DHOB (IU5SGN): Aruco calibration file format

mercoledì 14 giugno 2023

Aruco calibration file format

Ho provato a vedere se effettuare la calibrazione di tag Aruco con immagini in formato raw lossless od in formato jpg (lossy) poteva influenzare l'errore finale

Per fare cio' ho preso un telefono Android che permette di salvare la stessa immagine in formato DNG ed in formato JPG

Lo script di calcolo e' il seguente

import numpy as np
import cv2
import cv2.aruco as aruco

# Aruco parameters
aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_50)
aruco_params = aruco.DetectorParameters_create()

# Chessboard parameters
num_chessboard_corners_horizontal = 9
num_chessboard_corners_vertical = 6
chessboard_square_size = 0.025 # in meters

# Image directory and file extension
image_directory = 'path_to_images_directory/'
image_extension = '.jpg'

# Arrays to store object points and image points
obj_points = [] # 3D points in real-world coordinates
img_points = [] # 2D points in image plane

# Generate chessboard object points
chessboard_size = (num_chessboard_corners_horizontal,
num_chessboard_corners_vertical)
objp = np.zeros((num_chessboard_corners_horizontal *
num_chessboard_corners_vertical, 3), np.float32)
objp[:, :2] = np.mgrid[0:num_chessboard_corners_horizontal,
0:num_chessboard_corners_vertical].T.reshape(-1, 2)
objp = objp * chessboard_square_size

# Iterate through images and find chessboard corners
image_count = 0
while True:
# Load image
image_path = image_directory + str(image_count) + image_extension
image = cv2.imread(image_path)
if image is None:
break

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Find chessboard corners
ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None)

# If corners found, add object points and image points
if ret == True:
obj_points.append(objp)
img_points.append(corners)

# Draw and display corners
cv2.drawChessboardCorners(image, chessboard_size, corners, ret)
cv2.imshow('Chessboard Corners', image)
cv2.waitKey(500)

image_count += 1

cv2.destroyAllWindows()

# Perform Aruco calibration
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points,
img_points, gray.shape[::-1], None, None)

# Calculate reprojection error
mean_error = 0
for i in range(len(obj_points)):
img_points_proj, _ = cv2.projectPoints(obj_points[i], rvecs[i],
tvecs[i], mtx, dist)
error = cv2.norm(img_points[i], img_points_proj, cv2.NORM_L2) /
len(img_points_proj)
mean_error += error

calibration_error = mean_error / len(obj_points)
print("Calibration error: {}".format(calibration_error))

# Estimate additional error
reproj_error = 0
total_points = 0
for i in range(len(obj_points)):
img_points_proj, _ = cv2.projectPoints(obj_points[i], rvecs[i],
tvecs[i], mtx, dist)
error = cv2.norm(img_points[i], img_points_proj, cv2.NORM_L2)
reproj_error += error
total_points += len(obj_points[i])

additional_error = reproj_error / total_points
print("Additional error: {}".format(additional_error))