222 lines
6.8 KiB
Python
222 lines
6.8 KiB
Python
#!/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
"""
|
||
棋盘格检测程序
|
||
用于检测视频中的11x8内角点棋盘格，并用于相机校准
|
||
"""
|
||
|
||
import cv2
|
||
import numpy as np
|
||
import os
|
||
import json
|
||
from datetime import datetime
|
||
|
||
|
||
class ChessboardDetector:
|
||
def __init__(self, pattern_size=(11, 8), square_size=1.0):
|
||
"""
|
||
初始化棋盘格检测器
|
||
|
||
Args:
|
||
pattern_size: 棋盘格内角点数量 (列, 行)
|
||
square_size: 棋盘格方格实际尺寸(单位:mm或其他)
|
||
"""
|
||
self.pattern_size = pattern_size
|
||
self.square_size = square_size
|
||
|
||
# 准备棋盘格的3D坐标点
|
||
self.objp = np.zeros((pattern_size[0] * pattern_size[1], 3), np.float32)
|
||
self.objp[:, :2] = np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2)
|
||
self.objp *= square_size
|
||
|
||
# 存储所有图像的角点
|
||
self.obj_points = [] # 3D点
|
||
self.img_points = [] # 2D点
|
||
|
||
# 角点检测参数
|
||
self.criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
|
||
|
||
def detect_chessboard(self, image):
|
||
"""
|
||
检测单张图像中的棋盘格
|
||
|
||
Args:
|
||
image: 输入图像
|
||
|
||
Returns:
|
||
ret: 是否检测成功
|
||
corners: 角点坐标
|
||
gray: 灰度图
|
||
"""
|
||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||
|
||
# 查找棋盘格角点
|
||
ret, corners = cv2.findChessboardCorners(gray, self.pattern_size, None)
|
||
|
||
if ret:
|
||
# 亚像素精度优化
|
||
corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), self.criteria)
|
||
|
||
return ret, corners, gray
|
||
|
||
def process_video(self, video_path, output_dir='output', sample_interval=30):
|
||
"""
|
||
处理视频文件，检测棋盘格
|
||
|
||
Args:
|
||
video_path: 视频文件路径
|
||
output_dir: 输出目录
|
||
sample_interval: 采样间隔(帧数)
|
||
"""
|
||
if not os.path.exists(output_dir):
|
||
os.makedirs(output_dir)
|
||
|
||
cap = cv2.VideoCapture(video_path)
|
||
if not cap.isOpened():
|
||
print(f"无法打开视频: {video_path}")
|
||
return False
|
||
|
||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||
|
||
print(f"视频信息: 总帧数={total_frames}, FPS={fps}")
|
||
print(f"开始检测棋盘格 (内角点: {self.pattern_size[0]}x{self.pattern_size[1]})...")
|
||
|
||
frame_count = 0
|
||
detected_count = 0
|
||
|
||
while True:
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
break
|
||
|
||
frame_count += 1
|
||
|
||
# 按间隔采样
|
||
if frame_count % sample_interval != 0:
|
||
continue
|
||
|
||
# 检测棋盘格
|
||
success, corners, gray = self.detect_chessboard(frame)
|
||
|
||
if success:
|
||
detected_count += 1
|
||
self.obj_points.append(self.objp)
|
||
self.img_points.append(corners)
|
||
|
||
# 绘制角点
|
||
vis_img = frame.copy()
|
||
cv2.drawChessboardCorners(vis_img, self.pattern_size, corners, success)
|
||
|
||
# 保存结果图像
|
||
output_path = os.path.join(output_dir, f'detected_{detected_count:03d}_frame{frame_count}.jpg')
|
||
cv2.imwrite(output_path, vis_img)
|
||
|
||
print(f"✓ 帧 {frame_count}: 检测成功 (已保存 {detected_count} 张)")
|
||
else:
|
||
print(f"✗ 帧 {frame_count}: 未检测到棋盘格")
|
||
|
||
cap.release()
|
||
|
||
print(f"\n检测完成: 共处理 {frame_count} 帧, 成功检测 {detected_count} 张图像")
|
||
return detected_count > 0
|
||
|
||
def calibrate_camera(self, image_size):
|
||
"""
|
||
执行相机校准
|
||
|
||
Args:
|
||
image_size: 图像尺寸 (width, height)
|
||
|
||
Returns:
|
||
ret: 标定误差
|
||
camera_matrix: 相机内参矩阵
|
||
dist_coeffs: 畸变系数
|
||
rvecs: 旋转向量
|
||
tvecs: 平移向量
|
||
"""
|
||
if len(self.obj_points) < 3:
|
||
print("错误: 需要至少3张成功检测的图像进行校准")
|
||
return None
|
||
|
||
print(f"\n开始相机校准 (使用 {len(self.obj_points)} 张图像)...")
|
||
|
||
ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(
|
||
self.obj_points, self.img_points, image_size, None, None
|
||
)
|
||
|
||
print(f"标定完成! 重投影误差: {ret:.4f} 像素")
|
||
|
||
return ret, camera_matrix, dist_coeffs, rvecs, tvecs
|
||
|
||
def save_calibration_results(self, camera_matrix, dist_coeffs, ret, output_path='calibration_result.json'):
|
||
"""
|
||
保存校准结果到JSON文件
|
||
"""
|
||
result = {
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||
'pattern_size': self.pattern_size,
|
||
'square_size': self.square_size,
|
||
'num_images': len(self.obj_points),
|
||
'reprojection_error': float(ret),
|
||
'camera_matrix': camera_matrix.tolist(),
|
||
'distortion_coefficients': dist_coeffs.tolist()
|
||
}
|
||
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
json.dump(result, f, indent=4, ensure_ascii=False)
|
||
|
||
print(f"\n校准结果已保存到: {output_path}")
|
||
|
||
# 打印结果
|
||
print("\n=== 相机校准结果 ===")
|
||
print(f"重投影误差: {ret:.4f} 像素")
|
||
print(f"\n相机内参矩阵:")
|
||
print(camera_matrix)
|
||
print(f"\n畸变系数:")
|
||
print(dist_coeffs.ravel())
|
||
|
||
|
||
def main():
|
||
# 配置参数
|
||
VIDEO_PATH = 'Video_20260303114232727.avi'
|
||
OUTPUT_DIR = 'chessboard_detection_output'
|
||
PATTERN_SIZE = (11, 8) # 11x8 内角点
|
||
SQUARE_SIZE = 25.0 # 假设每个方格25mm，根据实际情况调整
|
||
SAMPLE_INTERVAL = 30 # 每30帧采样一次
|
||
|
||
# 创建检测器
|
||
detector = ChessboardDetector(pattern_size=PATTERN_SIZE, square_size=SQUARE_SIZE)
|
||
|
||
# 处理视频
|
||
success = detector.process_video(VIDEO_PATH, OUTPUT_DIR, SAMPLE_INTERVAL)
|
||
|
||
if not success:
|
||
print("未能检测到任何棋盘格，程序退出")
|
||
return
|
||
|
||
# 获取图像尺寸
|
||
cap = cv2.VideoCapture(VIDEO_PATH)
|
||
ret, frame = cap.read()
|
||
if ret:
|
||
image_size = (frame.shape[1], frame.shape[0])
|
||
cap.release()
|
||
|
||
# 执行相机校准
|
||
calib_result = detector.calibrate_camera(image_size)
|
||
|
||
if calib_result:
|
||
ret, camera_matrix, dist_coeffs, rvecs, tvecs = calib_result
|
||
|
||
# 保存校准结果
|
||
detector.save_calibration_results(
|
||
camera_matrix, dist_coeffs, ret,
|
||
os.path.join(OUTPUT_DIR, 'calibration_result.json')
|
||
)
|
||
|
||
print("\n✓ 校准完成！可以使用生成的校准参数进行图像矫正")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|