def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255)):
thresh_min, thresh_max = thresh
# Convert to grayscale
gray = rgb_to_gray(img)
# Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
elif orient == 'y':
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# Take the absolute value of the derivative or gradient
sobel = np.absolute(sobel)
# Scale to 8-bit (0 - 255) then convert to type = np.uint8
sobel = np.uint8(255 * sobel / np.max(sobel))
# Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
binary_output = np.zeros_like(sobel)
binary_output[(sobel >= thresh_min) & (sobel <= thresh_max)] = 1
# Return this mask as your binary_output image
return binary_output
def calibrate_camera():
for i, filepath in enumerate(images_glob):
# Load image
img = rgb_image(filepath)
# Convert image to grayscale
gray = rgb_to_gray(img)
# Find chessboard corners for image
ret, corners = cv2.findChessboardCorners(gray, chessboard_shape, None)
# Create an object points array
cols, rows = chessboard_shape
objp = np.zeros((cols * rows, 3), np.float32)
objp[:,:2] = np.mgrid[0:cols, 0:rows].T.reshape(-1, 2)
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
corners_img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
plt.imsave("./camera_cal/corners{:02d}.jpg" .format(i+1), corners_img)
#plt.pause(0.5)
# Use the object points and image points to calibrate a camera
_, mtx, dist, _, _ = cv2.calibrateCamera(objpoints, imgpoints, image_shape, None, None)
# Save the calibration data for use later
save_camera_calibration(output_file, mtx, dist)
def calibrate():
# List of images to be used for calibration
images_glob = glob.glob("images/calibration/calibration*.jpg")
# Shape of the calibration images
image_shape = rgb_image(images_glob[0]).shape[1::-1]
# Number of columns and rows for the chessboard
chessboard_shape = (9, 6)
# Where to save the calibration data
output_file = "./calibration.pkl"
objpoints = []
imgpoints = []
for filepath in images_glob:
# Load image
img = rgb_image(filepath)
# Convert image to grayscale
gray = rgb_to_gray(img)
# Find chessboard corners for image
pattern_was_found, corners = cv2.findChessboardCorners(
gray, chessboard_shape, None)
# Create an object points array
cols, rows = chessboard_shape
objp = np.zeros((cols * rows, 3), np.float32)
objp[:, :2] = np.mgrid[0:cols, 0:rows].T.reshape(-1, 2)
if pattern_was_found:
objpoints.append(objp)
imgpoints.append(corners)
# Use the object points and image points to calibrate a camera
_, mtx, dist, _, _ = cv2.calibrateCamera(objpoints, imgpoints, image_shape,
None, None)
# Save the calibration data for use later
save_calibration_data(output_file, mtx, dist)
def dir_thresh(img, sobel_kernel=3, thresh=(0, np.pi / 2)):
# Convert to grayscale
gray = rgb_to_gray(img)
# Take the gradient in x and y separately
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the x and y gradients
sobel_x = np.absolute(sobel_x)
sobel_y = np.absolute(sobel_y)
# Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction = np.arctan2(sobel_y, sobel_x)
# Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction)
binary_output[(direction >= thresh[0]) & (direction <= thresh[1])] = 1
# Return this mask as your binary_output image
return binary_output
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
gray = rgb_to_gray(img)
# Take the gradient in x and y separately
sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobel_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the magnitude
sobel_xy = np.sqrt(sobel_x**2 + sobel_y**2)
# Scale to 8-bit (0 - 255) and convert to type = np.uint8
sobel_xy = np.uint8(255 * sobel_xy / np.max(sobel_xy))
# Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(sobel_xy)
binary_output[(sobel_xy >= mag_thresh[0])
& (sobel_xy <= mag_thresh[1])] = 1
# Return this mask as your binary_output image
return binary_output