Added histogram equalization in computer vision

Author: mariamabidi

ciphers/base64_cipher.py

@@ -105,13 +105,13 @@ def base64_decode(encoded_data: str) -> bytes:
 
     # Check if the encoded string contains non base64 characters
     if padding:
-        assert all(char in B64_CHARSET for char in encoded_data[:-padding]), (
-            "Invalid base64 character(s) found."
-        )
+        assert all(
+            char in B64_CHARSET for char in encoded_data[:-padding]
+        ), "Invalid base64 character(s) found."
     else:
-        assert all(char in B64_CHARSET for char in encoded_data), (
-            "Invalid base64 character(s) found."
-        )
+        assert all(
+            char in B64_CHARSET for char in encoded_data
+        ), "Invalid base64 character(s) found."
 
     # Check the padding
     assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"

computer_vision/histogram_equalization.py

@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+
+"""
+Histogram Equalization for Image Enhancement
+https://en.wikipedia.org/wiki/Histogram_equalization
+"""
+
+
+def hist_equalization(image):
+    """
+    Returns the histogram equalization image
+    :param image: input image
+    """
+    L, a, b = cv2.split(image)
+    histogram = cv2.calcHist([L], [0], None, [256], [0, 256])
+    histogram_sum = np.sum(histogram)
+    probability_density_function = histogram / histogram_sum
+    cumulative_distribution_function = np.cumsum(probability_density_function)
+    lookup_table = np.round(cumulative_distribution_function * 255).astype(np.uint8)
+    equalized_L = cv2.LUT(L, lookup_table)
+    new_image = cv2.merge((equalized_L, a, b))
+    new_image = cv2.cvtColor(new_image, cv2.COLOR_LAB2BGR)
+    return new_image
+
+
+if __name__ == "__main__":
+    image = cv2.imread("path_to_image")
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
+    new_image = hist_equalization(image)
+    cv2.imshow("image", new_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()

data_structures/hashing/number_theory/prime_numbers.py

@@ -32,9 +32,9 @@ def is_prime(number: int) -> bool:
     """
 
     # precondition
-    assert isinstance(number, int) and (number >= 0), (
-        "'number' must been an int and positive"
-    )
+    assert isinstance(number, int) and (
+        number >= 0
+    ), "'number' must been an int and positive"
 
     if 1 < number < 4:
         # 2 and 3 are primes

data_structures/heap/min_heap.py

@@ -124,9 +124,9 @@ def is_empty(self):
         return len(self.heap) == 0
 
     def decrease_key(self, node, new_value):
-        assert self.heap[self.idx_of_element[node]].val > new_value, (
-            "newValue must be less that current value"
-        )
+        assert (
+            self.heap[self.idx_of_element[node]].val > new_value
+        ), "newValue must be less that current value"
         node.val = new_value
         self.heap_dict[node.name] = new_value
         self.sift_up(self.idx_of_element[node])

data_structures/kd_tree/tests/test_kdtree.py

@@ -48,14 +48,14 @@ def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_res
         assert kdtree is not None, "Expected a KDNode, got None"
 
         # Check if root has correct dimensions
-        assert len(kdtree.point) == num_dimensions, (
-            f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
-        )
+        assert (
+            len(kdtree.point) == num_dimensions
+        ), f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
 
         # Check that the tree is balanced to some extent (simplistic check)
-        assert isinstance(kdtree, KDNode), (
-            f"Expected KDNode instance, got {type(kdtree)}"
-        )
+        assert isinstance(
+            kdtree, KDNode
+        ), f"Expected KDNode instance, got {type(kdtree)}"
 
 
 def test_nearest_neighbour_search():

data_structures/suffix_tree/tests/test_suffix_tree.py

@@ -22,37 +22,37 @@ def test_search_existing_patterns(self) -> None:
         patterns = ["ana", "ban", "na"]
         for pattern in patterns:
             with self.subTest(pattern=pattern):
-                assert self.suffix_tree.search(pattern), (
-                    f"Pattern '{pattern}' should be found."
-                )
+                assert self.suffix_tree.search(
+                    pattern
+                ), f"Pattern '{pattern}' should be found."
 
     def test_search_non_existing_patterns(self) -> None:
         """Test searching for patterns that do not exist in the suffix tree."""
         patterns = ["xyz", "apple", "cat"]
         for pattern in patterns:
             with self.subTest(pattern=pattern):
-                assert not self.suffix_tree.search(pattern), (
-                    f"Pattern '{pattern}' should not be found."
-                )
+                assert not self.suffix_tree.search(
+                    pattern
+                ), f"Pattern '{pattern}' should not be found."
 
     def test_search_empty_pattern(self) -> None:
         """Test searching for an empty pattern."""
         assert self.suffix_tree.search(""), "An empty pattern should be found."
 
     def test_search_full_text(self) -> None:
         """Test searching for the full text."""
-        assert self.suffix_tree.search(self.text), (
-            "The full text should be found in the suffix tree."
-        )
+        assert self.suffix_tree.search(
+            self.text
+        ), "The full text should be found in the suffix tree."
 
     def test_search_substrings(self) -> None:
         """Test searching for substrings of the full text."""
         substrings = ["ban", "ana", "a", "na"]
         for substring in substrings:
             with self.subTest(substring=substring):
-                assert self.suffix_tree.search(substring), (
-                    f"Substring '{substring}' should be found."
-                )
+                assert self.suffix_tree.search(
+                    substring
+                ), f"Substring '{substring}' should be found."
 
 
 if __name__ == "__main__":

digital_image_processing/filters/gabor_filter.py

@@ -48,9 +48,9 @@ def gabor_filter_kernel(
             _y = -sin_theta * px + cos_theta * py
 
             # fill kernel
-            gabor[y, x] = np.exp(-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)) * np.cos(
-                2 * np.pi * _x / lambd + psi
-            )
+            gabor[y, x] = np.exp(
+                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)
+            ) * np.cos(2 * np.pi * _x / lambd + psi)
 
     return gabor
 

dynamic_programming/climbing_stairs.py

@@ -25,9 +25,9 @@ def climb_stairs(number_of_steps: int) -> int:
         ...
     AssertionError: number_of_steps needs to be positive integer, your input -7
     """
-    assert isinstance(number_of_steps, int) and number_of_steps > 0, (
-        f"number_of_steps needs to be positive integer, your input {number_of_steps}"
-    )
+    assert (
+        isinstance(number_of_steps, int) and number_of_steps > 0
+    ), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
     if number_of_steps == 1:
         return 1
     previous, current = 1, 1

dynamic_programming/iterating_through_submasks.py

@@ -37,9 +37,9 @@ def list_of_submasks(mask: int) -> list[int]:
 
     """
 
-    assert isinstance(mask, int) and mask > 0, (
-        f"mask needs to be positive integer, your input {mask}"
-    )
+    assert (
+        isinstance(mask, int) and mask > 0
+    ), f"mask needs to be positive integer, your input {mask}"
 
     """
     first submask iterated will be mask itself then operation will be performed

greedy_methods/fractional_knapsack.py

@@ -39,9 +39,11 @@ def frac_knapsack(vl, wt, w, n):
     return (
         0
         if k == 0
-        else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
-        if k != n
-        else sum(vl[:k])
+        else (
+            sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
+            if k != n
+            else sum(vl[:k])
+        )
     )