From 45ce034ca9311a594cf0a30d1f84937bedcd6d64 Mon Sep 17 00:00:00 2001
From: Gasper Spagnolo <gasper.spagnolo@outlook.com>
Date: Tue, 15 Nov 2022 10:46:07 +0100
Subject: [PATCH] Se 3b done

---
 assignment3/solution.py           | 53 +++++++++++++++++++++++++++++--
 assignment3/uz_framework/image.py | 38 ++++++++++++++++++++++
 2 files changed, 88 insertions(+), 3 deletions(-)

diff --git a/assignment3/solution.py b/assignment3/solution.py
index 433b0d3..13cc92c 100644
--- a/assignment3/solution.py
+++ b/assignment3/solution.py
@@ -226,8 +226,8 @@ def two_c():
 # EXCERCISE 2: Exercise 1: Edges in images #
 ############################################
 def ex3():
-    three_a()
-    #three_b()
+    #three_a()
+    three_b()
     #three_c()
 
 def three_a(): 
@@ -251,6 +251,53 @@ def three_a():
 
     plt.show()
 
+def three_b():
+    """
+    Implement the function hough_find_lines that accepts a binary image, the number
+    of bins for ϑ and ρ (allow the possibility of them being different) and a threshold.
+    Create an accumulator matrix A for the parameter space (ρ, ϑ). Parameter ϑ is
+    defined in the interval from −π/2 to π/2, ρ is defined on the interval from −D to
+    D, where D is the length of the image diagonal. For each nonzero pixel in the image,
+    generate a curve in the (ρ, ϑ) space by using the equation (7) for all possible values
+    of ϑ and increase the corresponding cells in A. Display the accumulator matrix. Test
+    the method on your own synthetic images ((e.g. 100 × 100 black image, with two
+    white pixels at (10, 10) and (10, 20)).
+    Finally, test your function on two synthetic images oneline.png and rectangle.png. First, you should obtain an edge map for each image using either your
+    function findedges or some inbuilt function. Run your implementation of the Hough
+    algorithm on the resulting edge maps.
+    """
+    SIGMA = 1
+    THETA = 0.02
+    T_LOW = 0.04
+    T_HIGH = 0.16
+
+    synthetic_image = np.zeros((100, 100))
+    synthetic_image[10, 10] = 1
+    synthetic_image[10, 20] = 1
+
+    oneline_image = uz_image.imread_gray('./images/oneline.png', uz_image.ImageType.float64)
+    rectangle_image = uz_image.imread_gray('./images/rectangle.png', uz_image.ImageType.float64)
+
+    oneline_image_edges = uz_image.find_edges_canny(oneline_image, SIGMA, THETA, T_LOW, T_HIGH)
+    rectangle_image_edges = uz_image.find_edges_canny(rectangle_image, SIGMA, THETA, T_LOW, T_HIGH)
+
+    fig, axs = plt.subplots(3, 3)
+
+    axs[0, 0].imshow(synthetic_image, cmap='gray')
+    axs[0, 0].set_title('Synthetic image')
+    axs[2, 0].imshow(uz_image.hough_find_lines(synthetic_image, 200, 200, 0.2))
+    axs[0, 1].imshow(oneline_image, cmap='gray')
+    axs[0, 1].set_title('Oneline image')
+    axs[1, 1].imshow(oneline_image_edges, cmap='gray')
+    axs[0, 2].imshow(rectangle_image, cmap='gray')
+    axs[0, 2].set_title('Rectangle image')
+    axs[1, 2].imshow(rectangle_image_edges, cmap='gray')
+    axs[1, 0].set_visible(False)
+    axs[2, 1].imshow(uz_image.hough_find_lines(oneline_image_edges, 200, 200, 0.2))
+    axs[2, 2].imshow(uz_image.hough_find_lines(rectangle_image_edges, 200, 200, 0.2))
+
+
+    plt.show()
 
 
 # ######## #
@@ -260,7 +307,7 @@ def three_a():
 def main():
     #ex1()
     #ex2()
-    #ex3()
+    ex3()
 
 if __name__ == '__main__':
     main()
diff --git a/assignment3/uz_framework/image.py b/assignment3/uz_framework/image.py
index a25f401..10f2179 100644
--- a/assignment3/uz_framework/image.py
+++ b/assignment3/uz_framework/image.py
@@ -653,4 +653,42 @@ def hough_transform_a_point(x: int, y: int, n_bins: int) -> npt.NDArray[np.float
         accumlator[int(r), i] += 1
 
     return accumlator
+  
+
+def hough_find_lines(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], n_bins_theta: int, n_bins_rho: int, treshold: float) -> npt.NDArray[np.uint64]:
+    """"
+    Accepts: bw image with lines, n_bins_theta, n_bins_rho, treshold
+    Returns: image points above treshold transformed into hough space
+    """
+
+    image = image.copy()
+    image[image < treshold] = 0
+    theta_values = np.linspace(-np.pi/2, np.pi/2, n_bins_theta)
+    rho_values = np.linspace(-np.sqrt(image.shape[0]**2 + image.shape[1]**2), np.sqrt(image.shape[0]**2 + image.shape[1]**2), n_bins_rho)
+    accumulator = np.zeros((n_bins_rho, n_bins_theta), dtype=np.uint64)
+
+    cos_precalculated = np.cos(theta_values)
+    sin_precalculated = np.sin(theta_values)
     
+    y_s, x_s = np.nonzero(image)
+   
+    # Loop through all nonzero pixels above treshold
+    for i in range(len(y_s)):
+        x = x_s[i]
+        y = y_s[i]
+        
+        # Precalculate rhos
+        rhos = []
+        for theta in range(n_bins_theta):
+            rho = int(np.round(x * cos_precalculated[theta] + y * sin_precalculated[theta]))
+            rhos.append(rho)
+       
+        # Bin the rhos
+        binned = np.digitize(rhos, rho_values)
+    
+        # Add to accumulator
+        for theta in range(n_bins_theta):
+            accumulator[binned[theta], theta] += 1
+
+    return accumulator
+