diff --git a/filter_images.py b/filter_images.py
index fb6dc5a86aca39208479eaefa4be06a0e2562b00..348940186e882418603ed7dc5aabcc6c1b9522fd 100644
--- a/filter_images.py
+++ b/filter_images.py
@@ -79,7 +79,6 @@ cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
while capture.isOpened():
-
ret, frame = capture.read()
if ret == True:
frame = cv2.resize(frame, (1280, 720))
diff --git a/filter_images_V1.py b/filter_images_V1.py
index 39314aaaf182b92dbd2ea05c584df98fc724e5d5..29e006c36404b668d61df3eee3d7c4047289acf9 100644
--- a/filter_images_V1.py
+++ b/filter_images_V1.py
@@ -14,9 +14,9 @@ diff = None
window = 'Filter'
# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
+min_threshold = 30
+max_threshold = 110
+img_threshold = 100
line_threshold = 30
def overlay_imgs(base, top):
@@ -31,6 +31,9 @@ def overlay_imgs(base, top):
return cv2.add(img1_bg, img2_fg)
+
+
+
def plot_points(x, y, px, py):
fig = plt.figure()
ax = fig.add_subplot()
@@ -60,16 +63,20 @@ def filter_image(image):
# Use canny edge detection
image = cv2.Canny(image, min_threshold, max_threshold)
- # Closing operation
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
+ # Closing operation elliptical shaped kernels
+ kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)) # use --> dilation = cv2.dilate(img,kernel,iterations = 1)
+ # Closing operation / closing small holes
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
# Minimum filter: Rauschen entfernen
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
+ # 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)
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=3)
#lines = cv2.HoughLinesP(image, cv2.HOUGH_PROBABILISTIC, np.pi/360, line_threshold, minLineLength=10, maxLineGap=20)
@@ -83,7 +90,9 @@ def filter_image(image):
return overlay
def sharpenImg(image):
+ # median of all the pixels under the kernel area
blur = cv2.medianBlur(image, 5)
+ # adding tow images
sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
return sharp
@@ -131,13 +140,13 @@ def calculate_function(overlay,image):
# print (p)
return overlay
-
+# 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))
@@ -145,8 +154,6 @@ while capture.isOpened():
filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
filter = sharpenImg(filter)
- #cv2.imshow(window, filter)
-
if last is not None:
diff = cv2.absdiff(filter, last) # Difference
overlay = filter_image(diff)
diff --git a/filter_images_V2.py b/filter_images_V2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1a25c399dc6eec76e603d48fa5d5b8097d0e1cd
--- /dev/null
+++ b/filter_images_V2.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+from datetime import datetime
+
+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
+
+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_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()
+
+
+ # construct a rectangular kernel from the current size / rect shaped kernel
+ kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
+
+
+ # Opening operation
+ image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
+
+
+ # Closing operation / closing small holes
+ image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
+ # Closing operation / closing small holes
+ image = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)
+
+
+ # dilation
+ kernel2 = np.ones((3,3),np.uint8)
+ image = cv2.dilate(image,kernel2,iterations = 3)
+
+
+ # perform erosion on the image
+ image = cv2.erode(image, kernel)
+
+ # 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=7)
+
+
+ return overlay
+
+def sharpenImg(image):
+ # median of all the pixels under the kernel area
+ blur = cv2.medianBlur(image, 5)
+ # 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)
+
+def calculate_function(overlay,image):
+ indices = np.where(overlay > [0])
+ if((indices[0].size>0)):
+ p=np.polyfit(indices[0],indices[1], 4)
+ x = np.arange(0, overlay.shape[1], 1)
+ y=np.polyval(p,x)
+ points=np.column_stack((x,y)).astype(np.int32)
+ indices = np.where(points < 0)
+ points = np.delete(points,indices,axis=0)
+ print(points)
+ cv2.polylines(image, [points], False, (255, 0, 0), thickness=3)
+ # plt.figure()
+ # plt.plot(x,y)
+ # plt.savefig("test.png")
+ # plt.show()
+ # time.sleep(20)
+ # print (p)
+ return overlay
+
+# 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(filter)
+
+ if last is not None:
+ diff = cv2.absdiff(filter, last) # Difference
+ overlay = filter_image(diff)
+
+ result = overlay_imgs(frame, overlay)
+
+ #function_img=calculate_function(overlay,result)
+
+ cv2.imshow(window, result)
+
+ last = filter
+
+ code = cv2.waitKey(33)
+ 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