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Mango Chutney

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firestar5683
2026-07-04 10:40:00 -05:00
parent 7e76fa9b41
commit e08745d519
2 changed files with 46 additions and 12 deletions
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starpilot/assets/vision_models/speed_limit_us_value_classifier.onnx
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starpilot/system/speed_limit_vision.py
+46 -12
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@@ -153,6 +153,8 @@ DETECTOR_CLASSIFIER_SUPPORT_BONUS = 0.06
DETECTOR_CLASSIFIER_REGULATORY_BONUS = 0.05
DETECTOR_CLASSIFIER_NON_REGULATORY_PENALTY = 0.03
DETECTOR_CLASSIFIER_SMALL_BOX_AREA_RATIO = 0.004
DETECTOR_CLASSIFIER_TINY_LOW_CONF_AREA_RATIO = 0.002
DETECTOR_CLASSIFIER_TINY_LOW_CONF_MIN_CONFIDENCE = 0.14
DETECTOR_CLASSIFIER_MIN_ACCEPT_WIDTH = 28
DETECTOR_CLASSIFIER_MIN_ACCEPT_HEIGHT = 40
DETECTOR_CLASSIFIER_RESCUE_MIN_WIDTH = 14
@@ -161,6 +163,12 @@ DETECTOR_CLASSIFIER_RESCUE_MIN_X_RATIO = 0.52
DETECTOR_CLASSIFIER_RESCUE_MIN_SUPPORT = 1
DETECTOR_CLASSIFIER_RESCUE_MIN_CONFIDENCE = 0.90
DETECTOR_CLASSIFIER_RESCUE_MAX_SCORE = 0.64
DETECTOR_CLASSIFIER_TRUSTED_MODEL_MAX_HEIGHT = 55
DETECTOR_CLASSIFIER_TRUSTED_MODEL_MAX_AREA_RATIO = 0.002
DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_PROPOSAL_CONFIDENCE = 0.18
DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_X_RATIO = 0.52
DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_READ_CONFIDENCE = 0.98
DETECTOR_CLASSIFIER_TRUSTED_MODEL_MIN_SUPPORT = 2
SCHOOL_ZONE_SPEED_PRIOR = 0.12
SCHOOL_ZONE_SUPPORT_BONUS = 0.08
SCHOOL_ZONE_MIN_SUPPORT = 2
@@ -1162,22 +1170,21 @@ class SpeedLimitVisionDaemon:
return None
normalized_mask = self._extract_value_template_mask(sign_crop)
if normalized_mask is None:
return None
classifier_input = cv2.cvtColor(normalized_mask, cv2.COLOR_GRAY2BGR)
padded_crop = self._square_resize(classifier_input, size=128)
blob = cv2.dnn.blobFromImage(padded_crop, scalefactor=1 / 255.0, size=(128, 128), swapRB=True, crop=False)
speed_class_count = len(US_CLASSIFIER_SPEED_VALUES)
if self.reject_classifier_net is not None:
self.reject_classifier_net.setInput(blob)
if normalized_mask is not None and self.reject_classifier_net is not None:
reject_input = cv2.cvtColor(normalized_mask, cv2.COLOR_GRAY2BGR)
reject_crop = self._square_resize(reject_input, size=128)
reject_blob = cv2.dnn.blobFromImage(reject_crop, scalefactor=1 / 255.0, size=(128, 128), swapRB=True, crop=False)
self.reject_classifier_net.setInput(reject_blob)
reject_scores = np.array(self.reject_classifier_net.forward()).reshape(-1)
if reject_scores.size == speed_class_count + 1:
reject_probabilities = self._normalize_classifier_output(reject_scores)
if float(reject_probabilities[speed_class_count]) >= US_REJECT_CLASSIFIER_MIN_CONFIDENCE:
return None
padded_crop = self._square_resize(sign_crop, size=128)
blob = cv2.dnn.blobFromImage(padded_crop, scalefactor=1 / 255.0, size=(128, 128), swapRB=True, crop=False)
self.classifier_net.setInput(blob)
scores = np.array(self.classifier_net.forward()).reshape(-1)
@@ -1220,6 +1227,13 @@ class SpeedLimitVisionDaemon:
):
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
)
if class_id == 2:
school_scores: dict[int, float] = {}
competing_scores: dict[int, float] = {}
@@ -1275,11 +1289,11 @@ class SpeedLimitVisionDaemon:
if score >= SCHOOL_ZONE_SHORT_CIRCUIT_CONFIDENCE:
return Detection(speed_limit_mph, score)
proposal_area_ratio = (box_width * box_height) / max(frame_width * frame_height, 1)
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] = {}
for expand_left, expand_top, expand_right, expand_bottom, expansion_weight in DETECTOR_CLASSIFIER_EXPANSIONS:
expanded_x1 = max(int(x1 - box_width * expand_left), 0)
@@ -1297,7 +1311,16 @@ class SpeedLimitVisionDaemon:
model_read = self._classify_speed_limit_from_model(sign_crop)
ocr_read = None
needs_ocr_confirmation = class_id != 2 and not is_regulatory
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
)
needs_ocr_confirmation = class_id != 2 and (not is_regulatory or is_tiny_low_conf_box) and not trusted_model_read
if model_read is None or needs_ocr_confirmation:
ocr_read = self._read_speed_limit_from_crop(sign_crop)
read_result = model_read or ocr_read
@@ -1310,6 +1333,7 @@ class SpeedLimitVisionDaemon:
read_result = (model_read[0], min(model_read[1], ocr_read[1]))
speed_limit_mph, read_confidence = read_result
score_is_regulatory = is_regulatory or trusted_model_read
if (
class_id == 2 and
proposal_confidence < SCHOOL_ZONE_FALLBACK_MIN_CONFIDENCE and
@@ -1318,7 +1342,7 @@ class SpeedLimitVisionDaemon:
continue
score = read_confidence * expansion_weight
if is_regulatory:
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
@@ -1330,6 +1354,8 @@ class SpeedLimitVisionDaemon:
speed_support_counts[speed_limit_mph] = speed_support_counts.get(speed_limit_mph, 0) + 1
if is_regulatory or class_id == 2:
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 not speed_scores:
continue
@@ -1343,6 +1369,11 @@ class SpeedLimitVisionDaemon:
)
if class_id == 2 and speed_limit_mph not in SCHOOL_ZONE_SPEED_VALUES:
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),
@@ -1371,7 +1402,10 @@ class SpeedLimitVisionDaemon:
else:
score = max(score - 0.06, 0.0)
elif is_small_box:
if speed_regulatory_support.get(speed_limit_mph, 0) < 1:
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