def pipeline(img):
chessboard_images = glob.glob('../data/main/camera_cal/*.jpg')
nx = 9 # chessboard corners in x direction
ny = 6 # chessboard corners in y direction
calibration_result = calibrate_camera(chessboard_images, (nx, ny))
mtx = calibration_result["mtx"]
dist = calibration_result["dist"]
undistorted_img = cv2.undistort(img, mtx, dist, None, mtx)
binary = find_line_edges(undistorted_img)
line_edges = np.dstack((binary, binary, binary)) * 255
return line_edges
def pipeline(img):
out = np.array(img, copy=True)
chessboard_images = glob.glob('../data/main/camera_cal/*.jpg')
nx = 9 # chessboard corners in x direction
ny = 6 # chessboard corners in y direction
calibration_result = calibrate_camera(chessboard_images, (nx, ny))
mtx = calibration_result["mtx"]
dist = calibration_result["dist"]
undistorted_img = cv2.undistort(img, mtx, dist, None, mtx)
binary = find_line_edges(undistorted_img)
l_line, r_line = hough_transformation(binary)
cv2.line(out, (l_line.get_x1(), l_line.get_y1()),
(l_line.get_x2(), l_line.get_y2()), (250, 0, 0), 10)
cv2.line(out, (r_line.get_x1(), r_line.get_y1()),
(r_line.get_x2(), r_line.get_y2()), (250, 0, 0), 10)
return out
import matplotlib.pyplot as plt
from image_processing.calibration import calibrate_camera
from image_processing.image_processing import find_line_edges
from image_processing.transform import perspective_transform
from image_processing.sliding_window import fit_polynomial
chessboard_images = glob.glob('../data/main/camera_cal/*.jpg')
nx = 9 # chessboard corners in x direction
ny = 6 # chessboard corners in y direction
calibration_result = calibrate_camera(chessboard_images, (nx, ny))
mtx = calibration_result["mtx"]
dist = calibration_result["dist"]
img = cv2.imread('../data/main/test_images/straight_lines1.jpg')
undistorted_img = cv2.undistort(img, mtx, dist, None, mtx)
img_rgb = cv2.cvtColor(undistorted_img, cv2.COLOR_BGR2RGB)
binary = find_line_edges(undistorted_img)
lines_img = np.dstack((binary, binary, binary)) * 255
warped, M, Minv, src, dst = perspective_transform(binary)
left_fitx, right_fitx, ploty, out_img = fit_polynomial(warped)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(24, 9))
ax1.imshow(img_rgb)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(lines_img)
ax2.set_title('Binary', fontsize=50)
ax3.plot(left_fitx, ploty, color='yellow')