def lane_detection(self, image_bgr):
# Convert to HSV
image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)
# Mask
global mask_lower_threshold, mask_upper_threshold
mask = cv2.inRange(image_hsv, mask_lower_threshold,
mask_upper_threshold)
# Publish mask image
if self.mask_pub.get_num_connections() > 0:
self.mask_pub.publish(
self.bridge.cv2_to_imgmsg(mask, encoding='passthrough'))
# Canny edge detection
edges = cv2.Canny(mask, 200, 400)
# Clear the top half of the edges image
edges[0:int(float(edges.shape[0]) / 2.), :] = 0.
# Publish edges image
if self.edges_image_pub.get_num_connections() > 0:
self.edges_image_pub.publish(
self.bridge.cv2_to_imgmsg(edges, encoding='passthrough'))
# Detect line segments
line_segments_tmp = cv2.HoughLinesP(edges, 1, np.pi / 180., 10, None,
8, 4)
if line_segments_tmp is None:
print('')
return [None, None]
# Remove extra array layer from line_segments_tmp
line_segments = []
for line_segment in line_segments_tmp:
line_segments.append(line_segment[0])
# Publish line segments image
if self.line_segments_image_pub.get_num_connections() > 0:
line_segments_image = plot_line_segments(image_bgr, line_segments)
self.line_segments_image_pub.publish(
self.bridge.cv2_to_imgmsg(line_segments_image,
encoding='bgr8'))
# Combine line segments
[left_lane,
right_lane] = functions.average_slope_intercept(line_segments)
if self.lanes_image_pub.get_num_connections() > 0:
lanes_image = plot_lanes(image_bgr, left_lane, right_lane)
self.lanes_image_pub.publish(
self.bridge.cv2_to_imgmsg(lanes_image, encoding='bgr8'))
return [left_lane, right_lane]
"""
canny_frm = functions.canny(frame)
# 4. cropped frame/image with the mask
cropped_frm = functions.region_of_interest(canny_frm)
# 5. Apply hough transform (r , teta) : shall be small for better precision
# HoughLinesP parameters : r = 2 pixels , teta=1 degree, threshold = 100, empty array, ...
lines = cv2.HoughLinesP(cropped_frm,
2,
np.pi / 180,
100,
np.array([]),
minLineLength=40,
maxLineGap=5)
# 6. optimizing
# average multiples slope in the lines to a single slope line
averaged_lines = functions.average_slope_intercept(frame, lines)
# display line frame/image
line_frm = functions.display_lines(frame, averaged_lines)
# merged the gradient frame/image into the colored frame/image
combo_frm = cv2.addWeighted(frame, 0.8, line_frm, 1, 1)
# Creates a window to display the line frame/image
cv2.imshow('result', combo_frm)
# Display the frame/image during 1msn and abort the system if the a keyboard is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()