lane detection matrices work on car!

ros2/UI/pages/Lane_Detection.py

@@ -11,7 +11,7 @@
 from ui_generics import *
 import ros2_numpy as rnp
 from geometry_msgs.msg import Transform
-
+from streamlit_image_coordinates import streamlit_image_coordinates as st_images
 # This class currently subscribes to some topics in cam_publisher, which I used to test. Please rewrite for lane Detection.
 
 
@@ -192,14 +192,16 @@ def input_handler(context):
         img = sub.reference_img
 
         if img is not None:
-            cols = st.columns(3)
-            cols[0].image(img, "Left reference image for transformation")
+            pixel_values_ref = st_images(img) 
+            st.write(pixel_values_ref)
             transformed_left = cv2.warpPerspective(img, matrix, (640, 480))
             hsv_transformed_left = cv2.cvtColor(
                 transformed_left, cv2.COLOR_BGR2HSV)
             mask = cv2.inRange(hsv_transformed_left, hsv[:][0], hsv[:][1])
-            cols[1].image(transformed_left, "Left Birds Eye View Preview")
-            cols[2].image(mask, "Left Mask Preview")
+            transformed_vales = st_images(transformed_left)
+            st.write(transformed_vales)
+            # # st.image(mask, "Left Mask Preview")
+            # st.write(values)
     # TODO: Make this button publish the custom ROS2 Message :)
     # TODO: Make this button do soemthing (Read and write to file)
         if(st.button("Publish")):

ros2/src/lanefollowingtest/LaneDetection.py

@@ -10,12 +10,13 @@
 from std_msgs.msg import String
 
 # Inputs from both cameras
-vidcap_left = cv2.VideoCapture("/dev/video3")
+vidcap_left = cv2.VideoCapture("/dev/video0")
 vidcap_left.set(3, 640)
 vidcap_left.set(4, 480)
-vidcap_right = cv2.VideoCapture("/dev/video1")
-vidcap_right.set(3, 640)
-vidcap_right.set(4, 480)
+vidcap_right = vidcap_left 
+# vidcap_right = cv2.VideoCapture("/dev/video1")
+# vidcap_right.set(3, 640)
+# vidcap_right.set(4, 480)
 
 
 # These are constants
@@ -107,7 +108,7 @@ def Plot_Line(self, smoothen=False, prevFrameCount=6):
         # And recast the x and y points into usable format for cv2.fillPoly()
         line_window1 = np.array(
             [np.transpose(np.vstack([fitx-margin, ploty]))])
-        line_window2 = np.array([np.flipud(np.transpose(np.vstack([fitx+margin,
+        line_window2 = np.array([(np.transpose(np.vstack([fitx+margin,
                                                                    ploty])))])
         line_pts = np.hstack((line_window1, line_window2))
 
@@ -136,10 +137,10 @@ def Plot_Line(self, smoothen=False, prevFrameCount=6):
 upper = np.array([u_h, u_s, u_v])
 
 # Coordinates for the 4 alignment points: again, should be handled by the UI
-bl = (12, 355)
-tl = (66, 304)
-br = (635, 344)
-tr = (595, 308)
+bl = (12, 472)
+tl = (90, 8)
+br = (499, 475)
+tr = (435, 24)
 # Aplying perspective transformation
 pts1 = np.float32([tl, bl, tr, br])
 pts2 = np.float32([[0, 0], [0, 480], [640, 0], [640, 480]])
@@ -158,7 +159,8 @@ def __init__(self):
         # Determine which lane we're in: Left lane means the right image is dashed
         self._Left_Lane = False
 
-
+        ###The ratio from  pixels to inches
+        conversion = 6.625
         timer_period = 0.01  # seconds
         self.timer = self.create_timer(timer_period, self.timer_callback)
 
@@ -229,6 +231,7 @@ def determine_lane_size(self, img):
     def timer_callback(self):
         success_l, image_l = vidcap_left.read()
         success_r, image_r = vidcap_right.read()
+        image_r = cv2.flip(image_r,0)
         images = [(image_l, "left"), (image_r, "right")]
         left_buffer = -1
         right_buffer = -1
@@ -242,8 +245,8 @@ def timer_callback(self):
             for point in (bl, tl, br, tr):
                 frame = cv2.circle(frame, point, 5, (0, 0, 255), -1)
 
-            transformed_frame = cv2.warpPerspective(
-                frame, matrix, (640, 480))
+            transformed_frame = cv2.rotate(cv2.warpPerspective(
+                frame, matrix, (640, 480)), cv2.ROTATE_90_CLOCKWISE)
             # Object Detection
             # Image Thresholding
             hsv_transformed_frame = cv2.cvtColor(