
import numpy as np
import matplotlib.pyplot as plt
import pykitti  # install using pip install pykitti
import os


basedir = '/home/bob/Downloads/data/'
date = '2011_09_26'
drive = '0009'

HRES = 0.35         # horizontal resolution (assuming 20Hz setting)
VRES = 0.4          # vertical res
VFOV = (-24.9, 2.0)    # Field of view (-ve, +ve) along vertical axis
Y_FUDGE = 5

frame_range = range(155, 156, 1)
dataset = pykitti.raw(basedir, date, drive, frames=frame_range)
#for frame_range in range(5):
#dataset = pykitti.raw(basedir, date, drive)
#a=len(dataset)

def lidar_to_2d_front_view(points,
                           v_res,
                           h_res,
                           v_fov,
                           val="depth",
                           cmap="jet",
                           saveto=None,
                           y_fudge=0.0
                           ):
    """ Takes points in 3D space from LIDAR data and projects them to a 2D
        "front view" image, and saves that image.

    Args:
        points: (np array)
            The numpy array containing the lidar points.
            The shape should be Nx4
            - Where N is the number of points, and
            - each point is specified by 4 values (x, y, z, reflectance)
        v_res: (float)
            vertical resolution of the lidar sensor used.
        h_res: (float)
            horizontal resolution of the lidar sensor used.
        v_fov: (tuple of two floats)
            (minimum_negative_angle, max_positive_angle)
        val: (str)
            What value to use to encode the points that get plotted.
            One of {"depth", "height", "reflectance"}
        cmap: (str)
            Color map to use to color code the `val` values.
            NOTE: Must be a value accepted by matplotlib's scatter function
            Examples: "jet", "gray"
        saveto: (str or None)
            If a string is provided, it saves the image as this filename.
            If None, then it just shows the image.
        y_fudge: (float)
            A hacky fudge factor to use if the theoretical calculations of
            vertical range do not match the actual data.

            For a Velodyne HDL 64E, set this value to 5.
    """

    # DUMMY PROOFING
    assert len(v_fov) ==2, "v_fov must be list/tuple of length 2"
    assert v_fov[0] <= 0, "first element in v_fov must be 0 or negative"
    assert val in {"depth", "height", "reflectance"}, \
        'val must be one of {"depth", "height", "reflectance"}'


    x_lidar = points[:, 0]
    y_lidar = points[:, 1]
    z_lidar = points[:, 2]
    r_lidar = points[:, 3] # Reflectance
    # Distance relative to origin when looked from top
    d_lidar1 = np.sqrt(x_lidar ** 2 + y_lidar ** 2)

    #print(d_lidar1)
    # Absolute distance relative to origin
    d_lidar = np.sqrt(x_lidar ** 2 + y_lidar ** 2 + z_lidar ** 2)

    #print(d_lidar)

    v_fov_total = -v_fov[0] + v_fov[1]

    # Convert to Radians
    v_res_rad = v_res * (np.pi/180)
    h_res_rad = h_res * (np.pi/180)

    # PROJECT INTO IMAGE COORDINATES
    x_img = np.arctan2(-y_lidar, x_lidar)/ h_res_rad
    y_img = np.arctan2(z_lidar, d_lidar)/ v_res_rad

    # SHIFT COORDINATES TO MAKE 0,0 THE MINIMUM
    x_min = -360.0 / h_res / 2  # Theoretical min x value based on sensor specs
    x_img -= x_min              # Shift
    x_max = 360.0 / h_res       # Theoretical max x value after shifting

    y_min = v_fov[0] / v_res    # theoretical min y value based on sensor specs
    y_img -= y_min              # Shift
    y_max = v_fov_total / v_res # Theoretical max x value after shifting

    y_max += y_fudge            # Fudge factor if the calculations based on
                                # spec sheet do not match the range of
                                # angles collected by in the data.

    #print(x_min, x_max, y_min, y_max)

    # WHAT DATA TO USE TO ENCODE THE VALUE FOR EACH PIXEL
    if val == "reflectance":
        pixel_values = r_lidar
    elif val == "height":
        pixel_values = z_lidar
    else:
        pixel_values = -d_lidar

    print(len(pixel_values))
    #plt.scatter(-pixel_values, z_lidar)
    #plt.show()

    # PLOT THE IMAGE
    cmap = "jet"            # Color map to use
    dpi = 100               # Image resolution
    fig, ax = plt.subplots(figsize=(x_max/dpi, y_max/dpi), dpi=dpi)
    ax.scatter(x_img,y_img, s=1, c=pixel_values, linewidths=0, alpha=1, cmap=cmap)
    ax.set_facecolor((0, 0, 0)) # Set regions with no points to black
    ax.axis('scaled')    # {equal, scaled}
    ax.xaxis.set_visible(False)    # Do not draw axis tick marks
    ax.yaxis.set_visible(False)    # Do not draw axis tick marks
    plt.xlim([0, x_max])   # prevent drawing empty space outside of horizontal FOV
    plt.ylim([0, y_max])   # prevent drawing empty space outside of vertical FOV

    #
    # if saveto is not None:
    #     fig.savefig(saveto, dpi=dpi, bbox_inches='tight', pad_inches=0.0)
    # else:
    #     fig.show()
    #ax.imshow(masked_image.astype(np.uint8))
    #ax.plot(x_img, y_img)
    #fig.show()
    plt.show()


i=0
#for i in range(len(dataset)):
for points in dataset.velo:

    #print(points[1])
    #for i in range(5):
    lidar_to_2d_front_view(points, v_res=VRES, h_res=HRES, v_fov=VFOV, val='depth', saveto=('/home/bob/Downloads/figure/lidar_depth %d.png'%(i)), y_fudge=Y_FUDGE)
    i = i + 1
    #lidar_to_2d_front_view(points, v_res=VRES, h_res=HRES, v_fov=VFOV, val='depth', saveto='/home/bob/Downloads/figure/lidar_depth.png', y_fudge=Y_FUDGE)
    #break
    #lidar_to_2d_front_view(points, v_res=VRES, h_res=HRES, v_fov=VFOV, val='height', saveto='/home/bob/Downloads/lidar_depth.png', y_fudge=Y_FUDGE)
    #lidar_to_2d_front_view(points, v_res=VRES, h_res=HRES, v_fov=VFOV, val='reflectance', saveto='/home/bob/Downloads/lidar_depth.png', y_fudge=Y_FUDGE)
    

