參考文獻：Deep_Head_Pose_Estimation_from_Depth_Data for In-car Automotive Applications

演算法步驟

Algorithm 1 Linear Interpolation Algorithm

procedure

• w : image width
• for row in image rows do
  • x_min = first foreground pixel in row
  • x_max = last foreground pixel in row
    • for x=0 to w-1 do
      • x_src = x/(w-1)*(x_max-x_min)
      • x_1 = floor(x_src)
      • x_2 = x_1+1
      • if x_2<=w then
        • lambda = x_2 - x_src
        • row_out[x]=row[x_1]lambda+row[x_2]*(1-lambda)
      • else
        • row_out[x]=row[x_1]

程式碼實現如下：

def scale_interpolation(filename):
	image = cv2.imread(filename,-1)
	height,width = image.shape[0],image.shape[1]
	#水平分割影象
	rows = np.vsplit(image,height)
	img_res = []
	for row in rows:
    	row = np.squeeze(row)
   		# get the foreground piexls index(nonzeros index)
    	index = np.argwhere(row)
    	#如果該行深度值全為零，則該行不處理
    	if index.size ==0:
    		row_result=np.zeros_like(row)
    	else:
    	    x_min = np.min(index)
    		x_max = np.max(index)
   			row_result=np.zeros_like(row)
	    	for col in range(width):
	            x_src = col/(width-1)*(x_max-x_min)
	         	x_1 = int(np.floor(x_src))
	         	x_2 = x_1 + 1
         
        	    if x_2 < width:
                	alpha = x_2 - x_src
                	row_result[col] = row[x_1]*alpha+row[x_2]*(1-alpha)
                else:
                	row_result[col] = row[x_1]
          img_res.append(row_result)
      
      img_res= np.vstack(img_res)
      return img_res
 

視覺化效果