Fix/Finding Lanes path resolution, frame validation, line averaging, and README (#467)

Finding_Lanes/README.md

@@ -7,7 +7,7 @@
 <img src="https://media3.giphy.com/media/8QC4kjqN9qDEk/giphy.gif?cid=ecf05e471kl80anlf7s2wx2yz9v5ssu5xxgkbsxq74j9afql&rid=giphy.gif&ct=g">
 
 ## 🛠️ <b> Description </b>
-A short description about the script must be mentioned here.
+Finding Lanes is a Python computer vision script built with OpenCV and NumPy to detect road lane markings in images and video streams. It performs RGB to Grayscale conversion, Gaussian blurring, Canny edge detection, Region of Interest (ROI) polygon masking, Hough Transform line detection, and linear regression slope/intercept averaging to compute and visualize clear lane boundaries.
 
 ## ⚙️ <b>Languages or Frameworks Used
 Run the following command: 

Finding_Lanes/lanes.py

@@ -1,3 +1,4 @@
+import os
 import cv2
 import numpy as np
 
@@ -14,30 +15,46 @@
 
 def make_coordinate(image, line_parameters):
     slope, intercept = line_parameters
+    if slope == 0:
+        return None
     y1 = image.shape[0]
-    y2 = int(y1*(3/5))
-    x1 = int((y1-intercept)/slope)
-    x2 = int((y2-intercept)/slope)
+    y2 = int(y1 * (3 / 5))
+    x1 = int((y1 - intercept) / slope)
+    x2 = int((y2 - intercept) / slope)
     return np.array([x1, y1, x2, y2])
 
 
 def average_lines_intercept(image, lines):
     left_fit = []
     right_fit = []
+    if lines is None:
+        return None
     for line in lines:
         x1, y1, x2, y2 = line.reshape(4)
+        if x1 == x2:
+            continue
         parameters = np.polyfit((x1, x2), (y1, y2), 1)
         slope = parameters[0]
         intercept = parameters[1]
         if slope < 0:
             left_fit.append((slope, intercept))
         else:
             right_fit.append((slope, intercept))
-    left_fit_average = np.average(left_fit, axis=0)
-    right_fit_average = np.average(right_fit, axis=0)
-    left_line = make_coordinate(image, left_fit_average)
-    right_line = make_coordinate(image, right_fit_average)
-    return np.array([left_line, right_line])
+
+    lines_list = []
+    if len(left_fit) > 0:
+        left_fit_average = np.average(left_fit, axis=0)
+        left_line = make_coordinate(image, left_fit_average)
+        if left_line is not None:
+            lines_list.append(left_line)
+
+    if len(right_fit) > 0:
+        right_fit_average = np.average(right_fit, axis=0)
+        right_line = make_coordinate(image, right_fit_average)
+        if right_line is not None:
+            lines_list.append(right_line)
+
+    return np.array(lines_list) if len(lines_list) > 0 else None
 
 
 def canny(image):
@@ -66,13 +83,20 @@ def roi(image):
     return masked_image
 
 
-cap = cv2.VideoCapture("Finding_Lanes/video.mp4")
+dir_path = os.path.dirname(os.path.abspath(__file__))
+video_path = os.path.join(dir_path, "video.mp4")
+if not os.path.exists(video_path):
+    video_path = "Finding_Lanes/video.mp4"
 
-while(cap.isOpened()):
-    _, frame = cap.read()
+cap = cv2.VideoCapture(video_path)
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret or frame is None:
+        break
     canny_image = canny(frame)
     cropped_image = roi(canny_image)
-    lines = cv2.HoughLinesP(cropped_image, 2, np.pi/180,
+    lines = cv2.HoughLinesP(cropped_image, 2, np.pi / 180,
                             100, np.array([]), minLineLength=40, maxLineGap=5)
     averaged_lines = average_lines_intercept(frame, lines)
     line_image = display_lines(frame, averaged_lines)

Finding_Lanes/sub.py

@@ -1,5 +1,12 @@
+import os
 import matplotlib.pyplot as plt
 import matplotlib.image as img
-img = img.imread("Finding_Lanes/picture.jpg")
-plt.imshow(img)
+
+dir_path = os.path.dirname(os.path.abspath(__file__))
+image_path = os.path.join(dir_path, "picture.jpg")
+if not os.path.exists(image_path):
+    image_path = "Finding_Lanes/picture.jpg"
+
+image = img.imread(image_path)
+plt.imshow(image)
 plt.show()