无人机摄像头标定及无人机图像校正-代码交接

MonocularCalibration.py

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+# 标定并校正
+
+import cv2
+import numpy as np
+import glob
+
+# Defining the dimensions of checkerboard
+CHECKERBOARD = (4, 4)
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+# Creating vector to store vectors of 3D points for each checkerboard image
+objpoints = []
+# Creating vector to store vectors of 2D points for each checkerboard image
+imgpoints = []
+
+# Defining the world coordinates for 3D points
+objp = np.zeros((1, CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)
+objp[0, :, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
+prev_img_shape = None
+
+# Extracting path of individual image stored in a given directory
+images = glob.glob('./images/*.JPG')
+for fname in images:
+    img = cv2.imread(fname)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # Find the chess board corners
+    # If desired number of corners are found in the image then ret = true
+    ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,
+                                             cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE)
+
+    """
+    If desired number of corner are detected,
+    we refine the pixel coordinates and display 
+    them on the images of checker board
+    """
+    if ret == True:
+        objpoints.append(objp)
+        # refining pixel coordinates for given 2d points.
+        corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
+        imgpoints.append(corners2)
+
+        # Draw and display the corners
+        img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
+    cv2.imwrite("./save/checkerboardCallbration.jpg", img)
+h, w = img.shape[:2]
+
+
+"""
+Performing camera calibration by 
+passing the value of known 3D points (objpoints)
+and corresponding pixel coordinates of the 
+detected corners (imgpoints)
+"""
+ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+mtx = mtx.astype(float)
+dist = dist.astype(float)
+
+print("Camera matrix : \n")  # 内参矩阵
+print(mtx)
+print("dist : \n")  # 畸变系数
+print(dist)
+print("rvecs : \n")  # 旋转向量
+print(rvecs)
+print("tvecs : \n")  # 平移向量
+print(tvecs)
+print("-----------------------------------------------------")
+
+# 畸变校正
+img = cv2.imread(r"./jiaoZheng/uav.JPG")
+
+print("------------------使用undistort函数-------------------")
+dst = cv2.undistort(img, mtx, dist, None, mtx)
+print("dst的大小为:", dst.shape)
+cv2.imwrite("./save/uav-dst-0418.JPG", dst)
+
+print("-------------------计算反向投影误差-----------------------")  # 利用反向投影误差对我们找到的参数的准确性进行估计，得到的结果越接近 0 越好。
+tot_error = 0
+for i in range(len(objpoints)):
+    img_points2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
+    error = cv2.norm(imgpoints[i], img_points2, cv2.NORM_L2) / len(img_points2)
+    print("error of " + str(i) + ": ", error)
+    tot_error += error
+
+mean_error = tot_error / len(objpoints)
+print("total error: ", tot_error)
+print("mean error: ", mean_error)
+
+
+
+
+
+