OpenCV & Python：快速将遮罩叠加在图像上而不会溢出

Question

我想在彩色图像上叠加一个二进制蒙版，这样蒙版为 "on" 的地方，像素值的变化量我可以设置。结果应如下所示：

我正在使用 OpenCV 2.4 和 Python 2.7.6。我有一种方法效果很好，但速度很慢，另一种方法速度很快，但存在上溢和下溢问题。这是更快代码的结果，带有 overflow/underflow 个工件：

这是我的代码，显示了快速版本和慢速版本：

def superimpose_mask_on_image(mask, image, color_delta = [20, -20, -20], slow = False):
    # superimpose mask on image, the color change being controlled by color_delta
    # TODO: currently only works on 3-channel, 8 bit images and 1-channel, 8 bit masks

    # fast, but can't handle overflows
    if not slow:
        image[:,:,0] = image[:,:,0] + color_delta[0] * (mask[:,:,0] / 255)
        image[:,:,1] = image[:,:,1] + color_delta[1] * (mask[:,:,0] / 255)
        image[:,:,2] = image[:,:,2] + color_delta[2] * (mask[:,:,0] / 255)

    # slower, but no issues with overflows
    else:
        rows, cols = image.shape[:2]
        for row in xrange(rows):
            for col in xrange(cols):
                if mask[row, col, 0] > 0:
                    image[row, col, 0] = min(255, max(0, image[row, col, 0] + color_delta[0]))
                    image[row, col, 1] = min(255, max(0, image[row, col, 1] + color_delta[1]))
                    image[row, col, 2] = min(255, max(0, image[row, col, 2] + color_delta[2]))

    return

有没有一种快速的方法（可能使用一些 numpy 的函数）来获得我的慢速代码当前产生的相同结果？

Answer 1

可能有更好的方法将着色蒙版应用到图像，但如果您想按照您建议的方式进行操作，那么这个简单的剪辑就可以满足您的要求：

import numpy as np

image[:, :, 0] = np.clip(image[:, :, 0] + color_delta[0] * (mask[:, :, 0] / 255), 0, 255)
image[:, :, 1] = np.clip(image[:, :, 1] + color_delta[1] * (mask[:, :, 0] / 255), 0, 255)
image[:, :, 2] = np.clip(image[:, :, 2] + color_delta[2] * (mask[:, :, 0] / 255), 0, 255)

结果是：

如果您的目标是将颜色应用到某个区域，另一种方法是简单地修改 hue/saturation。例如：

mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.bool)
mask[100:200, 100:500] = True

image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image[mask, 0] = 80
image[mask, 1] = 255
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)

Answer 2

一种方法使用 np.clip & np.einsum -

import numpy as np

# Get clipped values after broadcasted summing of image and color_delta
clipvals = np.clip(image + color_delta,0,255)

# Mask of image elements to be changed
mask1 = mask[:,:,0]>0

# Extract clipped values for TRUE values in mask1, otherwise keep image 
out = np.einsum('ijk,ij->ijk',clipvals,mask1) + np.einsum('ijk,ij->ijk',image,~mask1)

运行时测试

In [282]: # Setup inputs
     ...: M = 1000; N = 1000
     ...: image = np.random.randint(-255,255,(M,N,3))
     ...: imagecp = image.copy()
     ...: mask = np.random.randint(0,10,(M,N,3))
     ...: color_delta = np.random.randint(-255,255,(3))
     ...: 

In [283]: def clip_einsum(image,color_delta,mask):
     ...:     clipvals = np.clip(imagecp + color_delta,0,255)
     ...:     mask1 = mask[:,:,0]>0
     ...:     return np.einsum('ijk,ij->ijk',clipvals,mask1) +
                           np.einsum('ijk,ij->ijk',image,~mask1)
     ...: 

In [284]: def org_approach(image,color_delta,mask):
     ...:     rows, cols = image.shape[:2]
     ...:     #out = image.copy()
     ...:     for row in range(rows):
     ...:         for col in range(cols):
     ...:             if mask[row, col, 0] > 0:
     ...:                 image[row, col, 0] = min(255, max(0, 
                                 image[row, col, 0] + color_delta[0]))
     ...:                 image[row, col, 1] = min(255, max(0,
                                 image[row, col, 1] + color_delta[1]))
     ...:                 image[row, col, 2] = min(255, max(0,
                                 image[row, col, 2] + color_delta[2]))
     ...:                 

In [285]: %timeit clip_einsum(image,color_delta,mask)
10 loops, best of 3: 147 ms per loop

In [286]: %timeit org_approach(image,color_delta,mask)
1 loops, best of 3: 5.95 s per loop

OpenCV & Python：快速将遮罩叠加在图像上而不会溢出

OpenCV & Python: quickly superimpose mask over image without overflow

python

opencv

numpy

mask