From ac26e0f97b4fba0e3159d18e19f724cc5d737dcc Mon Sep 17 00:00:00 2001
From: Mohammad Khaleeliyeh <mohammad.khaleeliyeh@asinco.de>
Date: Fri, 12 May 2023 14:31:49 +0200
Subject: [PATCH] stabiles Verhalten
---
filter_images_V1.py | 9 ++-
filter_images_V2.py | 20 ++++-
filter_images_V3.py | 6 +-
filter_images_V4.py | 178 +++++++++++++++++++++++++++++++++++++++++++
filter_images_V5.py | 181 ++++++++++++++++++++++++++++++++++++++++++++
5 files changed, 383 insertions(+), 11 deletions(-)
create mode 100644 filter_images_V4.py
create mode 100644 filter_images_V5.py
diff --git a/filter_images_V1.py b/filter_images_V1.py
index 1be6379..a296968 100644
--- a/filter_images_V1.py
+++ b/filter_images_V1.py
@@ -69,15 +69,15 @@ def filter_image(image):
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
# Minimum filter: Rauschen entfernen
-<<<<<<< HEAD
+
# Closing operation elliptical shaped kernels
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3)) # use another kernal shapes
# perform erosion on the image
image = cv2.erode(image, kernel)
-=======
+
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
#image = cv2.erode(image, kernel)
->>>>>>> 15c1ee4b8b800ce409ac06c9128e25df62715fd1
+
points = find_points(image)
overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
@@ -168,7 +168,8 @@ while capture.isOpened():
#function_img=calculate_function(overlay,result)
cv2.imshow(window, image)
-
+ cv2.imshow('Rsult', result)
+
last = filter
code = cv2.waitKey(33)
diff --git a/filter_images_V2.py b/filter_images_V2.py
index a6c6f8b..978004f 100644
--- a/filter_images_V2.py
+++ b/filter_images_V2.py
@@ -28,7 +28,18 @@ def overlay_imgs(base, top):
img2_fg = cv2.bitwise_and(top,top,mask = mask)
return cv2.add(img1_bg, img2_fg)
-
+def find_points(image):
+ indices = np.where(image > 0)
+ if (indices[1].size > 0):
+ x_sort = np.sort(indices[1])
+ p = np.polyfit(indices[1], indices[0], 4)
+ x = np.arange(x_sort[0], x_sort[-1], 1)
+ y = np.polyval(p, x)
+
+ points = np.column_stack((x, y)).astype(np.int32)
+ return points
+
+ return None
def filter_image(image):
global min_threshold, max_threshold, line_threshold, result, window
image = image.copy()
@@ -40,9 +51,9 @@ def filter_image(image):
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
#lines = cv2.HoughLinesP(image, cv2.HOUGH_PROBABILISTIC, np.pi/360, line_threshold, minLineLength=10, maxLineGap=20)
-
+ points = find_points(image)
overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
-
+ cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=2)
#if lines is not None:
# for line in lines:
# x1,y1,x2,y2 = line[0]
@@ -102,7 +113,8 @@ while capture.isOpened():
result = overlay_imgs(frame, overlay)
cv2.imshow(window, image)
-
+ cv2.imshow('overlay', overlay)
+ cv2.imshow('result', result)
code = cv2.waitKey(33)
if code & 0xFF == ord('s'):
diff --git a/filter_images_V3.py b/filter_images_V3.py
index f1a25c3..4d8fa97 100644
--- a/filter_images_V3.py
+++ b/filter_images_V3.py
@@ -93,7 +93,7 @@ def filter_image(image):
cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=7)
- return overlay
+ return overlay, image
def sharpenImg(image):
# median of all the pixels under the kernel area
@@ -149,12 +149,12 @@ while capture.isOpened():
if last is not None:
diff = cv2.absdiff(filter, last) # Difference
- overlay = filter_image(diff)
+ overlay, image = filter_image(diff)
result = overlay_imgs(frame, overlay)
#function_img=calculate_function(overlay,result)
-
+ # cv2.imshow(window, image)
cv2.imshow(window, result)
last = filter
diff --git a/filter_images_V4.py b/filter_images_V4.py
new file mode 100644
index 0000000..f697431
--- /dev/null
+++ b/filter_images_V4.py
@@ -0,0 +1,178 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+from datetime import datetime
+import pandas as pd
+
+VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
+capture = cv2.VideoCapture(VIDEO)
+
+
+last = None
+frame = None
+diff = None
+window = 'Filter'
+
+# Einstellungen
+min_threshold = 30
+max_threshold = 110
+img_threshold = 100
+line_threshold = 30
+
+spanne = 2
+
+def overlay_imgs(base, top):
+ topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
+
+ _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
+ mask_inv = cv2.bitwise_not(mask)
+ # Now black-out the area of overlay
+ img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
+ # Take only region of from logo image.
+ img2_fg = cv2.bitwise_and(top,top,mask = mask)
+
+ return cv2.add(img1_bg, img2_fg)
+
+
+def plot_points(x, y, px, py):
+ fig = plt.figure()
+ ax = fig.add_subplot()
+ ax.scatter(px, py, c='r')
+ ax.plot(x, y)
+ ax.set_xlim([0, 1280])
+ ax.set_ylim([720, 0])
+ plt.show()
+
+def find_points(image):
+ indices = np.where(image > 0)
+ if (indices[1].size > 0):
+ x_so = indices[1]
+ y_so = indices[0]
+
+ list_xy = np.column_stack((x_so, y_so)).astype(np.int32)
+ # list_xy = np.sort(list_xy, axis=0)
+ # print( list_xy)
+
+ df = pd.DataFrame(list_xy,columns=['x','y'])
+
+ df = df.sort_values(by=['x'], ascending=True)
+
+ n_list = []
+ for el in df.x.unique():
+ med = (df.y.where(df.x >= el-spanne).where(el+spanne >= df.x)).median()
+ n_list.append([el,med])
+
+
+ n_list = np.array(n_list)
+
+
+ p = np.polyfit(n_list[:,0], n_list[:,1], 6)
+ x = np.arange(n_list[:,0][0], n_list[:,0][-1], 1)
+ y = np.polyval(p,x)
+ points = np.column_stack((x, y)).astype(np.int32)
+
+ return points
+
+ return None
+
+
+def filter_image(image):
+ global min_threshold, max_threshold, line_threshold, result, window
+ image = image.copy()
+
+
+ # construct a rectangular kernel from the current size / rect shaped kernel
+ kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,1))
+
+ # Opening operation
+ image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
+
+ # perform erosion on the image
+ image = cv2.erode(image, (3,3))
+
+ # Closing operation / closing small holes
+ # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (1,1))
+ image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (5,5))
+ # # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (4,4))
+
+
+ # dilation
+ kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
+ image = cv2.dilate(image,kernel2,iterations = 1)
+
+
+
+
+ # Use canny edge detection
+ image = cv2.Canny(image, min_threshold, max_threshold)
+
+ points = find_points(image)
+ overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
+ # draw a polygon on the image
+ cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=6)
+
+ return overlay, image
+
+def sharpenImg(image):
+ # median of all the pixels under the kernel area
+ blur = cv2.medianBlur(image, 7)
+ # adding tow images
+ sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
+ return sharp
+
+
+
+def line_callback(val):
+ global line_threshold, frame, diff
+
+ line_threshold = val
+ if diff is not None:
+ overlay = filter_image(diff)
+
+ result = overlay_imgs(frame, overlay)
+ cv2.imshow(window, result)
+
+
+# To control the Size of the Disply
+cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
+cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
+
+result = None
+while capture.isOpened():
+ # ret is the stat of the reading
+ ret, frame = capture.read()
+ if ret == True:
+ frame = cv2.resize(frame, (1280, 720))
+
+ # filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+ filter = sharpenImg(frame)
+
+ if last is not None:
+ diff = cv2.absdiff(filter, last) # Difference
+ cv2.imshow("diff", diff)
+ overlay, image = filter_image(diff)
+
+ result = overlay_imgs(frame, overlay)
+
+
+ cv2.imshow("image", image)
+ cv2.imshow("result", result)
+ # time.sleep(0.1)
+ last = filter
+
+ code = cv2.waitKey(10)
+ if code & 0xFF == ord('s'):
+ now = datetime.now()
+ str = now.strftime("%d_%m_%Y_%H_%M_%S")
+ cv2.imwrite(f'Filter/IMG_{str}.png', result)
+
+ if code & 0xFF == ord('q'):
+ break
+
+ else:
+ break
+
+capture.release()
+
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images_V5.py b/filter_images_V5.py
new file mode 100644
index 0000000..798d80d
--- /dev/null
+++ b/filter_images_V5.py
@@ -0,0 +1,181 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+from datetime import datetime
+import pandas as pd
+
+VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
+capture = cv2.VideoCapture(VIDEO)
+
+
+last = None
+frame = None
+diff = None
+window = 'Filter'
+
+# Einstellungen
+min_threshold = 30
+max_threshold = 110
+img_threshold = 100
+line_threshold = 30
+
+spanne = 2
+
+def overlay_imgs(base, top):
+ topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
+
+ _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
+ mask_inv = cv2.bitwise_not(mask)
+ # Now black-out the area of overlay
+ img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
+ # Take only region of from logo image.
+ img2_fg = cv2.bitwise_and(top,top,mask = mask)
+
+ return cv2.add(img1_bg, img2_fg)
+
+
+def plot_points(x, y, px, py):
+ fig = plt.figure()
+ ax = fig.add_subplot()
+ ax.scatter(px, py, c='r')
+ ax.plot(x, y)
+ ax.set_xlim([0, 1280])
+ ax.set_ylim([720, 0])
+ plt.show()
+
+def find_points(image):
+ indices = np.where(image > 0)
+ if (indices[1].size > 0):
+ x_so = indices[1]
+ y_so = indices[0]
+
+ list_xy = np.column_stack((x_so, y_so)).astype(np.int32)
+ # list_xy = np.sort(list_xy, axis=0)
+ # print( list_xy)
+
+ df = pd.DataFrame(list_xy,columns=['x','y'])
+
+ df = df.sort_values(by=['x'], ascending=True)
+
+ n_list = []
+
+ df_un = df.x.unique()
+ for el in df_un[::2]:
+
+ med = (df.y.where(df.x >= el-spanne).where(el+spanne >= df.x)).median()
+ n_list.append([el,med])
+
+
+ n_list = np.array(n_list)
+
+
+ p = np.polyfit(n_list[:,0], n_list[:,1], 6)
+ x = np.arange(n_list[:,0][0], n_list[:,0][-1], 1)
+ y = np.polyval(p,x)
+ points = np.column_stack((x, y)).astype(np.int32)
+
+ return points
+
+ return None
+
+
+def filter_image(image):
+ global min_threshold, max_threshold, line_threshold, result, window
+ image = image.copy()
+
+
+ # construct a rectangular kernel from the current size / rect shaped kernel
+ kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,1))
+
+ # Opening operation
+ image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
+
+ # perform erosion on the image
+ image = cv2.erode(image, (3,3))
+
+ # Closing operation / closing small holes
+ # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (1,1))
+ image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (5,5))
+ # # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (4,4))
+
+
+ # dilation
+ kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
+ image = cv2.dilate(image,kernel2,iterations = 1)
+
+
+
+
+ # Use canny edge detection
+ image = cv2.Canny(image, min_threshold, max_threshold)
+
+ points = find_points(image)
+ overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
+ # draw a polygon on the image
+ cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=6)
+
+ return overlay, image
+
+def sharpenImg(image):
+ # median of all the pixels under the kernel area
+ blur = cv2.medianBlur(image, 7)
+ # adding tow images
+ sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
+ return sharp
+
+
+
+def line_callback(val):
+ global line_threshold, frame, diff
+
+ line_threshold = val
+ if diff is not None:
+ overlay = filter_image(diff)
+
+ result = overlay_imgs(frame, overlay)
+ cv2.imshow(window, result)
+
+
+# To control the Size of the Disply
+cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
+cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
+
+result = None
+while capture.isOpened():
+ # ret is the stat of the reading
+ ret, frame = capture.read()
+ if ret == True:
+ frame = cv2.resize(frame, (1280, 720))
+
+ # filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+ filter = sharpenImg(frame)
+
+ if last is not None:
+ diff = cv2.absdiff(filter, last) # Difference
+ cv2.imshow("diff", diff)
+ overlay, image = filter_image(diff)
+
+ result = overlay_imgs(frame, overlay)
+
+
+ cv2.imshow("image", image)
+ cv2.imshow("result", result)
+ # time.sleep(0.1)
+ last = filter
+
+ code = cv2.waitKey(10)
+ if code & 0xFF == ord('s'):
+ now = datetime.now()
+ str = now.strftime("%d_%m_%Y_%H_%M_%S")
+ cv2.imwrite(f'Filter/IMG_{str}.png', result)
+
+ if code & 0xFF == ord('q'):
+ break
+
+ else:
+ break
+
+capture.release()
+
+cv2.destroyAllWindows()
\ No newline at end of file
--
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