Team Angeldrew Katraline

# Code based on user raja_961's instructable found here:
# https://www.instructables.com/Autonomous-Lane-Keeping-Car-Using-Raspberry-Pi-and/

import cv2
import numpy as np
import math
import sys
import time
import Adafruit_BBIO.PWM as PWM

# GPIO.setwarnings(False)

#throttle = physical pin 66
throttlePin = "P8_13" # Physical pin 22
# in3 = 23 # physical Pin 16
# in4 = 24 # physical Pin 18

#Steering
steeringPin = "P9_16" # Physical Pin 15
# in1 = 17 # Physical Pin 11
# in2 = 27 # Physical Pin 13


# Throttle
PWM.start(throttlePin,7.5, 50)
#PWM.set_duty_cycle(throttlePin,7.5)
# PWM.stop(throttlePin)

# Steering
PWM.start(steeringPin,7.5, 50)
#PWM.set_duty_cycle(steeringPin, 7.5)
# PWM.stop(steeringPin)

def detect_stop(hsv):
    #takes image in hsv as input

    #mask for the lower hue values
    red_lower = np.array([0,50,50], dtype = "uint8")
    red_higher = np.array([10,255,255], dtype = "uint8")
    #find pixels that are within the mask range
    mask0 = cv2.inRange(hsv, red_lower, red_higher)
    #mask for the higher hue values
    red_lower = np.array([170,50,50], dtype = "uint8")
    red_higher = np.array([180,255,255], dtype = "uint8")
    #find pixels within upper mask range
    mask1 = cv2.inRange(hsv, red_lower, red_higher)

    #get all pixels that are red
    superMask = mask0+mask1
    #sum up all values (0 if not red, 255 = logical 1 if red)
    totalRed = np.sum(superMask)

    #if more than 3/8's of total screen (3/4s of bottom half) is red -> there is a "stop sign"
    #return true if stop sign, false otherwise
    return True if totalRed >= 80*60/9*1*255 else False

def detect_edges(hsv):
    #changed to have hsv as input so that it doesnt need to be computed for edges and stops
    # filter for blue lane lines
    #cv2.imshow("HSV",hsv)
    lower_blue = np.array([90, 120, 0], dtype = "uint8")
    upper_blue = np.array([150, 255, 255], dtype="uint8")
    mask = cv2.inRange(hsv,lower_blue,upper_blue)
    #cv2.imshow("mask",mask)
    
    # detect edges
    edges = cv2.Canny(mask, 50, 100)
    #cv2.imshow("edges",edges)
    
    return edges

def region_of_interest(frame):
    #changed to find ROI from original frame
    height, width, depth = frame.shape
    mask = np.zeros_like(frame)
    cropped_frame = np.zeros_like(frame)
    # only focus lower half of the screen
    polygon = np.array([[
        (0, height//2),
        (width, height // 2),
        (width, height - 1),
        (0, height - 1)
    ]], np.int32)

    # on image "mask", fill a square with corner points "polygon" with 1's
    cv2.fillPoly(mask, polygon, (255,)*frame.shape[2])
    #blackens out top half of the screen
    cropped_frame = cv2.bitwise_and(frame, mask)
    # cv2.imshow("roi",cropped_frame)
    
    return cropped_frame

def detect_line_segments(cropped_edges):
    rho = 1  
    theta = np.pi / 180  
    min_threshold = 10  
    
    line_segments = cv2.HoughLinesP(cropped_edges, rho, theta, min_threshold, 
                                    np.array([]), minLineLength=5, maxLineGap=150)

    return line_segments

def average_slope_intercept(frame, line_segments):
    lane_lines = []
    
    if line_segments is None:
        #print("no line segments detected")
        return lane_lines

    height, width,_ = frame.shape
    left_fit = []
    right_fit = []

    boundary = 1/3
    left_region_boundary = width * (1 - boundary)
    right_region_boundary = width * boundary
    
    for line_segment in line_segments:
        for x1, y1, x2, y2 in line_segment:
            if x1 == x2:
                #print("skipping vertical lines (slope = infinity")
                continue
            
            fit = np.polyfit((x1, x2), (y1, y2), 1)
            slope = (y2 - y1) / (x2 - x1)
            intercept = y1 - (slope * x1)
            
            if slope < 0:
                if x1 < left_region_boundary and x2 < left_region_boundary:
                    left_fit.append((slope, intercept))
            else:
                if x1 > right_region_boundary and x2 > right_region_boundary:
                    right_fit.append((slope, intercept))

    left_fit_average = np.average(left_fit, axis=0)
    if len(left_fit) > 0:
        lane_lines.append(make_points(frame, left_fit_average))

    right_fit_average = np.average(right_fit, axis=0)
    if len(right_fit) > 0:
        lane_lines.append(make_points(frame, right_fit_average))

    return lane_lines

def make_points(frame, line):
    height, width, _ = frame.shape
    
    slope, intercept = line
    
    y1 = height  # bottom of the frame
    y2 = int(y1 / 2)  # make points from middle of the frame down
    
    if slope == 0:
        slope = 0.1
        
    x1 = int((y1 - intercept) / slope)
    x2 = int((y2 - intercept) / slope)
    
    return [[x1, y1, x2, y2]]

def display_lines(frame, lines, line_color=(0, 255, 0), line_width=6):
    line_image = np.zeros_like(frame)
    
    if lines is not None:
        for line in lines:
            for x1, y1, x2, y2 in line:
                cv2.line(line_image, (x1, y1), (x2, y2), line_color, line_width)
                
    line_image = cv2.addWeighted(frame, 0.8, line_image, 1, 1)
    
    return line_image


def display_heading_line(frame, steering_angle, line_color=(0, 0, 255), line_width=5 ):
    heading_image = np.zeros_like(frame)
    height, width, _ = frame.shape
    
    steering_angle_radian = steering_angle / 180.0 * math.pi
    
    x1 = int(width / 2)
    y1 = height
    x2 = int(x1 - height / 2 / math.tan(steering_angle_radian))
    y2 = int(height / 2)
    
    cv2.line(heading_image, (x1, y1), (x2, y2), line_color, line_width)
    heading_image = cv2.addWeighted(frame, 0.8, heading_image, 1, 1)
    
    return heading_image

def get_steering_angle(frame, lane_lines):
    
    height,width,_ = frame.shape
    
    if len(lane_lines) == 2:
        _, _, left_x2, _ = lane_lines[0][0]
        _, _, right_x2, _ = lane_lines[1][0]
        mid = int(width / 2)
        x_offset = (left_x2 + right_x2) / 2 - mid
        y_offset = int(height / 2)
        
    elif len(lane_lines) == 1:
        x1, _, x2, _ = lane_lines[0][0]
        x_offset = x2 - x1
        y_offset = int(height / 2)
        
    elif len(lane_lines) == 0:
        x_offset = 0
        y_offset = int(height / 2)
        
    angle_to_mid_radian = math.atan(x_offset / y_offset)
    angle_to_mid_deg = int(angle_to_mid_radian * 180.0 / math.pi)  
    steering_angle = angle_to_mid_deg + 90
    
    return steering_angle

video = cv2.VideoCapture(0)
video.set(cv2.CAP_PROP_FRAME_WIDTH,320)
video.set(cv2.CAP_PROP_FRAME_HEIGHT,240)

time.sleep(1)

##fourcc = cv2.VideoWriter_fourcc(*'XVID')
##out = cv2.VideoWriter('Original15.avi',fourcc,10,(320,240))
##out2 = cv2.VideoWriter('Direction15.avi',fourcc,10,(320,240))

speed = 7.96
lastTime = 0
lastError = 0

kp = 0.095
kd = kp * 0.07 #0.05
foundStop = False
red = 0
PWM.set_duty_cycle(throttlePin, speed)
PWM.set_duty_cycle(steeringPin, 7.5)

i = 0
while i < 300:
    ret,frame = video.read()
    frame = cv2.resize(frame, (80, 60), interpolation=cv2.INTER_AREA)
    # frame = cv2.flip(frame,-1)
    if red > 1:
        break
    #cv2.imshow("original",frame)
    #computed first so it doesnt have to be found each time for edges and stop sign detection
    roi = region_of_interest(frame)
    #moved outside detect edges to only convert roi frame once
    hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

    #check for stops
    if detect_stop(hsv):
        #makes sure it's detecting stop for the first time
        if not foundStop:
            #sleep for 3 seconds and set the speed to 0
            PWM.set_duty_cycle(throttlePin, 7.5)
            time.sleep(3)
            #notes that we detected it for the first time, will not run again until another stop sign
            foundStop = True
            speed += .025
            #print("STOOOOOOOOOOOOOOOOP")
            red += 1
            continue
        else:
            PWM.set_duty_cycle(throttlePin,speed)
            
    #if no stop sign but foundStop is true -> we just passed a stop sign
    elif foundStop:
        #set foundStop to false for the next time
        foundStop = False

        PWM.set_duty_cycle(throttlePin,speed)
    # print(i)    
    edges = detect_edges(hsv)
    line_segments = detect_line_segments(edges)
    lane_lines = average_slope_intercept(frame,line_segments)
    lane_lines_image = display_lines(frame,lane_lines)
    steering_angle = get_steering_angle(frame, lane_lines)
    heading_image = display_heading_line(lane_lines_image,steering_angle)
    #cv2.imshow("heading line",heading_image)

    now = time.time()
    dt = now - lastTime

    deviation = steering_angle - 90
    error = -1*deviation

    if deviation < 13 and deviation > -13:
        deviation = 0
        error = 0
    #    PWM.set_duty_cycle(steeringPin,7.5)

    # elif deviation > 5:
    #    PWM.set_duty_cycle(steeringPin,7.2)

    # elif deviation < -5:
    #     PWM.set_duty_cycle(steeringPin,7.9)

    derivative = kd * (error - lastError) / dt
    proportional = kp * (error)
    PD = 7.5 + derivative + proportional
    if PD > 8.7:
         PD = 8.7
    if PD < 6.3:
         PD = 6.3
    
    
    print(PD, speed, error, derivative, proportional)
    if abs(error - lastError) <= 10 and i < 100: # 5
        lastError = error
        lastTime = time.time()
        continue
    
    PWM.set_duty_cycle(steeringPin,PD)
    if abs(PD- 7.5) > .80:
        PWM.set_duty_cycle(throttlePin,speed-.005)
    else:
        PWM.set_duty_cycle(throttlePin,speed)
    lastError = error
    lastTime = time.time()
    
    
#    out.write(frame)
#    out2.write(heading_image)
    i += 1
    key = cv2.waitKey(1)
    if key == 27:
        break

PWM.set_duty_cycle(throttlePin,7.5)
PWM.set_duty_cycle(steeringPin,7.5)
#print("Reseted")

PWM.stop(throttlePin)
PWM.stop(steeringPin)
video.release()
##out.release()
##out2.release()
cv2.destroyAllWindows()
PWM.cleanup()