利用Mask_Rcnn與HSV 做簡易pixel-wise 的雲層檢測，並簡易推斷厚雲與薄雲的面積。
##過程與結果
先利用現有的資料(約略200張)做labelme的Mask標定，再轉至Mask_rcnn可訓練的資料做訓練，原始資料如下：
獲得Mask Rcnn初步雲朵位置，因這次的訓練資料較少，所以訓練10次，每次100筆資料訓練，loss如下圖：
這裡的結果沒有很精準是沒問題的，目的只是框選出大部分雲朵的pixel
統計Mask所選取的Pixel HSV中的V值，透過這統計來分析該張相片的厚、薄雲的V閥值
最後統計厚雲(紅色)與薄雲(綠色)之pixel統計值，並計算其在照片中所占面積比例。
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