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firestar5683
2026-07-05 17:31:32 -05:00
parent 8d9e97fdcd
commit 4243a5b055
2 changed files with 25 additions and 2 deletions
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starpilot/assets/vision_models/speed_limit_us_detector.onnx
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starpilot/system/speed_limit_vision.py
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@@ -26,7 +26,7 @@ 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)
DETECTOR_INPUT_SIZE_CANDIDATES = (640, 512, 448, 416, 384, 320, 288, 256)
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
@@ -150,7 +150,7 @@ US_DETECTOR_CLASSES = {
US_CLASSIFIER_SPEED_VALUES = (15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75)
SCHOOL_ZONE_SPEED_VALUES = frozenset((15, 20, 25))
US_DETECTOR_MIN_CONFIDENCE = 0.06
US_CLASSIFIER_MIN_CONFIDENCE = 0.50
US_CLASSIFIER_MIN_CONFIDENCE = 0.65
US_CLASSIFIER_REJECT_MIN_CONFIDENCE = 0.85
SEPARATE_REJECT_CLASSIFIER_ENABLED = False
US_REJECT_CLASSIFIER_MIN_CONFIDENCE = 0.85
@@ -315,6 +315,11 @@ class SpeedLimitVisionDaemon:
self.last_inference_interval = INFERENCE_INTERVAL
self.last_inference_interval_reason = "steady"
self.last_cpu_busy = False
self.last_frame_process_duration_s = 0.0
self.last_detector_forward_count = 0
self.last_detector_forward_duration_s = 0.0
self.last_classifier_forward_count = 0
self.last_classifier_forward_duration_s = 0.0
self.digit_templates = self._build_digit_templates()
self.speed_value_templates = self._build_speed_value_templates()
@@ -1195,7 +1200,10 @@ class SpeedLimitVisionDaemon:
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]:
@@ -1298,7 +1306,10 @@ class SpeedLimitVisionDaemon:
blob = cv2.dnn.blobFromImage(padded_crop, scalefactor=1 / 255.0, size=(128, 128), 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
@@ -1931,6 +1942,11 @@ class SpeedLimitVisionDaemon:
"lastInferenceIntervalS": round(float(self.last_inference_interval), 3),
"lastInferenceIntervalReason": self.last_inference_interval_reason,
"cpuBusy": self.last_cpu_busy,
"lastFrameProcessDurationS": round(self.last_frame_process_duration_s, 3),
"lastDetectorForwardCount": self.last_detector_forward_count,
"lastDetectorForwardDurationS": round(self.last_detector_forward_duration_s, 3),
"lastClassifierForwardCount": self.last_classifier_forward_count,
"lastClassifierForwardDurationS": round(self.last_classifier_forward_duration_s, 3),
"cpuUsagePercent": cpu_usage,
"livePoseInputsOK": live_pose_inputs_ok,
"publishedSpeedLimitMph": self.published_speed_limit_mph,
@@ -2128,6 +2144,12 @@ class SpeedLimitVisionDaemon:
buffer = self.client.recv() if self.client is not None else None
self.inference_count += 1
self.last_inference_at = now
inference_started_at = time.monotonic()
self.last_frame_process_duration_s = 0.0
self.last_detector_forward_count = 0
self.last_detector_forward_duration_s = 0.0
self.last_classifier_forward_count = 0
self.last_classifier_forward_duration_s = 0.0
if buffer is None or not buffer.data.any():
self.empty_frame_count += 1
stale_cleared = self._clear_published_detection_if_stale(now, "empty_frame")
@@ -2144,6 +2166,7 @@ class SpeedLimitVisionDaemon:
self.current_frame_bgr = frame_bgr
detection = self._detect_sign(frame_bgr)
self.last_frame_process_duration_s = time.monotonic() - inference_started_at
if detection is not None:
self.detection_count += 1
self._update_detection(detection)