android – 如何实时地在帧中加入png与alpha /透明度

我在OpenCV android 2.4.11的例子下工作,它使用相机检测面部.
我没有在找到的脸上画一个矩形,而是试图在脸上放一个面具(png图像).
但是为了在脸上显示图像,png图像带有透明度的黑色背景.

FdActivity.java

public void onCameraViewStarted(int width, int height) {
        mGray = new Mat();
        mRgba = new Mat();

        //Load my mask png
        Bitmap image = BitmapFactory.decodeResource(getResources(), R.drawable.mask_1);

        mask = new Mat();

        Utils.bitmapToMat(image, mask);

}

public Mat onCameraFrame(CvCameraViewFrame inputFrame) {

        mRgba = inputFrame.rgba();
        mGray = inputFrame.gray();

        if (mAbsoluteFaceSize == 0) {
            int height = mGray.rows();
            if (Math.round(height * mRelativeFaceSize) > 0) {
                mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
            }
            mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
        }

        MatOfRect faces = new MatOfRect();

        if (mDetectorType == JAVA_DETECTOR) {
            if (mJavaDetector != null)
                mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2,
                        new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
        }
        else if (mDetectorType == NATIVE_DETECTOR) {
            if (mNativeDetector != null)
                mNativeDetector.detect(mGray, faces);
        }
        else {
            Log.e(TAG, "Detection method is not selected!");
        }

        Rect[] facesArray = faces.toArray();


        for (int i = 0; i < facesArray.length; i++) {

              overlayImage(mRgba, mask, facesArray[i]);

        }

        return mRgba;
    }

    public Mat overlayImage(Mat background, Mat foregroundMask, Rect faceRect)
    {
        Mat mask = new Mat();

        Imgproc.resize(this.mask, mask, faceRect.size());

        Mat source = new Mat();
        Imgproc.resize(foregroundMask, source, background.size());

        mask.copyTo( background.submat( new Rect((int) faceRect.tl().x, (int) faceRect.tl().y, mask.cols(), mask.rows())) );

        source.release();
        mask.release();
        return background;
    }

注意：我将解释一般原理并在Python中给出一个示例实现,因为我没有设置Android开发环境.将它移植到Java应该相当简单.您可以将代码作为单独的答案发布.

您需要执行与addWeighted操作类似的操作,即操作

但是,在您的情况下,α需要是一个矩阵(即我们需要每个像素不同的混合系数).

样本图像

让我们使用一些示例图像来说明这一点.我们可以使用Lena图像作为样本面：

此图像作为透明覆盖：

这个图像作为没有透明度的叠加层：

混合矩阵

要获得alpha矩阵,我们可以使用阈值处理确定前景(叠加)和背景(面部)遮罩,或者如果可用,则使用输入图像中的alpha通道.

在值为0.0 .. 1.0的浮点图像上执行此操作非常有用.然后我们可以将两个面具之间的关系表达为

foreground_mask = 1.0 - background_mask

即加在一起的两个掩模导致所有掩模.

对于RGBA格式的叠加图像,我们得到以下前景和背景蒙版：

当我们在RGB格式的情况下使用阈值,侵蚀和模糊时,我们得到以下前景和背景蒙版：

加权和

现在我们可以计算两个加权部分：

foreground_part = overlay_image * foreground_mask
background_part = face_image * background_mask

对于RGBA覆盖,前景和背景部分如下所示：

对于RGB叠加,前景和背景部分看起来如下：

最后将它们组合在一起,并将图像转换回0-255范围内的8位整数.

操作结果如下(分别为RGBA和RGB叠加)：

代码示例 – RGB叠加

import numpy as np
import cv2

# ==============================================================================

def blend_non_transparent(face_img, overlay_img):
    # Let's find a mask covering all the non-black (foreground) pixels
    # NB: We need to do this on grayscale version of the image
    gray_overlay = cv2.cvtColor(overlay_img, cv2.COLOR_BGR2GRAY)
    overlay_mask = cv2.threshold(gray_overlay, 1, 255, cv2.THRESH_BINARY)[1]

    # Let's shrink and blur it a little to make the transitions smoother...
    overlay_mask = cv2.erode(overlay_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    overlay_mask = cv2.blur(overlay_mask, (3, 3))

    # And the inverse mask, that covers all the black (background) pixels
    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    # Create a masked out face image, and masked out overlay
    # We convert the images to floating point in range 0.0 - 1.0
    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    # And finally just add them together, and rescale it back to an 8bit integer image
    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0))

# ==============================================================================

# We load the images
face_img = cv2.imread("lena.png", -1)
overlay_img = cv2.imread("overlay.png", -1)

result_1 = blend_non_transparent(face_img, overlay_img)
cv2.imwrite("merged.png", result_1)

代码示例 – RGBA叠加

import numpy as np
import cv2

# ==============================================================================

def blend_transparent(face_img, overlay_t_img):
    # Split out the transparency mask from the colour info
    overlay_img = overlay_t_img[:,:,:3] # Grab the BRG planes
    overlay_mask = overlay_t_img[:,:,3:]  # And the alpha plane

    # Again calculate the inverse mask
    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    # Create a masked out face image, and masked out overlay
    # We convert the images to floating point in range 0.0 - 1.0
    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    # And finally just add them together, and rescale it back to an 8bit integer image    
    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0))

# ==============================================================================

# We load the images
face_img = cv2.imread("lena.png", -1)
overlay_t_img = cv2.imread("overlay_transparent.png", -1) # Load with transparency

result_2 = blend_transparent(face_img, overlay_t_img)
cv2.imwrite("merged_transparent.png", result_2)