Untitled

import numpy as np
from matplotlib import pyplot as plt
import cv2
import math
from PIL import Image
import Methods2 as m2
import Methods3 as m3
import a3_utils as a3


pierIm = np.asarray(Image.open("pier.jpg"))
pierIm_copy = np.copy(pierIm)
pierIm = m3.to_grayscale(pierIm)
bricksIm = np.asarray(Image.open("bricks.jpg"))
bricksIm_copy = np.copy(bricksIm)
bricksIm = m3.to_grayscale(bricksIm)


pierIm_edges = m3.findedges(pierIm, 1, 7)
bricksIm_edges = m3.findedges(bricksIm, 2, 10)

pierIm_supressed = m3.nonmaxima_supression_box(pierIm_edges)
bricksIm_supressed = m3.nonmaxima_supression_box(bricksIm_edges)

r1 = 150; t1 = 155
r2 = 100; t2 = 100
#if r1 >> t1, we get all of the lines on the walking surface
#if r1 << t1, we get all of the lines on the walking surface
pierIm_accumulator = m3.hough_find_lines(pierIm_supressed, r1, t1)
bricksIm_accumulator = m3.hough_find_lines(bricksIm_supressed, r2, t2)

#get the indices of the n=10 biggest elements in the accum. matrices

pier_indices = []
bricks_indices = []

for i in range(0, len(pierIm_accumulator)):
    for j in range(0, len(pierIm_accumulator[0])):
        pier_indices.append((pierIm_accumulator[i][j], i, j))

pier_indices = sorted(pier_indices, key=lambda sub: (sub[0]), reverse=True)

res_theta = t1
res_rho = r1
pier_diag = math.sqrt(len(pierIm)**2 + len(pierIm[0])**2)
theta_values = np.linspace(-90, 90, res_theta)
theta_values = np.deg2rad(theta_values)  # I am forced to work with radians
rho_values = np.linspace(-pier_diag, pier_diag, res_rho)

plt.subplot(1,2,1)
plt.title("r = " + str(r1) + ", t = " + str(t1))
for k in range(0, 11):
    (votes, y, x) = pier_indices[k]
    i = rho_values[y]
    j = theta_values[x]
    a3.draw_line(i, j, pier_diag)

plt.imshow(pierIm_copy)

#############BRICKS###########3
for i in range(0, len(bricksIm_accumulator)):
    for j in range(0, len(bricksIm_accumulator[0])):
        bricks_indices.append((bricksIm_accumulator[i][j], i, j))

bricks_indices = sorted(bricks_indices, key=lambda sub: (sub[0]), reverse=True)



res_theta = t2
res_rho = r2
bricks_diag = math.sqrt(len(bricksIm)**2 + len(bricksIm[0])**2)
theta_values = np.linspace(-90, 90, res_theta)
theta_values = np.deg2rad(theta_values)  # I am forced to work with radians
rho_values = np.linspace(-bricks_diag, bricks_diag, res_rho)

plt.subplot(1,2,2)
plt.title("r = " + str(r2) + ", t = " + str(t2))
for k in range(0, 14):
    (votes, y, x) = bricks_indices[k]
    i = rho_values[y]
    j = theta_values[x]
    a3.draw_line(i, j, bricks_diag)


plt.imshow(bricksIm_copy)

plt.show()