diff --git a/assignment1/UZ_utils.py b/assignment1/UZ_utils.py
index 34e7359..2ec175c 100644
--- a/assignment1/UZ_utils.py
+++ b/assignment1/UZ_utils.py
@@ -80,6 +80,9 @@ def convolve(I: np.ndarray, *ks):
         I = cv2.filter2D(I, cv2.CV_64F, k)
     return I
 
+def convert_float64_array_to_uint8_array(a: npt.NDArray[np.float64]) -> npt.NDArray[np.uint8]:
+    return a.astype(np.uint8)
+
 
 if __name__ == '__main__':  # False if this file is imported to another file and executed from there.
 
diff --git a/assignment1/solution.py b/assignment1/solution.py
index efac23f..8a20f84 100644
--- a/assignment1/solution.py
+++ b/assignment1/solution.py
@@ -2,6 +2,7 @@ import UZ_utils as uz
 import numpy as np
 import numpy.typing as npt
 from matplotlib import pyplot as plt
+import random
 
 #######################################
 # EXCERCISE 1: Basic image processing #
@@ -11,7 +12,7 @@ def excercise_one() -> None:
     image = one_a()
     one_b(image)
     one_c(image)
-    one_d(100, 200, 50, 200, image) # needs to be fixed
+    one_d(100, 200, 50, 200, image)
     one_e()
     
 def one_a() -> npt.NDArray[np.float64]:
@@ -124,8 +125,67 @@ def one_e() -> None:
 # EXCERCISE 2: Thresholding and histograms #
 ############################################
 
+def excercise_two() -> None:
+    """
+    Thresholding an image is an operation that produces a binary image (mask) of the same
+    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()
+
+def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
+    """
+    Create a binary mask from a grayscale image. The binary mask is a matrix the same
+    size as the image which contains 1 where some condition holds and 0 everywhere
+    else. In this case the condition is simply the original image intensity. Use the image
+    bird.jpg. Display both the image and the mask.
+    """
+    random_number = random.random()
+    TRESHOLD = 0.4
+
+    image = uz.imread_gray("./images/bird.jpg")
+    binary_mask = image.copy()
+ 
+    if random_number < 0.5:
+        binary_mask[binary_mask < TRESHOLD] = 0
+        binary_mask[binary_mask >= TRESHOLD] = 1
+    else:
+        binary_mask = np.where(binary_mask < TRESHOLD, 0, 1)
+    
+    binary_mask = uz.convert_float64_array_to_uint8_array(binary_mask) 
+
+    fig, (ax0, ax1) = plt.subplots(1, 2)
+    fig.suptitle("Birdie and its mask")
+
+
+    ax0.imshow(image, cmap="gray") 
+    ax1.imshow(binary_mask, cmap="gray")
+
+    ax0.set(title="Original image")
+    ax1.set(title="Mask of birdie")
+
+    plt.show()
+    return (image, binary_mask)
+
+def two_b() -> 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
+    represents the image histogram (the size should be equal to the number of bins, of
+    course).
+    The histogram is simply a count of pixels with same (or similar) intensity for all
+    bins. You can assume the values of the image are within the interval [0,255]. If you
+    use fewer than 255 bins, intensities will have to be grouped together, e.g. if using 10
+    bins, all values on the interval [0,25] will fall into bin 0. 
+
+    Write a script that calculates and displays histograms for different numbers of bins
+    using bird.jpg
+    """
+
+
 def main() -> None:
-    excercise_one() 
+    #excercise_one()
+    excercise_two()
 
 if __name__ == "__main__":
     main()