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2017 Week 15 Computer Vision 2
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2017 Week 15 Computer Vision 2
1.
電腦視覺(二) 日期:2017/06/01
2.
移動偵測
3.
4.
圖片灰階化處理 高斯平滑函式,功能是將圖片平滑化,來减少圖 片上的雜訊。參數為來源圖片、模板尺寸大小, 越大模糊效果越明顯、標準差。 將前一張圖與目前攝影機照到的畫面相減並取絕對值 如果相差值小於125就將該值設為0(黑色),大於125就設為255(白色) 膨脹運算的功用主要是將外形均勻的加粗。 dilate(圖片、使用圓形還是矩形進行膨脹處裡、執行次數,執行越多次膨脹效果越明顯)
5.
rectangle(圖片、矩形左上角的位置、矩形右下角的位置、顏色、粗細) 可用boundingRect()得到包覆此輪廓的最小矩形 putText(圖片、顯示的文字、文字的位置、字體樣式、字體大小、文字顏色、文字粗細)
6.
人臉辨識
7.
8.
已經訓練好的人臉辨識樣本 將人臉辨識樣本載入Cascade分類器 偵測到目標後會以矩形形式放在串列裡回傳 detectMultiScale(灰階圖片, 每次調整圖片時要縮小的比例, 至少有5次檢測到目標,才認為是人臉, 被辨識的目標範圍大小) 在frame裡面將臉部的位置以矩形畫出來 rectangle(圖片、矩形左上角的位置、矩形右下角的位置、顏色、粗細)
9.
手部偵測
10.
11.
建立一個矩陣全部為0 轉為凸多邊形。 moments算質心位置 contourArea()算面積 找出面積最大的多邊形
12.
畫圓來顯示質心的位置 approxPolyDP(圖片、門檻值,值越小折線越接近曲線、是否為封閉形狀) 凸多邊形的缺陷分析 質心的X座標 質心的Y座標
13.
作業十一:基本題 題目:利用手部辨識技術,作為開關GPIO的觸發事件。 範例:當webcam偵測到手勢比二(手勢自己定義)的時候,就開 啟綠色LED燈,並系統以語音回覆”綠色LED燈已開啟”,或 當webcam偵測到手指在畫面中的某個區塊時,就開啟綠色 LED燈,並系統以語音回覆”綠色LED燈已開啟”。
14.
作業十一:進階題 題目:訓練Cascade分類器,來辨識自己想辨識的物品。 範例:利用自己訓練的Cascade分類器,來辨識想辨識的物品,當 辨識成功時,讓系統說出該辨識的物品是什麼。
15.
附註
16.
下載訓練好的人臉辨識分類器到樹莓派
17.
Dilation (膨脹) 原圖 膨脹處理
18.
convexHull approxPolyDP(門檻值,值越小折線越接近曲線) Threadhold=15 Threadhold=5 原圖
19.
convexityDefects 黑色的輪廓線為convexity hull, 而convexity
hull與手掌之間的部分為convexity defects. 每個convexity defect區 域有四個特徵點:起始點(startPoint),結束點(endPoint),距離convexity hull最遠點(farPoint),最遠點到 convexity hull的距距離(depth)。
20.
convexity defects圖,藍色點是convexity defects的起始點和結束點,紅色點是 最遠點。
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