Normaliziraj brate

assignment1/solution.py

@@ -131,7 +131,8 @@ def excercise_two() -> None:
     size where the value of pixels is determined by whether the value of the corresponding
     pixels in the source image is greater or lower than the given threshold.
     """
-    two_a()
+    #two_a()
+    two_b('./images/bird.jpg', 100, 20)
 
 def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
     """
@@ -167,7 +168,12 @@ def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
     plt.show()
     return (image, binary_mask)
 
-def two_b() -> None:
+def count_image_bins(image: npt.NDArray[np.float64], number_of_bins: int) -> npt.NDArray[np.uint8]:
+    bins = np.arange(0, 1, 1 / number_of_bins)
+    binarray = np.digitize(image.reshape(-1), bins).astype(np.uint8)
+    return binarray
+
+def two_b(image_path: str, number_of_bins_first: int, number_of_bins_second: int) -> None:
     """
     Write a function myhist that accepts a grayscale image and the number of bins that
     will be used in building a histogram. The function should return a 1D array that
@@ -181,6 +187,22 @@ def two_b() -> None:
     Write a script that calculates and displays histograms for different numbers of bins
     using bird.jpg
     """
+    image = uz.imread_gray(image_path)
+
+    H1 = count_image_bins(image, number_of_bins_first)
+    H2 = count_image_bins(image, number_of_bins_second)
+
+    fig, (ax0, ax1, ax2) = plt.subplots(1, 3)
+    fig.suptitle("Birdie and its mask")
+
+
+    ax0.imshow(image, cmap="gray") 
+    ax1.hist(H1, bins=number_of_bins_first)
+    ax2.hist(H2, bins=number_of_bins_second)
+
+    plt.show()
+
+    
 
 
 def main() -> None: