185 lines
5.8 KiB
Python
185 lines
5.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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相机校准应用程序
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使用检测到的棋盘格参数进行图像矫正和去畸变
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"""
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import cv2
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import numpy as np
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import json
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import os
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class CameraCalibration:
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def __init__(self, calibration_file='chessboard_detection_output/calibration_result.json'):
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"""
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加载校准参数
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Args:
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calibration_file: 校准结果JSON文件路径
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"""
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self.calibration_file = calibration_file
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self.camera_matrix = None
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self.dist_coeffs = None
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self.load_calibration()
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def load_calibration(self):
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"""从JSON文件加载校准参数"""
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if not os.path.exists(self.calibration_file):
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raise FileNotFoundError(f"校准文件不存在: {self.calibration_file}")
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with open(self.calibration_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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self.camera_matrix = np.array(data['camera_matrix'])
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self.dist_coeffs = np.array(data['distortion_coefficients'])
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print("✓ 校准参数加载成功")
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print(f" 重投影误差: {data['reprojection_error']:.4f} 像素")
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print(f" 使用图像数: {data['num_images']}")
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def undistort_image(self, image):
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"""
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对图像进行去畸变处理
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Args:
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image: 输入图像
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Returns:
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undistorted: 去畸变后的图像
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"""
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h, w = image.shape[:2]
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new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
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self.camera_matrix, self.dist_coeffs, (w, h), 1, (w, h)
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)
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# 去畸变
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undistorted = cv2.undistort(image, self.camera_matrix, self.dist_coeffs, None, new_camera_matrix)
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# 裁剪图像
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x, y, w, h = roi
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undistorted = undistorted[y:y+h, x:x+w]
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return undistorted
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def undistort_video(self, input_video, output_video='undistorted_video.avi'):
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"""
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对视频进行去畸变处理
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Args:
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input_video: 输入视频路径
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output_video: 输出视频路径
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"""
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cap = cv2.VideoCapture(input_video)
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if not cap.isOpened():
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print(f"无法打开视频: {input_video}")
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return
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# 获取视频参数
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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# 读取第一帧获取尺寸
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ret, frame = cap.read()
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if not ret:
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print("无法读取视频帧")
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return
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h, w = frame.shape[:2]
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new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
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self.camera_matrix, self.dist_coeffs, (w, h), 1, (w, h)
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)
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x, y, w_roi, h_roi = roi
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# 创建视频写入器
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter(output_video, fourcc, fps, (w_roi, h_roi))
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print(f"开始处理视频 (共 {total_frames} 帧)...")
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# 重置到开头
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cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
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frame_count = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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# 去畸变
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undistorted = cv2.undistort(frame, self.camera_matrix, self.dist_coeffs, None, new_camera_matrix)
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undistorted = undistorted[y:y+h_roi, x:x+w_roi]
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out.write(undistorted)
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if frame_count % 50 == 0:
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print(f" 处理进度: {frame_count}/{total_frames} ({100*frame_count/total_frames:.1f}%)")
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cap.release()
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out.release()
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print(f"\n✓ 视频处理完成，已保存到: {output_video}")
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def compare_images(self, image_path, output_path='comparison.jpg'):
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"""
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生成原始图像和去畸变图像的对比图
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Args:
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image_path: 输入图像路径
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output_path: 输出对比图路径
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"""
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image = cv2.imread(image_path)
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if image is None:
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print(f"无法读取图像: {image_path}")
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return
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undistorted = self.undistort_image(image)
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# 调整尺寸以便并排显示
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h1, w1 = image.shape[:2]
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h2, w2 = undistorted.shape[:2]
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h = min(h1, h2)
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image_resized = cv2.resize(image, (int(w1 * h / h1), h))
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undistorted_resized = cv2.resize(undistorted, (int(w2 * h / h2), h))
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# 并排拼接
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comparison = np.hstack([image_resized, undistorted_resized])
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# 添加文字标注
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cv2.putText(comparison, 'Original', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 3)
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cv2.putText(comparison, 'Undistorted', (w1 + 50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 3)
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cv2.imwrite(output_path, comparison)
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print(f"✓ 对比图已保存到: {output_path}")
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def main():
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"""主函数 - 演示如何使用校准参数"""
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print("=== 相机校准应用程序 ===\n")
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# 加载校准参数
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calib = CameraCalibration()
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# 示例1: 对检测结果图像进行去畸变
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output_dir = 'chessboard_detection_output'
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detected_images = [f for f in os.listdir(output_dir) if f.startswith('detected_') and f.endswith('.jpg')]
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if detected_images:
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print(f"\n找到 {len(detected_images)} 张检测图像")
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sample_image = os.path.join(output_dir, detected_images[0])
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print(f"生成对比图: {sample_image}")
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calib.compare_images(sample_image, os.path.join(output_dir, 'comparison.jpg'))
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# 示例2: 对原始视频进行去畸变
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print("\n是否要对原始视频进行去畸变处理？")
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print("注意: 这将处理整个视频，可能需要一些时间")
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print("如需处理，请取消注释下面的代码行：")
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print("# calib.undistort_video('Video_20260303114232727.avi', 'undistorted_video.avi')")
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if __name__ == '__main__':
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main()
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