SAComputerVisionMachineLearning/LaneDetection/visualizer.py at master · appinho/SAComputerVisionMachineLearning · GitHub

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import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import cv2
import image_processing

def plot_two_images(image1, image2, title, save_directory=''):
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image1, cmap='gray')
    ax1.set_title('Image before ' + title, fontsize=20)
    ax2.imshow(image2, cmap='gray')
    ax2.set_title('Image after ' + title, fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
    plt.show()
    if save_directory:
        f.savefig(save_directory)
    plt.close(f)

def plot_result(image,save_directory=''):
    f = plt.figure()
    plt.imshow(image)
    plt.show()
    if save_directory:
        f.savefig(save_directory)
    plt.close(f)

def save_image(image, save_directory):
    plt.imshow(image)
    #plt.show()
    #print(save_directory)
    plt.imsave(save_directory+'.png',image)


def plot_colorspaces(image, save_directory=''):
    # Perform transformations
    original = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)

    f, axarr = plt.subplots(3, 4, figsize=(24, 12))
    f.tight_layout()
    axarr[0, 0].imshow(original)
    axarr[0, 0].set_title('Original', fontsize=20)
    axarr[0, 1].imshow(original[:,:,0], cmap='gray')
    gray = original[:,:,0]
    axarr[0, 1].set_title('Red channel', fontsize=20)
    axarr[0, 2].imshow(original[:,:,1], cmap='gray')
    axarr[0, 2].set_title('Green channel', fontsize=20)
    axarr[0, 3].imshow(original[:,:,2], cmap='gray')
    axarr[0, 3].set_title('Blue channel', fontsize=20)
    axarr[1, 0].imshow(gray, cmap='gray')
    axarr[1, 0].set_title('Gray channel', fontsize=20)
    axarr[1, 1].imshow(hls[:,:,0], cmap='gray')
    axarr[1, 1].set_title('Hue channel', fontsize=20)
    axarr[1, 2].imshow(hls[:,:,1], cmap='gray')
    axarr[1, 2].set_title('Lightness channel', fontsize=20)
    axarr[1, 3].imshow(hls[:,:,2], cmap='gray')
    axarr[1, 3].set_title('Saturation channel', fontsize=20)
    axarr[2, 0].imshow(image_processing.sobel_operator(
        gray,orient='x',thresh=[10,255]), cmap='gray')
    axarr[2, 0].set_title('Sobel x', fontsize=20)
    axarr[2, 1].imshow(image_processing.sobel_operator(
        gray,orient='y',thresh=[30,255]), cmap='gray')
    axarr[2, 1].set_title('Sobel y', fontsize=20)
    axarr[2, 2].imshow(image_processing.gradient_magnitude(
        gray,thresh=[30,255]), cmap='gray')
    axarr[2, 2].set_title('Gradient magnitude', fontsize=20)
    axarr[2, 3].imshow(image_processing.gradient_orientation(
        gray,thresh=[0.7,1.3]
    ), cmap='gray')
    axarr[2, 3].set_title('Gradient orientation', fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=1, bottom=0.)
    plt.show()
    if save_directory:
        f.savefig(save_directory + 'colorspaces')

    plt.close(f)

def plot_polynomial_fit(image1, image2, left_fitx, right_fitx, ploty, save_directory=''):
    # Plot original image and undistorted image
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image1, cmap='gray')
    ax1.set_title('Warped image', fontsize=20)
    ax2.imshow(image2, cmap='gray')
    ax2.plot(left_fitx, ploty, color='yellow')
    ax2.plot(right_fitx, ploty, color='yellow')
    # ax2.xlim(0, 1280)
    # ax2.ylim(720, 0)
    ax2.set_title('Found polynomial', fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
    plt.show()
    if save_directory:
        f.savefig(save_directory)
    plt.close(f)


def plot_two_times_four_images(images, save_directory=''):
    # Plot original image and undistorted image
    f, axarr = plt.subplots(2, 4, figsize=(24, 9))
    f.tight_layout()
    axarr[0, 0].imshow(images[0])
    axarr[0, 0].set_title('Original', fontsize=20)
    axarr[0, 1].imshow(images[1], cmap='gray')
    axarr[0, 1].set_title('1', fontsize=20)
    axarr[0, 2].imshow(images[2], cmap='gray')
    axarr[0, 2].set_title('2', fontsize=20)
    axarr[0, 3].imshow(images[3], cmap='gray')
    axarr[0, 3].set_title('3', fontsize=20)
    axarr[1, 0].imshow(images[4], cmap='gray')
    axarr[1, 0].set_title('Result', fontsize=20)
    axarr[1, 1].imshow(images[5], cmap='gray')
    axarr[1, 1].set_title('4', fontsize=20)
    axarr[1, 2].imshow(images[6], cmap='gray')
    axarr[1, 2].set_title('5', fontsize=20)
    axarr[1, 3].imshow(images[7], cmap='gray')
    axarr[1, 3].set_title('6', fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
    plt.show()
    if save_directory:
        f.savefig(save_directory)
    plt.close(f)


def plot_perspective_transform(image, topdownimage, src, dst,save_directory=''):
    xs, ys = zip(*src)  # create lists of x and y values
    xd, yd = zip(*dst)  # create lists of x and y values
    # print(xs,ys)
    # print(xd,yd)
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(image, cmap='gray')
    ax1.add_patch(patches.Polygon(xy=list(src), color='r', linewidth=2, fill=False))
    ax1.set_title('Undistorted Image', fontsize=20)
    ax2.imshow(topdownimage, cmap='gray')
    ax2.add_patch(patches.Polygon(xy=list(dst), color='g', linewidth=2, fill=False))
    ax2.set_title('Transformed Image', fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
    plt.show()
    if save_directory:
        f.savefig(save_directory)
    plt.close(f)

def plot_histogram(hist_left,hist_right,save_directory=''):
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.plot(hist_left)
    ax1.set_title('Left Histogram', fontsize=20)
    ax2.plot(hist_right)
    ax2.set_title('Right Histogram', fontsize=20)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
    plt.show()
    if save_directory:
        f.savefig(save_directory)
    plt.close(f)

def get_result(image,warped,pts,Minv,undist,curvature,lane_offset):
    warp_zero = np.zeros_like(warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))

    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
    # Combine the result with the original image
    result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)

    font = cv2.FONT_HERSHEY_SIMPLEX

    if curvature > 6000:
        str_curv = '>6000m | STRAIGHT'
    else:
        str_curv = str(curvature) + 'm'

    cv2.putText(result, 'Radius of Curvature = ' + str_curv,
                (230, 50), font, 1, (255, 255, 255), 2, cv2.LINE_AA)
    cv2.putText(result, 'Relative vehicle position to lane center = ' + str(lane_offset) + 'm',
                (230, 100), font, 1, (255, 255, 255), 2, cv2.LINE_AA)
    return result

def plot_history(detector,save_directory=''):
    lc = detector.left_lane.history_curvature
    rc = detector.right_lane.history_curvature
    slc = detector.left_lane.history_smoothed_curvature
    src = detector.right_lane.history_smoothed_curvature
    sac = [(src[i]+slc[i])/2 for i in range(len(slc))]
    ll = detector.left_lane.history_lane_center
    rl = detector.right_lane.history_lane_center
    sll = detector.left_lane.history_smoothed_lane_center
    srl = detector.right_lane.history_smoothed_lane_center
    sal = [(srl[i] + sll[i]) / 2 for i in range(len(sll))]
    tc = range(len(lc))
    tl = range(len(ll))

    al = [item[0] for item in detector.left_lane.history_polynomial]
    tal = range(len(al))

    ar = [item[0] for item in detector.right_lane.history_polynomial]
    tar = range(len(ar))

    fig1 = plt.figure()
    ax1 = fig1.add_subplot(411)
    # tc, lc, 'r--', tc, rc,'b--',
    ax1.plot(tc, slc, 'r', tc, src,'b',sac,'g')
    ax2 = fig1.add_subplot(412)
    # tl, ll, 'r--', tl, rl,'b--', tl,
    ax2.plot(sll, 'r', tl, srl,'b',sal,'g')
    ax3 = fig1.add_subplot(413)
    ax3.plot(tal, al, 'r')
    ax4 = fig1.add_subplot(414)
    ax4.plot(tar, ar, 'r')

    plt.show()
    if save_directory:
        plt.savefig(save_directory)