Genauere Berechnung der Regressionsgeraden

filter_pipeline.py

@@ -44,8 +44,8 @@ cv2.namedWindow(window, cv2.WINDOW_NORMAL)
 cv2.createTrackbar('Canny_Min', window, 90, 255, nothing_cb)
 cv2.createTrackbar('Canny_Max', window, 150, 255, nothing_cb)
 cv2.createTrackbar('Diff_Mix', window, 40, 100, nothing_cb)
-cv2.createTrackbar('HSV_Min', window, 40, 255, nothing_cb)
-cv2.createTrackbar('HSV_Max', window, 100, 255, nothing_cb)
+cv2.createTrackbar('HSV_Min', window, 50, 180, nothing_cb)
+cv2.createTrackbar('HSV_Max', window, 100, 180, nothing_cb)
 
 
 pipeline = [

filters.py

@@ -31,9 +31,9 @@ def gitter(info, image, state):
     ax.set_ylim([720, 0])
 
     ax.grid(True, which='major')
-    diff_x = 50
-    diff_y = 50
-    treshold = 50
+    diff_x = 40
+    diff_y = 40
+    treshold = 30
 
     ax.set_xticks(range(0,1280, diff_x))
     ax.set_yticks(range(0,720, diff_y))
@@ -47,17 +47,21 @@ def gitter(info, image, state):
                 y_so = indices[0] + y
 
                 list_xy = np.column_stack((x_so, y_so)).astype(np.int32)
-                poly_xy = polyfit2(list_xy, x, x + diff_x, y, y + diff_y)
+                complete, poly_xy = trendline(list_xy, x, x + diff_x, y, y + diff_y)
                 list_of_lines.append(poly_xy)
 
                 ax.scatter(x_so, y_so, c='r')
+                #ax.plot(complete[:,0], complete[:,1], 'g')
                 ax.plot(poly_xy[:,0], poly_xy[:,1], 'b')
 
-                #fig.show()
+                #print(complete)
+                if complete is None or poly_xy is None:
+                    print('Poly xy none')
                 
                 #plot_points(poly_xy[:,0], poly_xy[:,1], x_so, y_so)
 
-    plt.show()
+    
+
     return image, False
 
 # Cut green kanal 
@@ -174,6 +178,55 @@ def polyfit2(n_list, xs, xe, ys, ye):
     
     return None
 
+def trendline(n_list, x1, x2, y1, y2):
+    if n_list is not None and len(n_list) >= 2:
+        m, b = np.polyfit(n_list[:,0], n_list[:,1], 1)
+
+        # f_n(x) = m*x+b
+        # f_i(y) = (y-b)/m
+
+        # x1 < x2 and y1 < y2
+
+        # Ersten beide Punkte (links x1 und rechts x2)
+        y_f1 = m*x1+b  # (x1, y_f1)
+        y_f2 = m*x2+b  # (x2, y_f2)
+
+        # Letzten beide Punkte (oben y1 und unten y2)
+        if m != 0:
+            x_f1 = (y1-b)/m  # (x_f1, y1)
+            x_f2 = (y2-b)/m  # (x_f2, y2)
+        else:
+            x_f1 = x1 
+            x_f2 = x2
+
+        # Schnittpunkt mit vertikaler Grenze (links)
+        if y_f1 >= y1 and y_f1 <= y2:
+            p1 = [x1, y_f1]
+        # Schnittpunkt mit horizontaler Grenze (oben)
+        elif x_f1 >= x1 and x_f1 <= x2: 
+            p1 = [x_f1, y1]
+        # Schnittpunkt mit horizontaler Grenze (unten)
+        elif x_f2 >= x1 and x_f2 <= x2: 
+            p1 = [x_f2, y2]
+        else:
+            raise ValueError('Not possible')
+        
+        # Schnittpunkt mit vertikaler Grenze (rechts)
+        if y_f2 >= y1 and y_f2 <= y2:
+            p2 = [x2, y_f2]
+        # Schnittpunkt mit horizontaler Grenze (oben)
+        elif x_f1 >= x1 and x_f1 <= x2: 
+            p2 = [x_f1, y1]
+        # Schnittpunkt mit horizontaler Grenze (unten)
+        elif x_f2 >= x1 and x_f2 <= x2: 
+            p2 = [x_f2, y2]
+        else:
+            raise ValueError('Not possible')
+        
+        return [np.array([[x1, y_f1],  [x2, y_f2], [x_f1, y1], [x_f2, y2]]), np.array([p1, p2])]
+    
+    return None
+
 def polyfit(n_list,n=4):
     if n_list is not None:
         p = np.polyfit(n_list[:,0], n_list[:,1], n)