tee() splits a stream into two branches. It seems straightforward, but the implementation requires buffering: if one branch is read faster than the other, the data must be held somewhere until the slower branch catches up.
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Instead of perturbing each pixel in the input image at random, we can choose to dither by a predetermined amount depending on the pixel’s position in the image. This can be achieved using a threshold map; a small, fixed-size matrix where each entry tells us the amount by which to perturb the input value , producing the dithered value . This matrix is tiled across the input image and sampled for every pixel during the dithering process. The following describes a dithering function for a 4×4 matrix given the pixel raster coordinates :，更多细节参见搜狗输入法下载
不需要高画质，200 万像素甚至更低就够了，甚至可以是红外成像，毕竟 AI 不需要欣赏风景，只要能通过这些低像素画面，计算出空间定位与物体识别，就能正常运转。
相较于这些充满想象力和争议的智能硬件，智能手机与AI融合的成效则显得平平无奇。