Ex3 done and tested everything

assignment2/solution.py

@@ -288,10 +288,10 @@ def two_e():
 # EXCERCISE 3: Image Filtering #
 ################################
 def ex3():
-    #three_a()
-    # three_b()
-    # three_c()
-    #three_d()
+    three_a()
+    three_b()
+    three_c()
+    three_d()
     three_e()
 
 def three_a():
@@ -312,9 +312,12 @@ def three_a():
     """
     lena = uz_image.imread('./data/images/lena.png', uz_image.ImageType.float64)
     lena_grayscale = uz_image.transform_coloured_image_to_grayscale(lena.astype(np.float64))
+
+    # Gaussian noise 
     lena_gausssian_noise = uz_image.gauss_noise(lena_grayscale)
+    # Salt and pepper noise
     lena_salt_and_pepper = uz_image.sp_noise(lena_grayscale)
-    kernel = uz_image.get_gaussian_kernel(1)
+    kernel = np.array(uz_image.get_gaussian_kernel(2)) # MUST BE A 2D for TRANSPOSE
 
     # Denoised
     denosised_lena = cv2.filter2D(lena_gausssian_noise, cv2.CV_64F, kernel)
@@ -335,6 +338,7 @@ def three_a():
     axs[0, 2].set(title='Salt and Pepper applied')
     axs[1, 2].imshow(desalted_lena, cmap='gray')
     axs[1, 2].set(title='Desalted Lena')
+    axs[1, 0].set_visible(False)
     
     plt.show()
 
@@ -345,17 +349,10 @@ def three_b():
     """
     museum_grayscale = uz_image.imread_gray('./data/images/museum.jpg', uz_image.ImageType.uint8)
 
-    #https://blog.demofox.org/2022/02/26/image-sharpening-convolution-kernels/  # Pa tui na slajdih lepo pise
-
-    kernel = np.array([[-1, -1, -1], 
-                       [-1, 17, -1],
-                       [-1, -1,-1]])
-
-    kernel = kernel * 1./9.
-
-    museo = cv2.filter2D(museum_grayscale, -1, kernel)
 
+    museo = uz_image.sharpen_image(museum_grayscale, 1.2)
     fig, axs = plt.subplots(1, 2)
+    fig.suptitle('Sharpening operation')
     axs[0].imshow(museum_grayscale, cmap='gray')
     axs[0].set(title='Original')
     axs[1].imshow(museo, cmap='gray')
@@ -367,6 +364,8 @@ def three_c():
     signal[15:20] = 1.0
 
     fig, axs = plt.subplots(1, 4)
+    fig.suptitle('Signal manipulation')
+
     axs[0].plot(signal)
     axs[0].set(title='Original')
 
@@ -392,22 +391,18 @@ def three_d():
     and pepper noise. Compare the results with the Gaussian filter for multiple noise
     intensities and filter sizes.
     """
-    lena = uz_image.imread('./data/images/obama.jpg', uz_image.ImageType.float64)
+    lena = uz_image.imread('./data/images/lena.png', uz_image.ImageType.float64)
     lena_grayscale = uz_image.transform_coloured_image_to_grayscale(lena.astype(np.float64))
+    # Peppered
     lena_salt_and_pepper = uz_image.sp_noise(lena_grayscale)
-
     # Depeppered
     deppepered_lena = uz_image.apply_median_method_2D(lena_salt_and_pepper, 7)
     # Sharpened
-    kernel = np.array([[-1, -1, -1], 
-                       [-1, 17, -1],
-                       [-1, -1,-1]])
-
-    kernel = kernel * 1./9.
-    sharpened_lena = cv2.filter2D(deppepered_lena, cv2.CV_64F, kernel)
+    sharpened_lena = uz_image.sharpen_image(deppepered_lena,1) 
 
 
     fig, axs = plt.subplots(1, 4)
+    fig.suptitle('Common methods applied over Lena image')
     
     axs[0].imshow(lena_grayscale, cmap='gray')
     axs[0].set(title='Orginal image')

assignment2/uz_framework/image.py

@@ -326,6 +326,27 @@ def get_gaussian_kernel(sigma: float):
 
     return result / np.sum(result)
 
+def sharpen_image(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], sharpen_factor=1.0) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
+    """
+    Accepts: image & sharpen factor 
+
+    Returns: sharpened image
+    https://blog.demofox.org/2022/02/26/image-sharpening-convolution-kernels/ <-- sharpening kernel, but also on slides
+    good explanation
+    """
+    sharpened_image = image.copy()
+
+    KERNEL = np.array([[-1, -1, -1], 
+                       [-1, 17, -1],
+                       [-1, -1,-1]]) * 1./9. * sharpen_factor
+
+    if image.dtype.type == np.float64: 
+        sharpened_image = cv2.filter2D(sharpened_image, cv2.CV_64F, KERNEL)
+    elif image.dtype.type == np.uint8:
+        sharpened_image = cv2.filter2D(sharpened_image, cv2.CV_8U, KERNEL)
+
+    return sharpened_image
+
 def gaussfilter2D(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], sigma: float) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
     """
         Accepts: image, sigma  
@@ -340,10 +361,10 @@ def gaussfilter2D(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]],
     return filtered_image
 
 def simple_median(signal: npt.NDArray[np.float64], width: int):
-    signal = signal.copy()
     if width % 2 == 0:
         raise Exception('No u won\'t do that')
 
+    signal = signal.copy()
     for i in range(len(signal) - int(np.ceil(width/2))):
         middle_element = int(i + np.floor(width/2))
         signal[middle_element] = np.median(signal[i:i+width])
@@ -352,10 +373,10 @@ def simple_median(signal: npt.NDArray[np.float64], width: int):
 def apply_median_method_2D(image:Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], width: int):
     if width % 2 == 0:
         raise Exception('No u won\'t do that')
+
     image = image.copy()
     W_HALF = int(np.floor(width/2))
     padded_image = np.pad(image, W_HALF, mode='edge')
-    print(image.shape)
     IMAGE_HEIGHT = image.shape[0] # y
     IMAGE_WIDTH = image.shape[1] # x