def main():
from glob import glob
from camera_calibration import CameraCalibration
from thresholding import threshold
from transform import PerspectiveTransform
# Fixed image dimensions
height = 720
width = 1280
# Prepare camera calibration
print("Calibrating camera")
calibration = CameraCalibration.default()
# Prepare perspective transform
transform = PerspectiveTransform.default(height, width)
images = glob('test_images/straight*') + glob('test_images/test*')
for fname in images:
print("Processing", fname)
# Run pipeline
img = cv2.imread(fname)
img = calibration.undistort(img)
img, _ = threshold(img)
# img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, (5,5))
img = transform.transform(img)
# Find lines using sliding windows
left_lane, right_lane = find_lines_with_sliding_windows(img)
# Plot sliding windows
plot_sliding_windows(img, left_lane, right_lane)
# combined_binary, color_binary = threshold(img, stack=True)
#
# f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(32, 9))
# ax1.set_title('Stacked thresholds', fontsize=20)
# ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
#
# ax2.set_title('Result', fontsize=20)
# ax2.imshow(combined_binary, cmap='gray')
plt.savefig('output_images/sliding_windows_' + fname.split('/')[-1])
def main():
# # Fixed image dimensions
height = 720
width = 1280
transform = PerspectiveTransform.default(height, width)
from camera_calibration import CameraCalibration
calibration = CameraCalibration.default()
images = glob('test_images/straight*') + glob('test_images/test*')
for fname in images:
print("Processing", fname)
image = cv2.cvtColor(cv2.imread(fname), cv2.COLOR_BGR2RGB)
image = calibration.undistort(image)
polygonned = cv2.polylines(np.copy(image), [np.int32(transform.src)],
False,
color=255,
thickness=1)
transformed = transform.transform(np.copy(polygonned))
inversed = transform.invert().transform(np.copy(transformed))
f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(32, 9))
ax1.set_title('Original', fontsize=20)
ax1.imshow(image)
ax2.set_title('Area of interest', fontsize=20)
ax2.imshow(polygonned)
ax3.set_title('Transformed', fontsize=20)
ax3.imshow(transformed)
ax4.set_title('Transformed inversed', fontsize=20)
ax4.imshow(inversed)
plt.savefig('output_images/transform_' + fname.split('/')[-1])
def main():
# Fixed image dimensions
height = 720
width = 1280
# Prepare camera calibration
print("Calibrating camera")
calibration = CameraCalibration.default()
transform = PerspectiveTransform.default(height, width)
images = glob('test_images/straight*') + glob('test_images/test*')
for fname in images:
print("Processing", fname)
org_image = cv2.imread(fname)
if org_image.shape != (height, width, 3):
print("skipping image", fname, "invalid dimensions", org_image.shape)
continue
# Run the pipeline on a copy of the image
undistorted = calibration.undistort(np.copy(org_image))
transformed = transform.transform(undistorted)
warped_binary, _ = threshold(transformed, stack=False)
# combined, _ = threshold(undistorted, stack=False)
# warped_binary = transform.transform(combined)
lane = Lane()
lane.update(warped_binary)
final = lane.overlay(org_image, draw_lines=False, fill_lane=True, transform=transform.invert())
final = lane.overlay_text(final)
final = cv2.polylines(final, [np.int32(transform.src)], color=[255, 0, 0], isClosed=False)
cv2.imwrite('output_images/lane_{}'.format(fname.split('/')[-1]), final)
final = lane.overlay(np.dstack((warped_binary, warped_binary, warped_binary)) * 255, draw_lines=True,
fill_lane=False)
cv2.imwrite('output_images/lane_warped_{}'.format(fname.split('/')[-1]), final)
def create_camera_calibration():
return CameraCalibration.default()
