Ex1 done without star

assignment2/solution.py

@@ -10,10 +10,11 @@ import os
 # EXCERCISE 1: Exercise 1: Global approach to image description #
 #################################################################
 def ex1():
-    one_a()
+    #one_a()
-    one_b()
+    #one_b()
-    one_c()
+    #one_c()
-    one_d('./data/dataset_reduced/', 10)
+    image, distances, selected_distances = one_d('./data/dataset', './data/dataset_reduced/', 10)
+    one_e(image, distances, selected_distances)
 
 def one_a() -> npt.NDArray[np.float64]:
     """
@@ -27,9 +28,12 @@ def one_a() -> npt.NDArray[np.float64]:
     array with H = np.zeros((n_bins,n_bins,n_bins)). Take care that you normalize
     the resulting histogram.
     """
-    image = uz_image.imread('./data/dataset/object_01_1.png', uz_image.ImageType.float64)
+    lena = uz_image.imread('./data/images/lena.png', uz_image.ImageType.float64)
-    bins = uz_image.get_image_bins_ND(image, 20)
+    lincoln = uz_image.imread('./data/images/lincoln.jpg', uz_image.ImageType.float64)
-    return bins 
+    lena_h = uz_image.get_image_bins_ND(lena, 128)
+    lincoln_h = uz_image.get_image_bins_ND(lincoln, 128)
+    print(uz_image.compare_two_histograms(lena_h, lincoln_h, uz_image.DistanceMeasure.euclidian_distance))
+    return lena_h 
 
 def one_b() -> None:
     """
@@ -85,7 +89,7 @@ def one_c() -> None:
 
     plt.show()
 
-def one_d(directory: str, n_bins: int):
+def one_d(directory: str, reduced_directory: str, n_bins: int):
     """
     You will now implement a simple image retrieval system that will use histograms.
     Write a function that will accept the path to the image directory and the parameter
@@ -99,15 +103,15 @@ def one_d(directory: str, n_bins: int):
     does the retrieved sequence change if you use a different number of bins? Is the
     execution time affected by the number of bins?
     """
-    img_names = os.listdir(directory)
+    img_names = os.listdir(reduced_directory)
     methods=[uz_image.DistanceMeasure.euclidian_distance, uz_image.DistanceMeasure.chi_square_distance,
             uz_image.DistanceMeasure.intersection_distance, uz_image.DistanceMeasure.hellinger_distance ]
     imgs=[] 
     hists=[]
+    selected_dists=[]
     for i in range(len(img_names)):
-        imgs.append(uz_image.imread(f'{directory}/{img_names[i]}', uz_image.ImageType.float64))
+        imgs.append(uz_image.imread(f'{reduced_directory}/{img_names[i]}', uz_image.ImageType.float64))
         hists.append(uz_image.get_image_bins_ND(imgs[i], n_bins).reshape(-1))
-
     for method in methods:
         fig, axs = plt.subplots(2, len(imgs))
         fig.suptitle(f'Comparrison between different measures, using:{method.name}')
@@ -116,6 +120,7 @@ def one_d(directory: str, n_bins: int):
             distances.append(uz_image.compare_two_histograms(hists[0], hists[i], method))
 
         indexes = np.argsort(distances)
+        selected_dists.append(distances)
 
         for i in range(len(imgs)):
             axs[0, i].imshow(imgs[indexes[i]])
@@ -125,6 +130,44 @@ def one_d(directory: str, n_bins: int):
 
         plt.show()
 
+    img_names = os.listdir(directory)
+    h_image = uz_image.get_image_bins_ND(imgs[0], n_bins).reshape(-1)
+    all_dists = [[] for _ in range(len(methods))]
+
+    for i in range(len(img_names)):
+        im = uz_image.imread(f'{directory}/{img_names[i]}', uz_image.ImageType.float64)
+        h = uz_image.get_image_bins_ND(im, n_bins).reshape(-1)
+        for j in range(len(methods)):
+            all_dists[j].append(uz_image.compare_two_histograms(h_image, h, methods[j]))
+    
+    print(all_dists)
+    return hists[0], all_dists, selected_dists
+
+def one_e(hist: npt.NDArray[np.float64], distances: list, selected_dists: list):
+    """
+    You can get a better sense of the differences in the distance values if you plot all
+    of them at the same time. Use the function plt.plot() to display image indices
+    on the x axis and distances to the reference image on the y axis. Display both the
+    unsorted and the sorted image sequence and mark the most similar values using a
+    circle (see pyplot documentation)
+    """
+    methods=[uz_image.DistanceMeasure.euclidian_distance, uz_image.DistanceMeasure.chi_square_distance,
+            uz_image.DistanceMeasure.intersection_distance, uz_image.DistanceMeasure.hellinger_distance ]
+
+    for i in range(len(distances)): 
+        fig, axs = plt.subplots(1, 2)
+        fig.suptitle(f'Using {methods[i].name}')
+        indexes = np.arange(0, len(distances[i]) , 1)
+        makevery_indexes = []
+
+        for j in range(len(distances[i])):
+            print(distances[i][j])
+            if distances[i][j] in selected_dists[i]:
+                makevery_indexes.append(j)
+
+        axs[0].plot(indexes,distances[i],markevery=makevery_indexes,  markerfacecolor = "none", marker = "o", markeredgecolor = "orange")
+        axs[1].plot(indexes,np.sort(distances[i]),markevery=makevery_indexes,  markerfacecolor = "none", marker = "o", markeredgecolor = "orange")
+        plt.show()
 
     
 # ######## #