1核做网站,app下载量推广,千锋教育课程,用ip做网站同一物体旋转角度求取
直接上代码#xff1a;
import cv2, os
import numpy as np
import timedef perform_pca(image, num_components):# 将图像转换为浮点型img_float np.float32(image)img_flatten img_float.reshape(-1, 2)# 计算均值和协方差矩阵mean, eigenvectors …同一物体旋转角度求取
直接上代码
import cv2, os
import numpy as np
import timedef perform_pca(image, num_components):# 将图像转换为浮点型img_float np.float32(image)img_flatten img_float.reshape(-1, 2)# 计算均值和协方差矩阵mean, eigenvectors cv2.PCACompute(img_flatten, meanNone, maxComponentsnum_components)return mean, eigenvectorsdef rotate_coords(coords, theta):绕原点旋转坐标点集。参数:coords: 坐标点集每个元素是一个(x, y)的元组。theta: 旋转角度以弧度为单位。返回:旋转后的坐标点集。# 定义旋转矩阵rotation_matrix np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])# 将坐标点集转换为NumPy数组以便进行矩阵运算coords_array np.array(coords)# 应用旋转矩阵rotated_coords np.dot(coords_array, rotation_matrix)return rotated_coordsdef judge_direction1(mean, vector, center_x, center_y):v np.array([center_x - mean[0][0], center_y - mean[0][1]])if np.dot(v, vector) 0:vector -vectorreturn vectordef judge_direction2(mean, vector, image, img_path):edge_image cv2.Canny(image, 50, 200)file_name img_path.split(/)[-1]save_path /home/xxx/下载/mask/result/x_angle vector_angle(vector,np.array([1, 0]))edge_non_zero_coords cv2.findNonZero(edge_image)edge_non_zero_coords edge_non_zero_coords - mean[0]edge_non_zero_coords rotate_coords(edge_non_zero_coords, np.radians(x_angle))min_x, min_y np.min(edge_non_zero_coords, axis0)[0]max_x, max_y np.max(edge_non_zero_coords, axis0)[0]new_image np.zeros((int(max_y-min_y)1, int(max_x - min_x)1), np.uint8)for coord in edge_non_zero_coords:x, y coord[0]new_image[int(y - min_y), int(x - min_x)] 255# cv2.imwrite(save_path file_name, new_image)if max_x - min_x max_y - min_y:if abs(max_x) abs(min_x):vector -vectorelse:if abs(max_y) abs(min_y):vector -vectorreturn vectordef get_vector(img_path):image cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)h, w image.shapecenter_x int(w / 2)center_y int(h / 2)non_zero_coords cv2.findNonZero(image)# 执行PCAnum_components 2mean, eigenvectors perform_pca(non_zero_coords, num_components)vector eigenvectors[0]vector judge_direction2(mean, vector, image, img_path)return vectordef vector_angle(v1, v2):# 计算点积dot_product np.dot(v1, v2)# 计算向量的模norm_v1 np.linalg.norm(v1)norm_v2 np.linalg.norm(v2)# 计算余弦值cos_theta dot_product / (norm_v1 * norm_v2)# 计算弧度制的夹角theta_rad np.arccos(np.clip(cos_theta, -1.0, 1.0))# 将弧度制转换为角度制theta_deg np.degrees(theta_rad)# 确定角度的方向性cross_product np.cross(v1, v2)if cross_product 0:theta_deg 360 - theta_degreturn 360 - theta_degdef get_angle(img_path1, img_path2):vector1 get_vector(img_path1)vector2 get_vector(img_path2)angle vector_angle(vector1, vector2)print(vector1, vector2, angle)return angleif __name__ __main__:img_path1 /home/xxx/下载/mask/mask3/0.jpgimg_path2 /home/xxx/下载/mask/mask3/32.jpgget_angle(img_path1, img_path2)
