def canny(image):
blurred_image = gaussian_blur(image, kernel_size=9)
edge_filter = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
gradient_magnitude, gradient_direction = sobel_edge_detection(blurred_image, edge_filter)
new_image = non_max_suppression(gradient_magnitude, gradient_direction)
weak = 50
new_image = threshold(new_image, 5, 20, weak)
new_image = hysteresis(new_image, weak)
new_image = new_image.astype(np.uint8)
kernel = np.ones((5, 5))
img_dil = cv2.dilate(new_image, kernel, iterations=1)
form = getContours(img_dil, image, image)
return form
plt.show()
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
ap.add_argument("-v",
"--verbose",
type=bool,
default=False,
help="Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"], 0)
blurred_image = gaussian_blur(image, kernel_size=25, verbose=False)
edge_filter = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
new_image_x, new_image_y, gradient_magnitude, gradient_direction = sobel_edge_detection(
blurred_image,
edge_filter,
convert_to_degree=True,
verbose=args["verbose"])
new_image = non_max_suppression(gradient_magnitude,
gradient_direction,
verbose=args["verbose"])
weak = 50
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
ap.add_argument("-v",
"--verbose",
type=bool,
default=False,
help="Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
blurred_image = gaussian_blur(image,
kernel_size=9,
verbose=args["verbose"])
edge_filter = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
gradient_magnitude, gradient_direction = sobel_edge_detection(
blurred_image,
edge_filter,
convert_to_degree=True,
verbose=args["verbose"])
new_image = non_max_suppression(gradient_magnitude,
gradient_direction,
verbose=args["verbose"])
weak = 50
#kernel = np.ones((4, 3), np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# Performing an opening
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# Performing a closing
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=2)
# Displaying result of opening
cv2.imshow('Opening', opening)
cv2.waitKey()
# Displaying result of closing
cv2.imshow('Closing', closing)
cv2.waitKey()
blurred_image = gaussian_blur(closing, kernel_size=9, verbose=True)
edge_filter = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
gradient_magnitude, gradient_direction = sobel_edge_detection(
blurred_image, edge_filter, convert_to_degree=True, verbose=True)
new_image = non_max_suppression(gradient_magnitude,
gradient_direction,
verbose=True)
weak = 50
new_image = threshold(new_image, 5, 20, weak=weak, verbose=True)
new_image = hysteresis(new_image, weak)
new = cv2.convertScaleAbs(new_image, cv2.CV_8UC1)
final_image = top_to_bottom + bottom_to_top + right_to_left + left_to_right
final_image[final_image > 255] = 255
return final_image
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
ap.add_argument("-s", "--steps", type=bool, default=False, help="Show all the steps")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
blurred_image = gaussian_blur(image, kernel_size=9, steps=False)
edge_filter = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
gradient_magnitude, gradient_direction = sobel_edge_detection(blurred_image, edge_filter, convert_to_degree=True, steps=args["steps"])
new_image = non_max_suppression(gradient_magnitude, gradient_direction, steps=args["steps"])
weak = 50
new_image = threshold(new_image, 5, 20, weak=weak, steps=args["steps"])
new_image = hysteresis(new_image, weak)
plt.imshow(new_image, cmap='gray')
plt.title("Canny Edge Detector by Vito Kurnik")