需要形容
深度学习模型在Android挪动端部署的时候，对于采集到的的摄像头画面须要放弃宽高比的将Bitmap缩放到模型输出的大小，缩放后较指标尺寸像素缺失的局部采纳灰度填充形式, 以避免指标产生显著形变影响辨认成果。在本例中，深度模型是以MobileNetV2为Backbone网络的YOLOv3，并基于MNN挪动端推理框架部署。其中模型输出图像的尺寸大小为(320,320)。
实现
public static Bitmap scaleImage(Bitmap bm, int newWidth, int newHeight){ if (bm == null) { return null; } int width = bm.getWidth(); int height = bm.getHeight(); float scaleWidth = ((float) newWidth) / width; float scaleHeight = ((float) newHeight) / height; // 放弃宽高比缩放，以长边为主 float scaleRatio = Math.min(scaleHeight, scaleWidth); Matrix matrix = new Matrix(); matrix.postScale(scaleRatio, scaleRatio); Bitmap newBm = Bitmap.createBitmap(bm, 0, 0, width, height, matrix, true); // 创立指标大小Bitmap Bitmap scaledImage = Bitmap.createBitmap(newWidth, newHeight, Bitmap.Config.ARGB_8888); Canvas canvas = new Canvas(scaledImage); // 绘制背景色彩 Paint paint = new Paint(); paint.setColor(Color.GRAY); paint.setStyle(Paint.Style.FILL); canvas.drawRect(0, 0, canvas.getWidth(), canvas.getHeight(), paint); // 确定画面地位 float left = 0; float top = 0; if (width > height){ top = (float)((newBm.getWidth() - newBm.getHeight()) / 2.0); } else{ left = (float)((newBm.getHeight() - newBm.getWidth()) / 2.0); } canvas.drawBitmap( newBm, left , top, null ); if (!bm.isRecycled()){ bm.recycle(); } return scaledImage;}