From aea65430e038540acd3effd2ca6b86f5f5de3063 Mon Sep 17 00:00:00 2001
From: frasu <frasu@noreply.localhost>
Date: Mon, 15 Dec 2025 13:31:08 +0000
Subject: [PATCH] src/dopt_sensor_anomalies/detection.py aktualisiert

changed value computation in measured_length
---
 src/dopt_sensor_anomalies/detection.py | 37 +++++++++++++++++++-------
 1 file changed, 27 insertions(+), 10 deletions(-)

diff --git a/src/dopt_sensor_anomalies/detection.py b/src/dopt_sensor_anomalies/detection.py
index 6f7a8b2..038c8c9 100644
--- a/src/dopt_sensor_anomalies/detection.py
+++ b/src/dopt_sensor_anomalies/detection.py
@@ -97,15 +97,16 @@ def measure_length(
         box = cast(npt.NDArray[np.float32], perspective.order_points(box))
 
         (tl, tr, br, bl) = box
-        (tltrX, tltrY) = midpoint(tl, tr)
-        (blbrX, blbrY) = midpoint(bl, br)
-        (tlblX, tlblY) = midpoint(tl, bl)
-        (trbrX, trbrY) = midpoint(tr, br)
 
-        dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
-        dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
+        widthA = np.linalg.norm(br - bl)
+        widthB = np.linalg.norm(tr - tl)
+        max_width = int(max(widthA, widthB))
 
-        if dA < 100 or dB < 100:
+        heightA = np.linalg.norm(tr - br)
+        heightB = np.linalg.norm(tl - bl)
+        max_height = int(max(heightA, heightB))        
+
+        if max_width < 100 or max_height < 100:
             continue
 
         is_duplicate = any(
@@ -117,14 +118,30 @@ def measure_length(
         accepted_boxes.append(rbox)
         filtered_cnts.append(c)
 
-        dimA = dA / pixels_per_metric_Y
-        dimB = dB / pixels_per_metric_X
+        dimA = max_width / pixels_per_metric_Y
+        dimB = max_height / pixels_per_metric_X
+
+        offset = 20
+
+        dst = np.array([
+            [offset, offset],
+            [max_width - 1 + offset, offset],
+            [max_width - 1 + offset, max_height - 1 + offset],
+            [offset, max_height - 1 + offset]
+        ], dtype="float32")
+        
+        M = cv2.getPerspectiveTransform(box, dst)
+        warped = cv2.warpPerspective(orig, M, (max_width + 2 * offset, max_height + 2 * offset))
+        
+        gray_warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
+        _, binary_warped = cv2.threshold(gray_warped, 80, 255, cv2.THRESH_BINARY)
+        pixel_count = np.sum(binary_warped == 0)
 
         data_csv.extend(
             [
                 f"{dimB:.3f}".replace(".", ","),
                 f"{dimA:.3f}".replace(".", ","),
-                f"{dimA * dimB:.1f}".replace(".", ","),
+                f"{pixel_count / pixels_per_metric_X / pixels_per_metric_Y:.1f}".replace(".", ","),
             ]
         )