perspective_transformation.py

# -*- coding: utf-8 -*-
"""Perspective_Transformation.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1hxwUdHNmLGx36S-mtJG5fnTcqx8FIS3_

# Step 3 : **Perspective Transformation**

**Apply a perspective transform to rectify binary image ("birds-eye view").**
"""

# Commented out IPython magic to ensure Python compatibility.
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

import pickle
# %matplotlib inline

"""**Camera Calibration**"""

# Read in the saved camera matrix and distortion coefficients
cameraCalibrationImgs = pickle.load( open( "Camera_Calibration.p", "rb" ) )
mtx = cameraCalibrationImgs["mtx"]
dist = cameraCalibrationImgs["dist"]

! unzip test_images

# Load test images using glob.
# read test images using cv2.imread .
testImages = list(map(lambda imageFileName: (imageFileName, cv2.imread(imageFileName)), 
                      glob.glob('test_images/st*.jpg')))

# convert test images to RGB to show
Test_imgsToShow = []
for fname in testImages:
    fileName, img = fname
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    Test_imgsToShow.append(img)

def show_image(images):
    n: int = len(images)
    f = plt.figure(figsize=(20,10))
    for i in range(n):
        f.add_subplot(1,2, i + 1)
        plt.imshow(images[i])

show_image(Test_imgsToShow)
# plt.savefig("straight_lines_imgsTest.png")

"""**Undistort**

"""

def cal_undistort(img, mtx, dist) :

  """
  Undistort the image with `mtx`, `dist`.

  """
  #Undistorting a test image:
  undist = cv2.undistort(img, mtx, dist, None, mtx)

  return undist

def hls_select(img, thresh=(0, 255)):

    '''
    This function thresholds the S-channel of HLS

    '''
    hls = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
    s_channel = hls[:,:,2]
    binary_output = np.zeros_like(s_channel)
    binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 1
    return binary_output

def unwarp(img):
    
    
    # Grab the image shape
    img_size = (img.shape[1], img.shape[0])

    # For source points I'm grabbing the outer four detected corners
    src = np.float32(
    [[(img_size[0] / 2) - 62, img_size[1] / 2 + 100],
    [((img_size[0] / 6) - 10), img_size[1]],
    [(img_size[0] * 5 / 6) + 60, img_size[1]],
    [(img_size[0] / 2 + 62), img_size[1] / 2 + 100]])

    #print(src)

    # For destination points, I'm arbitrarily choosing some points to be
    # a nice fit for displaying our warped result 
    # again, not exact, but close enough for our purposes
    dst = np.float32(
    [[(img_size[0] / 4), 0],
    [(img_size[0] / 4), img_size[1]],
    [(img_size[0] * 3 / 4), img_size[1]],
    [(img_size[0] * 3 / 4), 0]])

    #print(dst)
        
    # Given src and dst points, calculate the perspective transform matrix
    M = cv2.getPerspectiveTransform(src, dst)
    # Warp the image using OpenCV warpPerspective()
    warped = cv2.warpPerspective(img, M,img_size , flags=cv2.INTER_LINEAR)

    return warped

# apply cal_undistort function to testimages and pask the images in a list 
Undist_images = []
for fname in testImages:
    fileName, img = fname
    Undist = cal_undistort(img, mtx, dist)
    Undist_images.append(Undist)

binary_hls = hls_select(Undist_images[1])
binary_hls = cv2.cvtColor(Undist_images[1], cv2.COLOR_RGB2GRAY)

binary_warped = unwarp(binary_hls)

# the first warped image
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(Test_imgsToShow[1])
ax1.set_title('Original Image 1', fontsize=50)
ax2.imshow(binary_warped)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.savefig("Undistorted_ Warped.png")

"""# Step 3 :  **Finding the Lines: Histogram Peaks**

"""

binary_warped.shape

import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

# Load our image
# `mpimg.imread` will load .jpg as 0-255, so normalize back to 0-1
img = binary_warped/255


def hist(img):
    # Grab only the bottom half of the image
    # Lane lines are likely to be mostly vertical nearest to the car
    bottom_half = img[img.shape[0]//2:,:]

    # Sum across image pixels vertically - make sure to set an `axis`
    # i.e. the highest areas of vertical lines should be larger values
    histogram = np.sum(bottom_half, axis=0)
    
    return histogram

# Create histogram of image binary activations
histogram = hist(img)

# Visualize the resulting histogram
plt.plot(histogram)
# plt.savefig("hist-of-binary-image.png")

binary_warped = mpimg.imread('warped-example.jpg')

binary_warped.shape

import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import cv2

# Load our image


def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    # Create an output image to draw on and visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]//2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # HYPERPARAMETERS
    # Choose the number of sliding windows
    nwindows = 9
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50

    # Set height of windows - based on nwindows above and image shape
    window_height = np.int(binary_warped.shape[0]//nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated later for each window in nwindows
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        
        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),
        (win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),
        (win_xright_high,win_y_high),(0,255,0), 2) 
        
        # Identify the nonzero pixels in x and y within the window #
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xleft_low) &  (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xright_low) &  (nonzerox < win_xright_high)).nonzero()[0]
        
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        
        # If you found > minpix pixels, recenter next window on their mean position
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices (previously was a list of lists of pixels)
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        # Avoids an error if the above is not implemented fully
        pass

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

   

    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    # Find our lane pixels first
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)

    # Fit a second order polynomial to each using `np.polyfit`
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)

    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    try:
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    except TypeError:
        # Avoids an error if `left` and `right_fit` are still none or incorrect
        print('The function failed to fit a line!')
        left_fitx = 1*ploty**2 + 1*ploty
        right_fitx = 1*ploty**2 + 1*ploty

    ## Visualization ##
    # Colors in the left and right lane regions
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]

    # Plots the left and right polynomials on the lane lines
    plt.plot(left_fitx, ploty, color='yellow')
    plt.plot(right_fitx, ploty, color='yellow')

    return out_img


out_img = fit_polynomial(binary_warped)


plt.imshow(out_img)
# plt.savefig("binary_warped.png")

import cv2
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

# Load our image - this should be a new frame since last time!
binary_warped = mpimg.imread('warped-example.jpg')


# Polynomial fit values from the previous frame
# Make sure to grab the actual values from the previous step in your project!
left_fit = np.array([ 2.13935315e-04, -3.77507980e-01,  4.76902175e+02])
right_fit = np.array([4.17622148e-04, -4.93848953e-01,  1.11806170e+03])

def fit_poly(img_shape, leftx, lefty, rightx, righty):
     ### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    # Generate x and y values for plotting
    ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    return left_fitx, right_fitx, ploty


def search_around_poly(binary_warped):
    # HYPERPARAMETER
    # Choose the width of the margin around the previous polynomial to search
    # The quiz grader expects 100 here, but feel free to tune on your own!
    margin = 100

    # Grab activated pixels
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    
    
    ### TO-DO: Set the area of search based on activated x-values ###
    ### within the +/- margin of our polynomial function ###
    ### Hint: consider the window areas for the similarly named variables ###
    ### in the previous quiz, but change the windows to our new search area ###
    left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + 
                    left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + 
                    left_fit[1]*nonzeroy + left_fit[2] + margin)))
    right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + 
                    right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + 
                    right_fit[1]*nonzeroy + right_fit[2] + margin)))
    
    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    # Fit new polynomials
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
    
    ## Visualization ##
    # Create an image to draw on and an image to show the selection window
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    window_img = np.zeros_like(out_img)
    # Color in left and right line pixels
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

    # Generate a polygon to illustrate the search window area
    # And recast the x and y points into usable format for cv2.fillPoly()
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, 
                              ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, 
                              ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
    
    # Plot the polynomial lines onto the image
    plt.plot(left_fitx, ploty, color='yellow')
    plt.plot(right_fitx, ploty, color='yellow')
    ## End visualization steps ##
    
    return result

# Run image through the pipeline
# Note that in your project, you'll also want to feed in the previous fits
result = search_around_poly(binary_warped)

# View your output
plt.imshow(result)
plt.savefig("Search-from-Prior.png")