Lane_Detection_Final.py

import cv2
import matplotlib.pyplot as plt
import numpy as np
import sys
import os

import serial
from queue import Queue
import threading
import time

import warnings

# Redirect stderr to null device
# sys.stderr = open(os.devnull, 'w')q

# Ignore all warnings
# warnings.filterwarnings("ignore")

# Open the serial port that your Arduino is connected to (e.g., COM3 on Windows, /dev/ttyUSB0 on Linux, /dev/tty.usbserial on MacOS).
# ser = serial.Serial('/dev/tty.usbmodem1401', 9600)  # Adjust this to match your connection
# def send_value(value):
#     ser.write(str(value).encode())  # Convert the integer to a string and encode it to bytes
#     ser.flush()
    # time.sleep(0.01) # Wait a bit for the Arduino to process the information

# # Read response
# def recieve_value():
#     if ser.in_waiting > 0:
#         ser_line = ser.readline().decode().strip()
#         print(f"Arduino responded with: {ser_line}")
# capture = cv2.VideoCapture(0)
# capture = cv2.VideoCapture('/Users/anavart.pandya/MY DRIVE D/Autonomous Vehicles Sem8 IITGN/Advanced-Lane-Lines-master/vid2.mp4')


def canny(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # kernel = 5
    kernel = 7
    blur = cv2.GaussianBlur(gray, (kernel,kernel), sigmaX=0, sigmaY=0)
    canny = cv2.Canny(blur,80,100)
    # canny = cv2.Canny(blur,100,160)
    return canny

def region_of_interest(img):
    height = img.shape[0]
    width = img.shape[1]
    mask = np.zeros_like(img)
    # triangle = np.array([[(50,height), (400,300), (900,height)]], np.int32)
    # triangle = np.array([[(0,height), (300,150), (900,height)]], np.int32)
    triangle = np.array([[(-100,height), (width//2,height//4), (width+100,height)]], np.int32)
    cv2.fillPoly(mask,triangle,255)
    masked_image = cv2.bitwise_and(img,mask)
    return masked_image

def region_of_interest_trapezium(img):
    height = img.shape[0]
    width = img.shape[1]
    mask = np.zeros_like(img)

    # Define coordinates for the trapezium
    # Adjust the points (x1, y1), (x2, y2), (x3, y3), (x4, y4) as needed
    bottom_left = (0, height)
    top_left = (0, height * 0.5)
    top_right = (width, height * 0.5)
    bottom_right = (width, height)

    # bottom_left = (width * 0.1, height)
    # top_left = (width * 0.4, height * 0.6)
    # top_right = (width * 0.6, height * 0.6)
    # bottom_right = (width * 0.9, height)

    # np.array expects points as [[first_point, second_point, third_point, fourth_point]]
    trapezium = np.array([[bottom_left, top_left, top_right, bottom_right]], np.int32)

    # Fill the polygon (trapezium here) with white (255)
    cv2.fillPoly(mask, [trapezium], 255)

    # Apply the mask
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def houghLines(img):
    houghLines = cv2.HoughLinesP(img,2,np.pi/180,100,np.array([]),minLineLength = 40, maxLineGap = 5)
    # houghLines = cv2.HoughLinesP(img,2,np.pi/180,100,np.array([]),minLineLength = 20, maxLineGap = 5)
    # houghLines = cv2.HoughLinesP(img,2,10*(np.pi/180),10,np.array([]),minLineLength = 15, maxLineGap = 5)
    return houghLines

def display_lines(img,lines,init_point,show_vehicle_centre):
    img_copy = img.copy()
    if lines is not None:
        for i in range(len(lines)):
            line = np.array(lines[i])
            if line.all() != np.array([[None,None,None,None]]).all():
                if i == 2:
                    for[x1,y1,x2,y2] in line:
                        cv2.line(img_copy, (x1,y1), (x2,y2), (255,0,0),10)
                        cv2.circle(img_copy, ((x1+x2)//2,(y1+y2)//2), 10, (255,0,0), 10)
                        # centre = ((x1+x2)//2,(y1+y2)//2)
                        if show_vehicle_centre == True:
                            cv2.circle(img_copy, init_point, 10, (255,0,255), 10)
                else:
                    for [x1,y1,x2,y2] in line:
                        cv2.line(img_copy, (x1,y1), (x2,y2), (255,0,0),10)
                        if show_vehicle_centre == True:
                            cv2.circle(img_copy, init_point, 10, (255,0,255), 10)
    return img_copy

def display_lines_with_filled_region(img,lines,init_point):
    img_copy = img.copy()
    centre = None
    if lines is not None:
        left_line = lines[0][0]  # Assuming first line is the left
        right_line = lines[1][0]  # Assuming second line is the right

        pts = np.array([[left_line[0], left_line[1]], [left_line[2], left_line[3]],
                        [right_line[2], right_line[3]], [right_line[0], right_line[1]]], np.int32)
        
        pts = pts.reshape((-1, 1, 2))
        cv2.fillPoly(img_copy, [pts], (144, 238, 144))  # Light green color
        
        for i in range(len(lines)):
            line = np.array(lines[i])
            if line.all() != np.array([[None,None,None,None]]).all():
                if i == 2:
                    for[x1,y1,x2,y2] in line:
                        cv2.line(img_copy, (x1,y1), (x2,y2), (255,0,0),10)
                        cv2.circle(img_copy, ((x1+x2)//2,(y1+y2)//2), 10, (255,0,0), 10)
                        cv2.circle(img_copy, init_point, 10, (255,0,255), 10)
                        centre = ((x1+x2)//2,(y1+y2)//2)
                else:
                    for [x1,y1,x2,y2] in line:
                        cv2.line(img_copy, (x1,y1), (x2,y2), (255,0,0),10)
                        cv2.circle(img_copy, init_point, 10, (255,0,255), 10)
    return img_copy, centre

def make_points(img,lineSI):
    slope, intercept = lineSI
    height = img.shape[0]
    y1 = int(height)
    # y2 = int(y1*4.0/5)
    y2 = int(y1*3/5)
    x1 = int((y1-intercept)/slope)
    x2 = int((y2-intercept)/slope)
    return [[x1,y1,x2,y2]]


def average_slope_intercept(img,lines):
    # lower_value_slope = 0.5
    # higher_value_slope = 3
    lower_value_slope = 0.5
    higher_value_slope = 2.5
    flag_left = True
    flag_right = True
    left_fit = []
    right_fit = []
    for line in lines:
        for x1,y1,x2,y2 in line:
            fit = np.polyfit((x1,x2),(y1,y2),1)
            slope = fit[0]
            intercept = fit[1]
            # print(intercept,slope)
            if slope < -lower_value_slope and slope >= -higher_value_slope:
                left_fit.append((slope, intercept))
            elif slope >= lower_value_slope and slope <= higher_value_slope :
                right_fit.append((slope,intercept))
    if left_fit == []:
        # left_fit = np.array([(0.0001,0.0001)])
        flag_left = False
    if right_fit == []:
        # right_fit = np.array([(0.0001,0.0001)])
        flag_right = False

    if flag_left:
        left_fit_average = np.average(left_fit,axis=0)
        left_line = make_points(img, left_fit_average)
    else:
        left_line = np.array([[None,None,None,None]])

    if flag_right:
        right_fit_average = np.average(right_fit,axis=0)
        right_line = make_points(img, right_fit_average)
    else:
        right_line = np.array([[None,None,None,None]])


    average_lines = [np.array(left_line),np.array(right_line)]
    return average_lines

def average_slope_intercept_with_centre(img,lines):
    # lower_value_slope = 0.5
    # higher_value_slope = 3
    lower_value_slope = 0.5
    higher_value_slope = 2.5
    flag_left = True
    flag_right = True
    left_fit = []
    right_fit = []
    for line in lines:
        for x1,y1,x2,y2 in line:
            fit = np.polyfit((x1,x2),(y1,y2),1)
            slope = fit[0]
            intercept = fit[1]
            # print(intercept,slope)
            if slope < -lower_value_slope and slope >= -higher_value_slope:
                left_fit.append((slope, intercept))
            elif slope >= lower_value_slope and slope <= higher_value_slope :
                right_fit.append((slope,intercept))
    if left_fit == []:
        # left_fit = np.array([(0.0001,0.0001)])
        flag_left = False
    if right_fit == []:
        # right_fit = np.array([(0.0001,0.0001)])
        flag_right = False

    if flag_left:
        left_fit_average = np.average(left_fit,axis=0)
        left_line = make_points(img, left_fit_average)
    else:
        left_line = np.array([[None,None,None,None]])

    if flag_right:
        right_fit_average = np.average(right_fit,axis=0)
        right_line = make_points(img, right_fit_average)
    else:
        right_line = np.array([[None,None,None,None]])

    if flag_left and flag_right:
        center_line = np.array([[0,0,0,0]])
        for i in range(4):
            center_line[0][i] = np.int32((left_line[0][i] + right_line[0][i])/2)
    else:
        center_line = np.array([[None,None,None,None]])

    average_lines = [np.array(left_line),np.array(right_line),np.array(center_line)]
    return average_lines

def color_filter_for_gray(img):
    # Convert the image to the HSV color space
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # Define range for gray colors in HSV
    # Gray color will have low saturation, so we use a higher lower bound for value to avoid very dark regions
    lower_gray = np.array([0, 0, 50], dtype="uint8")
    upper_gray = np.array([180, 50, 255], dtype="uint8")
    
    # Create mask for the gray color
    mask_gray = cv2.inRange(hsv, lower_gray, upper_gray)
    
    # Apply the mask to the input image
    masked_image = cv2.bitwise_and(img, img, mask=mask_gray)
    masked_image = cv2.cvtColor(masked_image, cv2.COLOR_HSV2BGR)
    
    return masked_image

left_line_history = []
right_line_history = []
# center_line_history = []
# history_length = 100  # Keep history of last 5 frames
history_length = 25  # Keep history of last 5 frames
# init_point = (428, 430)
# init_point = (283, 384)
# init_point = (267, 408)


def update_line_history(line_history, new_line, history_length=5):
    if new_line[0].all() == np.array([None,None,None,None]).all() and line_history:
        # Use the most recent valid line if the new line is invalid
        new_line = line_history[-1]
    line_history.append(new_line)
    if len(line_history) > history_length:
        line_history.pop(0)
    # print(line_history)
    return line_history


def average_line_from_history(line_history):
    if not line_history:
        return np.array([0, 0, 0, 0])
    avg_line = np.mean(np.array(line_history), axis=0, dtype=np.int32)
    return avg_line

class PIDController:
    def __init__(self, Kp, Ki, Kd, max_output=None, min_output=None):
        self.Kp = Kp
        self.Ki = Ki
        self.Kd = Kd
        self.max_output = max_output
        self.min_output = min_output
        self.integral = 0
        self.previous_error = 0

    def calculate(self, error, delta_time):
        # Proportional term
        proportional = self.Kp * error
        
        # Integral term
        self.integral += error * delta_time
        integral = self.Ki * self.integral
        
        # Derivative term
        derivative = self.Kd * (error - self.previous_error) / delta_time
        
        # Update previous error
        self.previous_error = error
        
        # Calculate total output
        output = proportional + integral + derivative
        
        # Clamp output to max and min values if specified
        if self.max_output is not None and output > self.max_output:
            output = self.max_output
        elif self.min_output is not None and output < self.min_output:
            output = self.min_output
            
        return output

# def update_pwm_value(new_value):
#     pwm_queue.put(new_value)  # Place the new PWM value in the queue

# capture = cv2.VideoCapture('/Users/anavart.pandya/MY DRIVE D/Autonomous Vehicles Sem8 IITGN/Advanced-Lane-Lines-master/vid2.mp4')
capture = cv2.VideoCapture(1)
# desired_fps = 15
# fps = capture.get(cv2.CAP_PROP_FPS)
# frame_start_pos = 90
# capture.set(cv2.CAP_PROP_POS_FRAMES, frame_start_pos)
# Capture properties for VideoWriter
# fps = capture.get(cv2.CAP_PROP_FPS)
# frame_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
# frame_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))

# # Initialize VideoWriter
# # fourcc = cv2.VideoWriter_fourcc(*'x264')  # or use 'XVID'
# fourcc = cv2.VideoWriter_fourcc(*'XVID')  # or use 'XVID'
# out = cv2.VideoWriter('IITGNvid2_output.mp4', fourcc, fps, (frame_width, frame_height))
counter = 0

pid_controller = PIDController(Kp=1000, Ki=0.0, Kd=0, max_output=255, min_output=-255)

previous_time = cv2.getTickCount()

# pwm_value = 0
# threading.Thread(target=send_value, args=(pwm_value,), daemon=True).start()
# pwm_queue = Queue()

while True:
    current_time = cv2.getTickCount()
    delta_time = (current_time - previous_time) / cv2.getTickFrequency()
    previous_time = current_time

    ret, frame = capture.read()
    if not ret:
        break
    counter += 1
    new_width = 640
    # aspect_ratio = original_width / original_height

    # # Calculate the new height maintaining the aspect ratio
    # new_height = int(new_width / aspect_ratio)
    new_height = 480

    frame = cv2.resize(frame, (new_width, new_height))
    original_height, original_width = frame.shape[0], frame.shape[1]
    # original_height, original_width = frame_height, frame_width
    # original_height, original_width = frame.shape[1], frame.shape[0]
    init_point = (original_width//2,384)

    # original_height, original_width = frame.shape[0], frame.shape[1]
    # new_width = 700
    # aspect_ratio = original_width / original_height

    # # Calculate the new height maintaining the aspect ratio
    # new_height = int(new_width / aspect_ratio)

    # frame = cv2.resize(frame, (new_width, new_height))

    # color_filtered_image = color_filter_for_gray(frame)
    # canny_output = canny(color_filtered_image)
    try:
        canny_output = canny(frame)
        # masked_output = region_of_interest(canny_output)
        masked_output = region_of_interest_trapezium(canny_output)
        # masked_output = canny_output
        lines = houghLines(masked_output)
        # line_image = display_lines(frame,lines)
        average_lines = average_slope_intercept(frame,lines)
        average_lines_with_centre = average_slope_intercept_with_centre(frame,lines)

        left_line = average_lines_with_centre[0]
        right_line = average_lines_with_centre[1]
        # center_line = average_lines_with_centre[2]

        left_line_history = update_line_history(left_line_history, left_line, history_length)
        right_line_history = update_line_history(right_line_history, right_line, history_length)
        # center_line_history = update_line_history(center_line_history, center_line, history_length)

        # Calculate averaged lines
        left_line_avg = average_line_from_history(left_line_history)
        right_line_avg = average_line_from_history(right_line_history)
        # center_line_avg = average_line_from_history(center_line_history)
        center_line_avg = np.array([[0,0,0,0]])
        for i in range(4):
            center_line_avg[0][i] = np.int32((left_line_avg[0][i] + right_line_avg[0][i])/2)

        average_lines_avg = np.array([np.array(left_line_avg),np.array(right_line_avg)])
        average_lines_with_centre_avg = np.array([np.array(left_line_avg),np.array(right_line_avg),np.array(center_line_avg)])

        # line_image_1 = display_lines(frame,average_lines_avg,init_point)
        # line_image_1_filled = display_lines_with_filled_region(frame,average_lines_avg,init_point)
        # line_image_2 = display_lines(frame,average_lines_with_centre_avg,init_point)
        line_image_2_filled, calculated_centre = display_lines_with_filled_region(frame,average_lines_with_centre_avg,init_point)

        error = np.sqrt((init_point[0] - calculated_centre[0])**2 + (init_point[1] - calculated_centre[1])**2)
        max_error = np.sqrt((init_point[0] - original_width)**2)
        error_normalised = error/max_error
        if calculated_centre[0] < init_point[0]:
            error_normalised = -error_normalised
        # Convert error to string and format it to display only two decimal places
        error_text = "Error: {:.2f}".format(error_normalised)

        # Display the error on the frame
        cv2.putText(line_image_2_filled, error_text, (original_width - 250, int(original_height*(2/5))), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)

        pwm_value = (pid_controller.calculate(error_normalised, delta_time))

        # Display PWM value on the frame for visualization
        pwm_text = "PWM: {:.2f}".format(pwm_value)
        cv2.putText(line_image_2_filled, pwm_text, (25, int(original_height*(2/5))), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)

        # send_value(int(pwm_value))
        # recieve_value()
    except:
        line_image_2_filled = frame
        pwm_value = 0 
        # send_value(int(pwm_value))
        # recieve_value()
    # Start PWM sending in a separate thread
    # send_value(int(pwm_value))
    # threading.Thread(target=send_value, args=(pwm_value,), daemon=True).start()
    # recieve_value()
    # out.write(line_image_2_filled)
    fps = capture.get(cv2.CAP_PROP_FPS)
    fps_text = "FPS: {:.2f}".format(fps)
    cv2.putText(line_image_2_filled, fps_text, (25, int(original_height*(4/5))), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
        

    # print(int(pwm_value))
    cv2.imshow('Frame', line_image_2_filled)
    # time.sleep(0.5)
    # if counter > 3500:
    #     print(counter)
    #     break
    if cv2.waitKey(25) & 0xFF == ord('q'):  # Press 'q' to exit
        print(counter)
        # ser.close()  # Close the serial port
        break
cv2.destroyAllWindows()
# ser.close()  # Close the serial port

# Close the redirected stderr (optional, depends on your script's structure)
# sys.stderr.close()

# Reset stderr to its original value if needed later in the script
# sys.stderr = sys.__stderr__