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code.py

+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import numpy as np
+import cv2
+import sys
+
+def draw_circle(circles, img):
+    if circles is not None:
+        print(len(circles))
+        circles = np.uint16(np.around(circles))
+        for i in circles[0, :]:
+            center = (i[0], i[1])
+            # circle center
+            cv2.circle(img, center, 1, (0, 100, 100), 3)
+            # circle outline
+            radius = i[2]
+            cv2.circle(img, center, radius, (255, 0, 255), 3)
+
+
+def draw_lines(img, houghLines, color=[0, 255, 0], thickness=1):
+    print(len(houghLines))
+    for line in houghLines:
+        for rho,theta in line:
+            a = np.cos(theta)
+            b = np.sin(theta)
+            x0 = a*rho
+            y0 = b*rho
+            x1 = int(x0 + 2000*(-b))
+            y1 = int(y0 + 2000*(a))
+            x2 = int(x0 - 2000*(-b))
+            y2 = int(y0 - 2000*(a))
+
+            cv2.line(img,(x1,y1),(x2,y2),color,thickness)
+
+
+
+def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):
+    return cv2.addWeighted(initial_img, α, img, β, λ)
+
+def run_segment(img):
+    original = img
+    image = img
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    blurred_image = cv2.GaussianBlur(img, (15, 15), 0)
+    #blurred_image_Copy = np.uint8(blurred_image)
+    src = cv2.Canny(blurred_image, 143, 43) #(cv2.Canny(bilde , lower threshold, upper threshold))
+    if src is None:
+        print ('Error opening image!')
+        print ('Usage: hough_lines.py [image_name -- default ' + default_file + '] \n')
+        return -1
+
+
+
+    cdst = cv2.cvtColor(src, cv2.COLOR_GRAY2BGR)
+    cdstP = np.copy(cdst)
+
+    rho_resolution = 1
+    theta_resolution = np.pi/180
+    threshold = 138
+
+    linesP = cv2.HoughLinesP(src, rho_resolution, theta_resolution, threshold, None, 2, 3)
+
+
+
+    if linesP is not None:
+        for i in range(0, len(linesP)):
+            l = linesP[i][0]
+            cv2.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv2.LINE_AA)
+
+    cv2.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
+
+    cv2.waitKey()
+    return 0
+
+def get_blur(img):
+    image = img
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    blurred_image_gray = cv2.GaussianBlur(gray_image, (9, 9), 0)
+    blurred_image_original = cv2.GaussianBlur(img, (9, 9), 0)
+
+
+    plt.figure(figsize = (30,30))
+    plt.subplot(131)
+    plt.imshow(img)
+    plt.subplot(132)
+    plt.imshow(blurred_image_gray, cmap='gray')
+    plt.subplot(133)
+    plt.imshow(blurred_image_original)
+
+    plt.show()
+
+def run_line(img):
+    image = img
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    blurred_image = cv2.GaussianBlur(gray_image, (9, 9), 0)
+    #blurred_image_Copy = np.uint8(blurred_image)
+    edges_image = cv2.Canny(blurred_image, 10, 100) #(cv2.Canny(bilde , lower threshold, upper threshold))
+
+
+    rho_resolution = 1
+    theta_resolution = np.pi/180
+    threshold = 175
+
+    hough_lines = cv2.HoughLines(edges_image, rho_resolution , theta_resolution , threshold)
+
+    hough_lines_image = np.zeros_like(image)
+    #draw_lines(hough_lines_image, hough_lines)
+    draw_lines(hough_lines_image, hough_lines)
+    original_image_with_hough_lines = weighted_img(hough_lines_image,image)
+
+    plt.figure(figsize = (10,10))
+    #plt.subplot(131)
+    #plt.imshow(gray_image)
+    #plt.subplot(132)
+    #plt.imshow(edges_image, cmap='gray')
+    plt.subplot(111)
+    plt.imshow(original_image_with_hough_lines, cmap='gray')
+
+    plt.show()