添加三种不同模式
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@ -1,10 +1,8 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Time :
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# @Author :
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# @File : Image_Registration_test.py
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import time
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import argparse
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import cv2
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import numpy as np
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@ -128,7 +126,7 @@ def Images_matching(img_base, img_target):
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print("Not enough matches are found - {}/{}".format(len(good), 4))
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return 0, None, 0
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else:
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# print(len(dst_pts), len(src_pts), "配准坐标点")
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print(len(dst_pts), len(src_pts), "配准坐标点")
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H = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC, 4) # 生成变换矩阵 H[0]: 3, 3 H[1]: 134, 1
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end = time.time()
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times = end - start
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@ -246,66 +244,76 @@ if __name__ == '__main__':
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time_all = 0
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dots = 0
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i = 0
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# fourcc = cv2.VideoWriter_fourcc(*'XVID')
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# capture = cv2.VideoCapture("video/20190926_141816_1_8/20190926_141816_1_8/infrared.mp4")
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# capture2 = cv2.VideoCapture("video/20190926_141816_1_8/20190926_141816_1_8/visible.mp4")
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# fps = capture.get(cv2.CAP_PROP_FPS)
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# out = cv2.VideoWriter('output2.mp4', fourcc, fps, (640, 480))
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# # 持续读取摄像头数据
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# while True:
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# read_code, frame = capture.read() # 红外帧
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# read_code2, frame2 = capture2.read() # 可见光帧
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# if not read_code:
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# break
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# i += 1
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# # frame = cv2.resize(frame, (1920, 1080))
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# # frame2 = cv2.resize(frame2, (640, 512))
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#
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# # 转换为灰度图（红外图像处理）
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# frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#
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# # 调用main函数进行融合和检测
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# flag, fusion, dot = main(frame2, frame_gray)
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#
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# if flag == 1:
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# # 显示带检测结果的融合图像
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# cv2.imshow("Fusion with YOLOv8 Detection", fusion)
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# out.write(fusion)
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#
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# if cv2.waitKey(1) == ord('q'):
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# break
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# # 释放资源
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# capture.release()
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# capture2.release()
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# cv2.destroyAllWindows()
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# ave = time_all / i
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# print(ave, "平均时间")
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# cv2.destroyAllWindows()
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args = parse_args()
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# === 新增静态图片测试代码 ===
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# 输入可见光和红外图像路径
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visible_path = "../test_images/visible.jpg" # 可见光图片路径
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infrared_path = "../test_images/infrared.jpg" # 红外图片路径
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# 读取图像
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img_visible = cv2.imread(visible_path)
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img_infrared = cv2.imread(infrared_path)
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if img_visible is None or img_infrared is None:
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print("Error: 图片加载失败，请检查路径！")
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exit()
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# 转换为灰度图（红外图像处理）
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img_inf_gray = cv2.cvtColor(img_infrared, cv2.COLOR_BGR2GRAY)
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# 执行融合与检测
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flag, fusion_result, _ = main(img_visible, img_inf_gray)
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if flag == 1:
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# 显示并保存结果
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cv2.imshow("Fusion with Detection", fusion_result)
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cv2.imwrite("../output/fusion_result.jpg", fusion_result)
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cv2.waitKey(0)
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if args.mode == 'video':
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if args.source == 'file':
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# ========== 视频流处理模式 ==========
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if not args.video1 or not args.video2:
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raise ValueError("视频模式需要指定 --video1 和 --video2 参数")
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capture = cv2.VideoCapture(args.video2)
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capture2 = cv2.VideoCapture(args.video1)
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elif args.source == 'camera':
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# ========== 摄像头处理模式 ==========
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capture = cv2.VideoCapture(args.camera_id1)
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capture2 = cv2.VideoCapture(args.camera_id2)
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else:
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raise ValueError("必须指定 --source 参数（camera 或 file）")
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# 公共视频处理逻辑
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fps = capture.get(cv2.CAP_PROP_FPS) if args.source == 'file' else 30
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter(args.output, fourcc, fps, (640, 480))
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while True:
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ret1, frame_vi = capture.read() # 可见光帧
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ret2, frame_ir = capture2.read() # 红外帧
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if not ret1 or not ret2:
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break
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# 红外图像转灰度
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frame_ir_gray = cv2.cvtColor(frame_ir, cv2.COLOR_BGR2GRAY)
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# 执行融合与检测
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flag, fusion, _ = main(frame_vi, frame_ir_gray)
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if flag == 1:
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cv2.imshow("Fusion with YOLOv8 Detection", fusion)
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out.write(fusion)
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if cv2.waitKey(1) == ord('q'):
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break
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# 释放资源
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capture.release()
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capture2.release()
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out.release()
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cv2.destroyAllWindows()
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else:
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print("融合失败！")
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elif args.mode == 'image':
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# ========= 图片处理模式 ==========
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if not args.infrared or not args.visible:
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raise ValueError("图片模式需要指定 --visible 和 --infrared 参数")
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# 读取图像
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img_visible = cv2.imread(args.visible)
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img_infrared = cv2.imread(args.infrared)
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if img_visible is None or img_infrared is None:
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print("Error: 图片加载失败，请检查路径！")
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exit()
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# 转换为灰度图（红外图像处理）
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img_inf_gray = cv2.cvtColor(img_infrared, cv2.COLOR_BGR2GRAY)
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# 执行融合与检测
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flag, fusion_result, _ = main(img_visible, img_inf_gray)
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if flag == 1:
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# 显示并保存结果
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cv2.imshow("Fusion with Detection", fusion_result)
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cv2.imwrite("../output/fusion_result.jpg", fusion_result)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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else:
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print("融合失败！")
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