ameenkhan07 · January 15, 2019 05:42 · Dec 3, 2018 · Dec 3, 2018
diff --git a/hough.py b/hough.py
@@ -95,3 +95,7 @@ def get_hough_lines(img, accumulator, diag_len, thresh=81, sin=0, cos=1, ang=90,
 
     get_hough_lines(img, acc_mat, diag_len, thresh=81, sin=0,
                     cos=1, ang=90, filename='red_line.jpg')
+
+    _cos, _sin = cos_t[55], sin_t[55]
+    get_hough_lines(img, acc_mat, diag_len, thresh=120,
+                    sin=_sin, cos=_cos, ang=55, filename='blue_line.jpg')
diff --git a/hough.py b/hough.py
@@ -0,0 +1,97 @@
+import matplotlib.pyplot as plt
+import cv2 as cv
+import numpy as np
+import os
+# import math
+from sobel import sobel
+
+OUTPUT_DIR = "outputs/"
+img_name = "./hough.jpg"
+
+
+def _save(filename, img):
+    """Saves the image with filename in output dir 
+    """
+    if not os.path.exists(OUTPUT_DIR):
+        os.makedirs(OUTPUT_DIR)
+    filename = os.path.join(OUTPUT_DIR, filename)
+    cv.imwrite(filename, img)
+
+
+def get_hough_transform_acc(img):
+    """
+    """
+    width, height = img.shape
+
+    thetas = np.deg2rad(np.arange(-90.0, 90.0))
+    num_thetas = len(thetas)
+
+    # Calculating Rhos for
+    diag_len = int(round(np.sqrt(width**2 + height**2)))
+
+    # Hough accumulator array of theta vs rho
+    accumulator = np.zeros((2 * diag_len, num_thetas), dtype=np.uint64)
+    y_idxs, x_idxs = np.nonzero(img)  # (row, col) indexes to edges
+
+    # Vote in the hough accumulator
+    cos_theta = np.cos(thetas)
+    sin_theta = np.sin(thetas)
+    for i in range(len(x_idxs)):
+        x = x_idxs[i]
+        y = y_idxs[i]
+        for t_idx in range(num_thetas):
+            # Calculate rho. diag_len is added for a positive index
+            rho = int(
+                round(x * cos_theta[t_idx] + y * sin_theta[t_idx]) + diag_len)
+            accumulator[rho, t_idx] += 1
+    return accumulator, diag_len, thetas, sin_theta, cos_theta
+
+
+def plot_hough(H):
+    """ Plot Hough accumulator matrix
+    """
+    plot_title = 'Hough Accumulator Plot'
+    fig = plt.figure(figsize=(10, 10))
+    fig.canvas.set_window_title(plot_title)
+
+    plt.imshow(H, cmap='jet')
+
+    plt.xlabel('Theta Direction'), plt.ylabel('Rho Direction')
+    plt.show()
+
+
+def get_hough_lines(img, accumulator, diag_len, thresh=81, sin=0, cos=1, ang=90, filename='lines.jpg'):
+    """
+    """
+    _img = np.copy(img)
+    acc = np.copy(accumulator)
+
+    acc = (acc[:, ang]) > thresh
+    rhos = np.nonzero(acc)[0]  # Return true indices
+
+    for i in range(0, len(rhos)):
+        rho = rhos[i]
+        rho = rho - diag_len
+        x0 = rho * cos
+        y0 = rho * sin
+        x1 = int(x0 + 1000*(-sin))
+        y1 = int(y0 + 1000*(cos))
+        x2 = int(x0 - 1000*(-sin))
+        y2 = int(y0 - 1000*(cos))
+        cv.line(_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+    _save(filename, _img)
+
+
+if __name__ == '__main__':
+
+    img, img_g = cv.imread(img_name), cv.imread(img_name, 0)
+
+    # Implement Canny Edge Detection Algo ?
+    edges = sobel(img_g)
+
+    acc_mat, diag_len, thetas, sin_t, cos_t = get_hough_transform_acc(edges)
+    # plot_hough(acc_mat)
+
+    get_hough_lines(img, acc_mat, diag_len, thresh=81, sin=0,
+                    cos=1, ang=90, filename='red_line.jpg')