Spaces:
Runtime error
Runtime error
File size: 7,658 Bytes
32603e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
import os
import torch # pytorch backend
import torchvision # CV models
import pygmtools as pygm
import matplotlib.pyplot as plt # for plotting
from matplotlib.patches import ConnectionPatch # for plotting matching result
import scipy.io as sio # for loading .mat file
import scipy.spatial as spa # for Delaunay triangulation
from sklearn.decomposition import PCA as PCAdimReduc
import itertools
import numpy as np
from PIL import Image
pygm.set_backend('pytorch') # set default backend for pygmtools
##################################################################
# Utils Func #
##################################################################
def plot_image_with_graph(img, kpt, A=None):
plt.imshow(img)
plt.scatter(kpt[0], kpt[1], c='w', edgecolors='k')
if A is not None:
for idx in torch.nonzero(A, as_tuple=False):
plt.plot((kpt[0, idx[0]], kpt[0, idx[1]]), (kpt[1, idx[0]], kpt[1, idx[1]]), 'k-')
def delaunay_triangulation(kpt):
d = spa.Delaunay(kpt.numpy().transpose())
A = torch.zeros(len(kpt[0]), len(kpt[0]))
for simplex in d.simplices:
for pair in itertools.permutations(simplex, 2):
A[pair] = 1
return A
def plot_image_with_graphs(img1, img2, kpts1, kpts2, A1=None, A2=None,
title_1: str="Image 1", title_2: str="Image 2", filename="examples.png"):
plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.title(title_1)
plot_image_with_graph(img1, kpts1, A1)
plt.subplot(1, 2, 2)
plt.title(title_2)
plot_image_with_graph(img2, kpts2, A2)
plt.savefig(filename)
def load_images(
img1_path: str,
img2_path: str,
kpts1_path: str,
kpts2_path: str,
obj_resize: tuple=(256, 256)
):
img1 = Image.open(img1_path)
img2 = Image.open(img2_path)
kpts1 = torch.tensor(sio.loadmat(kpts1_path)['pts_coord'])
kpts2 = torch.tensor(sio.loadmat(kpts2_path)['pts_coord'])
kpts1[0] = kpts1[0] * obj_resize[0] / img1.size[0]
kpts1[1] = kpts1[1] * obj_resize[1] / img1.size[1]
kpts2[0] = kpts2[0] * obj_resize[0] / img2.size[0]
kpts2[1] = kpts2[1] * obj_resize[1] / img2.size[1]
img1 = img1.resize(obj_resize, resample=Image.Resampling.BILINEAR)
img2 = img2.resize(obj_resize, resample=Image.Resampling.BILINEAR)
return img1, img2, kpts1, kpts2
##################################################################
# Process #
##################################################################
def pygm_rrwm(
img1_path: str,
img2_path: str,
kpts1_path: str,
kpts2_path: str,
obj_resize: tuple=(256, 256),
output_path: str="examples",
filename: str="example"
):
if not os.path.exists(output_path):
os.mkdir(output_path)
output_filename = os.path.join(output_path, filename) + "_{}.png"
# Load the images
img1, img2, kpts1, kpts2 = load_images(img1_path, img2_path, kpts1_path, kpts2_path, obj_resize)
plot_image_with_graphs(img1, img2, kpts1, kpts2, filename=output_filename.format(1))
# Build the graphs
A1 = delaunay_triangulation(kpts1)
A2 = delaunay_triangulation(kpts2)
A1 = ((kpts1.unsqueeze(1) - kpts1.unsqueeze(2)) ** 2).sum(dim=0) * A1
A1 = (A1 / A1.max()).to(dtype=torch.float32)
A2 = ((kpts2.unsqueeze(1) - kpts2.unsqueeze(2)) ** 2).sum(dim=0) * A2
A2 = (A2 / A2.max()).to(dtype=torch.float32)
# plot_image_with_graphs(img1, img2, kpts1, kpts2, A1, A2,
# "Image 1 with Graphs", "Image 2 with Graphs", output_filename.format(2))
# Extract node features
vgg16_cnn = torchvision.models.vgg16_bn(True)
torch_img1 = torch.from_numpy(np.array(img1, dtype=np.float32) / 256).permute(2, 0, 1).unsqueeze(0) # shape: BxCxHxW
torch_img2 = torch.from_numpy(np.array(img2, dtype=np.float32) / 256).permute(2, 0, 1).unsqueeze(0) # shape: BxCxHxW
with torch.set_grad_enabled(False):
feat1 = vgg16_cnn.features(torch_img1)
feat2 = vgg16_cnn.features(torch_img2)
# Normalize the features
num_features = feat1.shape[1]
def l2norm(node_feat):
return torch.nn.functional.local_response_norm(
node_feat, node_feat.shape[1] * 2, alpha=node_feat.shape[1] * 2, beta=0.5, k=0)
feat1 = l2norm(feat1)
feat2 = l2norm(feat2)
# Up-sample the features to the original image size
feat1_upsample = torch.nn.functional.interpolate(feat1, (obj_resize[1], obj_resize[0]), mode='bilinear')
feat2_upsample = torch.nn.functional.interpolate(feat2, (obj_resize[1], obj_resize[0]), mode='bilinear')
# Visualize the extracted CNN feature (dimensionality reduction via principle component analysis)
pca_dim_reduc = PCAdimReduc(n_components=3, whiten=True)
feat_dim_reduc = pca_dim_reduc.fit_transform(
np.concatenate((
feat1_upsample.permute(0, 2, 3, 1).reshape(-1, num_features).numpy(),
feat2_upsample.permute(0, 2, 3, 1).reshape(-1, num_features).numpy()
), axis=0)
)
feat_dim_reduc = feat_dim_reduc / np.max(np.abs(feat_dim_reduc), axis=0, keepdims=True) / 2 + 0.5
feat1_dim_reduc = feat_dim_reduc[:obj_resize[0] * obj_resize[1], :]
feat2_dim_reduc = feat_dim_reduc[obj_resize[0] * obj_resize[1]:, :]
# Plot
# plt.figure(figsize=(8, 4))
# plt.subplot(1, 2, 1)
# plt.title('Image 1 with CNN features')
# plot_image_with_graph(img1, kpts1, A1)
# plt.imshow(feat1_dim_reduc.reshape(obj_resize[1], obj_resize[0], 3), alpha=0.5)
# plt.subplot(1, 2, 2)
# plt.title('Image 2 with CNN features')
# plot_image_with_graph(img2, kpts2, A2)
# plt.imshow(feat2_dim_reduc.reshape(obj_resize[1], obj_resize[0], 3), alpha=0.5)
# plt.savefig(output_filename.format(3))
# Extract node features by nearest interpolation
rounded_kpts1 = torch.round(kpts1).to(dtype=torch.long)
rounded_kpts2 = torch.round(kpts2).to(dtype=torch.long)
node1 = feat1_upsample[0, :, rounded_kpts1[1], rounded_kpts1[0]].t() # shape: NxC
node2 = feat2_upsample[0, :, rounded_kpts2[1], rounded_kpts2[0]].t() # shape: NxC
# Build affinity matrix
conn1, edge1 = pygm.utils.dense_to_sparse(A1)
conn2, edge2 = pygm.utils.dense_to_sparse(A2)
import functools
gaussian_aff = functools.partial(pygm.utils.gaussian_aff_fn, sigma=1) # set affinity function
K = pygm.utils.build_aff_mat(node1, edge1, conn1, node2, edge2, conn2, edge_aff_fn=gaussian_aff)
# Plot affinity matrix
# plt.figure(figsize=(4, 4))
# plt.title(f'Affinity Matrix (size: {K.shape[0]}$\\times${K.shape[1]})')
# plt.imshow(K.numpy(), cmap='Blues')
# plt.savefig(output_filename.format(4))
# Solve graph matching problem by RRWM solver
X = pygm.rrwm(K, kpts1.shape[1], kpts2.shape[1])
X = pygm.hungarian(X)
# Plot the matching
plt.figure(figsize=(8, 4))
plt.suptitle('Image Matching Result by RRWM')
ax1 = plt.subplot(1, 2, 1)
plot_image_with_graph(img1, kpts1, A1)
ax2 = plt.subplot(1, 2, 2)
plot_image_with_graph(img2, kpts2, A2)
for i in range(X.shape[0]):
j = torch.argmax(X[i]).item()
con = ConnectionPatch(xyA=kpts1[:, i], xyB=kpts2[:, j], coordsA="data", coordsB="data",
axesA=ax1, axesB=ax2, color="red" if i != j else "green")
plt.gca().add_artist(con)
plt.savefig(output_filename.format(2)) |