Vincentqyw
update: rord
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import argparse
import numpy as np
import imageio
import torch
from tqdm import tqdm
import time
import scipy
import scipy.io
import scipy.misc
import os
import sys
from lib.model_test import D2Net
from lib.utils import preprocess_image
from lib.pyramid import process_multiscale
import cv2
import matplotlib.pyplot as plt
from PIL import Image
from skimage.feature import match_descriptors
from skimage.measure import ransac
from skimage.transform import ProjectiveTransform, AffineTransform
import pydegensac
parser = argparse.ArgumentParser(description='Feature extraction script')
parser.add_argument('imgs', type=str, nargs=2)
parser.add_argument(
'--preprocessing', type=str, default='caffe',
help='image preprocessing (caffe or torch)'
)
parser.add_argument(
'--model_file', type=str,
help='path to the full model'
)
parser.add_argument(
'--no-relu', dest='use_relu', action='store_false',
help='remove ReLU after the dense feature extraction module'
)
parser.set_defaults(use_relu=True)
parser.add_argument(
'--sift', dest='use_sift', action='store_true',
help='Show sift matching as well'
)
parser.set_defaults(use_sift=False)
def extract(image, args, model, device):
if len(image.shape) == 2:
image = image[:, :, np.newaxis]
image = np.repeat(image, 3, -1)
input_image = preprocess_image(
image,
preprocessing=args.preprocessing
)
with torch.no_grad():
keypoints, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
device=device
),
model,
scales=[1]
)
keypoints = keypoints[:, [1, 0, 2]]
feat = {}
feat['keypoints'] = keypoints
feat['scores'] = scores
feat['descriptors'] = descriptors
return feat
def rordMatching(image1, image2, feat1, feat2, matcher="BF"):
if(matcher == "BF"):
t0 = time.time()
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matches = bf.match(feat1['descriptors'], feat2['descriptors'])
matches = sorted(matches, key=lambda x:x.distance)
t1 = time.time()
print("Time to extract matches: ", t1-t0)
print("Number of raw matches:", len(matches))
match1 = [m.queryIdx for m in matches]
match2 = [m.trainIdx for m in matches]
keypoints_left = feat1['keypoints'][match1, : 2]
keypoints_right = feat2['keypoints'][match2, : 2]
np.random.seed(0)
t0 = time.time()
H, inliers = pydegensac.findHomography(keypoints_left, keypoints_right, 10.0, 0.99, 10000)
t1 = time.time()
print("Time for ransac: ", t1-t0)
n_inliers = np.sum(inliers)
print('Number of inliers: %d.' % n_inliers)
inlier_keypoints_left = [cv2.KeyPoint(point[0], point[1], 1) for point in keypoints_left[inliers]]
inlier_keypoints_right = [cv2.KeyPoint(point[0], point[1], 1) for point in keypoints_right[inliers]]
placeholder_matches = [cv2.DMatch(idx, idx, 1) for idx in range(n_inliers)]
draw_params = dict(matchColor = (0,255,0),
singlePointColor = (255,0,0),
# matchesMask = matchesMask,
flags = 0)
image3 = cv2.drawMatches(image1, inlier_keypoints_left, image2, inlier_keypoints_right, placeholder_matches, None, **draw_params)
plt.figure(figsize=(20, 20))
plt.imshow(image3)
plt.axis('off')
plt.show()
def siftMatching(img1, img2):
img1 = np.array(cv2.cvtColor(np.array(img1), cv2.COLOR_BGR2RGB))
img2 = np.array(cv2.cvtColor(np.array(img2), cv2.COLOR_BGR2RGB))
# surf = cv2.xfeatures2d.SURF_create(100)
surf = cv2.xfeatures2d.SIFT_create()
kp1, des1 = surf.detectAndCompute(img1, None)
kp2, des2 = surf.detectAndCompute(img2, None)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
good = []
for m, n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1, 2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1, 2)
model, inliers = pydegensac.findHomography(src_pts, dst_pts, 10.0, 0.99, 10000)
n_inliers = np.sum(inliers)
print('Number of inliers: %d.' % n_inliers)
inlier_keypoints_left = [cv2.KeyPoint(point[0], point[1], 1) for point in src_pts[inliers]]
inlier_keypoints_right = [cv2.KeyPoint(point[0], point[1], 1) for point in dst_pts[inliers]]
placeholder_matches = [cv2.DMatch(idx, idx, 1) for idx in range(n_inliers)]
image3 = cv2.drawMatches(img1, inlier_keypoints_left, img2, inlier_keypoints_right, placeholder_matches, None)
cv2.imshow('Matches', image3)
cv2.waitKey(0)
src_pts = np.float32([ inlier_keypoints_left[m.queryIdx].pt for m in placeholder_matches ]).reshape(-1, 2)
dst_pts = np.float32([ inlier_keypoints_right[m.trainIdx].pt for m in placeholder_matches ]).reshape(-1, 2)
return src_pts, dst_pts
if __name__ == '__main__':
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
args = parser.parse_args()
model = D2Net(
model_file=args.model_file,
use_relu=args.use_relu,
use_cuda=use_cuda
)
image1 = np.array(Image.open(args.imgs[0]))
image2 = np.array(Image.open(args.imgs[1]))
print('--\nRoRD\n--')
feat1 = extract(image1, args, model, device)
feat2 = extract(image2, args, model, device)
print("Features extracted.")
rordMatching(image1, image2, feat1, feat2, matcher="BF")
if(args.use_sift):
print('--\nSIFT\n--')
siftMatching(image1, image2)