LaneDes

gistfile1.txt

"""
Project Title : Lane Detection.
Developed By :- Vidit Shah,
                Vivek Shah,
                Jash Rele,
                Deep Thakker

"""
import cv2
import matplotlib.pyplot as plt
import numpy as np
from numpy import ones,vstack
from numpy.linalg import lstsq



img = cv2.imread('img.jpeg')

def canny(img, low_threshold, high_threshold):
    """Applies the Canny transform"""
    return cv2.Canny(img, low_threshold, high_threshold)

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)

#img_gray=grayscale(img)
#cv2.imshow("image",img_gray)
#cv2.waitKey(0)

def roi(img, vertices):
    # blank mask:
    mask = np.zeros_like(img)

    # filling pixels inside the polygon defined by "vertices" with the fill color
    cv2.fillPoly(mask, vertices, 255)

    # returning the image only where mask pixels are nonzero
    masked = cv2.bitwise_and(img, mask)
    return masked


def draw_lanes(img, lines, color=[0, 255, 255], thickness=1):
    # if this fails, go with some default line
    try:

        # finds the maximum y value for a lane marker
        # (since we cannot assume the horizon will always be at the same point.)

        ys = []
        for i in lines:
            for ii in i:
                ys += [ii[1], ii[3]]
        min_y = min(ys)
        max_y = 600
        new_lines = []
        line_dict = {}

        for idx, i in enumerate(lines):
            for xyxy in i:
                # These four lines:
                # modified from http://stackoverflow.com/questions/21565994/method-to-return-the-equation-of-a-straight-line-given-two-points
                # Used to calculate the definition of a line, given two sets of coords.
                x_coords = (xyxy[0], xyxy[2])
                y_coords = (xyxy[1], xyxy[3])
                A = vstack([x_coords, ones(len(x_coords))]).T
                m, b = lstsq(A, y_coords)[0]

                # Calculating our new, and improved, xs
                x1 = (min_y - b) / m
                x2 = (max_y - b) / m

                line_dict[idx] = [m, b, [int(x1), min_y, int(x2), max_y]]
                new_lines.append([int(x1), min_y, int(x2), max_y])

        final_lanes = {}

        for idx in line_dict:
            final_lanes_copy = final_lanes.copy()
            m = line_dict[idx][0]
            b = line_dict[idx][1]
            line = line_dict[idx][2]

            if len(final_lanes) == 0:
                final_lanes[m] = [[m, b, line]]

            else:
                found_copy = False

                for other_ms in final_lanes_copy:

                    if not found_copy:
                        if abs(other_ms * 1.2) > abs(m) > abs(other_ms * 0.8):
                            if abs(final_lanes_copy[other_ms][0][1] * 1.2) > abs(b) > abs(
                                            final_lanes_copy[other_ms][0][1] * 0.8):
                                final_lanes[other_ms].append([m, b, line])
                                found_copy = True
                                break
                        else:
                            final_lanes[m] = [[m, b, line]]

        line_counter = {}

        for lanes in final_lanes:
            line_counter[lanes] = len(final_lanes[lanes])

        top_lanes = sorted(line_counter.items(), key=lambda item: item[1])[::-1][:2]

        lane1_id = top_lanes[0][0]
        lane2_id = top_lanes[1][0]

        def average_lane(lane_data):
            x1s = []
            y1s = []
            x2s = []
            y2s = []
            for data in lane_data:
                x1s.append(data[2][0])
                y1s.append(data[2][1])
                x2s.append(data[2][2])
                y2s.append(data[2][3])
            return int(np.mean(x1s)), int(np.mean(y1s)), int(np.mean(x2s)), int(np.mean(y2s))

        l1_x1, l1_y1, l1_x2, l1_y2 = average_lane(final_lanes[lane1_id])
        l2_x1, l2_y1, l2_x2, l2_y2 = average_lane(final_lanes[lane2_id])

        return [l1_x1, l1_y1, l1_x2, l1_y2], [l2_x1, l2_y1, l2_x2, l2_y2]
    except Exception as e:
        print(str(e))


def process_img(image):
    original_image = image
    # convert to gray
    processed_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # edge detection
    processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)

    processed_img = cv2.GaussianBlur(processed_img, (5, 5), 0)

    vertices = np.array([[10, 500], [10, 300], [300, 200], [500, 200], [800, 300], [800, 500],
                         ], np.int32)

    processed_img = roi(processed_img, [vertices])

    # more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
    #                                     rho   theta   thresh  min length, max gap:
    lines = cv2.HoughLinesP(processed_img, 1, np.pi / 180, 180, 20, 15)
    try:
        l1, l2 = draw_lanes(original_image, lines)
        cv2.line(original_image, (l1[0], l1[1]), (l1[2], l1[3]), [0, 255, 0], 30)
        cv2.line(original_image, (l2[0], l2[1]), (l2[2], l2[3]), [0, 255, 0], 30)
    except Exception as e:
        print(str(e))
        pass
    try:
        for coords in lines:
            coords = coords[0]
            try:
                cv2.line(processed_img, (coords[0], coords[1]), (coords[2], coords[3]), [255, 0, 0], 3)


            except Exception as e:
                print(str(e))
    except Exception as e:
        pass

    return processed_img,original_image

new_screen,original_image = process_img(img)
cv2.imshow('window', new_screen)
cv2.imshow('window2',cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))

cv2.waitKey(0)