import cv2 import numpy as np import glob from scipy.optimize import minimize import model import math import calc_way def needs_correction(dist_coeffs, error_threshold=0.5): k1, k2, p1, p2, k3 = dist_coeffs.ravel() if (abs(k1) > 0.1 or abs(k2) > 0.01 or abs(p1) > 0.005 or abs(p2) > 0.005 or abs(k3) > 0.01): return True return False def calibrate(image_fold, columns, rows, size): # 设置棋盘格参数 chessboard_size = (columns, rows) # 内部角点数量 (columns, rows) square_size = size # 棋盘格方块实际大小（单位：毫米/厘米/英寸等） # 准备对象点 (0,0,0), (1,0,0), (2,0,0) ..., (8,5,0) objp = np.zeros((chessboard_size[0] * chessboard_size[1], 3), np.float32) objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) * square_size # 存储对象点和图像点的数组 objpoints = [] # 3D点（真实世界坐标） imgpoints = [] # 2D点（图像坐标） # 获取标定图像 images = glob.glob(image_fold) # print("找到的图像文件:", images) for fname in images: img = cv2.imread(fname) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 查找棋盘格角点 ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) # 如果找到，添加对象点和图像点 if ret: objpoints.append(objp) # 亚像素级精确化 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) imgpoints.append(corners2) # 绘制并显示角点 cv2.drawChessboardCorners(img, chessboard_size, corners2, ret) cv2.imshow('Corners', img) cv2.waitKey(500) cv2.destroyAllWindows() # 相机标定 ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera( objpoints, imgpoints, gray.shape[::-1], None, None) # 输出标定结果 print("相机内参矩阵（K矩阵）:\n", camera_matrix) print("\n畸变系数（k1, k2, p1, p2, k3）:\n", dist_coeffs) print("\n重投影误差:", ret) if needs_correction(dist_coeffs): print("需要矫正：畸变系数过大") else: print("无需矫正：畸变可忽略") return camera_matrix, dist_coeffs, rvecs, tvecs def find_corners(image_path, columns, rows): # 读取图像 image = cv2.imread(image_path) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 定义棋盘格尺寸 (内角点数量，非方格数) pattern_size = (columns, rows) # 例如8x8的棋盘有7x7内角点 # 查找棋盘格角点 ret, corners = cv2.findChessboardCorners(gray, pattern_size, None) if ret: # 提高角点检测精度 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) # 绘制检测结果 cv2.drawChessboardCorners(image, pattern_size, corners, ret) cv2.imshow('Chessboard Corners', image) cv2.waitKey(0) cv2.destroyAllWindows() else: print("未检测到棋盘格") print(corners) # 将形状从 (N,1,2) 转换为 (N,2) corners_reshaped = corners.reshape(-1, 2) # print("所有角点坐标:\n", corners_reshaped) fixed_value = 960 # 固定值 column_index = 1 # 操作第2列（索引从0开始） # 方法：固定值 - 列值 corners_reshaped[:, column_index] = fixed_value - corners_reshaped[:, column_index] print(corners_reshaped) return corners_reshaped def fun_test(x,cls,corners): print("标定开始") # print(corners) error = 0 error_y = 0 model = cls() f = model.f H = model.H - 9 gamma = math.atan(1 / (math.tan(x[0]) * math.tan(x[1]))) seta = math.atan(1 / math.sqrt(pow(math.tan(x[1]), 2) + 1 / pow(math.tan(x[0]), 2))) column = 0 k = 0 for index, value in enumerate(corners): if index % 11 == 10: column += 1 index += 1 k += 1 continue Xw, Yw = calc_way.calc_distance(value[0], value[1], x[0], x[1]) Xw1, Yw1 = calc_way.calc_distance(corners[index+1][0], corners[index+1][1], x[0], x[1]) print(f"第{index}个点") print(f"Xw: {Xw}, Yw: {Yw}") print(f"Xw1: {Xw1}, Yw1: {Yw1}") d2 = math.sqrt((Xw1 - Xw) ** 2 + (Yw1 - Yw) ** 2) print(f"两点距离为：{d2:.2f}") error = error + abs(d2 - 60) d_y = abs(570-60*k-50+Yw) error_y = error_y + d_y print(f"平均误差为：{error/80:.2f}") print(f"errpr_y:{error/80:.2f}") return error/80 + error_y/80 def get_result_test(cls,corners): params = cls,corners bounds = [(0.1, 1.7), (0.1, 1.7)] result = minimize( fun_test, x0=[0.7, 0.9], args=params, # method='Nelder-Mead', # 或 'trust-constr' method='L-BFGS-B', bounds=bounds, tol=1e-6, # 高精度容差 options={'gtol': 1e-6, 'maxiter': 1000} ) return result def undistort_image(image_path, camera_matrix, dist_coeffs): # 读取图像 img = cv2.imread(image_path) # 获取图像尺寸 h, w = img.shape[:2] # 优化相机矩阵 new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix( camera_matrix, dist_coeffs, (w, h), 1, (w, h)) # 使用undistort dst = cv2.undistort(img, camera_matrix, dist_coeffs, None, new_camera_matrix) # 裁剪图像(使用roi) x, y, w, h = roi dst = dst[y:y + h, x:x + w] return dst # 示例使用 # 假设你已经通过相机标定获得了相机矩阵和畸变系数 # result = calibrate(r"C:\Users\Administrator\Desktop\BYD\20250711\*.jpg",11,8,60) # camera_matrix = result[0] # dist_coeffs = result[1] # corrected_img = undistort_image(r"C:\Users\Administrator\Desktop\BYD\20250711\frame_2100_3.jpg", camera_matrix, dist_coeffs) # cv2.imwrite("corrected.jpg", corrected_img) # result = get_result_test(model.Model,find_corners("corrected.jpg",11,8)) # print(result) # find_corners("corrected.jpg",11,8) # x_zeros,y_zeros = calc_way.calc_zeros_yto0(-200) # print(x_zeros,y_zeros) # # 读取图像 # img = cv2.imread("corrected.jpg") # # # 定义两点坐标 # pt1 = (int(x_zeros[0]), int(960-y_zeros[0])) # pt2 = (int(x_zeros[-1]), int(960-y_zeros[-1])) # # # 画红色线条，粗细为3 # cv2.line(img, pt1, pt2, (0, 0, 255), 3) # # # 保存结果 # cv2.imwrite("output.jpg", img)