Merge pull request #76 from zmdlw/main

Finding Load Lanes

Author: ndleah

Finding_Lanes/README.md

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+<!--Please do not remove this part-->
+![Star Badge](https://img.shields.io/static/v1?label=%F0%9F%8C%9F&message=If%20Useful&style=style=flat&color=BC4E99)
+![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)
+
+# <b>🚘 Finding Lanes </b>
+### Let's go!
+<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.
+
+## ⚙️ <b>Languages or Frameworks Used
+Run the following command: 
+```sh
+$ python -m pip install --upgrade pip
+$ python -m pip install opencv-python
+```
+## <img src="https://cdn-icons-png.flaticon.com/512/109/109197.png" width="30" height="30"> <b>How to run</b>
+open a terminal in the folder where your script is located and run the following command:
+```sh
+$ python lanes.py
+```
+## <img src="https://cdn-icons-png.flaticon.com/512/61/61180.png" width="30" height="30"><b> How to close</b>
+
+Just press q
+
+## 📺 <b> Demo </b>
+
+<img src="Finding_Lanes/capture.png" width=100% height=100%>
+
+## 🤖 <b> Author</b>
+zmdlw (https://github.com/zmdlw)

Finding_Lanes/capture.png

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+import cv2
+import numpy as np
+
+
+# 1. convert the image to gray scale
+# 2. blur the image
+# 3. detect the edges
+# 4. create a mask
+# 5. apply the mask to the image
+# 6. detect the lines
+# 7. average the lines
+# 8. display the lines
+
+
+def make_coordinate(image, line_parameters):
+    slope, intercept = line_parameters
+    y1 = image.shape[0]
+    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 = []
+    for line in lines:
+        x1, y1, x2, y2 = line.reshape(4)
+        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])
+
+
+def canny(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    canny = cv2.Canny(blur, 50, 150)
+    return canny
+
+
+def display_lines(image, lines):
+    line_image = np.zeros_like(image)
+    if lines is not None:
+        for x1, y1, x2, y2 in lines:
+            cv2.line(line_image, (x1, y1), (x2, y2), (0, 0, 255), 10)
+    return line_image
+
+
+def roi(image):
+    height = image.shape[0]
+    polygons = np.array([
+        [(200, height), (1100, height), (550, 250)]
+    ])
+    mask = np.zeros_like(image)
+    cv2.fillPoly(mask, polygons, 255)
+    masked_image = cv2.bitwise_and(image, mask)
+    return masked_image
+
+
+cap = cv2.VideoCapture("Finding_Lanes/video.mp4")
+
+while(cap.isOpened()):
+    _, frame = cap.read()
+    canny_image = canny(frame)
+    cropped_image = roi(canny_image)
+    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)
+    combo_image = cv2.addWeighted(frame, 0.8, line_image, 1, 1)
+    cv2.imshow("result", combo_image)
+    if cv2.waitKey(10) == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

Finding_Lanes/lanes.py

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+import matplotlib.pyplot as plt
+import matplotlib.image as img
+img = img.imread("Finding_Lanes/picture.jpg")
+plt.imshow(img)
+plt.show()