erweiterung

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))

filter_images_V1.py

@@ -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)

filter_images_V2.py

+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