積分圖？

即是當前位置的畫素值是為左上角到當前位置的畫素總和，用途有計算haar特徵、濾波等

程式程式碼：

def integral(img):
    rows,cols=img.shape[0],img.shape[1]
    integral_img=np.zeros((rows,cols)).astype("uint32")
    for i in range(rows):
        for j in range(cols):
            sum1=0
            for k in range(i+1):
                for p in range(j+1):
                    sum1+=img[k,p]
            integral_img[i,j]=sum1
 

這樣的寫法很不符合python，for迴圈能避免儘量避免

numpy 用一句可以 實現：

intergal_image=np.cumsum(np.cumsum(img,0),1) 

卷積？

影象卷積即是濾波，有三種方式：valid、same 、full

卷積後的圖大小=1+（影象大小-卷積核大小+2*p）/s 

程式碼如下：

def conv(image,filter_mask,padding):
    h,w=image.shape[:]
    if padding==False:
        result=[]
        for i in range(0,h-len(filter_mask)+1):
            r=[]
            for j in range(0,w-len(filter_mask)+1):
                r.append(image[i,j]*filter_mask[0,0]+image[i,j+1]*filter_mask[0,1]
                +image[i,j+2]*filter_mask[0,2]+image[i+1,j]*filter_mask[1,0]+ 
                image[i+1,j+1]*filter_mask[1,1]+image[i+1,j+2]*filter_mask[1,2]
                +image[i+2,j]*filter_mask[2,0]+image[i+2,j+1]*filter_mask[2,1]
                +image[i+2,j+2]*filter_mask[2,2])
            result.append(r)
    if padding==True:
        image_new=np.zeros((h+2,w+2))
        image_new[0:h,0:w]=image
        result=[]
        for i in range(0,h+2-len(filter_mask)+1):
            r=[]
            for j in range(0,w+2-len(filter_mask)+1):
                r.append(image_new[i,j]*filter_mask[0,0]+image_new[i,j+1]*filter_mask[0,1]
                    +image_new[i,j+2]*filter_mask[0,2]+image_new[i+1,j]*filter_mask[1,0]+ 
                    image_new[i+1,j+1]*filter_mask[1,1]+image_new[i+1,j+2]*filter_mask[1,2]
                +image_new[i+2,j]*filter_mask[2,0]+image_new[i+2,j+1]*filter_mask[2,1]
                +image_new[i+2,j+2]*filter_mask[2,2])
            result.append(r)   
    return np.array(result)