Update extension-1.py

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")