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静态图片测试

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This commit is contained in:
myh 2025-04-19 13:08:15 +08:00
parent 5e72ac28cc
commit 5df0e15baf
1 changed files with 65 additions and 36 deletions
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93
image_fusion/Image_Registration_test.py
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@ -13,7 +13,7 @@ from ultralytics import YOLO
# 添加YOLOv8模型初始化
yolo_model = YOLO("yolov8n.pt") # 可替换为yolov8s/m/l等
yolo_model.to('cuda') # 启用GPU加速（可选）
yolo_model.to('cuda') # 启用GPU加速
def sift_registration(img1, img2):
@ -125,6 +125,7 @@ def Images_matching(img_base, img_target):
src_pts = np.array([kp1[m.queryIdx].pt for m in good]) # 查询图像的特征描述子索引 # 134, 2
dst_pts = np.array([kp2[m.trainIdx].pt for m in good]) # 训练(模板)图像的特征描述子索引
if len(src_pts) <= 4:
print("Not enough matches are found - {}/{}".format(len(good), 4))
return 0, None, 0
else:
# print(len(dst_pts), len(src_pts), "配准坐标点")
@ -204,38 +205,66 @@ if __name__ == '__main__':
time_all = 0
dots = 0
i = 0
fourcc = cv2.VideoWriter_fourcc(*'XVID')
capture = cv2.VideoCapture("video/20190926_141816_1_8/20190926_141816_1_8/infrared.mp4")
capture2 = cv2.VideoCapture("video/20190926_141816_1_8/20190926_141816_1_8/visible.mp4")
fps = capture.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter('output2.mp4', fourcc, fps, (640, 480))
# 持续读取摄像头数据
while True:
read_code, frame = capture.read() # 红外帧
read_code2, frame2 = capture2.read() # 可见光帧
if not read_code:
break
i += 1
# frame = cv2.resize(frame, (1920, 1080))
# frame2 = cv2.resize(frame2, (640, 512))
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
# capture = cv2.VideoCapture("video/20190926_141816_1_8/20190926_141816_1_8/infrared.mp4")
# capture2 = cv2.VideoCapture("video/20190926_141816_1_8/20190926_141816_1_8/visible.mp4")
# fps = capture.get(cv2.CAP_PROP_FPS)
# out = cv2.VideoWriter('output2.mp4', fourcc, fps, (640, 480))
# # 持续读取摄像头数据
# while True:
# read_code, frame = capture.read() # 红外帧
# read_code2, frame2 = capture2.read() # 可见光帧
# if not read_code:
# break
# i += 1
# # frame = cv2.resize(frame, (1920, 1080))
# # frame2 = cv2.resize(frame2, (640, 512))
#
# # 转换为灰度图（红外图像处理）
# frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#
# # 调用main函数进行融合和检测
# flag, fusion, dot = main(frame2, frame_gray)
#
# if flag == 1:
# # 显示带检测结果的融合图像
# cv2.imshow("Fusion with YOLOv8 Detection", fusion)
# out.write(fusion)
#
# if cv2.waitKey(1) == ord('q'):
# break
# # 释放资源
# capture.release()
# capture2.release()
# cv2.destroyAllWindows()
# ave = time_all / i
# print(ave, "平均时间")
# cv2.destroyAllWindows()
# 转换为灰度图（红外图像处理）
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# === 新增静态图片测试代码 ===
# 输入可见光和红外图像路径
visible_path = "../test_images/visible.jpg" # 可见光图片路径
infrared_path = "../test_images/infrared.jpg" # 红外图片路径
# 调用main函数进行融合和检测
flag, fusion, dot = main(frame2, frame_gray)
# 读取图像
img_visible = cv2.imread(visible_path)
img_infrared = cv2.imread(infrared_path)
if flag == 1:
# 显示带检测结果的融合图像
cv2.imshow("Fusion with YOLOv8 Detection", fusion)
out.write(fusion)
if img_visible is None or img_infrared is None:
print("Error: 图片加载失败，请检查路径！")
exit()
if cv2.waitKey(1) == ord('q'):
break
# 释放资源
capture.release()
capture2.release()
cv2.destroyAllWindows()
ave = time_all / i
print(ave, "平均时间")
cv2.destroyAllWindows()
# 转换为灰度图（红外图像处理）
img_inf_gray = cv2.cvtColor(img_infrared, cv2.COLOR_BGR2GRAY)
# 执行融合与检测
flag, fusion_result, _ = main(img_visible, img_inf_gray)
if flag == 1:
# 显示并保存结果
cv2.imshow("Fusion with Detection", fusion_result)
cv2.imwrite("../output/fusion_result.jpg", fusion_result)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
print("融合失败！")