Added histogram equalization in computer vision

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()

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])
+        )
     )
 
 

matrix/matrix_class.py

@@ -204,9 +204,11 @@ def cofactors(self) -> Matrix:
         return Matrix(
             [
                 [
-                    self.minors().rows[row][column]
-                    if (row + column) % 2 == 0
-                    else self.minors().rows[row][column] * -1
+                    (
+                        self.minors().rows[row][column]
+                        if (row + column) % 2 == 0
+                        else self.minors().rows[row][column] * -1
+                    )
                     for column in range(self.minors().num_columns)
                 ]
                 for row in range(self.minors().num_rows)