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import cv2
import numpy as np
def resize_and_paste_back_with_repair_mask(
image_path,
mask_path,
scale_width=1.0,
scale_height=1.0,
output_image_path="output_with_resized_obj.jpg",
output_repair_mask_path="repair_mask.png"
):
"""
抠出物体 → 缩放 → 贴回原图，并生成用于 AI 修复的 mask。
Returns:
output_img: 合成后的图像
repair_mask: 修复用二值 mask（白色=需修复）
"""
# 1. 加载原图和掩码
image = cv2.imread(image_path)
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
if image is None or mask is None:
raise FileNotFoundError("Image or mask not found!")
h, w = image.shape[:2]
assert mask.shape == (h, w), "Mask and image size mismatch!"
# 2. 找到物体边界框
coords = cv2.findNonZero(mask)
if coords is None:
raise ValueError("No object in mask!")
x, y, obj_w, obj_h = cv2.boundingRect(coords)
# 3. 计算原物体中心
center_x = x + obj_w // 2
center_y = y + obj_h // 2
# 4. 抠出 RGBA 物体
rgba = np.dstack([image, mask])
obj_rgba = rgba[y:y+obj_h, x:x+obj_w]
# 5. 缩放物体
new_w = max(1, int(obj_w * scale_width))
new_h = max(1, int(obj_h * scale_height))
resized_obj = cv2.resize(obj_rgba, (new_w, new_h), interpolation=cv2.INTER_AREA)
# 6. 计算新位置（保持中心对齐）
new_x = center_x - new_w // 2
new_y = center_y - new_h // 2
# 7. 计算贴图的有效区域（保证 src 和 dst 尺寸一致）
dst_x1 = max(0, new_x)
dst_y1 = max(0, new_y)
dst_x2 = min(w, new_x + new_w)
dst_y2 = min(h, new_y + new_h)
src_x1 = dst_x1 - new_x
src_y1 = dst_y1 - new_y
src_x2 = src_x1 + (dst_x2 - dst_x1)
src_y2 = src_y1 + (dst_y2 - dst_y1)
# Clamp to resized_obj bounds
src_x1 = max(0, int(src_x1))
src_y1 = max(0, int(src_y1))
src_x2 = min(new_w, int(src_x2))
src_y2 = min(new_h, int(src_y2))
# Update final coordinates
dst_x_start, dst_y_start = dst_x1, dst_y1
dst_x_end, dst_y_end = dst_x2, dst_y2
src_x_start, src_y_start = src_x1, src_y1
src_x_end, src_y_end = src_x2, src_y2
# 8. 创建修复 mask
repair_mask = np.zeros((h, w), dtype=np.uint8)
repair_mask[mask > 0] = 255 # 原物体区域需修复
# 9. 合成图像 & 构建新物体 mask
background = image.copy()
background[mask > 0] = [255, 255, 255] # 白色填充原物体区域
output_img = background.copy()
new_obj_mask = np.zeros((h, w), dtype=np.uint8)
if src_x_end > src_x_start and src_y_end > src_y_start:
# 提取有效区域
obj_part = resized_obj[src_y_start:src_y_end, src_x_start:src_x_end]
alpha = obj_part[:, :, 3].astype(np.float32) / 255.0
alpha_uint8 = (alpha * 255).astype(np.uint8)
# Alpha blending
bg_part = output_img[dst_y_start:dst_y_end, dst_x_start:dst_x_end]
fg_part = obj_part[:, :, :3]
blended = fg_part * alpha[..., None] + bg_part * (1 - alpha[..., None])
output_img[dst_y_start:dst_y_end, dst_x_start:dst_x_end] = blended.astype(np.uint8)
# 记录新物体覆盖区域
new_obj_mask[dst_y_start:dst_y_end, dst_x_start:dst_x_end] = alpha_uint8
# 10. 生成 repair mask：原区域 - 新物体覆盖区域
kernel = np.ones((5, 5), np.uint8)
new_obj_mask_dilated = cv2.dilate(new_obj_mask, kernel, iterations=1)
repair_mask = cv2.subtract(repair_mask, new_obj_mask_dilated)
repair_mask = np.clip(repair_mask, 0, 255).astype(np.uint8)
repair_mask = cv2.dilate(repair_mask, np.ones((3, 3), np.uint8), iterations=1)
# 11. 保存
cv2.imwrite(output_image_path, output_img)
cv2.imwrite(output_repair_mask_path, repair_mask)
print(f"✅ Image saved to: {output_image_path}")
print(f"✅ Repair mask saved to: {output_repair_mask_path}")
return output_img, repair_mask
img, repair_mask = resize_and_paste_back_with_repair_mask(
image_path="/mnt/prev_nas/qhy_1/datasets/flux_gen_images/allocentric_002.png",
mask_path="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_masks/allocentric_002.png",
scale_width=1.0,
scale_height=1.4,
output_image_path="/mnt/prev_nas/qhy_1/GenSpace/example/imageedit/29/allocentric_002.png",
output_repair_mask_path="fatter_repair_mask.png"
) |