android– 如何实时地在帧中加入png与alpha 透明度

概述我在OpenCVandroid2.4.11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.javapublicvoidonCameraViewStarted(intwidth,intheight){mGray

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;    }

解决方法:

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 *** 作类似的 *** 作,即 *** 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 *** 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_maskbackground_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.

*** 作结果如下(分别为RGBA和RGB叠加)：

代码示例 – RGB叠加

import numpy as npimport 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 imagesface_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 npimport 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 imagesface_img = cv2.imread("lena.png", -1)overlay_t_img = cv2.imread("overlay_transparent.png", -1) # Load with transparencyresult_2 = blend_transparent(face_img, overlay_t_img)cv2.imwrite("merged_transparent.png", result_2)

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