def main_image():
""" this is the function that processes single image pointed by a name mentioned below """
# orig_image_name = "straight_lines1.jpg"
orig_image_name = "test3.jpg"
file_distortion_corrected = "output_images/lines_undist_{}".format(orig_image_name)
file_to_process = "output_images/warped_lines_undist_{}".format(orig_image_name)
original = cv2.imread("test_images/{}".format(orig_image_name))
original = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
undistorted = cv2.imread(file_distortion_corrected)
undistorted = cv2.cvtColor(undistorted, cv2.COLOR_BGR2RGB)
warped, _, _, minv = perspective_transform(file_distortion_corrected)
result, output = search_for_lines(file_to_process)
curvature_output = \
determine_lane_curvature(output['left_lane_inds'],
output['right_lane_inds'],
output['nonzerox'],
output['nonzeroy'])
print(curvature_output)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
result = draw_lane(original, undistorted, warped, output['left_fitx'], output['right_fitx'], output['ploty'], minv)
avg_curve = (curvature_output['left_curverad'] + curvature_output['right_curverad']) / 2
label_curve = 'Radius of curvature: %.1f m' % avg_curve
result = cv2.putText(result, label_curve, (30, 40), 0, 1, (0, 0, 0), 2, cv2.LINE_AA)
display_two_images(original, "Original Image", result, "Drawn Lane")
def draw_lane_pipeline():
""" this is the function that processes single image pointed by a name mentioned below """
# orig_image_name = "straight_lines1.jpg"
files_to_process = glob("test_images/*.jpg")
for file in files_to_process:
file_distortion_corrected = "output_images/lines_undist_{}".format(file.split('\\')[-1])
file_to_process = "output_images/warped_lines_undist_{}".format(file.split('\\')[-1])
original = cv2.imread(file)
original = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
undistorted = cv2.imread(file_distortion_corrected)
undistorted = cv2.cvtColor(undistorted, cv2.COLOR_BGR2RGB)
warped, _, _, minv = perspective_transform(file_distortion_corrected)
result, output = search_for_lines(file_to_process)
curvature_output = \
determine_lane_curvature(output['left_lane_inds'],
output['right_lane_inds'],
output['nonzerox'],
output['nonzeroy'])
print(curvature_output)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
result = draw_lane(original, undistorted, warped, output['left_fitx'], output['right_fitx'], output['ploty'], minv)
avg_curve = (curvature_output['left_curverad'] + curvature_output['right_curverad']) / 2
label_curve = 'Radius of curvature: %.1f m' % avg_curve
result = cv2.putText(result, label_curve, (30, 40), 0, 1, (0, 0, 0), 2, cv2.LINE_AA)
file_to_write = 'output_images/annotated_'+file.split('\\')[-1]
cv2.imwrite(file_to_write, result)
def draw_lane_pipeline(files=None, display_images=False):
""" this is the function that processes single image pointed by a name mentioned below """
# orig_image_name = "straight_lines1.jpg"
if files is None:
files_to_process = glob("test_images/*.jpg")
else:
files_to_process = list()
files_to_process.append(files)
for file in files_to_process:
if "\\" in file:
file_distortion_corrected = "output_images/lines_undist_{}".format(
file.split('\\')[-1])
file_to_process = "output_images/warped_lines_undist_{}".format(
file.split('\\')[-1])
else:
file_distortion_corrected = "output_images/lines_undist_{}".format(
file.split('/')[-1])
file_to_process = "output_images/warped_lines_undist_{}".format(
file.split('/')[-1])
original = cv2.imread(file)
original = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
undistorted = cv2.imread(file_distortion_corrected)
undistorted = cv2.cvtColor(undistorted, cv2.COLOR_BGR2RGB)
warped, _, _, minv = perspective_transform(file_distortion_corrected)
result, output = search_for_lines(file_to_process)
# Calculate vehicle center
vehicleposition_msg = vehicle_position(original, output)
curvature_output = \
determine_lane_curvature(output['left_lane_inds'],
output['right_lane_inds'],
output['nonzerox'],
output['nonzeroy'])
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
result = draw_lane(original, undistorted, warped, output['left_fitx'],
output['right_fitx'], output['ploty'], minv)
avg_curve = (curvature_output['left_curverad'] +
curvature_output['right_curverad']) / 2
label_curve = 'Radius of curvature: %.1f m' % avg_curve
result = cv2.putText(result, label_curve, (30, 40), 0, 1, (0, 0, 0), 2,
cv2.LINE_AA)
result = cv2.putText(result, vehicleposition_msg, (30, 80), 0, 1,
(0, 0, 0), 2, cv2.LINE_AA)
file_to_write = 'output_images/annotated_' + file.split('\\')[-1]
result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
if display_images:
result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
plt.imshow(result)
plt.show()
else:
cv2.imwrite(file_to_write, result)
def annotate_image(image, image_file=None):
"""
this function is used to annotate each video frame
this function can annotate either an image or it can read image from a file
:param image: image to annotate
:param image_file: file name of the image to annotate
:return: annotated image/frame
"""
# if not image and not image_file:
# raise ValueError("annotate_image: wrong function arguments (both of them are null")
if image.any() and image_file:
raise ValueError("annotate_image: wrong function arguments (both of them are not null)")
if not image.any():
raise NotImplementedError("this function accepts only input in the form of image")
# removing distortion
undistorted = distortion_removal(calibration, imageFile=None, image=image)
# discovering lines
gradient = apply_gradients_thresholds(image=undistorted)
# changing perspective
warped, _, _, minv = perspective_transform(src_file=None, image=gradient)
_, output = search_for_lines(img=warped, file_image=None)
# discovering curvature
curvature_output = \
determine_lane_curvature(output['left_lane_inds'],
output['right_lane_inds'],
output['nonzerox'],
output['nonzeroy'])
# drawing lane & annotating the image
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
res = draw_lane(image, undistorted, warped, output['left_fitx'], output['right_fitx'], output['ploty'], minv)
avg_curve = (curvature_output['left_curverad'] + curvature_output['right_curverad']) / 2
label_curve = 'Radius of curvature: %.1f m' % avg_curve
res = cv2.putText(res, label_curve, (30, 40), 0, 1, (0, 0, 0), 2, cv2.LINE_AA)
return cv2.cvtColor(res, cv2.COLOR_BGR2RGB)
def annotate_image(image, image_file=None):
"""
this function is used to annotate each video frame
this function can annotate either an image or it can read image from a file
:param image: image to annotate
:param image_file: file name of the image to annotate
:return: annotated image/frame
"""
global left_line, right_line
# if not image and not image_file:
# raise ValueError("annotate_image: wrong function arguments (both of them are null")
if image.any() and image_file:
raise ValueError(
"annotate_image: wrong function arguments (both of them are not null)"
)
if not image.any():
raise NotImplementedError(
"this function accepts only input in the form of image")
# removing distortion
undistorted = distortion_removal(calibration,
imageFile=None,
image=image)
# discovering lines
gradient = apply_gradients_thresholds(image=undistorted)
# changing perspective
warped, _, _, minv = perspective_transform(src_file=None,
image=gradient)
_, output = search_for_lines(img=warped, file_image=None)
# discovering curvature
curvature_output = \
determine_lane_curvature(output['left_lane_inds'],
output['right_lane_inds'],
output['nonzerox'],
output['nonzeroy'])
left_line.current_fit = output['left_fit']
left_line.allx = output['left_fitx']
left_line.radius_of_curvature = curvature_output['left_curverad']
left_line.ally = output['ploty']
if not left_line.detected:
for i in range(0, left_line.N_Average):
left_line.recent_xfitted.append(output['left_fit'])
left_line.Counter = 0
left_line.best_fit = output['left_fit']
left_line.detected = True
left_line.line_base_pos = image.shape[1] / 2 - output['left_fitx'][-1]
left_line.radius_of_curvature = curvature_output['left_curverad']
left_line.add_fit(output['left_fit'])
right_line.current_fit = output['right_fit']
right_line.allx = output['right_fitx']
right_line.radius_of_curvature = curvature_output['right_curverad']
right_line.ally = output['ploty']
if not right_line.detected:
for i in range(0, right_line.N_Average):
right_line.recent_xfitted.append(output['right_fit'])
right_line.Counter = 0
right_line.best_fit = output['right_fit']
right_line.detected = True
right_line.radius_of_curvature = curvature_output['right_curverad']
right_line.line_base_pos = image.shape[1] / 2 - output['right_fitx'][-1]
right_line.add_fit(output['right_fit'])
car_position_msg = vehicle_position_lines(left_line, right_line)
# drawing lane & annotating the image
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
res = draw_lane(image, undistorted, warped, left_line.bestx,
right_line.bestx, left_line.ally, minv)
avg_curve = (left_line.radius_of_curvature +
right_line.radius_of_curvature) / 2
label_curve = 'Radius of curvature: %.1f m' % avg_curve
res = cv2.putText(res, label_curve, (30, 40), 0, 1, (0, 0, 0), 2,
cv2.LINE_AA)
res = cv2.putText(res, car_position_msg, (30, 80), 0, 1, (0, 0, 0), 2,
cv2.LINE_AA)
return res