Python：在opencv中手动合并通道

Question

def frame_processing(frame):
out_frame = np.zeros((frame.shape[0],frame.shape[1],4),dtype = np.uint8)
b,g,r = cv2.split(frame)
alpha = np.zeros_like(b , dtype=np.uint8)
print(out_frame.shape)
print(b.shape);print(g.shape);print(r.shape);print(alpha.shape)
for i in range(frame.shape[0]):
    for j in range(frame.shape[1]):
        a = (frame[i,j,0],frame[i,j,1],frame[i,j,2])
        b = (225,225,225)
        if all(i > j for i, j in zip(a,b)):  #all(a>b) :
            alpha[i,j] = 0
        else:
            alpha[i,j] = 255
out_frame[:,:,0] = b
out_frame[:,:,1] = g
out_frame[:,:,2] = r
out_frame[:,:,3] = alpha
#out_frame = cv2.merge((b,g,r,alpha))
return out_frame

想添加一个alpha通道; 尝试了cv2.Merge()和手动堆叠频道但失败了。

使用cv2.merge() ：

error: OpenCV(3.4.2) C:\projects\opencv- 
python\opencv\modules\core\src\merge.cpp:458: error: (-215:Assertion failed) 
mv[i].size == mv[0].size && mv[i].depth() == depth in function 'cv::merge'

手动添加频道时：

ValueError: could not broadcast input array from shape (3) into shape 
(225,225)

Answer 1

使用cv2.inRange查找掩码，然后将它们与np.dstack合并：

#!/use/bin/python3
# 2018/09/24 11:51:31 (CST)
import cv2
import numpy as np

#frame = ...
mask = cv2.inRange(frame, (225,225,225), (255,255,255))

#dst = np.dstack((frame, 255-mask))
dst = np.dstack((frame, mask))

cv2.imwrite("dst.png", dst)

要找到特定的颜色，也许您会对这个问题感兴趣：

使用`cv :: inRange`（OpenCV）为颜色检测选择正确的上下HSV边界

Answer 2

这是一个简单的错字。 您正在更改for循环中的变量“b”，它与蓝色通道的变量冲突。 将b = (225,225,225)更改为threshold = (225, 255, 255) b = (225,225,225)并将zip(a,b)更改为zip(a, threshold)解决问题。
顺便说一下，您可以使用它来创建您的Alpha通道：

alpha = np.zeros(b.shape, dtype=b.dtype)

如果您需要更快的速度（可以测量时差），也可以像这样填充Alpha通道：

alpha[~((b[:,:]>threshold[0]) & (g[:,:]>threshold[1]) & (r[:,:]>threshold[2]))] = 255

所以你的功能变成了：

def frame_processing(frame):
    #  split channels
    b,g,r = cv2.split(frame)

    #  initialize alpha to zeros
    alpha = np.zeros(b.shape, dtype=b.dtype)

    #  fill alpha values
    threshold = (225, 225, 225)
    alpha[~((b[:,:]>threshold[0]) & (g[:,:]>threshold[1]) & (r[:,:]>threshold[2]))] = 255

    #  merge all channels back
    out_frame = cv2.merge((b, g, r, alpha))

    return out_frame