From 88559f8a201b33727d54afc0b3f0952e1f512428 Mon Sep 17 00:00:00 2001
From: Midras Lappe <midras.lappe@hs-bochum.de>
Date: Fri, 23 Jun 2023 15:28:08 +0200
Subject: [PATCH] Gitter erweitert sodass Felder mit weniger als zwei Nnachbarn
entfernt werden
---
filter_pipeline.py | 4 ++-
filters.py | 63 ++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 64 insertions(+), 3 deletions(-)
mode change 100644 => 100755 filters.py
diff --git a/filter_pipeline.py b/filter_pipeline.py
index a1d7fee..c2a9a59 100644
--- a/filter_pipeline.py
+++ b/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)
]
diff --git a/filters.py b/filters.py
old mode 100644
new mode 100755
index 3b344d5..fdd1487
--- a/filters.py
+++ b/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
@@ -39,16 +40,26 @@ 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))
-
+
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):
+ 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
--
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