Lane_error

gistfile1.txt

import numpy as np
import cv2
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
    you should call plt.imshow(gray, cmap='gray')"""
    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.
    """
    # 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(img, lines, color=[255, 0, 0], thickness=10):
    """
    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
    """
    imshape = img.shape
    left_x1 = []
    left_x2 = []
    right_x1 = []
    right_x2 = []
    y_min = img.shape[0]
    y_max = int(img.shape[0] * 0.611)
    for line in lines:
        for x1, y1, x2, y2 in line:
            if ((y2 - y1) / (x2 - x1)) < 0:
                mc = np.polyfit([x1, x2], [y1, y2], 1)
                left_x1.append(np.int(np.float((y_min - mc[1])) / np.float(mc[0])))
                left_x2.append(np.int(np.float((y_max - mc[1])) / np.float(mc[0])))
            # cv2.line(img, (xone, imshape[0]), (xtwo, 330), color, thickness)
            elif ((y2 - y1) / (x2 - x1)) > 0:
                mc = np.polyfit([x1, x2], [y1, y2], 1)
                right_x1.append(np.int(np.float((y_min - mc[1])) / np.float(mc[0])))
                right_x2.append(np.int(np.float((y_max - mc[1])) / np.float(mc[0])))
                #           cv2.line(img, (xone, imshape[0]), (xtwo, 330), color, thickness)
    l_avg_x1 = np.int(np.nanmean(left_x1))
    l_avg_x2 = np.int(np.nanmean(left_x2))
    r_avg_x1 = np.int(np.nanmean(right_x1))
    r_avg_x2 = np.int(np.nanmean(right_x2))
    #     print([l_avg_x1, l_avg_x2, r_avg_x1, r_avg_x2])
    cv2.line(img, (l_avg_x1, y_min), (l_avg_x2, y_max), color, thickness)
    cv2.line(img, (r_avg_x1, y_min), (r_avg_x2, y_max), 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, dtype=np.uint8)
    draw_lines(line_img, lines)
    return line_img


def process_image(img):
    img_test = grayscale(img)
    img_test = gaussian_blur(img_test, 7)
    img_test = canny(img_test, 50, 150)
    imshape = img.shape
    vertices = np.array([[(100,imshape[0]),(400, 330), (600, 330), (imshape[1],imshape[0])]], dtype=np.int32)
    img_test = region_of_interest(img_test, vertices)
    rho = 2 # distance resolution in pixels of the Hough grid
    theta = np.pi/180 # angular resolution in radians of the Hough grid
    threshold = 55     # minimum number of votes (intersections in Hough grid cell)
    min_line_length = 40 #minimum number of pixels making up a line
    max_line_gap = 100    # maximum gap in pixels between connectable line segments
    line_image = np.copy(img)*0 # creating a blank to draw lines on
    img_test = hough_lines(img_test, rho, theta, threshold, min_line_length, max_line_gap)
    return img_test

img = cv2.imread("img.jpeg")
res=process_image(img)
cv2.imshow("Image",res)
cv2.waitKey(0)

Error:
/Users/ViditShah/anaconda/envs/py27/bin/python /Users/ViditShah/Downloads/untitled1/detection2.py
/Users/ViditShah/Downloads/untitled1/detection2.py:85: RuntimeWarning: Mean of empty slice
  l_avg_x1 = np.int(np.nanmean(left_x1))
Traceback (most recent call last):
  File "/Users/ViditShah/Downloads/untitled1/detection2.py", line 124, in <module>
    res=process_image(img)
  File "/Users/ViditShah/Downloads/untitled1/detection2.py", line 120, in process_image
    img_test = hough_lines(img_test, rho, theta, threshold, min_line_length, max_line_gap)
  File "/Users/ViditShah/Downloads/untitled1/detection2.py", line 103, in hough_lines
    draw_lines(line_img, lines)
  File "/Users/ViditShah/Downloads/untitled1/detection2.py", line 85, in draw_lines
    l_avg_x1 = np.int(np.nanmean(left_x1))
ValueError: cannot convert float NaN to integer

Process finished with exit code 1