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 __init__(self, birdseye):
self.birdseye = birdseye
def draw_overlays(self, image, lanes):
return overlay_detected_lane_data(image=image,
lanes=lanes,
birdseye=self.birdseye)
if __name__ == "__main__":
np.seterr(all='ignore')
birdseye = BirdsEyeView()
distortion = Distortion(
calibration_data_filepath="./camera_cal/wide_dist_pickle.p")
overlay = OverlayDetectedLaneData(birdseye=birdseye)
images_glob = glob.glob("./test_images/test1.jpg")
for filepath in images_glob:
image = rgb_image(filepath)
bin_image = distortion.undistort(image)
bin_image = birdseye.transform_to_birdseye(bin_image)
bin_image, gb = combined_thresholds.pipeline(bin_image)
lanes, _ = detect_lane_lines(bin_image)
output_image = overlay.draw_overlays(image, lanes)
plt.imshow(output_image)
plt.show()
from image_helper import rgb_image, rgb_to_gray
import cv2
import glob
import numpy as np
import pickle
import matplotlib.pyplot as plt
# List of images to be used for calibration
images_glob = sorted(glob.glob("./camera_cal/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 = "./camera_cal/wide_dist_pickle.p"
objpoints = []
imgpoints = []
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)