# Path: self_driving/optical_flow/python/video.py
# class VideoSynthBase(object):
# class Book(VideoSynthBase):
# class Cube(VideoSynthBase):
# class Chess(VideoSynthBase):
#     def __init__(self, size=None, noise=0.0, bg=None, **params):
#     def render(self, dst):
#     def read(self, dst=None):
#     def isOpened(self):
#     def __init__(self, **kw):
#     def read(self, dst=None):
#     def __init__(self, **kw):
#     def read(self, dst=None):
#     def __init__(self, **kw):
#     def draw_quads(self, img, quads, color=(0, 255, 0)):
#     def render(self, dst):
# def create_capture(source=0, fallback=presets['chess']):


import numpy as np
import cv2
import sys
from self_driving.optical_flow.python import video
from scipy import misc
            if .5 <= abs(slope) < 1.0: # Discard unlikely slopes
                if slope > 0:
                    left_xs.extend([x1, x2])
                    left_ys.extend([y1, y2])
                else:
                    right_xs.extend([x1, x2])
                    right_ys.extend([y1, y2])

    left_fit = np.polyfit(left_xs, left_ys, 1)
    right_fit = np.polyfit(right_xs, right_ys, 1)

    y1 = img.shape[0] # Bottom of image
    y2 = img.shape[0] / 2+ 50 # Middle of view
    x1_left = (y1 - left_fit[1]) / left_fit[0]
    x2_left = (y2 - left_fit[1]) / left_fit[0]
    x1_right = (y1 - right_fit[1]) / right_fit[0]
    x2_right = (y2 - right_fit[1]) / right_fit[0]
    y1 = int(y1); y2 = int(y2);
    x1_left = int(x1_left); x2_left = int(x2_left);
    x1_right = int(x1_right); x2_right = int(x2_right);

    cv2.line(img, (x1_left, y1), (x2_left, y2), color, thickness)
    cv2.line(img, (x1_right, y1), (x2_right, y2), color, thickness)


if __name__ == '__main__':
    try:
        fn = sys.argv[1]
    except IndexError:
        fn = 0

# Path: self_driving/optical_flow/python/video.py
# class VideoSynthBase(object):
# class Book(VideoSynthBase):
# class Cube(VideoSynthBase):
# class Chess(VideoSynthBase):
#     def __init__(self, size=None, noise=0.0, bg=None, **params):
#     def render(self, dst):
#     def read(self, dst=None):
#     def isOpened(self):
#     def __init__(self, **kw):
#     def read(self, dst=None):
#     def __init__(self, **kw):
#     def read(self, dst=None):
#     def __init__(self, **kw):
#     def draw_quads(self, img, quads, color=(0, 255, 0)):
#     def render(self, dst):
# def create_capture(source=0, fallback=presets['chess']):


# Path: self_driving/lane_detect/udacity_lane_detect.py
import numpy as np
import cv2
import sys
from self_driving.optical_flow.python import video
from scipy import misc

            if .5 <= abs(slope) < 1.0: # Discard unlikely slopes
                if slope > 0:
                    left_xs.extend([x1, x2])
                    left_ys.extend([y1, y2])
                else:
                    right_xs.extend([x1, x2])
                    right_ys.extend([y1, y2])

    left_fit = np.polyfit(left_xs, left_ys, 1)
    right_fit = np.polyfit(right_xs, right_ys, 1)

    y1 = img.shape[0] # Bottom of image
    y2 = img.shape[0] / 2+ 50 # Middle of view
    x1_left = (y1 - left_fit[1]) / left_fit[0]
    x2_left = (y2 - left_fit[1]) / left_fit[0]
    x1_right = (y1 - right_fit[1]) / right_fit[0]
    x2_right = (y2 - right_fit[1]) / right_fit[0]
    y1 = int(y1); y2 = int(y2);
    x1_left = int(x1_left); x2_left = int(x2_left);
    x1_right = int(x1_right); x2_right = int(x2_right);

    cv2.line(img, (x1_left, y1), (x2_left, y2), color, thickness)
    cv2.line(img, (x1_right, y1), (x2_right, y2), color, thickness)


if __name__ == '__main__':
    try:
        fn = sys.argv[1]
    except IndexError:
        fn = 0

    cam = video.create_capture(fn)