增加部署代码

LK/GMM_IMAGE.py

@@ -0,0 +1,39 @@
+import cv2
+import numpy as np
+import glob
+import os
+
+# 图片文件夹路径
+img_dir = r"/home/hx/yolo/LK/frames"
+img_files = sorted(glob.glob(os.path.join(img_dir, "*.jpg")))
+
+print("找到图片数量:", len(img_files))
+if len(img_files) == 0:
+    raise FileNotFoundError("❌ 没有找到图片，请检查路径和后缀！")
+
+alpha = 0.01  # 学习率
+mean_bg = None
+
+for idx, img_path in enumerate(img_files):
+    frame = cv2.imread(img_path)
+    if frame is None:
+        print(f"⚠️ 跳过无法读取的图片: {img_path}")
+        continue
+
+    frame = frame.astype(np.float32)
+
+    if mean_bg is None:
+        mean_bg = frame.copy()
+    else:
+        mean_bg = (1 - alpha) * mean_bg + alpha * frame
+
+    # 每张都打印
+    print(f"处理进度: {idx+1}/{len(img_files)}")
+
+# 最终背景图
+bg_image = mean_bg.astype(np.uint8)
+
+# 保存到 frames 文件夹下
+save_path = os.path.join(img_dir, "background.jpg")
+cv2.imwrite(save_path, bg_image)
+print(f"✅ 背景图已保存: {save_path}")

LK/GMM_video.py

@@ -0,0 +1,30 @@
+import cv2
+import numpy as np
+
+# 创建背景建模器 (MOG2)
+bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=16, detectShadows=True)
+
+cap = cv2.VideoCapture("your_video.mp4")  # 或者用图片序列
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # 应用背景建模器
+    fg_mask = bg_subtractor.apply(frame)
+
+    # 获取背景图像
+    bg_image = bg_subtractor.getBackgroundImage()
+
+    # 显示
+    cv2.imshow("Frame", frame)
+    cv2.imshow("Foreground Mask", fg_mask)
+    if bg_image is not None:
+        cv2.imshow("Background Model", bg_image)
+
+    if cv2.waitKey(30) & 0xFF == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()

LK/divid_diff.py

@@ -0,0 +1,63 @@
+import cv2
+import numpy as np
+
+def simple_background_subtraction(background_path, foreground_path, output_path, threshold=30):
+    """
+    使用前后两张图做差分，分离前景
+    """
+    bg = cv2.imread(background_path)
+    fg_img = cv2.imread(foreground_path)
+
+    if bg is None or fg_img is None:
+        raise FileNotFoundError("图片没找到！检查路径")
+
+    # 确保两图大小一致
+    if bg.shape != fg_img.shape:
+        print("⚠️ 图片大小不一致，正在调整...")
+        fg_img = cv2.resize(fg_img, (bg.shape[1], bg.shape[0]))
+
+    # 转灰度 + 差分
+    bg_gray = cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY)
+    fg_gray = cv2.cvtColor(fg_img, cv2.COLOR_BGR2GRAY)
+    diff = cv2.absdiff(fg_gray, bg_gray)
+
+    # 二值化
+    _, mask = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)
+
+    # 形态学去噪
+    kernel = np.ones((5,5), np.uint8)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+
+    # 提取前景：背景变白
+    result_white = fg_img.copy()
+    result_white[mask == 0] = [255, 255, 255]  # 背景白
+
+    # 提取前景：背景透明（PNG）
+    b, g, r = cv2.split(fg_img)
+    alpha = np.where(mask > 0, 255, 0).astype(np.uint8)
+    result_transparent = cv2.merge([b, g, r, alpha])
+
+    # 保存
+    cv2.imwrite(output_path + "_white.jpg", result_white)
+    cv2.imwrite(output_path + "_transparent.png", result_transparent)
+
+    print(f"✅ 保存成功：\n  {output_path}_white.jpg\n  {output_path}_transparent.png")
+
+    # 显示（可选）
+    cv2.imshow('Original', fg_img)
+    cv2.imshow('Diff', diff)
+    cv2.imshow('Mask', mask)
+    cv2.imshow('Foreground', result_white)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# === 使用 ===
+if __name__ == '__main__':
+    simple_background_subtraction(
+        background_path='f.jpg',
+        foreground_path='b.jpg',
+        output_path='output/foreground',
+        threshold=30
+    )