pre written code.txt

# # import tensorflow as tf
# import pandas as pd
# import matplotlib.pyplot as py
# # import cv2
#
# data = pd.read_csv('./Dataset/train.csv')
# print(data.head())
# print(data.isnull())
# py.bar("hello",data['Age'])
# py.show()
# import matplotlib.pyplot as plt
# import matplotlib.image as mpimg
# import numpy as np
# # Read in the image and print out some stats
# image = mpimg.imread('test.jpg')
# print('This image is: ',type(image),
#          'with dimensions:', image.shape)
#
# # Grab the x and y size and make a copy of the image
# ysize = image.shape[0]
# xsize = image.shape[1]
# # Note: always make a copy rather than simply using "="
# color_select = np.copy(image)
# # Define our color selection criteria
# # Note: if you run this code, you'll find these are not sensible values!!
# # But you'll get a chance to play with them soon in a quiz
# red_threshold = 200
# green_threshold = 200
# blue_threshold = 200
# rgb_threshold = [red_threshold, green_threshold, blue_threshold]
# # Identify pixels below the threshold
# thresholds = (image[:,:,0] < rgb_threshold[0]) | (image[:,:,1] < rgb_threshold[1]) | (image[:,:,2] < rgb_threshold[2])
# color_select[thresholds] = [0,0,0]
#
# # Display the image
# plt.imshow(color_select)
# plt.show()






# import matplotlib.pyplot as plt
# import matplotlib.image as mpimg
# import numpy as np
#
# # Read in the image
# image = mpimg.imread('test.jpg')
#
# # Grab the x and y sizes and make two copies of the image
# # With one copy we'll extract only the pixels that meet our selection,
# # then we'll paint those pixels red in the original image to see our selection
# # overlaid on the original.
# ysize = image.shape[0]
# xsize = image.shape[1]
# color_select= np.copy(image)
# line_image = np.copy(image)
#
# # Define our color criteria
# red_threshold = 210
# green_threshold = 210
# blue_threshold = 210
# rgb_threshold = [red_threshold, green_threshold, blue_threshold]
#
# # Define a triangle region of interest (Note: if you run this code,
# # Keep in mind the origin (x=0, y=0) is in the upper left in image processing
# # you'll find these are not sensible values!!
# # But you'll get a chance to play with them soon in a quiz ;)
# left_bottom = [0, 539]
# right_bottom = [900, 539]
# apex = [450, 0]
#
# fit_left = np.polyfit((left_bottom[0], apex[0]), (left_bottom[1], apex[1]), 1)
# fit_right = np.polyfit((right_bottom[0], apex[0]), (right_bottom[1], apex[1]), 1)
# fit_bottom = np.polyfit((left_bottom[0], right_bottom[0]), (left_bottom[1], right_bottom[1]), 1)
#
# # Mask pixels below the threshold
# color_thresholds = (image[:,:,0] < rgb_threshold[0]) | \
#                     (image[:,:,1] < rgb_threshold[1]) | \
#                     (image[:,:,2] < rgb_threshold[2])
#
# # Find the region inside the lines
# XX, YY = np.meshgrid(np.arange(0, xsize), np.arange(0, ysize))
# region_thresholds = (YY > (XX*fit_left[0] + fit_left[1])) & \
#                     (YY > (XX*fit_right[0] + fit_right[1])) & \
#                     (YY < (XX*fit_bottom[0] + fit_bottom[1]))
# # Mask color selection
# color_select[color_thresholds] = [0,0,0]
# # Find where image is both colored right and in the region
# line_image[~color_thresholds & region_thresholds] = [255,0,0]
#
# # Display our two output images
# plt.imshow(color_select)
# plt.show()
# plt.imshow(line_image)
# plt.show()
# # uncomment if plot does not display














# import matplotlib.pyplot as plt
# import matplotlib.image as mpimg
# image = mpimg.imread('exit-ramp1.jpg')
# plt.imshow(image)
# import cv2  #bringing in OpenCV libraries
# gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) #grayscale conversion
# # plt.imshow(gray, cmap='gray')
# # plt.show()
# kernel_size = 3
# blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size), 0)
# edges = cv2.Canny(gray, 100, 200)
# plt.imshow(edges, cmap='gray')
# plt.show()













# Do relevant imports
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2

# Read in and grayscale the image
image = mpimg.imread('exit-ramp.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)

# Define a kernel size and apply Gaussian smoothing
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

# Define our parameters for Canny and apply
low_threshold = 100
high_threshold = 200
masked_edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Define the Hough transform parameters
# Make a blank the same size as our image to draw on
rho = 1
theta = np.pi/180
threshold = 1
# min_line_length = 150
# max_line_gap = 20
#<<--actual---->>
min_line_length = 10
max_line_gap = 1
line_image = np.copy(image)*0
#creating a blank to draw lines on

# Run Hough on edge detected image
lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
                            min_line_length, max_line_gap)

# Iterate over the output "lines" and draw lines on the blank
for line in lines:
    for x1,y1,x2,y2 in line:
        cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)

# Create a "color" binary image to combine with line image
color_edges = np.dstack((masked_edges, masked_edges, masked_edges))

# Draw the lines on the edge image
combo = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0)
plt.imshow(combo)
plt.show()