#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline

#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')

#printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', image.shape)
plt.imshow(image)  # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')

**Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:**

`cv2.inRange()` for color selection  
`cv2.fillPoly()` for regions selection  
`cv2.line()` to draw lines on an image given endpoints  
`cv2.addWeighted()` to coadd / overlay two images
`cv2.cvtColor()` to grayscale or change color
`cv2.imwrite()` to output images to file  
`cv2.bitwise_and()` to apply a mask to an image

**Check out the OpenCV documentation to learn about these and discover even more awesome functionality!**

import math

def grayscale(img):
    """Applies the Grayscale transform
    This will return an image with only one color channel
    but NOTE: to see the returned image as grayscale
    (assuming your grayscaled image is called 'gray')
    you should call plt.imshow(gray, cmap='gray')"""
    #plt.imshow(gray, cmap='gray')
    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Or use BGR2GRAY if you read an image with cv2.imread()
    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
def canny(img, low_threshold, high_threshold):
    """Applies the Canny transform"""
    return cv2.Canny(img, low_threshold, high_threshold)

def gaussian_blur(img, kernel_size):
    """Applies a Gaussian Noise kernel"""
    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)

def region_of_interest(img, vertices):
    """
    Applies an image mask.
    
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    `vertices` should be a numpy array of integer points.
    """
    #defining a blank mask to start with
    mask = np.zeros_like(img)   
    
    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
        
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    
    #returning the image only where mask pixels are nonzero
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def draw_lines_ori(img, lines, color=[255, 0, 0], thickness=7):
    """
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 
    the lines and extrapolate to the top and bottom of the lane.
    
    This function draws `lines` with `color` and `thickness`.    
    Lines are drawn on the image inplace (mutates the image).
    If you want to make the lines semi-transparent, think about combining
    this function with the weighted_img() function below
    """
    

    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(img, (x1, y1), (x2, y2), color, thickness)
            

def draw_lines(img, lines, color=[255, 0, 0], thickness=7):
    
     #list to get positives and negatives values
    
    x_bottom_pos = []
    x_upperr_pos = []
    x_bottom_neg = []
    x_upperr_neg = []
    
    y_bottom = 540
    y_upperr = 315
    
    slope = 0
    b = 0
    
    #get x upper and bottom to lines with slope positive and negative    
    for line in lines:
        for x1,y1,x2,y2 in line:
            #test and filter values to slope
            if ((y2-y1)/(x2-x1)) > 0.5 and ((y2-y1)/(x2-x1)) < 0.8 :
                
                slope = ((y2-y1)/(x2-x1))
                b = y1 - slope*x1
                
                x_bottom_pos.append((y_bottom - b)/slope)
                x_upperr_pos.append((y_upperr - b)/slope)
                                      
            elif ((y2-y1)/(x2-x1)) < -0.5 and ((y2-y1)/(x2-x1)) > -0.8:
            
                slope = ((y2-y1)/(x2-x1))
                b = y1 - slope*x1
                
                x_bottom_neg.append((y_bottom - b)/slope)
                x_upperr_neg.append((y_upperr - b)/slope)
               
    #creating a new 2d array with means
    lines_mean = np.array([[int(np.mean(x_bottom_pos)), int(np.mean(y_bottom)), int(np.mean(x_upperr_pos)), int(np.mean(y_upperr))],
                           [int(np.mean(x_bottom_neg)), int(np.mean(y_bottom)), int(np.mean(x_upperr_neg)), int(np.mean(y_upperr))]])
    
    #Drawing the lines
    for i in range(len(lines_mean)):
        cv2.line(img, (lines_mean[i,0], lines_mean[i,1]), (lines_mean[i,2], lines_mean[i,3]), color, thickness)
            
            
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    `img` should be the output of a Canny transform.
        
    Returns an image with hough lines drawn.
    """
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, lines)
    return line_img

# Python 3 has support for cool math symbols.

def weighted_img(img, initial_img, α=0.8, β=1., γ=0.):
    """
    `img` is the output of the hough_lines(), An image with lines drawn on it.
    Should be a blank image (all black) with lines drawn on it.
    
    `initial_img` should be the image before any processing.
    
    The result image is computed as follows:
    
    initial_img * α + img * β + γ
    NOTE: initial_img and img must be the same shape!
    """
    return cv2.addWeighted(initial_img, α, img, β, γ)

import os
imgpath = os.listdir("test_images/")
print(imgpath)

image = mpimg.imread('test_images/solidWhiteCurve.jpg')
color_select = np.copy(image)

# gray transform
gray = grayscale(color_select)

# Define a kernel size for Gaussian smoothing / blurring
kernel_size = 5
blur_gray = gaussian_blur(gray,kernel_size)

# Define parameters for Canny and run it
low_threshold = 100
high_threshold = 250
edges = canny(blur_gray, low_threshold, high_threshold)

# Display the image
plt.figure(figsize=(8,6))
plt.imshow(edges, cmap='Greys_r')

image = mpimg.imread('test_images/solidWhiteCurve.jpg')
color_select = np.copy(image)

# gray transform
gray = grayscale(color_select)

# Define a kernel size for Gaussian smoothing / blurring
kernel_size = 5
blur_gray = gaussian_blur(gray,kernel_size)

# Define parameters for Canny and run it
low_threshold = 100
high_threshold = 250
edges = canny(blur_gray, low_threshold, high_threshold)

# Display the image
plt.figure(figsize=(8,6))
plt.imshow(edges, cmap='Greys_r')

# This time we are defining a four sided polygon to mask
imshape = image.shape
left_bottom = (0,imshape[0]) # 1
right_bottom = (imshape[1],imshape[0]) # 4
right_top =  ((imshape[1]/2)+50, (imshape[0]/2 + 60)) # 3
left_top = ((imshape[1]/2 )-50, (imshape[0]/2 + 60)) # 2

vertices = np.array([[left_bottom,left_top,right_top, right_bottom]], dtype=np.int32)

selectimg = region_of_interest(color_select,vertices)

plt.figure(figsize=(8,6))
plt.imshow(selectimg)

selectedges = region_of_interest(edges,vertices)

plt.figure(figsize=(8,6))
plt.imshow(selectedges, cmap='Greys_r')

rho = 1
theta = np.pi/90
threshold = 30
min_line_length = 40
max_line_gap = 400

lines = hough_lines(selectedges, rho, theta, threshold,min_line_length, max_line_gap)

plt.figure(figsize=(8,6))
plt.imshow(lines)

lines_edges = weighted_img(lines, image, α=0.8, β=1., γ=0.)

plt.figure(figsize=(8,6))
plt.imshow(lines_edges)

def pipeline_img(image, 
                   kernel_size = 5, 
                   low_threshold = 100, high_threshold=250, 
                   rho=1, theta=np.pi/90, threshold=30,
                   min_line_len=40, max_line_gap=400): 
    
    color_select = np.copy(image)
    
    # gray transform
    gray = grayscale(color_select)
    
    # Gaussian smoothing / blurring
    blur_gray = gaussian_blur(gray, kernel_size)
    
    #finding edges
    edges = canny(blur_gray, low_threshold, high_threshold)
    
    #setting vertices
    imshape = image.shape
    left_bottom = (0,imshape[0]) # 1
    right_bottom = (imshape[1],imshape[0]) # 4
    right_top =  ((imshape[1]/2)+50, (imshape[0]/2 + 60)) # 3
    left_top = ((imshape[1]/2 )-50, (imshape[0]/2 + 60)) # 2

    vertices = np.array([[left_bottom,left_top,right_top, right_bottom]], dtype=np.int32)
    
    #creating a region of interest
    masked_edges = region_of_interest(edges, vertices)
    
    #finding lines
    lines = hough_lines(masked_edges, rho, theta, threshold, min_line_len, max_line_gap)
    
    #merge with original image
    lines_edges = weighted_img(lines, image, α=0.8, β=1., γ=0.)
    
    return lines_edges

import os
imgname = os.listdir("test_images/")

for img in imgname:
    imgpath = "test_images/" + img
    image = mpimg.imread(imgpath)
    
    image_processed = pipeline_img(image, 
                   kernel_size = 5, 
                   low_threshold = 100, high_threshold=250, 
                   rho=1, theta=np.pi/90, threshold=30,
                   min_line_len=40, max_line_gap=400)
    mpimg.imsave("test_images_output/" + img, image_processed)

# Import everything needed to edit/save/watch video clips

from moviepy.editor import VideoFileClip
from IPython.display import HTML

def process_image(image):
    # NOTE: The output you return should be a color image (3 channel) for processing video below
    # TODO: put your pipeline here,
    # you should return the final output (image where lines are drawn on lanes)
    
    result = pipeline_img(image, 
                    kernel_size = 5, 
                   low_threshold = 100, high_threshold=250, 
                   rho=1, theta=np.pi/90, threshold=30,
                   min_line_len=40, max_line_gap=400)
    
    return result

white_output = 'test_videos_output/solidWhiteRight.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(white_output))

yellow_output = 'test_videos_output/solidYellowLeft.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)
clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)

def draw_lines2(img, lines, color=[255, 0, 0], thickness=7):
    
     #list to get positives and negatives values
    
    x_bottom_pos = []
    x_upperr_pos = []
    x_bottom_neg = []
    x_upperr_neg = []
    
    y_bottom = 720
    y_upperr = 450
    
    slope = 0
    b = 0
    
    #get x upper and bottom to lines with slope positive and negative    
    for line in lines:
        for x1,y1,x2,y2 in line:
            #test and filter values to slope
            if ((y2-y1)/(x2-x1)) > 0.5 and ((y2-y1)/(x2-x1)) < 0.8 :
                
                slope = ((y2-y1)/(x2-x1))
                b = y1 - slope*x1
                
                x_bottom_pos.append((y_bottom - b)/slope)
                x_upperr_pos.append((y_upperr - b)/slope)
                                      
            elif ((y2-y1)/(x2-x1)) < -0.5 and ((y2-y1)/(x2-x1)) > -0.8:
            
                slope = ((y2-y1)/(x2-x1))
                b = y1 - slope*x1
                
                x_bottom_neg.append((y_bottom - b)/slope)
                x_upperr_neg.append((y_upperr - b)/slope)
                
    if (np.isnan(np.mean(x_bottom_pos)) == False) and (np.isnan(np.mean(x_upperr_pos)) == False) \
            and  (np.isnan(np.mean(x_bottom_neg)) == False) and (np.isnan(np.mean(x_upperr_neg)) == False) :
        #creating a new 2d array with means
        lines_mean = np.array([[int(np.mean(x_bottom_pos)), int(np.mean(y_bottom)), int(np.mean(x_upperr_pos)), int(np.mean(y_upperr))],
                           [int(np.mean(x_bottom_neg)), int(np.mean(y_bottom)), int(np.mean(x_upperr_neg)), int(np.mean(y_upperr))]])
       #Drawing the lines
        for i in range(len(lines_mean)):
            cv2.line(img, (lines_mean[i,0], lines_mean[i,1]), (lines_mean[i,2], lines_mean[i,3]), color, thickness)     

def hough_lines2(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    `img` should be the output of a Canny transform.
        
    Returns an image with hough lines drawn.
    """
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines2(line_img, lines)
    return line_img

def process_image2(image, 
                   kernel_size = 7, 
                   low_threshold = 150, high_threshold=250, 
                   rho=1, theta=np.pi/90, threshold=15,
                   min_line_len=40, max_line_gap=150): 
    
    color_select = np.copy(image)
    
    # gray transform
    gray = grayscale(color_select)
    
    # Gaussian smoothing / blurring
    blur_gray = gaussian_blur(gray, kernel_size)
    
    #finding edges
    edges = canny(blur_gray, low_threshold, high_threshold)
    
    #setting vertices
    imshape = image.shape
    left_bottom = (100,imshape[0]) # 1
    right_bottom =  (imshape[1]-100,imshape[0]) # 4
    right_top =  (490, 325)#((imshape[1]/2)+50, (imshape[0]/2 + 60)) # 3
    left_top = (470, 325)#((imshape[1]/2 )-50, (imshape[0]/2 + 60)) # 2

    vertices = np.array([[left_bottom,left_top,right_top, right_bottom]], dtype=np.int32)
    
    #creating a region of interest
    masked_edges = region_of_interest(edges, vertices)
    
    #finding lines
    lines = hough_lines2(masked_edges, rho, theta, threshold, min_line_len, max_line_gap)
    
    #merge with original image
    lines_edges = weighted_img(lines, image, α=0.8, β=1., γ=0.)
    
    return lines_edges

challenge_output = 'test_videos_output/challenge.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)
clip3 = VideoFileClip('test_videos/challenge.mp4')
challenge_clip = clip3.fl_image(process_image2)
%time challenge_clip.write_videofile(challenge_output, audio=False)

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(challenge_output))

import imageio
imageio.plugins.ffmpeg.download()

pip install --trusted-host pypi.python.org moviepy