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assignment6/uz_framework/image.py

@@ -1467,6 +1467,11 @@ def sift(grayscale_image: npt.NDArray[np.float64],
 
 def get_disparity(left_image: npt.NDArray[np.float64], right_image: npt.NDArray[np.float64],\
              window_size: int = 35, patch_size: int = 11, reduce_size: float = 0.3) -> npt.NDArray[np.float64]:
+    """
+    Disparity algorithm 
+    Accepts left & right image, window size, patch size, and reduce size 
+    Returns diff in images, from left image prespsective
+    """
 
     if(patch_size % 2 == 0 or window_size % 2 == 0):
         raise ValueError("Patch/window size must be odd")
@@ -1548,6 +1553,11 @@ def draw_epiline(l,h,w):
 	plt.gca().invert_yaxis()
 
 def fundamential_matrix(P1, P2):
+    """
+    Fundamental matrix estimation
+    accepts points from left and right image
+    Returns fundamental matrix
+    """
     # P1 and P2 are Nx2 vectors of points
 
     # returns: fundamental matrix
@@ -1581,6 +1591,11 @@ def fundamential_matrix(P1, P2):
 
 
 def get_epipolar_lines(p1, p2):
+    """
+    compute Epipolar lines 
+    Accepts points p1 and p2 from images
+    Returns epipolar lines
+    """
     F = fundamential_matrix(p1, p2)
 
     p1 = np.hstack((p1, np.ones((p1.shape[0], 1))))
@@ -1593,6 +1608,11 @@ def get_epipolar_lines(p1, p2):
 
 
 def reprojection_error(F, p1, p2):
+    """
+    compute reprojection error 
+    Accepts points p1 and p2 from images and fundamental matrix
+    Returns reprojection error
+    """
 
     # Add a column of ones to the points, even if there is single point
     p1 = np.hstack((p1, np.ones((p1.shape[0], 1))))
@@ -1659,9 +1679,9 @@ def ransac_fundamental(correspondences_a: npt.NDArray[np.float64], correspondenc
             iterations: int = 5000,
                        threshold: float = 3):
     """
-    RANSAC algorithm for estimating homography.
+    RANSAC algorithm for estimating fundamental matrix.
     Accepts two images and their corresponding keypoints.
-    Returns the best homography matrix and the inliers.
+    Returns the best fundamental matrix matrix and the inliers.
     """
     # Find the best homography
     best_inliers = [] 
@@ -1726,8 +1746,11 @@ def drawEllipse(mu, cov, n_std=1):
 	plt.gca().add_patch(ellipse)
 
 def compute_PCA(points: npt.NDArray[np.float64], plot: bool = False):
-    # 1. b)
-    # Build matrix X
+    """
+    Algorithm to compute PCA
+    accepts points, and plot bool variable
+    returns U, S, VT and mean
+    """
     X = points.T.copy()
     mean = np.mean(X, axis=1)
 
@@ -1756,6 +1779,10 @@ def compute_PCA(points: npt.NDArray[np.float64], plot: bool = False):
 
 
 def plot_histogram_pca(U: npt.NDArray[np.float64], S: npt.NDArray[np.float64], VT: npt.NDArray[np.float64]):
+    """
+    Accepts U, S, VT
+    Plots histogram pca
+    """
     eigvals = S.copy()
     # Normalize eginevalues
     eigvals = eigvals / np.sum(eigvals)
@@ -1765,6 +1792,11 @@ def plot_histogram_pca(U: npt.NDArray[np.float64], S: npt.NDArray[np.float64], V
     plt.show()
 
 def dual_PCA(points: npt.NDArray[np.float64]):
+    """
+    Algorithm to compute dual PCA 
+    accepts points, and plot bool variable
+    returns U, S, VT and mean
+    """
     X = points.T.copy()
     mean = np.mean(X, axis=1)
     X = X - mean[:, np.newaxis]
@@ -1780,6 +1812,11 @@ def dual_PCA(points: npt.NDArray[np.float64]):
     return U, S, VT, mean
 
 def read_images(data_path: str):
+    """
+    Read images from directory
+    Accepts path to direcgtory
+    returns vectorized images
+    """
     imgs = np.array([])
     for filename in os.listdir(data_path):
         img = imread_gray(os.path.join(data_path, filename), ImageType.float64)
@@ -1792,6 +1829,11 @@ def read_images(data_path: str):
     return imgs.T
 
 def LDA(points, c, n):
+    """
+    LDA algorithm
+    Accepts points, number of participants in every class, number of classes
+    returns reprojected points into LDA space
+    """
     MM = np.mean(points)
     
     Ms = []
@@ -1808,8 +1850,6 @@ def LDA(points, c, n):
 
     eig_vals, eig_vecs = np.linalg.eig(SWB)
 
-    print(eig_vecs)
-
     Ms = np.array(Ms)
 
     Ms = eig_vecs.T.dot(Ms.T).T