Separate stacked speed-limit panels before reading numbers

openpilot/starpilot/speed_limits/vision/model.py

@@ -267,6 +267,25 @@ class HistoryEntry:
   created_at: float
   strong_consensus: bool = False
 
+def _primary_regulatory_panel(image):
+  height, width = image.shape[:2]
+  if image.dtype != np.uint8 or width < 24 or height < 1.8 * width:
+    return image
+
+  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+  _, mask = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+  for foreground in (mask >= 128, mask < 128):
+    occupied = foreground[:, int(width * .2):int(width * .8)].mean(1) > .8
+    edges = np.diff(np.r_[False, occupied, False].astype(np.int8))
+    runs = list(zip(np.flatnonzero(edges == 1), np.flatnonzero(edges == -1), strict=False))
+    for (top, bottom), (next_top, next_bottom) in zip(runs, runs[1:], strict=False):
+      if (top <= .3 * width and .75 * width <= bottom - top <= 1.6 * width and
+          next_bottom - next_top >= .5 * width and .02 * width <= next_top - bottom <= .15 * width):
+        # Keep a separate lower plate, such as MINIMUM, out of the number reader.
+        return image[:min(height, int(bottom + .03 * width))]
+  return image
+
+
 class VisionModelCore:
   """The frozen active detector/classifier path, with no camera or Params ownership."""
 
@@ -692,6 +711,7 @@ class VisionModelCore:
     speed_class_count = len(US_CLASSIFIER_SPEED_VALUES)
 
     input_size = self.classifier_input_size
+    sign_crop = _primary_regulatory_panel(sign_crop)
     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)

openpilot/starpilot/speed_limits/vision/tests/test_speed_panels.py

@@ -0,0 +1,60 @@
+import unittest
+
+import numpy as np
+
+from openpilot.starpilot.speed_limits.vision.model import (
+  US_CLASSIFIER_SPEED_VALUES, VisionModelCore, _primary_regulatory_panel,
+)
+
+
+def stacked_panels():
+  image = np.full((200, 100, 3), 200, dtype=np.uint8)
+  image[5:105, 10:90] = 20
+  image[108:190, 10:90] = 20
+  return image
+
+
+class TestSpeedPanels(unittest.TestCase):
+  def test_separate_lower_plate_is_excluded(self):
+    image = stacked_panels()
+    crop = _primary_regulatory_panel(image)
+    self.assertGreaterEqual(crop.shape[0], 105)
+    self.assertLessEqual(crop.shape[0], 108)
+    np.testing.assert_array_equal(crop, image[:crop.shape[0]])
+
+  def test_single_plate_and_short_header_keep_original_input(self):
+    single = np.full((200, 100, 3), 200, dtype=np.uint8)
+    single[5:195, 10:90] = 20
+    header = single.copy()
+    header[30:40] = 200
+    for image in (single, header, stacked_panels().astype(np.float32), np.zeros((40, 30, 3), dtype=np.uint8)):
+      self.assertIs(_primary_regulatory_panel(image), image)
+
+  def test_read_uses_one_existing_classifier_forward(self):
+    class Net:
+      calls = 0
+
+      def setInput(self, blob):
+        self.blob = blob
+
+      def forward(self):
+        self.calls += 1
+        scores = np.zeros((1, len(US_CLASSIFIER_SPEED_VALUES) + 1), dtype=np.float32)
+        scores[0, US_CLASSIFIER_SPEED_VALUES.index(70)] = .99
+        return scores
+
+    core = object.__new__(VisionModelCore)
+    core.classifier_net = Net()
+    core.classifier_input_size = 128
+    core.classifier_logits_layer = None
+    core.last_classifier_forward_count = 0
+    core.last_classifier_forward_duration_s = 0.
+    result = core._classify_speed_limit_from_model(stacked_panels())
+    assert result is not None
+    self.assertEqual(result[0], 70)
+    self.assertEqual(core.classifier_net.calls, 1)
+    self.assertEqual(core.last_classifier_forward_count, 1)
+
+
+if __name__ == '__main__':
+  unittest.main()

prebuilt.json

@@ -1 +1 @@
-{"protected_inventory_sha256": "46a4163eb60a227519f3e01c28cfad309e5eefd27132298487d072c43be4a31c", "schema_version": 1}
+{"protected_inventory_sha256": "3f48edce7f387d0e45d93fd3e6dd8cdad10f0e88def45bb818e65882bf2d977e", "schema_version": 1}