All done

assignment2/solution.py

@@ -187,8 +187,8 @@ def one_e(distances: list, selected_dists: list):
 
 def ex2():
     two_b()
-    #two_d()
-    #two_e()
+    two_d()
+    two_e()
     
 def two_b():
     """
@@ -209,7 +209,7 @@ def two_b():
     """
     signal = uz_text.read_data('./data/signal.txt')
     kernel = uz_text.read_data('./data/kernel.txt')
-    convolved_signal = uz_image.simple_convolution_improved(signal, kernel)
+    convolved_signal = uz_image.simple_convolution(signal, kernel)
 
     cv2_convolved_signal = cv2.filter2D(signal, cv2.CV_64F, kernel)
 
@@ -287,6 +287,7 @@ def two_e():
 ################################
 # EXCERCISE 3: Image Filtering #
 ################################
+
 def ex3():
     three_a()
     three_b()
@@ -454,9 +455,9 @@ def three_e():
 # ######## #
 
 def main():
-    #ex1()
+    ex1()
     #ex2()
-    ex3()
+    #ex3()
 
 if __name__ == '__main__':
     main()

assignment2/uz_framework/image.py

@@ -219,7 +219,10 @@ def get_image_bins(image: Union[npt.NDArray[np.float64],  npt.NDArray[np.uint8]]
     return counts
 
 def get_image_bins_ND(image: Union[npt.NDArray[np.float64],  npt.NDArray[np.uint8]], number_of_bins: int) -> npt.NDArray[np.float64]:
-    
+    """
+    Accepts image in the float64 format or uint8 and number of bins
+    Returns normailzed image histogram bins
+    """ 
     bs = [] 
     hist = np.zeros((number_of_bins, number_of_bins, number_of_bins))
     if image.dtype.type == np.uint8:
@@ -239,6 +242,10 @@ def get_image_bins_ND(image: Union[npt.NDArray[np.float64],  npt.NDArray[np.uint
     return hist / np.sum(hist)
 
 def compare_two_histograms(h1: npt.NDArray[np.float64], h2: npt.NDArray[np.float64], method: DistanceMeasure) -> float:
+    """
+    Accepts two histograms and method of comparison
+    Returns distance between them
+    """
 
     if method == DistanceMeasure.euclidian_distance:
         d = np.sqrt(np.sum(np.square(h1 - h2)))
@@ -361,6 +368,10 @@ 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):
+    """
+    Accepts: signal & width
+    returns signal improved using median filter
+    """
     if width % 2 == 0:
         raise Exception('No u won\'t do that')
 
@@ -371,6 +382,10 @@ def simple_median(signal: npt.NDArray[np.float64], width: int):
     return signal
 
 def apply_median_method_2D(image:Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], width: int):
+    """
+    Accepts: image & filter width
+    returns: image with median filter applied
+    """
     if width % 2 == 0:
         raise Exception('No u won\'t do that')
 
@@ -394,6 +409,10 @@ def apply_median_method_2D(image:Union[npt.NDArray[np.float64], npt.NDArray[np.u
     return image
 
 def filter_laplace(image:Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], sigma: float):
+    """
+    Accepts: image & sigma
+    returns: image with laplace filter applied
+    """
     # Prepare unit impulse and gauss kernel
     unit_impulse = np.zeros((1, 2 * int(np.ceil(3*sigma)) + 1))
     unit_impulse[0][int(np.ceil(unit_impulse.size /2)) - 1]= 1
@@ -409,6 +428,10 @@ def filter_laplace(image:Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]],
     return applied_by_y
 
 def gauss_noise(I, magnitude=.1):
+    """
+    Accepts: image & magnitude
+    Returns: image with gaussian noise applied
+    """
     # input: image, magnitude of noise
     # output: modified image
     I = I.copy()
@@ -417,6 +440,10 @@ def gauss_noise(I, magnitude=.1):
 
 
 def sp_noise(I, percent=.1):
+    """
+    Accepts: image & percent
+    Returns: image with salt and pepper noise applied
+    """
     # input: image, percent of corrupted pixels
     # output: modified image
 
@@ -427,6 +454,10 @@ def sp_noise(I, percent=.1):
     return res
 
 def sp_noise1D(signal, percent=.1):
+    """
+    Accepts: signal & percent
+    Returns: signal with salt and pepper noise applied
+    """
     signal = signal.copy()
     signal[np.random.rand(signal.shape[0]) < percent / 2] = 2
     signal[np.random.rand(signal.shape[0]) < percent / 2] = 1
@@ -435,5 +466,10 @@ def sp_noise1D(signal, percent=.1):
     return signal
 
 def sum_two_grayscale_images(image_a: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], image_b :Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
+    """
+    Accepts: image_a, image_b
+    Returns: image_a + image_b
+    """
+
     # Merge image_a and image_b
     return (image_a + image_b)/ 2