From 89d8f4c0df0dad9f0838ff4919ed1d2590f48835 Mon Sep 17 00:00:00 2001 From: myh Date: Tue, 22 Apr 2025 16:35:29 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E8=AF=84=E4=BB=B7=E6=8C=87?= =?UTF-8?q?=E6=A0=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- image_fusion/Image_Registration_test.py | 66 +++++++++++++++++++++++-- 1 file changed, 62 insertions(+), 4 deletions(-) diff --git a/image_fusion/Image_Registration_test.py b/image_fusion/Image_Registration_test.py index d07b4a3..9de54ac 100644 --- a/image_fusion/Image_Registration_test.py +++ b/image_fusion/Image_Registration_test.py @@ -8,12 +8,41 @@ import cv2 import numpy as np from ultralytics import YOLO +from skimage.metrics import structural_similarity as ssim # 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 +def calculate_en(img): + """计算信息熵（处理灰度图）""" + hist = cv2.calcHist([img], [0], None, [256], [0, 256]) + hist = hist / hist.sum() + return -np.sum(hist * np.log2(hist + 1e-10)) + + +def calculate_sf(img): + """计算空间频率（处理灰度图）""" + rf = np.sqrt(np.mean(np.square(np.diff(img, axis=0)))) + cf = np.sqrt(np.mean(np.square(np.diff(img, axis=1)))) + return np.sqrt(rf ** 2 + cf ** 2) + + +def calculate_mi(img1, img2): + """计算互信息（处理灰度图）""" + hist_2d = np.histogram2d(img1.ravel(), img2.ravel(), 256)[0] + pxy = hist_2d / hist_2d.sum() + px = np.sum(pxy, axis=1) + py = np.sum(pxy, axis=0) + return np.sum(pxy * np.log2(pxy / (px[:, None] * py[None, :] + 1e-10) + 1e-10)) + + +def calculate_ssim(img1, img2): + """计算SSIM（处理灰度图）""" + return ssim(img1, img2, data_range=255) + + # 裁剪线性RGB对比度拉伸：（去掉2%百分位以下的数，去掉98%百分位以上的数，上下百分位数一般相同，并设置输出上下限） def truncated_linear_stretch(image, truncated_value=2, maxout=255, min_out=0): """ @@ -145,20 +174,42 @@ def main(matchimg_vi, matchimg_in): orimg_vi = matchimg_vi orimg_in = matchimg_in h, w = orimg_vi.shape[:2] # 480 640 - flag, H, dot = Images_matching(matchimg_vi, matchimg_in) # (3, 3)//获取对应的配准坐标点 + # (3, 3)//获取对应的配准坐标点 + flag, H, dot = Images_matching(matchimg_vi, matchimg_in) if flag == 0: return 0, None, 0 else: + # 配准处理 matched_ni = cv2.warpPerspective(orimg_in, H, (w, h)) matched_ni, left, right, top, bottom = removeBlackBorder(matched_ni) + + # 裁剪可见光图像 + cropped_vi = orimg_vi[left:right, top:bottom] + # fusion = fusions(orimg_vi[left:right, top:bottom], matched_ni) - fusion = fusions(orimg_vi, matched_ni) + fusion = fusions(cropped_vi, matched_ni) + + # 转换为灰度计算指标 + fusion_gray = cv2.cvtColor(fusion, cv2.COLOR_RGB2GRAY) + cropped_vi_gray = cv2.cvtColor(cropped_vi, cv2.COLOR_BGR2GRAY) + matched_ni_gray = matched_ni # 红外图已经是灰度 + + # 计算指标 + en = calculate_en(fusion_gray) + sf = calculate_sf(fusion_gray) + mi_visible = calculate_mi(fusion_gray, cropped_vi_gray) + mi_infrared = calculate_mi(fusion_gray, matched_ni_gray) + mi_total = mi_visible + mi_infrared + ssim_visible = calculate_ssim(fusion_gray, cropped_vi_gray) + ssim_infrared = calculate_ssim(fusion_gray, matched_ni_gray) + ssim_avg = (ssim_visible + ssim_infrared) / 2 # YOLOv8目标检测 results = yolo_model(fusion) # 输入融合后的图像 annotated_image = results[0].plot() # 绘制检测框 - return 1, annotated_image, dot # 返回带检测结果的图像 + # 返回带检测结果的图像 + return 1, annotated_image, dot, en, sf, mi_total, ssim_avg except Exception as e: print(f"Error in fusion/detection: {e}") return 0, None, 0 @@ -272,9 +323,16 @@ if __name__ == '__main__': 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) + flag, fusion_result, dot, en, sf, mi, ssim_val = main(img_visible, img_inf_gray) if flag == 1: + # 展示评价指标 + print("\n======== 融合质量评价 ========") + print(f"信息熵（EN）: {en:.2f}") + print(f"空间频率（SF）: {sf:.2f}") + print(f"互信息（MI）: {mi:.2f}") + print(f"结构相似性（SSIM）: {ssim_val:.4f}") + # 显示并保存结果 cv2.imshow("Fusion with Detection", fusion_result) cv2.imwrite("output/fusion_result.jpg", fusion_result)