Adjustment to depth_map.py file.

[Jiahao-Ma]

The essence of creating dense depth map is to calculate weighted depth information according to the distance of neighborhood. The original repo is based on inverse distance weighted. When it comes to the distance calculation, I have mads some adjustments, replacing the np.round() with np.int32(). Also, in the double loop statements, I have deleted - grid - 1.
`

  def dense_map(Pts, n, m, grid):
      ng = 2 * grid + 1
      mX = np.zeros((m,n)) + np.float("inf")
      mY = np.zeros((m,n)) + np.float("inf")
      mD = np.zeros((m,n))
      mX[np.int32(Pts[1]),np.int32(Pts[0])] = Pts[0] - np.int32(Pts[0])#np.round(Pts[0])  # NOTICE
      mY[np.int32(Pts[1]),np.int32(Pts[0])] = Pts[1] - np.int32(Pts[1])#np.round(Pts[1])  # NOTICE
      mD[np.int32(Pts[1]),np.int32(Pts[0])] = Pts[2]
      
      KmX = np.zeros((ng, ng, m - ng, n - ng))
      KmY = np.zeros((ng, ng, m - ng, n - ng))
      KmD = np.zeros((ng, ng, m - ng, n - ng))
      
      for i in range(ng):
          for j in range(ng):
              KmX[i,j] = mX[i : (m - ng + i), j : (n - ng + j)] + i #- grid - 1  # NOTICE
              KmY[i,j] = mY[i : (m - ng + i), j : (n - ng + j)] + j #- grid - 1  # NOTICE
              KmD[i,j] = mD[i : (m - ng + i), j : (n - ng + j)]
      S = np.zeros_like(KmD[0,0])
      Y = np.zeros_like(KmD[0,0])

      # Inverse distance weighted
      for i in range(ng):
          for j in range(ng):
              s = 1/np.sqrt(KmX[i,j] * KmX[i,j] + KmY[i,j] * KmY[i,j])
              Y = Y + s * KmD[i,j]
              S = S + s
      
      S[S == 0] = 1
      out = np.zeros((m,n))
      out[grid + 1 : -grid, grid + 1 : -grid] = Y/S
      return out

`

[charlielito]

The essence of creating dense depth map is to calculate weighted depth information according to the distance of neighborhood. The original repo is based on inverse distance weighted. When it comes to the distance calculation, I have mads some adjustments, replacing the np.round() with np.int32(). Also, in the double loop statements, I have deleted - grid - 1. `

  def dense_map(Pts, n, m, grid):
      ng = 2 * grid + 1
      mX = np.zeros((m,n)) + np.float("inf")
      mY = np.zeros((m,n)) + np.float("inf")
      mD = np.zeros((m,n))
      mX[np.int32(Pts[1]),np.int32(Pts[0])] = Pts[0] - np.int32(Pts[0])#np.round(Pts[0])  # NOTICE
      mY[np.int32(Pts[1]),np.int32(Pts[0])] = Pts[1] - np.int32(Pts[1])#np.round(Pts[1])  # NOTICE
      mD[np.int32(Pts[1]),np.int32(Pts[0])] = Pts[2]
      
      KmX = np.zeros((ng, ng, m - ng, n - ng))
      KmY = np.zeros((ng, ng, m - ng, n - ng))
      KmD = np.zeros((ng, ng, m - ng, n - ng))
      
      for i in range(ng):
          for j in range(ng):
              KmX[i,j] = mX[i : (m - ng + i), j : (n - ng + j)] + i #- grid - 1  # NOTICE
              KmY[i,j] = mY[i : (m - ng + i), j : (n - ng + j)] + j #- grid - 1  # NOTICE
              KmD[i,j] = mD[i : (m - ng + i), j : (n - ng + j)]
      S = np.zeros_like(KmD[0,0])
      Y = np.zeros_like(KmD[0,0])

      # Inverse distance weighted
      for i in range(ng):
          for j in range(ng):
              s = 1/np.sqrt(KmX[i,j] * KmX[i,j] + KmY[i,j] * KmY[i,j])
              Y = Y + s * KmD[i,j]
              S = S + s
      
      S[S == 0] = 1
      out = np.zeros((m,n))
      out[grid + 1 : -grid, grid + 1 : -grid] = Y/S
      return out

`

did you see any big difference using this?