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firestar5683
2026-07-05 11:33:59 -05:00
parent 2fb5fbf3ad
commit 8ab1b723ae
4 changed files with 212 additions and 36 deletions
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starpilot/system/speed_limit_vision.py
+109 -29
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@@ -25,6 +25,10 @@ LIVE_POSE_RECOVERY_THROTTLE_SECONDS = 2.0
LIVE_POSE_RECOVERY_INFERENCE_INTERVAL = 1.0
RUNTIME_TELEMETRY_INTERVAL_SECONDS = 2.0
DEBUG_HEARTBEAT_INTERVAL_SECONDS = 30.0
DEFAULT_DETECTOR_INPUT_SIZE = 640
DETECTOR_INPUT_SIZE_CANDIDATES = (640, 512, 448, 416, 384, 320)
FULL_FRAME_OCR_FALLBACK_ENABLED = False
DETECTOR_CLASSIFIER_REGION_MODE = "right_roi" # full, right_roi, full_and_right_roi
DEVICE_BUSY_AVG_CPU_USAGE_PERCENT = 78.0
DEVICE_BUSY_MAX_CPU_USAGE_PERCENT = 92.0
DEVICE_BUSY_HOT_CORE_COUNT = 4
@@ -66,7 +70,7 @@ ROI_WINDOWS = (
{"bounds": (0.48, 0.00, 0.98, 0.42), "min_confidence": MIN_DETECTION_CONFIDENCE},
{"bounds": (0.52, 0.02, 0.97, 0.58), "min_confidence": 0.22},
{"bounds": (0.62, 0.02, 0.99, 0.68), "min_confidence": 0.18},
{"bounds": (0.72, 0.05, 1.00, 0.82), "min_confidence": 0.15},
{"bounds": (0.45, 0.00, 1.00, 0.82), "min_confidence": 0.10},
)
EDGE_MARGIN_RATIO = 0.03
MAX_BOX_AREA_RATIO = 0.22
@@ -98,10 +102,18 @@ REGULATORY_RED_LOW_HUE_MAX = 12
REGULATORY_RED_HIGH_HUE_MIN = 168
REGULATORY_RED_SAT_MIN = 80
REGULATORY_RED_VALUE_MIN = 60
REGULATORY_GREEN_HUE_MIN = 45
REGULATORY_GREEN_HUE_MAX = 90
REGULATORY_BLUE_HUE_MIN = 90
REGULATORY_BLUE_HUE_MAX = 135
REGULATORY_COLORED_SAT_MIN = 70
REGULATORY_COLORED_VALUE_MIN = 70
REGULATORY_MIN_WHITE_RATIO = 0.08
REGULATORY_MIN_DARK_RATIO = 0.01
REGULATORY_MAX_YELLOW_RATIO = 0.12
REGULATORY_MAX_RED_RATIO = 0.10
REGULATORY_MAX_GREEN_RATIO = 0.35
REGULATORY_MAX_BLUE_RATIO = 0.35
REGULATORY_MIN_WHITE_COMPONENT_RATIO = 0.012
REGULATORY_MIN_COMPONENT_FILL = 0.36
REGULATORY_MIN_COMPONENT_HEIGHT_RATIO = 0.2
@@ -125,6 +137,7 @@ SPEED_LIMIT_CLASSES = {
}
VALID_SPEED_LIMITS_MPH = set(range(10, 125, 5))
MIN_PUBLISHABLE_SPEED_LIMIT_MPH = 20
LEGACY_MODEL_PATH = Path(__file__).resolve().parents[1] / "assets" / "vision_models" / "speed_limit_vision.onnx"
US_DETECTOR_MODEL_PATH = Path(__file__).resolve().parents[1] / "assets" / "vision_models" / "speed_limit_us_detector.onnx"
US_CLASSIFIER_MODEL_PATH = Path(__file__).resolve().parents[1] / "assets" / "vision_models" / "speed_limit_us_value_classifier.onnx"
@@ -248,6 +261,7 @@ class SpeedLimitVisionDaemon:
self.net = None
self.classifier_net = None
self.model_mode = "legacy"
self.detector_input_size = DEFAULT_DETECTOR_INPUT_SIZE
self.last_error = ""
self.last_inference_at = -float("inf")
self.last_detection_at = 0.0
@@ -749,14 +763,37 @@ class SpeedLimitVisionDaemon:
self.last_inference_interval_reason = reason
return interval
@staticmethod
def _read_onnx_square_input_size(model_path):
try:
import onnx
model = onnx.load(str(model_path), load_external_data=False)
if not model.graph.input:
return DEFAULT_DETECTOR_INPUT_SIZE
shape = model.graph.input[0].type.tensor_type.shape.dim
if len(shape) < 4:
return DEFAULT_DETECTOR_INPUT_SIZE
height = int(shape[2].dim_value)
width = int(shape[3].dim_value)
if height == width and height in DETECTOR_INPUT_SIZE_CANDIDATES:
return height
except Exception:
pass
return DEFAULT_DETECTOR_INPUT_SIZE
def _load_model(self):
self.net = None
self.classifier_net = None
self.reject_classifier_net = None
self.model_mode = "legacy"
self.detector_input_size = DEFAULT_DETECTOR_INPUT_SIZE
if US_DETECTOR_MODEL_PATH.is_file() and US_CLASSIFIER_MODEL_PATH.is_file():
try:
self.detector_input_size = self._read_onnx_square_input_size(US_DETECTOR_MODEL_PATH)
self.net = cv2.dnn.readNetFromONNX(str(US_DETECTOR_MODEL_PATH))
self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
@@ -787,6 +824,7 @@ class SpeedLimitVisionDaemon:
return
try:
self.detector_input_size = self._read_onnx_square_input_size(LEGACY_MODEL_PATH)
self.net = cv2.dnn.readNetFromONNX(str(LEGACY_MODEL_PATH))
self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
@@ -917,23 +955,35 @@ class SpeedLimitVisionDaemon:
def _detect_sign(self, frame_bgr):
if self.net is None:
return self._detect_sign_from_ocr_candidates(frame_bgr)
if FULL_FRAME_OCR_FALLBACK_ENABLED:
return self._publishable_detection(self._detect_sign_from_ocr_candidates(frame_bgr))
return None
if self.model_mode == "detector_classifier" and self.classifier_net is not None:
detector_detection = self._detect_sign_from_detector_classifier(frame_bgr)
if detector_detection is not None:
return detector_detection
return self._detect_sign_from_ocr_candidates(frame_bgr)
return self._publishable_detection(detector_detection)
if FULL_FRAME_OCR_FALLBACK_ENABLED:
return self._publishable_detection(self._detect_sign_from_ocr_candidates(frame_bgr))
return None
model_detection = self._detect_sign_from_model_proposals(frame_bgr)
if model_detection is not None and model_detection.confidence >= MODEL_DETECTION_SHORT_CIRCUIT_CONFIDENCE:
return model_detection
return self._publishable_detection(model_detection)
ocr_detection = self._detect_sign_from_ocr_candidates(frame_bgr)
if ocr_detection is not None and (model_detection is None or ocr_detection.confidence > model_detection.confidence):
return ocr_detection
if FULL_FRAME_OCR_FALLBACK_ENABLED:
ocr_detection = self._detect_sign_from_ocr_candidates(frame_bgr)
if ocr_detection is not None and (model_detection is None or ocr_detection.confidence > model_detection.confidence):
return self._publishable_detection(ocr_detection)
return model_detection
return self._publishable_detection(model_detection)
def _publishable_detection(self, detection):
if detection is None:
return None
if detection.speed_limit_mph < MIN_PUBLISHABLE_SPEED_LIMIT_MPH:
return None
return detection
def _is_regulatory_speed_sign(self, sign_crop):
if sign_crop.size == 0:
@@ -962,11 +1012,25 @@ class SpeedLimitVisionDaemon:
(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
@@ -974,6 +1038,10 @@ class SpeedLimitVisionDaemon:
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)
@@ -1069,8 +1137,9 @@ class SpeedLimitVisionDaemon:
return []
frame_height, frame_width = frame_bgr.shape[:2]
letterboxed, ratio, pad_width, pad_height = self._letterbox(frame_bgr)
blob = cv2.dnn.blobFromImage(letterboxed, scalefactor=1 / 255.0, size=(640, 640), swapRB=True, crop=False)
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)
predictions = np.squeeze(self.net.forward())
@@ -1118,8 +1187,9 @@ class SpeedLimitVisionDaemon:
return []
region_height, region_width = frame_bgr.shape[:2]
letterboxed, ratio, pad_width, pad_height = self._letterbox(frame_bgr)
blob = cv2.dnn.blobFromImage(letterboxed, scalefactor=1 / 255.0, size=(640, 640), swapRB=True, crop=False)
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)
predictions = np.squeeze(self.net.forward())
@@ -1171,17 +1241,19 @@ class SpeedLimitVisionDaemon:
return []
frame_height, frame_width = frame_bgr.shape[:2]
candidates = self._collect_detector_classifier_proposals_from_region(
frame_bgr,
0,
0,
frame_width,
frame_height,
US_DETECTOR_MIN_CONFIDENCE,
)
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 ROI_WINDOWS:
if DETECTOR_CLASSIFIER_REGION_MODE in ("right_roi", "full_and_right_roi") and ROI_WINDOWS:
left_ratio, top_ratio, right_ratio, bottom_ratio = ROI_WINDOWS[-1]["bounds"]
left = int(frame_width * left_ratio)
top = int(frame_height * top_ratio)
@@ -1431,11 +1503,15 @@ class SpeedLimitVisionDaemon:
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:
score = min(score + 0.06, 0.95)
selection_score = min(score + 0.06, 0.95)
published_score = selection_score
else:
score = max(score - 0.06, 0.0)
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
@@ -1446,11 +1522,13 @@ class SpeedLimitVisionDaemon:
continue
if read_confidence < DETECTOR_CLASSIFIER_RESCUE_MIN_CONFIDENCE:
continue
score = min(score, DETECTOR_CLASSIFIER_RESCUE_MAX_SCORE)
if score > best_score:
best_score = score
best_detection = Detection(speed_limit_mph, score)
if best_score >= MODEL_DETECTION_SHORT_CIRCUIT_CONFIDENCE:
published_score = min(score, DETECTOR_CLASSIFIER_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)
if best_detection is not None and best_detection.confidence >= MODEL_DETECTION_SHORT_CIRCUIT_CONFIDENCE:
return best_detection
return best_detection
@@ -1820,6 +1898,8 @@ class SpeedLimitVisionDaemon:
"started": started,
"startedPrev": self.started_prev,
"modelMode": self.model_mode,
"detectorInputSize": self.detector_input_size,
"detectorRegionMode": DETECTOR_CLASSIFIER_REGION_MODE,
"stream": self.stream_name,
"cameraConnected": camera_connected,
"debugSession": self.debug_session_id,