def ablation(opts, cv_cover, cv_desk):
sigmas = np.arange(0.1, 1.0, 0.1)
ratios = np.arange(0.1, 1.0, 0.1)
for s in sigmas:
for r in ratios:
opts.ratio = r
opts.sigma = s
print("Using ratio {:.2f}, sigma {:.2f}".format(r, s))
cvc = cv_cover.copy()
cvd = cv_desk.copy()
matches, locs1, locs2 = matchPics(cvc, cvd, opts)
plotMatches(cv_cover, cv_desk, matches, locs1, locs2, opts)
from helper import plotMatches
from opts import get_opts
# Q2.1.6
opts = get_opts()
# Read the image and convert to grayscale, if necessary
cover = cv2.imread('../data/cv_cover.jpg')
histogram = []
choice = [1, 17, 35]
for i in range(36):
# Rotate Image
cover_rotate = scipy.ndimage.rotate(cover, i * 10, reshape=False)
# Compute features, descriptors and Match features
matches, locs1, locs2 = matchPics(cover, cover_rotate, opts)
if i in choice:
plotMatches(cover, cover_rotate, matches, locs1, locs2)
# Update histogram
histogram.append(matches.shape[0])
# Display histogram
chosen_histogram = [histogram[1]] + [histogram[17]] + [histogram[35]]
plt.bar(np.arange(3), chosen_histogram)
plt.xlabel('Rotation Degrees')
plt.ylabel('Matches Count')
plt.xticks(np.arange(3), choice * 10)
plt.title('Number of Matches for BRIEF Rotation')
plt.show()
import cv2
from matchPics import matchPics
from helper import plotMatches
from opts import get_opts
opts = get_opts()
cv_cover = cv2.imread('../data/cv_cover.jpg')
cv_desk = cv2.imread('../data/cv_desk.png')
matches, locs1, locs2 = matchPics(cv_cover, cv_desk, opts)
#display matched features
plotMatches(cv_cover, cv_desk, matches, locs1, locs2)
opts = get_opts()
#Q2.1.6
#Read the image and convert to grayscale, if necessary
cv_cover = cv2.imread('../data/cv_cover.jpg')
matchesnumber = np.zeros((36))
for i in range(36):
#Rotate Image
cv_cover_rot = scipy.ndimage.rotate(cv_cover, 10 * i)
#Compute features, descriptors and Match features
matches, locs1, locs2 = matchPics(cv_cover, cv_cover_rot, opts)
#Update histogram
matchesnumber[i] = np.size(matches, axis=0)
if (i == 3):
plotMatches(cv_cover, cv_cover_rot, matches, locs1, locs2)
if (i == 15):
plotMatches(cv_cover, cv_cover_rot, matches, locs1, locs2)
if (i == 30):
plotMatches(cv_cover, cv_cover_rot, matches, locs1, locs2)
#Display histogram
# plt.hist(matchesnumber, bins = 36)
# plt.show()
# x = np.arange(36)
# plt.xlabel("Rotation iteration")
# plt.ylabel("No. of matches")
# plt.bar(x,matchesnumber)
from matplotlib import pyplot as plt
opts = get_opts()
#Write script for Q4.2x
pano_left = cv2.imread('../IMG-7543.jpg')
pano_right = cv2.imread('../IMG-7544.jpg')
H1 = np.size(pano_left, axis = 0)
W1 = np.size(pano_left, axis = 1)
H2 = np.size(pano_right, axis = 0)
W2 = np.size(pano_right, axis = 1)
matches, locs1, locs2 = matchPics(pano_left, pano_right, opts)
plotMatches(pano_left, pano_right, matches, locs1, locs2)
locs1 = locs1[matches[:,0]]
locs2 = locs2[matches[:,1]]
locs1[:,[1,0]] = locs1[:,[0,1]]
locs2[:,[1,0]] = locs2[:,[0,1]]
H2to1, inliers = computeH_ransac(locs1, locs2, opts)
corners_right = np.array([[0,0,W2-1,W2-1],[0,H2-1,H2-1,0],[1,1,1,1]])
corners_left_pred = H2to1 @ corners_right
corners_left_pred = (corners_left_pred/corners_left_pred[-1,:]).round().astype(int)
Hmax = np.amax([H1,np.amax(corners_left_pred[1,:])])
from opts import get_opts
opts = get_opts()
cv_cover = cv2.imread('../data/cv_cover.jpg')
matches_hist = list()
for i in range(36):
#Rotate Image
rotated_img = rotate(cv_cover, 10 * (i))
#Compute features, descriptors and Match features
matches, locs1, locs2 = matchPics(cv_cover, rotated_img, opts)
#Update histogram
matches_hist.append(len(matches))
if (i % 11 == 0):
plotMatches(cv_cover, rotated_img, matches, locs1, locs2)
print(matches_hist)
#Display histogram
m_hist = np.asarray(matches_hist)
ang = np.asarray([i for i in range(0, 360, 10)]).T
y = np.arange(len(ang))
plt.bar(y, m_hist.T, align='center')
plt.xticks(y, ang)
plt.ylabel('matches')
import numpy as np
import cv2
import skimage.io
import skimage.color
from opts import get_opts
#Import necessary functions
from matchPics import matchPics
from planarH import computeH_ransac
from helper import plotMatches
#Write script for Q2.2.4
opts = get_opts()
# Read cv_cover.jpg, cv_desk.png and hp_cover.jpg
img1 = cv2.imread('D:/Academic/CMU/Course/2020Fall/CV/Homework/HW2_Handout/HW2_Handout/data\cv_cover.jpg',1)
img2 = cv2.imread('D:/Academic/CMU/Course/2020Fall/CV/Homework/HW2_Handout/HW2_Handout/data\cv_desk.png',1)
img3 = cv2.imread('D:/Academic/CMU/Course/2020Fall/CV/Homework/HW2_Handout/HW2_Handout/data\hp_cover.jpg',1)
matches, locs1, locs2 = matchPics(img1,img1, opts)
locs1 = locs1[matches[:,0],:]
locs2 = locs2[matches[:,1],:]
plotMatches(img1, img2, matches, locs1, locs2)
result = computeH_ransac(locs2, locs1, opts)
import numpy as np
import cv2
from matchPics import matchPics
from helper import plotMatches
from opts import get_opts
from briefRotTest import rotTest
from planarH import computeH_norm
## COLLABORATORS: CORINNE ALINI, HUSAM WADI, DANIEL BRONSTEIN, LIU JINKUN, JONATHAN SCHWARTZ
opts = get_opts()
'''
cv_cover = cv2.imread('../data/cv_cover.jpg')
cv_desk = cv2.imread('../data/cv_desk.png')
matches, locs1, locs2 = matchPics(cv_cover, cv_desk, opts)
# display matched features
plotMatches(cv_cover, cv_desk, matches, locs1, locs2)
# Q2.1.6 RotTest
rotTest()
#Q2.2.2 for debug
'''
x1 = np.array([[1, 2],
[3,4],
[5,6],
[7,8]])
x2 = np.array([[10,20],