Learn to plot the correct image idiot

assignment1/solution.py

@@ -388,7 +388,7 @@ def excercise_three() -> None:
     three_a()
 
 
-def three_a():
+def three_a() -> None:
     """
     We will perform two basic morphological operations on the image mask.png, erosion
     and dilation. We will also experiment with combinations of both operations, named
@@ -398,51 +398,57 @@ def three_a():
     Answer: Opening = Erosion then dialation
             Closing = Dialation then erosion
     """
-    img_orig = uz.imread_gray('./images/bird.jpg', uz.ImageType.float64)
+    img_orig = uz.imread_gray('./images/mask.png', uz.ImageType.float64)
 
     img_er_dil = img_orig.copy()
     img_dil_er = img_orig.copy()
     img_comb = img_orig.copy()
-    img_er_dil_N = 5
-    SE = np.ones((img_er_dil_N, img_er_dil_N), np.float64)
+    SE_size = 2
+    SE = np.ones((SE_size, SE_size), np.float64)
     
-
     fig, axs = plt.subplots(3, 4)
     fig.suptitle("Image after N iterations of Opening, Closing and combination of opening and closing")
     
-    imgs = []
     for im_ix in range(4):
+        SE = np.ones((SE_size, SE_size), np.float64)
         img_er_dil = cv2.erode(img_er_dil, SE)
         img_er_dil = cv2.dilate(img_er_dil, SE)
-        imgs.append(img_er_dil.copy())
+        axs[0, im_ix].imshow(img_er_dil.copy(), cmap='gray')
+        axs[0, im_ix].set(title=f"after {im_ix + 1} iterations of Opening, SE: {SE_size} x {SE_size}")
+        SE_size +=1
 
-    for im_ix in range(4):
-        axs[0, im_ix].imshow(imgs[im_ix], cmap='gray')
-        axs[0, im_ix].set(title=f"image after {im_ix + 1} iterations of opening")
 
-    imgs=[]
+    SE_size = 2
     for im_ix in range(4):
-        img_dil_er = cv2.erode(img_dil_er, SE)
+        SE = np.ones((SE_size, SE_size), np.float64)
         img_dil_er = cv2.dilate(img_dil_er, SE)
-        imgs.append(img_dil_er.copy())
+        img_dil_er = cv2.erode(img_dil_er, SE)
+        axs[1, im_ix].imshow(img_dil_er.copy(), cmap='gray')
+        axs[1, im_ix].set(title=f"after {im_ix + 1} iterations of Closing, SE: {SE_size} x {SE_size}")
+        SE_size +=1
     
-    for im_ix in range(4):
-        axs[1, im_ix].imshow(imgs[im_ix], cmap='gray')
-        axs[1, im_ix].set(title=f"Image after {im_ix + 1} iterations of Closing")
-
-    imgs = []
+    SE_size = 2
     for im_ix in range(4):
         img_comb = cv2.erode(img_comb, SE)
         img_comb = cv2.dilate(img_comb, SE)
         img_comb = cv2.dilate(img_comb, SE)
         img_comb = cv2.erode(img_comb, SE)
-        imgs.append(img_comb)
+        axs[2, im_ix].imshow(img_comb.copy(), cmap='gray')
+        axs[2, im_ix].set(title=f"after {im_ix + 1} iterations of O+C, SE: {SE_size} x {SE_size}")
+        SE_size +=1
 
-    for im_ix in range(4):
-        axs[2, im_ix].imshow(imgs[im_ix], cmap='gray')
-        axs[2, im_ix].set(title=f"Image after {im_ix + 1} iterations of O + C")
         
     plt.show()
+
+
+    def three_b() -> None:
+        """
+        Try to clean up the mask of the image bird.jpg using morphological operations
+        as shown in the image. Experiment with different sizes of the structuring element.
+        You can also try different shapes, like cv2.getStructuringElement(cv2.MORPH_-
+        ELLIPSE,(n,n)).
+        """
+
     
 
 def main() -> None: