From 46c7ff872d6e7f7d5c85ffe047e107d27ec422b2 Mon Sep 17 00:00:00 2001 From: Xi Xu Date: Wed, 13 Nov 2024 15:31:50 +0800 Subject: [PATCH] Update extension-1.py --- 10/extension-1.py | 61 +++-------------------------------------------- 1 file changed, 3 insertions(+), 58 deletions(-) diff --git a/10/extension-1.py b/10/extension-1.py index 490dd66..ad6976b 100644 --- a/10/extension-1.py +++ b/10/extension-1.py @@ -1,16 +1,12 @@ -# 利用OpenCV实现多张2D图像的3D重构。每张图片的内外方位元素从templeRing_par.txt文件中读取 - import cv2 import numpy as np import plotly.express as px import plotly.io as pio -# 设置plotly在浏览器中显示 pio.renderers.default = "browser" def read_camera_parameters(file_path): - """读取相机参数文件""" cameras = [] with open(file_path, "r") as f: n_cameras = int(f.readline()) @@ -41,55 +37,29 @@ def read_camera_parameters(file_path): def reconstruct_3d_points(img1, img2, K, R1, t1, R2, t2): - """从两张图片重构3D点云""" - # 转换为灰度图 gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) - - # 创建SIFT特征检测器 sift = cv2.SIFT_create() - - # 检测特征点和计算描述符 kp1, des1 = sift.detectAndCompute(gray1, None) kp2, des2 = sift.detectAndCompute(gray2, None) - - # 创建BFMatcher对象 bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True) - - # 进行特征匹配 matches = bf.match(des1, des2) - - # 按距离排序 matches = sorted(matches, key=lambda x: x.distance) - - # 选择前N个最佳匹配点 N_BEST_MATCHES = 100 matches = matches[:N_BEST_MATCHES] - - # 提取匹配点的坐标 pts1 = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) pts2 = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) - - # 计算投影矩阵 P1 = K @ np.hstack((R1, t1)) P2 = K @ np.hstack((R2, t2)) - - # 使用RANSAC进行几何验证 E, mask = cv2.findEssentialMat( pts1, pts2, K, method=cv2.RANSAC, prob=0.999, threshold=1.0 ) - - # 只保留内点 matches = [m for i, m in enumerate(matches) if mask[i][0]] pts1 = pts1[mask.ravel() == 1] pts2 = pts2[mask.ravel() == 1] - - # 三角测量得到3D点 points_4D = cv2.triangulatePoints(P1, P2, pts1, pts2) points_3D = points_4D / points_4D[3] points_3D = points_3D[:3].T - - # 可选：显示匹配结果 img_matches = cv2.drawMatches( img1, kp1, @@ -100,30 +70,20 @@ def reconstruct_3d_points(img1, img2, K, R1, t1, R2, t2): flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS, ) cv2.imshow("Matches", img_matches) - cv2.waitKey(1) # 显示1毫秒 - + cv2.waitKey(1) return points_3D def main(): - # 读取相机参数 cameras = read_camera_parameters(r"10\templeRing\templeR_par.txt") - - # 初始化空数组存储所有3D点 all_points_3D = [] - - # 从连续的图像对重构3D点 for i in range(len(cameras) - 1): print(f"Processing image pair {i+1}/{len(cameras)-1}") - - # 读取图像 img1 = cv2.imread(f"10/templeRing/{cameras[i]['image']}") img2 = cv2.imread(f"10/templeRing/{cameras[i+1]['image']}") - if img1 is None or img2 is None: print(f"Failed to load images {i} and {i+1}") continue - points_3D = reconstruct_3d_points( img1, img2, @@ -133,36 +93,21 @@ def main(): cameras[i + 1]["R"], cameras[i + 1]["t"], ) - - # 过滤异常点 distances = np.sqrt(np.sum(points_3D**2, axis=1)) - mask = distances < np.percentile(distances, 95) # 移除最远的5%的点 + mask = distances < np.percentile(distances, 95) points_3D = points_3D[mask] - all_points_3D.append(points_3D) - - # 关闭所有窗口 cv2.destroyAllWindows() - - # 组合所有点 all_points_3D = np.vstack(all_points_3D) - - # 使用plotly进行3D可视化 fig = px.scatter_3d( x=all_points_3D[:, 0], y=all_points_3D[:, 1], z=all_points_3D[:, 2], title="3D Reconstruction from Multiple Views", ) - - # 调整点的大小和视角 fig.update_traces(marker=dict(size=1)) - fig.update_layout(scene=dict(aspectmode="data")) # 保持真实比例 - - # 显示结果 + fig.update_layout(scene=dict(aspectmode="data")) fig.show() - - # 保存结果 fig.write_html("10/templeR_multi.html")