first commit

stitching-test-yuan.py

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+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+from tqdm import tqdm
+import time
+
+
+def lr_match(left_path, right_path):
+    left_img = cv2.imread(left_path)
+    right_img = cv2.imread(right_path)
+
+    # 配准
+    left_gray = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY)
+    right_gray = cv2.cvtColor(right_img, cv2.COLOR_BGR2GRAY)
+
+    h, w = left_gray.shape
+    print(f"图像尺寸: 宽度={w}像素, 高度={h}像素")
+
+    template = left_gray[100:h - 100, w - 100:w]
+    print(f"模板区域: 上边界=100, 下边界={h - 100}, 左边界={w - 100}, 右边界={w}")
+
+    search_area = right_gray[:, :200]
+    print(f"搜索区域: 宽度={200}, 高度={h}")
+
+    res = cv2.matchTemplate(search_area, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(f"匹配质量: {max_val:.4f} (1.0=完美匹配)")
+
+    match_x, match_y = max_loc
+    print(f"匹配位置: x={match_x}, y={match_y} (在搜索区域内)")
+
+    dx = w - 100 - match_x
+    dy = 100 - match_y
+    print(f"计算平移量: dx={dx}, dy={dy}")
+
+    return dx, dy
+
+
+def tb_match(top_path, bottom_path):
+    top_img = cv2.imread(top_path)
+    bottom_img = cv2.imread(bottom_path)
+
+    # 配准
+    top_gray = cv2.cvtColor(top_img, cv2.COLOR_BGR2GRAY)
+    bottom_gray = cv2.cvtColor(bottom_img, cv2.COLOR_BGR2GRAY)
+
+    h, w = top_gray.shape
+    print(f"图像尺寸: 宽度={w}像素, 高度={h}像素")
+
+    template = top_gray[h - 100:h, 100:w - 100]
+    print(f"模板区域: 上边界={h - 100}, 下边界={h}, 左边界={100}, 右边界={w - 100}")
+
+    search_area = bottom_gray[:200, :]
+    print(f"搜索区域: 宽度={w}, 高度={200}")
+
+    res = cv2.matchTemplate(search_area, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(f"匹配质量: {max_val:.4f} (1.0=完美匹配)")
+
+    match_x, match_y = max_loc
+    print(f"匹配位置: x={match_x}, y={match_y} (在搜索区域内)")
+
+    dx = 100 - match_x
+    dy = h - 100 - match_y
+    print(f"计算平移量: dx={dx}, dy={dy}")
+
+    return dx, dy
+
+
+if __name__ == "__main__":
+    start_time = time.time()
+    # ====== 1. 计算相对配准关系 ======
+    max_x, max_y = 9, 9
+
+    # 假设图像范围：x 从 0 到 max_x, y 从 0 到 max_y
+    right_disp = [[None] * (max_y+1) for _ in range(max_x)]  # 大小为 [max_x][max_y+1]
+    down_disp = [[None] * max_y for _ in range(max_x+1)]      # 大小为 [max_x+1][max_y]
+
+    # 计算左右相邻位移（横向关系）
+    for x in range(max_x):          # x 从 0 到 max_x-1
+        for y in range(max_y + 1):  # y 从 0 到 max_y
+            img_left = f"cell/{x}-{y}.jpg"
+            img_right = f"cell/{x+1}-{y}.jpg"
+            print(f"Matching: {img_left} and {img_right}")
+            dx, dy = lr_match(img_left, img_right)  # 右图相对于左图的位移
+            right_disp[x][y] = (dx, dy)  # 存储结果
+
+    # 计算上下相邻位移（纵向关系）
+    for x in range(max_x + 1):      # x 从 0 到 max_x
+        for y in range(max_y):      # y 从 0 到 max_y-1
+            img_top = f"cell/{x}-{y}.jpg"
+            img_bottom = f"cell/{x}-{y+1}.jpg"
+            dx, dy = tb_match(img_top, img_bottom)  # 下图相对于上图的位移
+            down_disp[x][y] = (dx, dy)  # 存储结果
+
+    # ====== 2. 计算绝对配准关系，即相对于 0-0 的配准关系 ======
+    global_disp = [[(0, 0)] * (max_y + 1) for _ in range(max_x + 1)]  # 初始化
+
+    # 初始化第一行 (y=0)
+    for x in range(1, max_x + 1):
+        dx_prev, dy_prev = global_disp[x-1][0]   # 左邻居 (x-1,0) 的位移
+        dx_right, dy_right = right_disp[x-1][0]   # (x-1,0) 到 (x,0) 的位移
+        global_disp[x][0] = (dx_prev + dx_right, dy_prev + dy_right)
+
+    # 初始化第一列 (x=0)
+    for y in range(1, max_y + 1):
+        dx_prev, dy_prev = global_disp[0][y-1]   # 上邻居 (0,y-1) 的位移
+        dx_down, dy_down = down_disp[0][y-1]      # (0,y-1) 到 (0,y) 的位移
+        global_disp[0][y] = (dx_prev + dx_down, dy_prev + dy_down)
+
+    # 计算内部点 (x≥1, y≥1)
+    for x in range(1, max_x + 1):
+        for y in range(1, max_y + 1):
+            # 通过左侧邻居 (x-1,y) 计算
+            dx_left, dy_left = global_disp[x-1][y]
+            dx_r, dy_r = right_disp[x-1][y]  # (x-1,y) 到 (x,y) 的位移
+            from_left = (dx_left + dx_r, dy_left + dy_r)
+
+            # 通过上方邻居 (x,y-1) 计算
+            dx_top, dy_top = global_disp[x][y-1]
+            dx_d, dy_d = down_disp[x][y-1]  # (x,y-1) 到 (x,y) 的位移
+            from_top = (dx_top + dx_d, dy_top + dy_d)
+
+            # 取平均值（整数四舍五入）
+            global_disp[x][y] = (
+                round((from_left[0] + from_top[0]) / 2),
+                round((from_left[1] + from_top[1]) / 2)
+            )
+
+    print(global_disp)
+
+    # ====== 3. 正数化全局坐标 ======
+    min_dx = 0
+    min_dy = 0
+
+    # 遍历所有位移值，找到最小偏移量
+    for x in range(len(global_disp)):
+        for y in range(len(global_disp[0])):
+            dx, dy = global_disp[x][y]
+            min_dx = min(min_dx, dx)
+            min_dy = min(min_dy, dy)
+
+    # 计算偏移量（将所有位移变为非负）
+    offset_x = -min_dx  # 当 min_dx 为负时，offset_x 为正
+    offset_y = -min_dy  # 当 min_dy 为负时，offset_y 为正
+
+    # 创建新矩阵存储非负位移
+    global_disp_abs = []
+    for x in range(len(global_disp)):
+        row = []
+        for y in range(len(global_disp[0])):
+            dx, dy = global_disp[x][y]
+            # 应用偏移使所有值非负
+            abs_dx = dx + offset_x
+            abs_dy = dy + offset_y
+            row.append((abs_dx, abs_dy))
+        global_disp_abs.append(row)
+
+    print(global_disp_abs)
+
+    # ====== 4. 根据全局坐标进行拼接 ======
+    # 计算画布尺寸，创建空白画布
+    sample_img = cv2.imread("cell/0-0.jpg")
+    h, w = sample_img.shape[:2]
+
+    max_dx = max(dx for row in global_disp_abs for dx, dy in row)
+    max_dy = max(dy for row in global_disp_abs for dx, dy in row)
+    
+    canvas_width = max_dx + w
+    canvas_height = max_dy + h
+
+    canvas = np.zeros((canvas_height, canvas_width, 3), dtype=np.uint8)
+
+    total_images = len(global_disp_abs) * len(global_disp_abs[0])
+    with tqdm(total=total_images, desc="拼接图像") as pbar:
+        for x in range(len(global_disp_abs)):
+            for y in range(len(global_disp_abs[0])):
+                img = cv2.imread(f"cell/{x}-{y}.jpg")
+                
+                # 获取该图像的位移
+                dx, dy = global_disp_abs[x][y]
+                
+                # 将图像放置到画布上
+                try:
+                    # 将图像复制到画布对应位置
+                    canvas[dy:dy+h, dx:dx+w] = img
+                except ValueError:
+                    # 处理可能的尺寸不匹配问题
+                    print(f"错误: 无法放置图像 cell/{x}-{y}.jpg 在位置({dx},{dy})")
+                    continue
+                
+                pbar.update(1)
+
+    # 保存拼接结果
+    cv2.imwrite("result.jpg", canvas)
+    print(f"拼接完成！结果保存至: result.jpg")
+
+    end_time = time.time()
+    print(f"总耗时：{end_time - start_time}s")

stitching-test.py

@@ -0,0 +1,89 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+def lr_match(left_path, right_path):
+    left_img = cv2.imread(left_path)
+    right_img = cv2.imread(right_path)
+
+    # 配准
+    left_gray = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY)
+    right_gray = cv2.cvtColor(right_img, cv2.COLOR_BGR2GRAY)
+
+    h, w = left_gray.shape
+    print(f"图像尺寸: 宽度={w}像素, 高度={h}像素")
+
+    template = left_gray[100:h - 100, w - 100:w]
+    print(f"模板区域: 上边界=100, 下边界={h - 100}, 左边界={w - 100}, 右边界={w}")
+
+    search_area = right_gray[:, :200]
+    print(f"搜索区域: 宽度={200}, 高度={h}")
+
+    res = cv2.matchTemplate(search_area, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(f"匹配质量: {max_val:.4f} (1.0=完美匹配)")
+
+    match_x, match_y = max_loc
+    print(f"匹配位置: x={match_x}, y={match_y} (在搜索区域内)")
+
+    dx = w - 100 - match_x
+    dy = 100 - match_y
+    print(f"计算平移量: dx={dx}, dy={dy}")
+
+    return dx, dy
+
+
+def tb_match(top_path, bottom_path):
+    top_img = cv2.imread(top_path)
+    bottom_img = cv2.imread(bottom_path)
+
+    # 配准
+    top_gray = cv2.cvtColor(top_img, cv2.COLOR_BGR2GRAY)
+    bottom_gray = cv2.cvtColor(bottom_img, cv2.COLOR_BGR2GRAY)
+
+    h, w = top_gray.shape
+    print(f"图像尺寸: 宽度={w}像素, 高度={h}像素")
+
+    template = top_gray[h - 100:h, 100:w - 100]
+    print(f"模板区域: 上边界={h - 100}, 下边界={h}, 左边界={100}, 右边界={w - 100}")
+
+    search_area = bottom_gray[:200, :]
+    print(f"搜索区域: 宽度={w}, 高度={200}")
+
+    res = cv2.matchTemplate(search_area, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(f"匹配质量: {max_val:.4f} (1.0=完美匹配)")
+
+    match_x, match_y = max_loc
+    print(f"匹配位置: x={match_x}, y={match_y} (在搜索区域内)")
+
+    dx = 100 - match_x
+    dy = h - 100 - match_y
+    print(f"计算平移量: dx={dx}, dy={dy}")
+
+    return dx, dy
+
+
+if __name__ == "__main__":
+    row, col = 10, 10
+
+    left_matrix = np.zeros((row - 1, col, 2), dtype=np.int32)
+    top_matrix = np.zeros((row, col - 1, 2), dtype=np.int32)
+
+    # 计算纵向配准
+    for i in range(row):
+        for j in range(col - 1):
+            print(f"{i}-{j}.jpg and {i}-{j + 1}.jpg")
+            dx, dy = tb_match(f"cell/{i}-{j}.jpg", f"cell/{i}-{j + 1}.jpg")
+            top_matrix[i, j] = dx, dy
+
+    print(top_matrix)
+
+    # 计算横向配准
+    for j in range(col):
+        for i in range(row - 1):
+            print(f"{i}-{j}.jpg and {i + 1}-{j}.jpg")
+            dx, dy = lr_match(f"cell/{i}-{j}.jpg", f"cell/{i + 1}-{j}.jpg")
+            left_matrix[i, j] = dx, dy
+
+    print(left_matrix)

two-stitching-lr.py

@@ -0,0 +1,68 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+if __name__ == "__main__":
+    left_img = cv2.imread("left.jpg")
+    right_img = cv2.imread("right.jpg")
+
+    # 配准
+    left_gray = cv2.cvtColor(left_img, cv2.COLOR_BGR2GRAY)
+    right_gray = cv2.cvtColor(right_img, cv2.COLOR_BGR2GRAY)
+
+    h, w = left_gray.shape
+    print(f"图像尺寸: 宽度={w}像素, 高度={h}像素")
+
+    template = left_gray[100:h - 100, w - 100:w]
+    print(f"模板区域: 上边界=100, 下边界={h - 100}, 左边界={w - 100}, 右边界={w}")
+
+    search_area = right_gray[:, :200]
+    print(f"搜索区域: 宽度={200}, 高度={h}")
+
+    res = cv2.matchTemplate(search_area, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(f"匹配质量: {max_val:.4f} (1.0=完美匹配)")
+
+    match_x, match_y = max_loc
+    print(f"匹配位置: x={match_x}, y={match_y} (在搜索区域内)")
+
+    dx = w - 100 - match_x
+    dy = 100 - match_y
+    print(f"计算平移量: dx={dx}, dy={dy}")
+
+    # 拼接
+    new_h, new_w = h + abs(dy), w + dx
+    canvas = np.zeros((new_h, new_w, 3), dtype=np.uint8)
+    left_mask = np.zeros((new_h, new_w), dtype=np.uint8)
+    right_mask = np.zeros((new_h, new_w), dtype=np.uint8)
+    iou = np.zeros((new_h, new_w), dtype=np.uint8)
+
+    if dy >= 0:
+        canvas[0:h, 0:w] = left_img
+        canvas[dy:new_h, dx:new_w] = right_img
+
+        left_mask[0:h, 0:w] = 255
+        right_mask[dy:new_h, dx:new_w] = 255
+        iou = cv2.bitwise_and(left_mask, right_mask)
+    else:
+        dy = -dy
+        canvas[dy:new_h, 0:w] = left_img
+        canvas[0:h, dx:new_w] = right_img
+
+        left_mask[dy:new_h, 0:w] = 255
+        right_mask[0:h, dx:new_w] = 255
+        iou = cv2.bitwise_and(left_mask, right_mask)
+
+    plt.subplot(221)
+    plt.imshow(cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB))
+
+    plt.subplot(222)
+    plt.imshow(iou, cmap='hot')
+
+    plt.subplot(223)
+    plt.imshow(left_mask, cmap='hot')
+
+    plt.subplot(224)
+    plt.imshow(right_mask, cmap='hot')
+
+    plt.show()

two-stitching-tb.py

@@ -0,0 +1,70 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+if __name__ == "__main__":
+    top_img = cv2.imread("top.jpg")
+    bottom_img = cv2.imread("bottom.jpg")
+
+    # 配准
+    top_gray = cv2.cvtColor(top_img, cv2.COLOR_BGR2GRAY)
+    bottom_gray = cv2.cvtColor(bottom_img, cv2.COLOR_BGR2GRAY)
+
+    h, w = top_gray.shape
+    print(f"图像尺寸: 宽度={w}像素, 高度={h}像素")
+
+    template = top_gray[h - 100:h, 100:w - 100]
+    print(f"模板区域: 上边界={h - 100}, 下边界={h}, 左边界={100}, 右边界={w - 100}")
+
+    search_area = bottom_gray[:200, :]
+    print(f"搜索区域: 宽度={w}, 高度={200}")
+
+    res = cv2.matchTemplate(search_area, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    print(f"匹配质量: {max_val:.4f} (1.0=完美匹配)")
+
+    match_x, match_y = max_loc
+    print(f"匹配位置: x={match_x}, y={match_y} (在搜索区域内)")
+
+    dx = 100 - match_x
+    dy = h - 100 - match_y
+    print(f"计算平移量: dx={dx}, dy={dy}")
+
+    # 拼接
+    new_h, new_w = h + dy, w + abs(dx)
+    canvas = np.zeros((new_h, new_w, 3), dtype=np.uint8)
+    top_mask = np.zeros((new_h, new_w), dtype=np.uint8)
+    bottom_mask = np.zeros((new_h, new_w), dtype=np.uint8)
+    iou = np.zeros((new_h, new_w), dtype=np.uint8)
+
+    if dx >= 0:
+        canvas[0:h, 0:w] = top_img
+        canvas[dy:new_h, dx:new_w] = bottom_img
+
+        top_mask[0:h, 0:w] = 255
+        bottom_mask[dy:new_h, dx:new_w] = 255
+        iou = cv2.bitwise_and(top_mask, bottom_mask)
+    else:
+        dx = -dx
+        canvas[0:h, dx:new_w] = top_img
+        canvas[dy:new_h, 0:w] = bottom_img
+
+        top_mask[0:h, dx:new_w] = 255
+        bottom_mask[dy:new_h, 0:w] = 255
+        iou = cv2.bitwise_and(top_mask, bottom_mask)
+
+    plt.subplot(221)
+    plt.imshow(cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB))
+
+    plt.subplot(222)
+    plt.imshow(iou, cmap='hot')
+
+    plt.subplot(223)
+    plt.imshow(top_mask, cmap='hot')
+
+    plt.subplot(224)
+    plt.imshow(bottom_mask, cmap='hot')
+
+    plt.show()
+
+    cv2.imwrite("stitching-tb.jpg", canvas)