diff --git a/10/3.py b/10/3.py new file mode 100644 index 0000000..cdb6728 --- /dev/null +++ b/10/3.py @@ -0,0 +1,113 @@ +# 利用OpenCV实现两张2D图像的3D重构。 + +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" + +# 读取图像 +img1 = cv2.imread(r"10\templeRing\templeR0003.png") +img2 = cv2.imread(r"10\templeRing\templeR0005.png") + +# 定义相机内参 +fx = 0.25 * 1520.4 +fy = 0.25 * 1525.9 +cx = 302.32 +cy = 246.87 + +K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]]) + +# 定义第一个相机的外参矩阵 +R1 = np.array( + [ + [-0.01625331773280620100, 0.98386957700862299000, -0.17814736905031653000], + [0.97668439268305030000, -0.02252259937820530100, -0.21349550254417543000], + [-0.21406407160478280000, -0.17746376518636725000, -0.96056399333613396000], + ] +) +t1 = np.array([[-0.0283090812583], [-0.0366442193256], [0.529139415773]]) + +# 定义第二个相机的外参矩阵 +R2 = np.array( + [ + [-0.05235090589954815400, 0.98479784589115438000, -0.16562785206491965000], + [0.88539496349116698000, -0.03093875352325572600, -0.46380874523331522000], + [-0.46188217250287084000, -0.17092687400923678000, -0.87031538103463302000], + ] +) +t2 = np.array([[-0.0269600886818], [-0.0469344855587], [0.53860946783]]) + +# 转换图像为灰度图 +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（可以调整这个数值） +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)) + +# 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：移除一些可能的异常点 +# 这里使用简单的距离阈值进行过滤 +distances = np.sqrt(np.sum(points_3D**2, axis=1)) +mask = distances < np.percentile(distances, 95) # 移除最远的5%的点 +points_3D = points_3D[mask] + +# 使用plotly进行3D可视化 +fig = px.scatter_3d( + x=points_3D[:, 0], y=points_3D[:, 1], z=points_3D[:, 2], title="3D Reconstruction" +) + +# 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.show() + +# 保存结果 +fig.write_html("10/templeR.html") + +# 可选：显示匹配结果 +img_matches = cv2.drawMatches( + img1, + kp1, + img2, + kp2, + matches, + None, + flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS, +) +cv2.imshow("Matches", img_matches) +cv2.waitKey(0) +cv2.destroyAllWindows() diff --git a/10/extension-1.py b/10/extension-1.py new file mode 100644 index 0000000..44f1e53 --- /dev/null +++ b/10/extension-1.py @@ -0,0 +1,160 @@ +# 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()) + for _ in range(n_cameras): + line = f.readline().split() + camera = { + "image": line[0], + "K": np.array( + [ + [float(line[1]), 0, float(line[3])], + [0, float(line[5]), float(line[6])], + [0, 0, 1], + ] + ), + "R": np.array( + [ + [float(line[10]), float(line[11]), float(line[12])], + [float(line[13]), float(line[14]), float(line[15])], + [float(line[16]), float(line[17]), float(line[18])], + ] + ), + "t": np.array( + [[float(line[19])], [float(line[20])], [float(line[21])]] + ), + } + cameras.append(camera) + return cameras + + +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)) + + # 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, + img2, + kp2, + matches, + None, + flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS, + ) + cv2.imshow("Matches", img_matches) + cv2.waitKey(1) # 显示1毫秒 + + 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, + cameras[i]["K"], + cameras[i]["R"], + cameras[i]["t"], + 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 + 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.show() + + # 保存结果 + fig.write_html("10/templeR_multi.html") + + +if __name__ == "__main__": + main() diff --git a/10/templeRing/README.txt b/10/templeRing/README.txt new file mode 100644 index 0000000..0458043 --- /dev/null +++ b/10/templeRing/README.txt @@ -0,0 +1,23 @@ +templeRing data set -- 47 views sampled on a ring + +The object is a plaster replica of "Temple of the Dioskouroi" in Agrigento (Sicily) + +The (tight) bounding box for the templeRing model is +(-0.023121 -0.038009 -0.091940) +(0.078626 0.121636 -0.017395) + +-------------------------- +Created by Steve Seitz, James Diebel, Daniel Scharstein, Brian Curless, and Rick Szeliski + +This directory contains images with camera calibration parameters. + +The images were captured using the Stanford spherical light field gantry, and calibrated by the above people. + +*.png: images in png format +*_par.txt: camera parameters. There is one line for each image. The format for each line is: + "imgname.png k11 k12 k13 k21 k22 k23 k31 k32 k33 r11 r12 r13 r21 r22 r23 r31 r32 r33 t1 t2 t3" + The projection matrix for that image is given by K*[R t] + The image origin is top-left, with x increasing horizontally, y vertically +*_ang.txt: latitude, longitude angles for each image. Not needed to compute scene->image mapping, but may be helpful for visualization. + +Note that (lat, lon) corresponds to the same image as (-lat, 180 + lon), rotated 180 degrees in the image plane. While it would therefore be sufficient in principle to capture only positive latitude images, in practice some images are not useable because of shadows where the gantry occludes the light source. Because the gantry is in a different configuration for positive vs. negative latitude images, this gives us two chances to capture each viewpoint without shadows. It is for this reason that some images may have positive and others negative latitudes. 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