First lane detection alg

laneDetection/detect.py

+import numpy as np
+import cv2
+# from time import sleep
+
+# Converts picture to grayscale and applies filter to picture
+# params
+# pic : a numpy array of pixel values to represent a picture
+def formatImg(pic):
+
+    # Convert picture to gray scale
+    gray = cv2.cvtColor(pic, cv2.COLOR_BGR2GRAY)
+
+    # Apply filter to image
+    img_filter = cv2.GaussianBlur(gray, (5, 5), 0)
+
+    return img_filter
+
+# pre-processes and performs edge detection on an image
+# params
+# pic: a numpy array of pixel values for an image in gray scale
+def detect_edge(pic):
+
+    # Perform canny edge detection
+    img_edge = cv2.Canny(pic, 50, 150)
+
+    # return new edge image
+    return img_edge
+
+def ROI(pic):
+    return 0
+
+def applyLanes(original_pic, edge_pic):
+    lines = cv2.HoughLinesP(edge_pic, 1, np.pi / 180, 50, maxLineGap=70, minLineLength=10)
+
+    for line in lines:
+        x1, y1, x2, y2 = line[0]
+        print(x1)
+        cv2.line(original_pic, (x1, y1), (x2, y2), (0, 255, 0), 3)
+
+    return original_pic
+
+
+video = cv2.VideoCapture("test_data.mp4")
+
+while True:
+    ret, frame = video.read()
+    # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    frame_edge = detect_edge(frame)
+
+    if not ret:
+        video = cv2.VideoCapture("test_data.mp4")
+        continue
+
+    new_img = formatImg(frame)
+
+    wEdges = detect_edge(new_img)
+
+    lanes = applyLanes(frame, wEdges)
+
+    cv2.imshow("frame", wEdges)
+
+    key = cv2.waitKey(25)
+    if key == 27:
+        break
+
+video.release()
+cv2.destroyAllWindows()
+
+'''
+# Load image
+img = cv2.imread('lol_image.jpg')
+
+cv2.imshow("original", img)
+
+new_img = formatImg(img)
+
+wEdges = detect_edge(new_img)
+
+lanes = applyLanes(img, wEdges)
+
+
+cv2.imshow("edges", wEdges)
+cv2.imshow("with lines", lanes)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+'''
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