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164 lines
5.3 KiB
164 lines
5.3 KiB
import math
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('TkAgg')
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elevation=0
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azimuth=0
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def circle3d():
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global elevation,azimuth
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# 创建一个新的图和一个3D坐标轴
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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# 定义圆的参数
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radius = 1 # 半径
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theta = np.linspace(0, 2 * np.pi, 100) # 参数角度
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x = radius * np.cos(theta) # x坐标
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y = radius * np.sin(theta) # y坐标
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z = np.zeros_like(theta) # z坐标（这里圆在xy平面上）
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# 绘制圆
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# ax.plot(x, y, z, label='3D Circle', color='b')
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# 绘制圆的正俯视图
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# 正视图（xz平面）
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ax.plot(x, np.zeros_like(theta), z, label='z View', color='r')
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ax.plot(np.zeros_like(theta), y, z, label='h View', color='b')
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# 俯视图（xy平面）
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ax.plot(x, y, np.zeros_like(theta), label='Top View', color='g')
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# 设置坐标轴范围
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ax.set_xlim([-2, 2])
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ax.set_ylim([-2, 2])
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ax.set_zlim([-2, 2])
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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# 添加图例
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ax.legend()
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# 设置初始视角
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# ax.view_init(elev=53, azim=-48,roll=-38) # 俯仰角30度，方位角45度
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ax.view_init(elev=90, azim=0, roll=0) # 俯仰角30度，方位角45度
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# 隐藏整个坐标轴
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# ax.axis('off')
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# 设置窗口透明度
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fig.canvas.manager.window.attributes('-alpha', 0.6) # 设置窗口透明度为 0.6
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# 显示图形
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plt.show()
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# input("请用鼠标转动 调整角度 elevation and azimuth: ")
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# 获取当前的 elevation 和 azimuth
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elevation = ax.elev
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azimuth = ax.azim
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def perspective_transform_by_angle(img, _elevation, _azimuth):
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h, w = img.shape[:2]
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# 根据角度计算目标点位置
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fov = math.radians(45) # 假设视场角45度
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dz = 1 / math.tan(_elevation)
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dx = dz * math.tan(_azimuth)
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# 源图像四个角点(原始斜视图)
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src_points = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
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# 计算目标点(俯视图)
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dst_points = np.float32([
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[w * 0.5 * (1 - dx), h * 0.5 * (1 - dz)], # 左上
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[w * 0.5 * (1 + dx), h * 0.5 * (1 - dz)], # 右上
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[w * 0.5 * (1 + dx), h * 0.5 * (1 + dz)], # 右下
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[w * 0.5 * (1 - dx), h * 0.5 * (1 + dz)] # 左下
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])
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# 获取变换矩阵并应用
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M = cv2.getPerspectiveTransform(src_points, dst_points)
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transformed_image=cv2.warpPerspective(img, M, (h,w))
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# 显示原始图像和变换后的图像
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fig, ax = plt.subplots(1, 2,figsize= (12, 6))
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ax[0].imshow(img)
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ax[0].set_title("Original Image")
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ax[1].imshow(transformed_image)
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ax[1].set_title("Transformed Image")
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plt.show()
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def trans_img(image_path ,x,y):
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global elevation, azimuth
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image = cv2.imread(image_path)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# 获取图像尺寸
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height, width = image.shape[:2]
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center_x, center_y = 532,385 #圆心
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# 定义源图像的四个点（假设为矩形）
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sx1=center_x-100
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sx2=center_x+100
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sy1=center_y-100
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sy2=center_y + 100
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src_points = np.float32([
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[sx1, sy1], # 左上
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[sx2, sy1], # 右上
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[sx2, sy2], # 右下
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[sx1, sy2] # 左下
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])
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# src_points = np.float32([
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# [center_x, sy1], # 上
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# [sx2, center_y], # 右
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# [center_x, sy2], # 下
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# [sx1, center_y] # 左
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# ])
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# 根据 elevation 和 azimuth 计算目标点
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# 这里是一个简化的计算方法，可以根据实际需求调整
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# 假设目标点在透视变换后的坐标
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# 将斜视的画面变成俯视的画面
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radius = 100
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# 根据俯仰角和方位角计算目标点的偏移
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offset_x = radius * np.sin(elevation) * np.cos(azimuth)
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offset_y = radius * np.sin(elevation) * np.sin(azimuth)
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offset_z = radius * np.cos(elevation)
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print(f"offset_x={offset_x},offset_y={offset_y}")
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# 计算目标点
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# dst_points = np.float32([
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# [0 - offset_x, 0 - offset_y], # 左上
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# [width + offset_x, 0 - offset_y], # 右上
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# [width + offset_x, height + offset_y], # 右下
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# [0 - offset_x, height + offset_y] # 左下
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# ])
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dst_points = np.float32([
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[sx1- offset_x, sy1- offset_y], # 上
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[sx2 + offset_x, sy1 - offset_y], # 右上
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[sx2 + offset_x, sy2+ offset_y], # 右下
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[sx1 - offset_x, sy2 + offset_y] # 下
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])
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# 计算透视变换矩阵
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matrix = cv2.getPerspectiveTransform(src_points, dst_points)
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# 应用透视变换
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transformed_image = cv2.warpPerspective(image, matrix, (width, height))
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# 显示原始图像和变换后的图像
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fig, ax = plt.subplots(1, 2,figsize= (12, 6))
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ax[0].imshow(image)
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ax[0].set_title("Original Image")
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ax[1].imshow(transformed_image)
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ax[1].set_title("Transformed Image")
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plt.show()
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if __name__ == '__main__':
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circle3d()
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print("测试============")
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print(f"elevation: {elevation}")
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print(f" azimuth: {azimuth}")
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img_path="images/trans/subRawImg.jpg"
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rawimg=cv2.imread(img_path)
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cv2.imshow("raw Image", rawimg)
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# trans_img(img_path,elevation,azimuth)
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elev = math.radians(elevation)
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azim = math.radians(azimuth)
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perspective_transform_by_angle(rawimg, elev,azim)
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