目录
[驾驶模拟左拐右拐 平移](#驾驶模拟左拐右拐 平移)
根据4个点确定投影变换关系:
python
import cv2
import numpy as np
def apply_perspective_transform(image, src_points, dst_points):
# 将选择的点转换为浮点数数组
pts1 = np.float32(src_points)
pts2 = np.float32(dst_points)
# 计算透视变换矩阵
perspective_matrix = cv2.getPerspectiveTransform(pts1, pts2)
# 应用透视变换
result = cv2.warpPerspective(image, perspective_matrix, (image.shape[1], image.shape[0]))
return result
# 加载图像
image = cv2.imread(r'0831_1825_12.jpg')
# 定义源点和目标点(可以自行修改这些点的坐标)
src_points = [(614,251), (615,385), (821,388), (821,249)] # 示例:原始图像的四个顶点
dst_points =[(614+100,251), (615+100,385-20), (821+110,388-20), (821+110,249)] # 示例:原始图像的四个顶点
# 应用透视变换
result=apply_perspective_transform(image, src_points, dst_points)
cv2.imshow("image", image)
cv2.imshow("Transformed Image", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
驾驶模拟左拐右拐 平移
python
import random
import cv2
import numpy as np
def toushi(turn_right,step):
rows, cols = image.shape[:2]
# 定义原图像中的四个角点
pts1 = np.float32([[0, 0], [cols, 0], [0, rows], [cols, rows]])
angle_x=4
np.random.uniform(-angle_x, angle_x)
scale= step*10+angle_x
# 定义变换后的四个点,模拟左转并保持车辆近似垂直向上
# 将左侧点向右偏移,右侧点向左偏移,模拟车辆左转
# 同时保持车辆近似垂直向上
if turn_right:
offset_x_left = -scale # 左侧点向右偏移量
offset_x_right = scale # 右侧点向左偏移量
else:
offset_x_left = scale # 左侧点向右偏移量
offset_x_right = -scale # 右侧点向左偏移量
offset_y = 0 # 垂直偏移量
pts2 = np.float32([
[offset_x_left, offset_y], # 左上角
[cols - offset_x_right, 0], # 右上角
[offset_x_left, rows - offset_y], # 左下角
[cols - offset_x_right, rows] # 右下角
])
# 计算透视变换矩阵
M = cv2.getPerspectiveTransform(pts1, pts2)
# 应用透视变换
transformed_image = cv2.warpPerspective(image, M, (cols, rows))
return transformed_image
if __name__ == '__main__':
# 读取图像a
image = cv2.imread(r'B:\project\jijia\lixiang\data_util\imgs\000002\0831_1824_1.jpg')
image = cv2.imread(r'B:\project\jijia\lixiang\data_util\imgs\000002\0831_1825_12.jpg')
# 图像的尺寸
# 图像的尺寸
while True:
turn_right = random.choice([0, 1])
for step in range(1,12):
print(turn_right,step)
transformed_image=toushi(turn_right, step)
# 显示原图和变换后的图像
cv2.imshow('Original Image', image)
cv2.imshow('Transformed Image', transformed_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
四个点选
python
import cv2
import numpy as np
# 初始化全局变量
points = []
# 定义鼠标回调函数,用于记录用户点击的点
def select_points(event, x, y, flags, param):
global points
if event == cv2.EVENT_LBUTTONDOWN:
points.append((x, y))
cv2.circle(image, (x, y), 5, (0, 255, 0), -1)
cv2.imshow("Original Image", image)
if len(points) == 4:
sorted_points = sort_points_clockwise(points)
apply_perspective_transform(sorted_points)
# 将点按顺时针顺序排序的函数
def sort_points_clockwise(pts):
# 将点转换为NumPy数组
pts = np.array(pts)
# 计算中心点
center = np.mean(pts, axis=0)
# 计算点相对于中心点的角度
angles = np.arctan2(pts[:, 1] - center[1], pts[:, 0] - center[0])
# 按照角度排序,逆时针顺序,所以使用负号将其变为顺时针排序
sorted_pts = pts[np.argsort(angles)]
return sorted_pts
# 应用透视变换的函数
def apply_perspective_transform(sorted_points):
# 定义目标图像的四个顶点
width, height = image.shape[1], image.shape[0]
pts1 = np.float32(sorted_points)
print(pts1)
# 目标点矩阵, 我们选择目标图像的四个角落作为目标点
pts2 = np.float32([[0, 0], [width, 0], [width, height], [0, height]])
# 计算透视变换矩阵
perspective_matrix = cv2.getPerspectiveTransform(pts1, pts2)
# 应用透视变换
result = cv2.warpPerspective(image_copy, perspective_matrix, (width, height))
# 显示结果
cv2.imshow("Transformed Image", result)
# 加载图像
image = cv2.imread(r'B:\project\jijia\lixiang\data_util\imgs\000002\0831_1825_12.jpg')
height, width = image.shape[:2]
center_x = width // 2
cv2.line(image, (center_x, 0), (center_x, height), (255, 0, 0), 2) # 蓝色垂直线
image_copy = image.copy()
# 在图像中绘制一条垂直线
# 创建窗口并设置鼠标回调函数
cv2.imshow("Original Image", image)
cv2.setMouseCallback("Original Image", select_points)
print("请点击图像中的四个点来选择透视变换的区域,按ESC键退出。")
# 主循环
while True:
key = cv2.waitKey(1) & 0xFF
if key == 27: # 按ESC键退出
break
elif key == ord('r'): # 按'r'键重置选择
points = []
image = image_copy.copy()
cv2.line(image, (center_x, 0), (center_x, height), (255, 0, 0), 2) # 重绘垂直线
cv2.imshow("Original Image", image)
cv2.destroyAllWindows()