, int] = {}
|
|
speed_model_only_rescue_support: dict[int, int] = {}
|
|
speed_direct_model_support: dict[int, int] = {}
|
|
speed_strong_model_support: dict[int, int] = {}
|
|
|
|
expansions = getattr(self, "detector_classifier_expansions", DETECTOR_CLASSIFIER_EXPANSIONS)
|
|
for expand_left, expand_top, expand_right, expand_bottom, expansion_weight in expansions:
|
|
expanded_x1 = max(int(x1 - box_width * expand_left), 0)
|
|
expanded_y1 = max(int(y1 - box_height * expand_top), 0)
|
|
expanded_x2 = min(int(x2 + box_width * expand_right), frame_width)
|
|
expanded_y2 = min(int(y2 + box_height * expand_bottom), frame_height)
|
|
sign_crop = frame_bgr[expanded_y1:expanded_y2, expanded_x1:expanded_x2]
|
|
if sign_crop.size == 0:
|
|
continue
|
|
|
|
raw_is_regulatory = self._is_regulatory_speed_sign(sign_crop)
|
|
is_regulatory = raw_is_regulatory
|
|
if class_id == 2:
|
|
is_regulatory = True
|
|
|
|
model_read = self._classify_speed_limit_from_model(sign_crop)
|
|
trusted_model_read = (
|
|
class_id == 0 and
|
|
model_read is not None and
|
|
x1 >= frame_width * DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_X_RATIO and
|
|
box_height <= DETECTOR_CLASSIFIER_TRUSTED_MODEL_MAX_HEIGHT and
|
|
proposal_area_ratio <= DETECTOR_CLASSIFIER_TRUSTED_MODEL_MAX_AREA_RATIO and
|
|
proposal_confidence >= DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_PROPOSAL_CONFIDENCE and
|
|
model_read[1] >= DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_READ_CONFIDENCE
|
|
)
|
|
strong_model_read = (
|
|
class_id == 0 and
|
|
model_read is not None and
|
|
not is_small_box and
|
|
proposal_confidence >= DETECTOR_CLASSIFIER_STRONG_MODEL_MIN_PROPOSAL_CONFIDENCE and
|
|
model_read[1] >= DETECTOR_CLASSIFIER_STRONG_MODEL_MIN_READ_CONFIDENCE
|
|
)
|
|
strong_model_consensus_read = (
|
|
class_id == 0 and
|
|
model_read is not None and
|
|
not is_small_box and
|
|
proposal_confidence >= DETECTOR_CLASSIFIER_STRONG_MODEL_MIN_PROPOSAL_CONFIDENCE and
|
|
model_read[1] >= DETECTOR_CLASSIFIER_STRONG_MODEL_CONSENSUS_MIN_READ_CONFIDENCE
|
|
)
|
|
model_only_consensus_read = (
|
|
class_id == 0 and
|
|
model_read is not None and
|
|
not is_small_box and
|
|
model_read[1] >= DETECTOR_CLASSIFIER_MODEL_ONLY_CONSENSUS_MIN_CONFIDENCE and
|
|
(is_regulatory or proposal_confidence >= DETECTOR_CLASSIFIER_STRONG_MODEL_MIN_PROPOSAL_CONFIDENCE)
|
|
)
|
|
needs_ocr_confirmation = (
|
|
class_id != 2 and
|
|
(not is_regulatory or is_tiny_low_conf_box) and
|
|
not trusted_model_read and
|
|
not strong_model_read
|
|
)
|
|
read_result = model_read
|
|
if read_result is None:
|
|
continue
|
|
|
|
if needs_ocr_confirmation and not trusted_model_read and not strong_model_read and not model_only_consensus_read:
|
|
continue
|
|
|
|
speed_limit_mph, read_confidence = read_result
|
|
score_is_regulatory = is_regulatory or trusted_model_read or strong_model_read
|
|
if (
|
|
class_id == 2 and
|
|
proposal_confidence < SCHOOL_ZONE_FALLBACK_MIN_CONFIDENCE and
|
|
not raw_is_regulatory
|
|
):
|
|
continue
|
|
|
|
score = read_confidence * expansion_weight
|
|
if score_is_regulatory:
|
|
score += DETECTOR_CLASSIFIER_REGULATORY_BONUS
|
|
elif proposal_area_ratio >= DETECTOR_CLASSIFIER_SMALL_BOX_AREA_RATIO:
|
|
score -= DETECTOR_CLASSIFIER_NON_REGULATORY_PENALTY
|
|
if class_id == 2:
|
|
score += SCHOOL_ZONE_SPEED_PRIOR if speed_limit_mph in SCHOOL_ZONE_SPEED_VALUES else -SCHOOL_ZONE_SPEED_PRIOR
|
|
|
|
speed_scores[speed_limit_mph] = speed_scores.get(speed_limit_mph, 0.0) + score
|
|
speed_best_confidences[speed_limit_mph] = max(speed_best_confidences.get(speed_limit_mph, 0.0), read_confidence)
|
|
speed_support_counts[speed_limit_mph] = speed_support_counts.get(speed_limit_mph, 0) + 1
|
|
if is_regulatory or class_id == 2 or strong_model_read:
|
|
speed_regulatory_support[speed_limit_mph] = speed_regulatory_support.get(speed_limit_mph, 0) + 1
|
|
if trusted_model_read:
|
|
speed_trusted_model_support[speed_limit_mph] = speed_trusted_model_support.get(speed_limit_mph, 0) + 1
|
|
if strong_model_consensus_read:
|
|
speed_strong_model_support[speed_limit_mph] = speed_strong_model_support.get(speed_limit_mph, 0) + 1
|
|
if needs_ocr_confirmation and model_only_consensus_read:
|
|
speed_model_only_rescue_support[speed_limit_mph] = speed_model_only_rescue_support.get(speed_limit_mph, 0) + 1
|
|
elif model_read is not None:
|
|
speed_direct_model_support[speed_limit_mph] = speed_direct_model_support.get(speed_limit_mph, 0) + 1
|
|
|
|
if not speed_scores:
|
|
continue
|
|
|
|
speed_limit_mph = max(
|
|
speed_scores,
|
|
key=lambda speed: (
|
|
speed_scores[speed] + max(speed_support_counts[speed] - 1, 0) * DETECTOR_CLASSIFIER_SUPPORT_BONUS,
|
|
speed_best_confidences[speed],
|
|
),
|
|
)
|
|
if class_id == 2 and speed_limit_mph not in SCHOOL_ZONE_SPEED_VALUES:
|
|
continue
|
|
model_only_rescue_support = speed_model_only_rescue_support.get(speed_limit_mph, 0)
|
|
if (
|
|
speed_direct_model_support.get(speed_limit_mph, 0) < 1 and
|
|
0 < model_only_rescue_support < DETECTOR_CLASSIFIER_MODEL_ONLY_CONSENSUS_MIN_SUPPORT
|
|
):
|
|
continue
|
|
if (
|
|
speed_regulatory_support.get(speed_limit_mph, 0) < 1 and
|
|
0 < speed_trusted_model_support.get(speed_limit_mph, 0) < DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_SUPPORT
|
|
):
|
|
continue
|
|
if class_id != 2 and speed_limit_mph in SCHOOL_ZONE_SPEED_VALUES:
|
|
competing_speed_limit_mph = max(
|
|
(speed for speed in speed_scores if speed not in SCHOOL_ZONE_SPEED_VALUES),
|
|
key=lambda speed: (speed_best_confidences[speed], speed_scores[speed]),
|
|
default=None,
|
|
)
|
|
if competing_speed_limit_mph is not None:
|
|
read_confidence = speed_best_confidences[speed_limit_mph]
|
|
competing_confidence = speed_best_confidences[competing_speed_limit_mph]
|
|
if (
|
|
competing_confidence >= NON_SCHOOL_LOW_SPEED_COMPETING_MIN_CONFIDENCE and
|
|
competing_confidence >= read_confidence
|
|
):
|
|
speed_limit_mph = competing_speed_limit_mph
|
|
read_confidence = speed_best_confidences[speed_limit_mph]
|
|
support_count = speed_support_counts[speed_limit_mph]
|
|
strong_rescue = (
|
|
DETECTOR_CLASSIFIER_STRONG_MODEL_CONSENSUS_ENABLED and
|
|
speed_strong_model_support.get(speed_limit_mph, 0) >= DETECTOR_CLASSIFIER_STRONG_MODEL_CONSENSUS_MIN_SUPPORT
|
|
)
|
|
score = min(
|
|
read_confidence * 0.72 +
|
|
proposal_confidence * 0.24 +
|
|
max(support_count - 1, 0) * DETECTOR_CLASSIFIER_SUPPORT_BONUS,
|
|
0.95,
|
|
)
|
|
selection_score = score
|
|
published_score = score
|
|
if class_id == 2:
|
|
if speed_limit_mph in SCHOOL_ZONE_SPEED_VALUES:
|
|
selection_score = min(score + 0.06, 0.95)
|
|
published_score = selection_score
|
|
else:
|
|
selection_score = max(score - 0.06, 0.0)
|
|
published_score = selection_score
|
|
elif is_small_box:
|
|
if (
|
|
speed_regulatory_support.get(speed_limit_mph, 0) < 1 and
|
|
speed_trusted_model_support.get(speed_limit_mph, 0) < DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_SUPPORT
|
|
):
|
|
continue
|
|
if support_count < DETECTOR_CLASSIFIER_RESCUE_MIN_SUPPORT:
|
|
continue
|
|
if read_confidence < DETECTOR_CLASSIFIER_RESCUE_MIN_CONFIDENCE:
|
|
continue
|
|
strong_rescue = strong_rescue or (
|
|
speed_trusted_model_support.get(speed_limit_mph, 0) >= DETECTOR_CLASSIFIER_STRONG_RESCUE_MIN_SUPPORT and
|
|
proposal_confidence >= DETECTOR_CLASSIFIER_STRONG_RESCUE_MIN_PROPOSAL_CONFIDENCE and
|
|
read_confidence >= DETECTOR_CLASSIFIER_STRONG_RESCUE_MIN_READ_CONFIDENCE
|
|
)
|
|
rescue_max_score = (
|
|
DETECTOR_CLASSIFIER_STRONG_RESCUE_MAX_SCORE if strong_rescue else DETECTOR_CLASSIFIER_RESCUE_MAX_SCORE
|
|
)
|
|
published_score = min(score, rescue_max_score)
|
|
if speed_trusted_model_support.get(speed_limit_mph, 0) < DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_SUPPORT:
|
|
selection_score = published_score
|
|
if selection_score > best_score:
|
|
best_score = selection_score
|
|
best_detection = Detection(speed_limit_mph, published_score, strong_rescue)
|
|
self._remember_detector_proposal(
|
|
proposal_confidence, class_id, (x1, y1, x2, y2), speed_limit_mph, preferred=True,
|
|
)
|
|
if best_detection is not None and best_detection.confidence >= MODEL_DETECTION_SHORT_CIRCUIT_CONFIDENCE:
|
|
return best_detection
|
|
|
|
return best_detection
|
|
|
|
|
|
def _prune_history(self, now):
|
|
while self.history and now - self.history[0].created_at > HISTORY_SECONDS:
|
|
self.history.popleft()
|
|
|
|
|
|
def _confirm_detection(self):
|
|
if not self.history:
|
|
return None
|
|
|
|
counts = Counter(entry.speed_limit_mph for entry in self.history)
|
|
candidate_speed_limit, candidate_count = counts.most_common(1)[0]
|
|
matching_entries = [entry for entry in self.history if entry.speed_limit_mph == candidate_speed_limit]
|
|
matching_confidences = sorted((entry.confidence for entry in matching_entries), reverse=True)
|
|
best_confidence = max(entry.confidence for entry in matching_entries)
|
|
has_strong_consensus = any(entry.strong_consensus for entry in matching_entries)
|
|
current_speed_limit = self.published_speed_limit_mph
|
|
current_count = counts.get(current_speed_limit, 0) if current_speed_limit > 0 else 0
|
|
|
|
if current_speed_limit > 0 and candidate_speed_limit != current_speed_limit:
|
|
required_count = CHANGE_CONSISTENT_DETECTIONS
|
|
allow_single_frame_confirmation = (
|
|
has_strong_consensus or best_confidence >= CHANGE_SINGLE_READ_MIN_CONFIDENCE
|
|
)
|
|
if current_speed_limit >= 30 and candidate_speed_limit < 30:
|
|
required_count = LOW_SPEED_CHANGE_CONSISTENT_DETECTIONS
|
|
allow_single_frame_confirmation = (
|
|
best_confidence >= CHANGE_SINGLE_READ_MIN_CONFIDENCE or
|
|
(has_strong_consensus and LOW_SPEED_CHANGE_ALLOW_STRONG_CONSENSUS)
|
|
)
|
|
if best_confidence < LOW_SPEED_CHANGE_MIN_CONFIDENCE:
|
|
return None
|
|
if candidate_count < required_count and not allow_single_frame_confirmation:
|
|
return None
|
|
if (
|
|
not allow_single_frame_confirmation and
|
|
matching_confidences[required_count - 1] < CHANGE_REPEAT_MIN_CONFIDENCE
|
|
):
|
|
return None
|
|
if candidate_count <= current_count:
|
|
return None
|
|
return candidate_speed_limit, best_confidence
|
|
|
|
if has_strong_consensus or best_confidence >= STRONG_DETECTION_CONFIDENCE or candidate_count >= CONSISTENT_DETECTIONS:
|
|
return candidate_speed_limit, best_confidence
|
|
return None
|