Gitter erweitert sodass Felder mit weniger als zwei Nnachbarn entfernt werden

filter_pipeline.py

@@ -7,6 +7,7 @@ import pandas as pd
 import filters
 
 VIDEO = 'Videomaterial/WIN_20230602_14_45_52_Pro.mp4'
+#VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
 capture = cv2.VideoCapture(VIDEO)
 capture.set(cv2.CAP_PROP_POS_FRAMES, 30 * 0) # skip 40 seconds
 
@@ -57,8 +58,9 @@ pipeline = [
     ('Green filter abs', filters.green_absfilter),
     ('Canny edge detection', filters.filter_canny),
     ('Morph close', filters.filter_close),
-    ('gitter_filter', filters.gitter),
+    # ('gitter_filter', filters.gitter),
     #('Morph open', filters.filter_open),
+    ('Einzelne Rauswerfen', filters.sortOut),
     ('Point extraction', filters.points_extract),
     ('Polyfit lines', filters.points_overlay)
 ]

filters.py

@@ -2,6 +2,7 @@ import cv2 as cv
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt 
+from scipy import signal 
 from collections import deque
 
 currentfig = None
@@ -39,7 +40,8 @@ def gitter(info, image, state):
     ax.grid(True, which='major')
     diff_x = 40
     diff_y = 40
-    treshold = 30
+    treshold = 5
+    haspoints = np.zeros([(int)(1280/diff_x),(int)(720/diff_y)])
 
     ax.set_xticks(range(0,1280, diff_x))
     ax.set_yticks(range(0,720, diff_y))
@@ -49,6 +51,15 @@ def gitter(info, image, state):
             subimg = image[y:y+diff_y,x:x+diff_x]
             indices = np.where(subimg > 0)
             if (indices[1].size > treshold):
+                haspoints[(int)(x/diff_x),(int)(y/diff_y)]=1
+    neighbours= findneighbours(haspoints)
+    
+    for x in range(0,1280,diff_x):
+        for y in range(0,720,diff_y):
+            subimg = image[y:y+diff_y,x:x+diff_x]
+            indices = np.where(subimg > 0)
+            if (neighbours[(int)(x/diff_x),(int)(y/diff_y)]>2):
+                
                 x_so = indices[1] + x
                 y_so = indices[0] + y
 
@@ -68,8 +79,56 @@ def gitter(info, image, state):
 
     
 
+    
+    
     return image, False
 
+def sortOut(info, image, state):
+    
+
+    # image Range ((1280, 720))
+
+
+    diff_x = 40
+    diff_y = 40
+    treshold = 30
+    haspoints = np.zeros([(int)(1280/diff_x),(int)(720/diff_y)])
+    
+    
+    for x in range(0,1280,diff_x):
+        for y in range(0,720,diff_y):
+            subimg = image[y:y+diff_y,x:x+diff_x]
+            indices = np.where(subimg > 0)
+            if (indices[1].size > treshold):
+                haspoints[(int)(x/diff_x),(int)(y/diff_y)]=1
+    neighbours= findneighbours(haspoints)
+    
+    for x in range(0,1280,diff_x):
+        for y in range(0,720,diff_y):
+            if (neighbours[(int)(x/diff_x),(int)(y/diff_y)]<3):
+                image[y:y+diff_y,x:x+diff_x] = 0
+    
+    
+    return image, False
+    
+
+def findneighbours(haspoints):
+    #print(haspoints)
+    #flat = np.ravel(haspoints)
+    # Definition des Kernel-Filters für die Nachbarschaft (umliegenden Felder)
+    #kernel = np.ones((3, 3))  # 3x3 Kernel aus Einsen
+
+    # Berechnung der Summe der umliegenden Felder
+    #sum_neighbors = np.convolve2d(haspoints, kernel, mode='same')
+    
+    kernel = np.ones([3,3])  # 3x3 Kernel aus Einsen
+    result = signal.convolve2d(haspoints, kernel, mode='same')
+    result[result < 3] = 0
+    result = result * haspoints
+    return result
+
+    
+
 # Cut green kanal 
 def greenfilter(info, image, state):
     # Umwandeln des Bildes in den Farbraum HSV