问题

我一直在使用OpenCV将一个PNG图像贴在背景上。我的目标是模拟水下的一种塑料袋效果。这样，我想应用Alpha值，使PNG图像在背景上呈半透明状态，但当我这样做时，PNG的黑色遮罩也变得透明。

    y1, y2 = yoff, yoff + png.shape[0]
    x1, x2 = xoff, xoff + png.shape[1]

    alpha_png = png[:, :, 3] / 255.0
    alpha_bg = 0.5 - alpha_png

    bg_copy = bg.copy()

    for c in range(0, 3):
        bg_copy[y1:y2, x1:x2, c] = (alpha_png * png[:, :, c] + alpha_bg * bg_copy[y1:y2, x1:x2, c])
    cv2.imwrite("result.png", bg_copy)

我在网上找到了这段代码，并改变了alpha_bg以使图像更或少透明。

结果是：

我需要移除黑色的半透明背景，但PNG的其余部分应保持半透明，另外袋子应该获得背景的绿色或水下颜色（但我稍后会处理这个）。

任何建议都非常感谢，也考虑使用PIL代替OpenCV。

编辑：

使用的图片是：

前景图

背景图：

<details>
<summary>英文:</summary>

I&#39;ve been using opencv to paste a png image over a background. My target is to simulate a sort of plastic bag underwater.
In that way, I would like to apply Alpha in order to have the png image semi-transparent over the background, but when I do, the black mask of the png becomes transparent too.

    y1, y2 = yoff, yoff + png.shape[0]
    x1, x2 = xoff, xoff + png.shape[1]

    alpha_png = png[:, :, 3] / 255.0
    alpha_bg = 0.5 - alpha_png

    bg_copy = bg.copy()

    for c in range(0, 3):
        bg_copy[y1:y2, x1:x2, c] = (alpha_png * png[:, :, c] + alpha_bg * bg_copy[y1:y2, x1:x2, c])
    cv2.imwrite(&quot;result.png&quot;, bg_copy)
I found this code online, and I changed the alpha_bg in order to make the image more or less transparent.

And the result is
[![png image on background][1]][1]


I need to remove the black semi-transparent background, but the rest of the png should remain semi-transparent, plus the bag should get the background green color or underwater color (But I&#39;ll work later on this).

Any suggestion is really appreciated, also considering to use PIL instead of opencv.

Edit:

The used images are:
Foreground 
[![foreground][2]][2]


And Background:
[![background][3]][3]



  [1]: https://i.stack.imgur.com/UNhSa.jpg
  [2]: https://i.stack.imgur.com/bONUF.png
  [3]: https://i.stack.imgur.com/75dWi.jpg

</details>


# 答案1
**得分**: 1

这是一种在Python/OpenCV中实现的方法。

- 读取背景（水）图像并获取其形状
- 读取前景（袋子）图像并获取其形状
- 分离袋子的BGR通道和alpha通道
- 用透明黑色扩展袋子的BGR图像作为掩码 - 将袋子插入到水图像大小的黑色水中心
- 同样，将袋子的alpha通道扩展到黑色的水图像大小中心（或任何您想要的位置）
- 定义混合因子
- 将掩码转换为浮点数，除以255并乘以混合量
- 将袋子的BGR转换为浮点数并使用掩码与水混合
- 裁剪结果并转换回uint8
- 保存结果

水图像:

![enter image description here](https://i.stack.imgur.com/Of7Wf.jpg)

袋子图像:

![enter image description here](https://i.stack.imgur.com/6JHqm.png)

```python
import cv2
import numpy as np

# 读取背景图像
water = cv2.imread('background.jpg')
h, w = water.shape[:2]
cx = w//2
cy = h//2

# 读取前景图像
bag = cv2.imread('bag.png', cv2.IMREAD_UNCHANGED)
hh, ww = bag.shape[:2]

# 分离袋子图像中的BGR和alpha通道
bag_bgr = bag[:,:,0:3]
bag_alpha = bag[:,:,3]

# 用黑色和透明度扩展袋子到水的大小，然后插入到水的中心
bag_ext = np.zeros((h,w,3), dtype=np.uint8)
bag_ext[cy:cy+hh, cx:cx+ww] = bag_bgr

mask = np.zeros((h,w), dtype=np.uint8)
mask[cy:cy+hh, cx:cx+ww] = bag_alpha
mask = cv2.merge([mask,mask,mask])

# 使用混合因子混合bag_bgr和水图像
blend = 0.5
mask = blend*mask.astype(np.float64)/255
result = bag_ext.astype(np.float64) * mask + water.astype(np.float64) * (1-mask)
result = result.clip(0,255).astype(np.uint8)

# 保存结果
cv2.imwrite('water_bag.jpg', result)

# 显示结果
cv2.imshow('result', result)
cv2.waitKey(0)

混合程度为0.5的结果:

import cv2
import numpy as np

# read the background image
water = cv2.imread('background.jpg')
h, w = water.shape[:2]
cx = w//2
cy = h//2

# read the foreground image
bag = cv2.imread('bag.png', cv2.IMREAD_UNCHANGED)
hh, ww = bag.shape[:2]

# separate bgr and alpha in bag image
bag_bgr = bag[:,:,0:3]
bag_alpha = bag[:,:,3]

# extend bag to size of water with added area black and transparent
# then insert bag at center of water
bag_ext = np.zeros((h,w,3), dtype=np.uint8)
bag_ext[cy:cy+hh, cx:cx+ww] = bag_bgr

mask = np.zeros((h,w), dtype=np.uint8)
mask[cy:cy+hh, cx:cx+ww] = bag_alpha
mask = cv2.merge([mask,mask,mask])

# blend bag_bgr with water using bag_alpha
blend = 0.5
mask = blend*mask.astype(np.float64)/255
result = bag_ext.astype(np.float64) * mask + water.astype(np.float64) * (1-mask)
result = result.clip(0,255).astype(np.uint8)

# save results
cv2.imwrite('water_bag.jpg', result)

# show results
cv2.imshow('result', result)
cv2.waitKey(0)

答案2

得分: 1

与fmw42的回答等效的替代方法：

composite = water.copy()
roi = composite[cy:cy+hh, cx:cx+ww]

water_alpha = 0.5 # 它对下层对象的影响程度

# 将叠加物（水）的alpha的余数/补充部分烘焙到底层（包）的alpha中
# 同时除以255，以使值范围在0.0到1.0之间
# float32是一种更便宜的数据类型
alpha = bag_alpha * np.float32((1 - water_alpha) / 255)

# 添加一个维度以进行沿着颜色通道的广播
alpha = alpha[..., None]

# 凸组合，无需剪裁
roi[:] = bag_bgr * alpha + roi * (1 - alpha)

结果相同。

composite = water.copy()
roi = composite[cy:cy+hh, cx:cx+ww]

water_alpha = 0.5 # how much it affects underlaid objects

# bake the _remainder/complement_ of the overlay's (water) alpha into the underlay's (bag) alpha
# also /255 so the value range is 0.0 to 1.0
# float32 is a cheaper data type
alpha = bag_alpha * np.float32((1 - water_alpha) / 255)

# add a dimension for broadcasting along color channels
alpha = alpha[..., None]

# convex combination, no need to clip
roi[:] = bag_bgr * alpha + roi * (1 - alpha)

答案3

得分: 1

根据Christoph Rackwitz的回答中提供的细节，我们首先从水图像中裁剪出所需位置的大小，然后使用来自alpha通道的掩码与包包图像（bgr）混合。然后将生成的合成图像插入到水图像的所需位置。

import cv2
import numpy as np

# 读取背景图像
water = cv2.imread('background.jpg')
h, w = water.shape[:2]
cx = w//2
cy = h//2

# 读取前景图像
bag = cv2.imread('bag.png', cv2.IMREAD_UNCHANGED)
hh, ww = bag.shape[:2]

# 将包包图像分成BGR和alpha通道
bag_bgr = bag[:,:,0:3]
bag_alpha = bag[:,:,3]

# 在中心裁剪水图像以适应包包大小
water2 = water.copy()
water_crop = water2[cy:cy+hh, cx:cx+ww]

blend = 0.5
mask = blend * bag_alpha.astype(np.float32) / 255
mask = mask[..., None]
composite = bag_bgr.astype(np.float32) * mask + water_crop.astype(np.float32) * (1 - mask)
composite = composite.astype(np.uint8)

# 将合成图像插入到水图像中
result = water.copy()
result[cy:cy+hh, cx:cx+ww] = composite

# 保存结果
cv2.imwrite('water_bag2.jpg', result)

# 显示结果
cv2.imshow('result', result)
cv2.waitKey(0)

结果图像:

import cv2
import numpy as np

# read the background image
water = cv2.imread('background.jpg')
h, w = water.shape[:2]
cx = w//2
cy = h//2

# read the foreground image
bag = cv2.imread('bag.png', cv2.IMREAD_UNCHANGED)
hh, ww = bag.shape[:2]

# separate bgr and alpha in bag image
bag_bgr = bag[:,:,0:3]
bag_alpha = bag[:,:,3]

# crop the water image at center to size of bag
water2 = water.copy()
water_crop = water2[cy:cy+hh, cx:cx+ww]

blend = 0.5
mask = blend*bag_alpha.astype(np.float32)/255
mask = mask[..., None]
composite = bag_bgr.astype(np.float32) * mask + water_crop.astype(np.float32) * (1-mask)
composite = composite.astype(np.uint8)

# insert composite into water
result = water.copy()
result[cy:cy+hh, cx:cx+ww] = composite

# save results
cv2.imwrite('water_bag2.jpg', result)

# show results
cv2.imshow('result', result)
cv2.waitKey(0)

答案4

得分: 0

以下是翻译好的内容：

正确的搜索术语是“alpha混合”，但以下是一个快速示例，将Python标志添加到您的图像中。

源图像：从这里获取，以及从您的帖子获取。

from PIL import Image
import numpy as np

with Image.open("python-logo-only.png") as img: # 您可能需要自行调整文件名以运行此代码
    image_with_alpha = np.array(img)

with Image.open("UNhSa.jpg") as img:
    background = np.array(img) # 没有alpha通道的图像

# 我们需要相同大小的图像，以便轻松将它们相加
foreground = np.zeros_like(background)

# 将图像颜色数据复制到前景层
w, h, _ = image_with_alpha.shape
foreground[:w, :h, :] = image_with_alpha[:,:,:3] # 复制颜色数据而不是alpha通道

# 制作透明度蒙版
transparency = np.zeros(background.shape[:2], dtype = float)
# 从前景图像复制透明度层数据
transparency[:w, :h] = image_with_alpha[:,:,3] / 255 # 将0-255范围转换为0-1

# 将透明度乘以颜色数据以获得前景在最终图像中的贡献
foreground_multiplied = foreground * transparency[:,:,None] # 添加 None (np.newaxis) 以获取正确的ndim
# 将反向透明度乘以背景以获得背景在最终图像中的贡献
background_multiplied = background * (1-transparency[:,:,None])
# 将两个图像相加
final_image = foreground_multiplied + background_multiplied

img = Image.fromarray(final_image.astype(np.uint8))
img.show()

最终图像：

PIL 还具有内置方法可执行大部分操作，如 PIL.Image.alpha_composite 和 PIL.Image.composite，但仍然需要调整图像大小。Image.paste 是一个几乎一行代码的几乎完整的解决方案。

from PIL import Image
import numpy as np

with Image.open(r"python-logo-only.png") as img: #you'll probably have to adjust file names to run this yourself
    image_with_alpha = np.array(img)
    
with Image.open(r"UNhSa.jpg") as img:
    background = np.array(img) #image without alpha
    
#we need images of the same size so we can add them together easily
foreground = np.zeros_like(background)

#copy image color data to forground layer
w, h, _ = image_with_alpha.shape
foreground[:w, :h, :] = image_with_alpha[:,:,:3] #copy color data not alpha layer

#make a transparency mask
transparency = np.zeros(background.shape[:2], dtype = float)
#copy transparency layer data from foreground image
transparency[:w, :h] = image_with_alpha[:,:,3] / 255 #convert 0-255 range to 0-1

#multiply transparency by color data to get forground contribution to final image
foreground_multiplied = foreground * transparency[:,:,None] #add None (np.newaxis) to get correct ndim
#multiply inverse transparency by background to get background contribution to final image
background_multiplied = background * (1-transparency[:,:,None])
#add the two images
final_image = foreground_multiplied + background_multiplied

img = Image.fromarray(final_image.astype(np.uint8))
img.show()