Merge pull request prathimacode-hub#893 from harshved/featureBranch

Added Code to Detect Lane from Image

Author: prathimacode-hub

ImageProcessingScripts/Image Lane Detector/Images/Detected_Lane.jpg

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+# Package/Script Name
+
+## Aim 
+
+The main aim of the project is to detect the Lane from the image.
+
+## Purpose
+
+Purpose of the script is to detect Lane from the Image and make it more and more accurate.
+
+## Short description of package/script
+
+- This project help us to detect the Road Lane.
+- Libraries Imported :
+    - CV2
+    - Matplotlib
+    - Numpy
+
+
+## Workflow of the Project
+
+- Here Firstly the image is read from Images folder using OpenCV library.
+- Setting the region of interest between which the road lane is their. (mentioned data is according to the given input, If u want to try your own input image try to change the value and after testing fix the region).
+- Converting the image to grey scale image.
+- Using canny's edge detection function, detecting the edge.
+- Using HoughLines function detecting the points from the canny's detected image.
+- At last drawing the line and showing the output as Detected_Lane.jpg file. 
+
+
+
+## Setup instructions
+
+First install the above mentioned library, If you want to detect lane on your given input just changge the path in cv2.imread function and set your path and run the file.
+
+## Compilation Steps:
+   - Install CV2, Matplotlib and Numpy library in your system. (syntax : pip install library_name)
+   - Clone the project on local system.
+   - Execute/Run .py file and get the output.
+
+## Output
+- Input Image
+    - ![road](https://user-images.githubusercontent.com/69030530/127738802-a2c310ba-e59e-4c2f-9960-16bd2fc4faab.jpg)
+
+- Output Image
+    - ![Detected Lane](https://user-images.githubusercontent.com/69030530/127738814-3cc4f9c3-eca5-4cb1-a2e2-8bcb91b7bf36.jpg)
+ 
+
+
+## Author(s)
+
+Harsh Ved
+

ImageProcessingScripts/Image Lane Detector/Images/road.jpg

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+#Importing the library
+import matplotlib.pylab as plt
+import cv2
+import numpy as np
+
+#Function to find region of interest
+def region_of_interest(img, vertices):
+    mask = np.zeros_like(img)
+    match_mask_color = 255
+    cv2.fillPoly(mask, vertices, match_mask_color)
+    masked_image = cv2.bitwise_and(img, mask)
+    return masked_image
+
+#Function to draw the line on output image
+def draw_the_lines(img, lines):
+    img_copy = np.copy(img)
+    blank_image = np.zeros((img_copy.shape[0], img_copy.shape[1], 3), np.uint8)
+
+    for line in lines:
+        for x1, y1, x2, y2 in line:
+            cv2.line(blank_image, (x1,y1), (x2,y2), (0, 255, 0), thickness=10)
+
+    img = cv2.addWeighted(img, 0.8, blank_image, 1, 0.0)
+    return img
+
+#Fetching the image and printing the shape
+image = cv2.imread('image/road.jpg')
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+print(image.shape)
+height = image.shape[0]
+width = image.shape[1]
+
+#setting the region of interest
+region_of_interest_vertices = [
+    (50, height),
+    (width/2.2, height/3),
+    (width-240, height)
+]
+
+gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+
+#adjusting the threshold value
+canny_image = cv2.Canny(gray_image, 20, 100)
+cropped_image = region_of_interest(canny_image,
+                np.array([region_of_interest_vertices], np.int32),)
+
+lines = cv2.HoughLinesP(cropped_image,
+                        rho=2,
+                        theta=np.pi/260,
+                        threshold=100,
+                        lines=np.array([]),
+                        minLineLength=100,
+                        maxLineGap=50)
+image_with_lines = draw_the_lines(image, lines)
+
+plt.imshow(image_with_lines)
+plt.show()
+
+cv2.imwrite("Detected_Lane.jpg",image_with_lines)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+

ImageProcessingScripts/Image Lane Detector/README.md

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+The libraries required to install and import in the project are:
+  - CV2
+  - Matplotlib
+  - Numpy