我需要一个基于pytorch完成全景图像拼接的代码 要求输入图片,并且生成拼接之后的图片
时间: 2024-05-10 19:18:49 浏览: 22
我可以为您提供一个基于PyTorch的全景图像拼接代码,以下是代码实现:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import functional as TF
from torchvision import transforms
from PIL import Image
class PanoramaNet(nn.Module):
def __init__(self):
super(PanoramaNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(512 * 6 * 6, 1024)
self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Linear(512, 128)
self.fc4 = nn.Linear(128, 3)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x = self.pool(F.relu(self.conv5(x)))
x = x.view(-1, 512 * 6 * 6)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return x
def panorama_stitching(image_list):
# 加载模型
net = PanoramaNet()
net.load_state_dict(torch.load("panorama_net.pth"))
net.eval()
# 对输入图片进行调整
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# 对输入图片进行预处理
image_tensor_list = []
for image_path in image_list:
image = Image.open(image_path)
image_tensor = transform(image).unsqueeze(0)
image_tensor_list.append(image_tensor)
# 进行全景图像拼接
with torch.no_grad():
pred_corners = []
for i in range(len(image_tensor_list)):
for j in range(len(image_tensor_list)):
if i == j:
continue
image1 = image_tensor_list[i].cuda()
image2 = image_tensor_list[j].cuda()
pred1 = net(image1)
pred2 = net(image2)
pred_corners.append((pred1, pred2))
max_inliers = 0
best_pred = None
for i in range(len(pred_corners)):
for j in range(len(pred_corners)):
if i == j:
continue
pred1, pred2 = pred_corners[i]
pred3, pred4 = pred_corners[j]
H = compute_homography(pred1, pred2, pred3, pred4)
if H is not None:
inliers = compute_inliers(H, pred1, pred2, pred3, pred4)
if inliers > max_inliers:
max_inliers = inliers
best_pred = (pred1, pred2, pred3, pred4, H)
if best_pred is None:
raise ValueError("No good homography found.")
# 拼接图像
new_image_size, corners = compute_new_image_size(best_pred[1], best_pred[3], best_pred[4])
panorama = Image.new("RGB", new_image_size)
for i, image_path in enumerate(image_list):
image = Image.open(image_path)
w, h = image.size
if i == 0:
panorama.paste(image, (corners[0][0], corners[0][1]))
else:
image = TF.rotate(image, compute_rotation(best_pred[i - 1], corners[i - 1]))
panorama.paste(image, (corners[i - 1][0], corners[i - 1][1]))
return panorama
def compute_homography(pred1, pred2, pred3, pred4):
"""
计算单应性矩阵
"""
M = torch.zeros((8, 9)).cuda()
M[0, :3] = pred1
M[0, 6:] = -pred1 * pred3[0]
M[1, 3:6] = pred1
M[1, 6:] = -pred1 * pred3[1]
M[2, :3] = pred2
M[2, 6:] = -pred2 * pred4[0]
M[3, 3:6] = pred2
M[3, 6:] = -pred2 * pred4[1]
M[4, :3] = pred1
M[4, 6:] = -pred1 * pred3[0]
M[5, 3:6] = pred1
M[5, 6:] = -pred1 * pred3[1]
M[6, :3] = pred2
M[6, 6:] = -pred2 * pred4[0]
M[7, 3:6] = pred2
M[7, 6:] = -pred2 * pred4[1]
U, S, V = torch.svd(M)
H = V[-1, :].view(3, 3)
H /= H[2, 2]
return H
def compute_inliers(H, pred1, pred2, pred3, pred4, threshold=5.0):
"""
计算内点数量
"""
inliers = 0
for i in range(len(pred1)):
x1, y1 = pred1[i].cpu().numpy()
x2, y2 = pred2[i].cpu().numpy()
x3, y3 = pred3[i].cpu().numpy()
x4, y4 = pred4[i].cpu().numpy()
p1 = torch.tensor([x1, y1, 1]).cuda()
p2 = torch.tensor([x2, y2, 1]).cuda()
p3 = torch.tensor([x3, y3, 1]).cuda()
p4 = torch.tensor([x4, y4, 1]).cuda()
q1 = H @ p1
q2 = H @ p2
q1 /= q1[2]
q2 /= q2[2]
error1 = torch.norm(q1 - p3)
error2 = torch.norm(q2 - p4)
if error1 < threshold and error2 < threshold:
inliers += 1
return inliers
def compute_rotation(pred, corner):
"""
计算旋转角度
"""
x1, y1 = pred[0].cpu().numpy()
x2, y2 = pred[1].cpu().numpy()
x3, y3 = pred[2].cpu().numpy()
x4, y4 = pred[3].cpu().numpy()
p1 = torch.tensor([x1, y1, 1]).cuda()
p2 = torch.tensor([x2, y2, 1]).cuda()
p3 = torch.tensor([x3, y3, 1]).cuda()
p4 = torch.tensor([x4, y4, 1]).cuda()
q1 = corner[0][0] + pred[0][0]
q2 = corner[0][1] + pred[0][1]
r1 = corner[1][0] + pred[1][0]
r2 = corner[1][1] + pred[1][1]
dx = r1 - q1
dy = r2 - q2
angle = -torch.atan2(dy, dx) * 180.0 / 3.1415926
return angle
def compute_new_image_size(pred1, pred2, H):
"""
计算新的图像大小和角点
"""
corners = []
for pred in [pred1, pred2]:
x, y = pred.cpu().numpy()
p = torch.tensor([x, y, 1]).cuda()
q = H @ p
q /= q[2]
corners.append((int(q[0]), int(q[1])))
new_width = max(corners[1][0], corners[0][0])
new_height = max(corners[1][1], corners[0][1])
new_size = (new_width, new_height)
return new_size, corners
```
您需要将输入的图片路径存储在一个列表中,然后调用`panorama_stitching()`函数即可得到拼接后的全景图像。其中,`PanoramaNet`是一个基于卷积神经网络的特征提取模型,用于提取输入图像的特征向量;`compute_homography()`函数用于计算单应性矩阵;`compute_inliers()`函数用于计算内点数量;`compute_rotation()`函数用于计算旋转角度;`compute_new_image_size()`函数用于计算新的图像大小和角点。
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