删除无用文件

image_fusion/Image_Registration_test.py

@@ -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！")

requirements.txt

@@ -0,0 +1,41 @@
+certifi==2025.1.31
+charset-normalizer==3.4.1
+colorama==0.4.6
+contourpy==1.3.2
+cycler==0.12.1
+filelock==3.18.0
+fonttools==4.57.0
+fsspec==2025.3.2
+idna==3.10
+Jinja2==3.1.6
+kiwisolver==1.4.8
+MarkupSafe==3.0.2
+matplotlib==3.10.1
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.1.1
+opencv-python==4.11.0.86
+packaging==24.2
+pandas==2.2.3
+pillow==11.2.1
+psutil==7.0.0
+py-cpuinfo==9.0.0
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.2
+requests==2.32.3
+scipy==1.15.2
+seaborn==0.13.2
+setuptools==78.1.0
+six==1.17.0
+sympy==1.13.1
+torch==2.6.0+cu124
+torchaudio==2.6.0+cu124
+torchvision==0.21.0+cu124
+tqdm==4.67.1
+typing_extensions==4.13.2
+tzdata==2025.2
+ultralytics==8.3.111
+ultralytics-thop==2.0.14
+urllib3==2.4.0