diff --git a/HW01/.gitignore b/HW01/.gitignore
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+# IDE files
+.idea/
diff --git a/HW01/hw1_images.zip b/HW01/hw1_images.zip
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diff --git a/HW01/hw1_images/input_img.jpg b/HW01/hw1_images/input_img.jpg
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diff --git a/HW01/hw1_images/ref_img.jpg b/HW01/hw1_images/ref_img.jpg
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diff --git a/HW01/scripts/hist_modif.py b/HW01/scripts/hist_modif.py
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index 0000000..e69de29
diff --git a/HW01/scripts/hist_utils.py b/HW01/scripts/hist_utils.py
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+++ b/HW01/scripts/hist_utils.py
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+#
+# histogram utilities file
+#
+#
+# author: Christos Choutouridis <cchoutou@ece.auth.gr>
+# date:   29/04/2025
+#
+
+try:
+    import numpy as np
+    from PIL import Image
+except ImportError as e:
+    print("Missing package: ", e)
+    print("Run: pip install -r requirements.txt to install.")
+    exit(1)
+
+
+def calculate_hist_of_img(
+        img_array: np.ndarray,
+        return_normalized: bool
+) -> dict:
+    """
+    Calculate the histogram of an input image array.
+
+    Args:
+        img_array (np.ndarray):   2D grayscale image array with float values in [0, 1].
+        return_normalized (bool): If True, return normalized histogram (probabilities),
+                                  otherwise return absolute counts.
+
+    Returns:
+        dict: A dictionary mapping each unique input level to its count or probability.
+    """
+    # Flatten the 2D image into a 1D array for easier processing
+    flat_img = img_array.flatten()
+
+    # Create an empty dictionary to store the histogram
+    hist = {}
+
+    # Traverse each pixel value
+    for value in flat_img:
+        # Increment the count for the corresponding pixel value
+        if value in hist:
+            hist[value] += 1
+        else:
+            hist[value] = 1
+
+    if return_normalized:
+        total_pixels = flat_img.size
+        for key in hist:
+            hist[key] /= total_pixels
+
+    return hist
+
+
+
+def apply_hist_modification_transform(
+        img_array: np.ndarray,
+        modification_transform: dict
+) -> np.ndarray:
+    """
+    Apply a histogram modification transform to an image.
+
+    Args:
+        img_array (np.ndarray):        2D grayscale image array with float values.
+        modification_transform (dict): Dictionary mapping each input level to an output level.
+
+    Returns:
+        np.ndarray: The modified image array.
+    """
+    # Create a copy of the input image to avoid modifying it in-place
+    modified_img = np.copy(img_array)
+
+    # Get the unique levels in the image
+    unique_levels = np.unique(modified_img)
+
+    # Apply the transform
+    for level in unique_levels:
+        if level in modification_transform:
+            modified_img[modified_img == level] = modification_transform[level]
+        else:
+            # Optional: raise an error or warning if level is missing
+            raise ValueError(f"Input level {level} not found in modification_transform.")
+
+    return modified_img
+
+
+
+
+
+
+
+
+
+#
+# =========================== Functional tests ===========================
+#
+# To execute the tests run:
+#   python hist_utils.py
+#
+
+def test_calculate_hist_counts():
+    """
+    Test calculate_hist_of_img with return_normalized = False.
+    """
+    # Create a dummy 3x3 image
+    img = np.array([
+        [0.0, 0.5, 1.0],
+        [0.5, 0.5, 0.0],
+        [1.0, 0.0, 1.0]
+    ])
+
+    # Call our function
+    my_hist = calculate_hist_of_img(img, return_normalized=False)
+
+    # Use numpy to verify
+    values, counts = np.unique(img, return_counts=True)
+    np_hist = dict(zip(values, counts))
+
+    # Compare
+    print("Test case: Counts")
+    #print("Our histogram:", my_hist)
+    #print("Numpy histogram:", np_hist)
+    print("Test passed:", my_hist == np_hist)
+    print()
+
+
+def test_calculate_hist_normalized():
+    """
+    Test calculate_hist_of_img with return_normalized = True.
+    """
+    # Create a dummy 3x3 image
+    img = np.array([
+        [0.0, 0.5, 1.0],
+        [0.5, 0.5, 0.0],
+        [1.0, 0.0, 1.0]
+    ])
+
+    # Call our function
+    my_hist = calculate_hist_of_img(img, return_normalized=True)
+
+    # Use numpy to verify (manual normalization)
+    values, counts = np.unique(img, return_counts=True)
+    total_pixels = img.size
+    np_hist = dict(zip(values, counts / total_pixels))
+
+    # Compare
+    print("Test case: Normalized")
+    #print("Our histogram:", my_hist)
+    #print("Numpy histogram:", np_hist)
+
+    # Because of floating point division, use np.isclose instead of exact equality
+    test_passed = True
+    for key in my_hist:
+        if not np.isclose(my_hist[key], np_hist[key]):
+            test_passed = False
+            break
+
+    print("Test passed:", test_passed)
+    print()
+
+
+
+def test_apply_hist_modification_identity():
+    """
+    Test apply_hist_modification_transform with identity transform (no changes).
+    """
+    # Create a simple 3x3 image
+    img = np.array([
+        [0.0, 0.5, 1.0],
+        [0.5, 0.5, 0.0],
+        [1.0, 0.0, 1.0]
+    ])
+
+    # Define an identity transform (each value maps to itself)
+    mod_transform = {
+        0.0: 0.0,
+        0.5: 0.5,
+        1.0: 1.0
+    }
+
+    # Apply our function
+    modified_img = apply_hist_modification_transform(img, mod_transform)
+
+    # Compare
+    print("Test case: Identity transform")
+    # print("Modified image:\n", modified_img)
+    # print("Original image:\n", img)
+    print("Test passed:", np.allclose(modified_img, img))
+    print()
+
+
+def test_apply_hist_modification_simple():
+    """
+    Test apply_hist_modification_transform with a simple transform.
+    """
+    # Create a simple 3x3 image
+    img = np.array([
+        [0.0, 0.5, 1.0],
+        [0.5, 0.5, 0.0],
+        [1.0, 0.0, 1.0]
+    ])
+
+    # Define a simple modification transform
+    mod_transform = {
+        0.0: 0.2,
+        0.5: 0.7,
+        1.0: 1.0
+    }
+
+    # Expected result
+    expected_img = np.array([
+        [0.2, 0.7, 1.0],
+        [0.7, 0.7, 0.2],
+        [1.0, 0.2, 1.0]
+    ])
+
+    # Apply our function
+    modified_img = apply_hist_modification_transform(img, mod_transform)
+
+    # Compare
+    print("Test case: Simple transform")
+    # print("Modified image:\n", modified_img)
+    # print("Expected image:\n", expected_img)
+    print("Test passed:", np.allclose(modified_img, expected_img))
+    print()
+
+
+if __name__ == '__main__':
+    test_calculate_hist_counts()
+    test_calculate_hist_normalized()
+    test_apply_hist_modification_identity()
+    test_apply_hist_modification_simple()
diff --git a/HW01/scripts/requirements.txt b/HW01/scripts/requirements.txt
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index 0000000..0d59398
--- /dev/null
+++ b/HW01/scripts/requirements.txt
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+numpy
+Pillow