[Swift]LeetCode661. 图片平滑器 | Image Smoother
★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★
➤微信公众号：山青咏芝（shanqingyongzhi）
➤博客园地址：山青咏芝（https://www.cnblogs.com/strengthen/）
➤GitHub地址：https://github.com/strengthen/LeetCode
➤原文地址：https://www.cnblogs.com/strengthen/p/10488878.html
➤如果链接不是山青咏芝的博客园地址，则可能是爬取作者的文章。
➤原文已修改更新！强烈建议点击原文地址阅读！支持作者！支持原创！
★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★
Given a 2D integer matrix M representing the gray scale of an image, you need to design a smoother to make the gray scale of each cell becomes the average gray scale (rounding down) of all the 8 surrounding cells and itself. If a cell has less than 8 surrounding cells, then use as many as you can.
Example 1:
Input: [[1,1,1], [1,0,1], [1,1,1]] Output: [[0, 0, 0], [0, 0, 0], [0, 0, 0]] Explanation: For the point (0,0), (0,2), (2,0), (2,2): floor(3/4) = floor(0.75) = 0 For the point (0,1), (1,0), (1,2), (2,1): floor(5/6) = floor(0.83333333) = 0 For the point (1,1): floor(8/9) = floor(0.88888889) = 0
Note:
- The value in the given matrix is in the range of [0, 255].
- The length and width of the given matrix are in the range of [1, 150].
包含整数的二维矩阵 M 表示一个图片的灰度。你需要设计一个平滑器来让每一个单元的灰度成为平均灰度 (向下舍入) ，平均灰度的计算是周围的8个单元和它本身的值求平均，如果周围的单元格不足八个，则尽可能多的利用它们。
示例 1:
输入: [[1,1,1], [1,0,1], [1,1,1]] 输出: [[0, 0, 0], [0, 0, 0], [0, 0, 0]] 解释: 对于点 (0,0), (0,2), (2,0), (2,2): 平均(3/4) = 平均(0.75) = 0 对于点 (0,1), (1,0), (1,2), (2,1): 平均(5/6) = 平均(0.83333333) = 0 对于点 (1,1): 平均(8/9) = 平均(0.88888889) = 0
注意:
- 给定矩阵中的整数范围为 [0, 255]。
- 矩阵的长和宽的范围均为 [1, 150]。
456ms
1 class Solution { 2 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 3 if M.count == 0 || M[0].count == 0 { 4 return M 5 } 6 7 var result = M 8 let rowCount = M.count 9 let columnCount = M[0].count 10 11 for row in 0..<rowCount { 12 for column in 0..<columnCount { 13 let edges = getAllEdges(row: row, column: column, m: M) 14 let currVal = M[row][column] 15 let sum = currVal + edges.sumOfEdges 16 let totalNodes = edges.totalEdges + 1 17 let avg: Int = Int(sum/totalNodes) 18 result[row][column] = avg 19 } 20 } 21 22 return result 23 } 24 25 func getAllEdges(row: Int, column: Int, m:[[Int]]) -> (sumOfEdges: Int, totalEdges: Int) { 26 27 if m.count == 0 || m[0].count == 0 { 28 return (sumOfEdges: 0, totalEdges: 0) 29 } 30 31 var rowCount = m.count 32 var columnCount = m[0].count 33 34 var result = (sumOfEdges: 0, totalEdges: 0) 35 36 guard row >= 0 && 37 row < rowCount && 38 column >= 0 && 39 column < columnCount else { 40 return result 41 } 42 var edges = 0 43 var sum = 0 44 // top Edge 45 if row != 0 { 46 let top = m[row - 1][column] 47 sum = sum + top 48 edges = edges + 1 49 } 50 51 // left 52 if column != 0 { 53 let left = m[row][column - 1] 54 sum = sum + left 55 edges = edges + 1 56 } 57 58 // right 59 if column != columnCount-1 { 60 let right = m[row][column + 1] 61 sum = sum + right 62 edges = edges + 1 63 } 64 65 //bottom 66 if row != rowCount-1 { 67 let bottom = m[row + 1][column] 68 sum = sum + bottom 69 edges = edges + 1 70 } 71 72 //top left 73 if row != 0 && column != 0 { 74 let topLeft = m[row-1][column-1] 75 sum = sum + topLeft 76 edges = edges + 1 77 } 78 79 //top right 80 if row != 0 && column != columnCount-1 { 81 let topRight = m[row-1][column+1] 82 sum = sum + topRight 83 edges = edges + 1 84 } 85 86 //bottom Left 87 if row != rowCount-1 && column != 0 { 88 let bottomLeft = m[row+1][column-1] 89 sum = sum + bottomLeft 90 edges = edges + 1 91 } 92 93 //bottom right 94 if row != rowCount-1 && column != columnCount-1 { 95 let bottomRight = m[row+1][column+1] 96 sum = sum + bottomRight 97 edges = edges + 1 98 } 99 100 result.sumOfEdges = sum 101 result.totalEdges = edges 102 103 return result 104 } 105 }
Runtime: 532 ms
Memory Usage: 19.6 MB
1 class Solution { 2 3 let transform = [[-1, -1], [0, -1], [1, -1], 4 [-1, 0], [0, 0], [1, 0], 5 [-1, 1], [0, 1], [1, 1]] 6 7 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 8 var N: [[Int]] = [] 9 let row = M.count 10 let colum = M[0].count 11 for i in 0..<row { 12 var array: [Int] = [] 13 for j in 0..<colum { 14 var value = 0 15 var count = 0 16 for k in 0..<9 { 17 let xx = i + transform[k][0] 18 let yy = j + transform[k][1] 19 if xx >= 0 && xx < row && yy >= 0 && yy < colum { 20 value = value + M[xx][yy] 21 count = count + 1 22 } 23 } 24 array.append(Int(value / count)) 25 } 26 N.append(array) 27 } 28 return N 29 } 30 }
Runtime: 544 ms
Memory Usage: 19.6 MB
1 class Solution { 2 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 3 if M.isEmpty || M[0].isEmpty {return [[]]} 4 var m:Int = M.count 5 var n:Int = M[0].count 6 var res:[[Int]] = M 7 var dirs:[[Int]] = [[0,-1],[-1,-1],[-1,0],[-1,1],[0,1],[1,1],[1,0],[1,-1]] 8 9 for i in 0..<m 10 { 11 for j in 0..<n 12 { 13 var cnt:Int = M[i][j] 14 var all:Int = 1 15 for dir in dirs 16 { 17 var x:Int = i + dir[0] 18 var y:Int = j + dir[1] 19 if x < 0 || x >= m || y < 0 || y >= n 20 { 21 continue 22 } 23 all += 1 24 cnt += M[x][y] 25 } 26 res[i][j] = cnt / all 27 } 28 } 29 return res 30 } 31 }
564ms
1 class Solution { 2 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 3 var M = M 4 let rows = M.count 5 let cols = M.first!.count 6 var retval: [[Int]] = Array(repeating: Array(repeating: 0, count: cols), count: rows) 7 8 for r in 0..<rows { 9 for c in 0..<cols { 10 retval[r][c] = ns(&M, r, c) 11 } 12 } 13 14 return retval 15 } 16 17 func ns(_ M: inout [[Int]], _ row: Int, _ col: Int) -> Int { 18 let rows = M.count 19 let cols = M.first!.count 20 21 var count = 0 22 var n = 0 23 if row > 0 { 24 n += (M[row - 1][col]) 25 count += 1 26 } 27 if row > 0 && col > 0 { 28 n += (M[row - 1][col - 1]) 29 count += 1 30 } 31 if row > 0 && col + 1 < cols { 32 n += (M[row - 1][col + 1]) 33 count += 1 34 } 35 if col > 0 { 36 n += (M[row][col - 1]) 37 count += 1 38 } 39 if col + 1 < cols { 40 n += (M[row][col + 1]) 41 count += 1 42 } 43 if row + 1 < rows { 44 n += (M[row + 1][col]) 45 count += 1 46 } 47 if row + 1 < rows && col > 0 { 48 n += (M[row + 1][col - 1]) 49 count += 1 50 } 51 if row + 1 < rows && col + 1 < cols { 52 n += (M[row + 1][col + 1]) 53 count += 1 54 } 55 n += (M[row][col]) 56 count += 1 57 58 return Int(floor(Double(n)/Double(count))) 59 } 60 }
612ms
1 class Solution { 2 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 3 var result = [[Int]]() 4 for i in 0..<M.count { 5 var row = [Int]() 6 for j in 0..<M[i].count { 7 var sum = 0 8 var count = 0 9 for r in -1...1 { 10 for c in -1...1 { 11 if i+r >= 0 && i+r < M.count && j+c >= 0 && j+c < M[0].count { 12 sum += M[i+r][j+c] 13 count += 1 14 } 15 } 16 } 17 row.append(sum/count) 18 } 19 result.append(row) 20 } 21 return result 22 } 23 }
752ms
1 class Solution { 2 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 3 4 var ans:[[Int]] = [[Int]](repeating: [Int](repeating: 0, count: M[0].count), count: M.count) 5 6 7 for indexX in 0..<M.count { 8 for indexY in 0..<M[0].count { 9 ans[indexX][indexY] = helper(M, indexX, indexY) 10 } 11 } 12 return ans 13 } 14 15 func helper(_ M: [[Int]],_ indexX:Int, _ indexY:Int) -> Int { 16 17 let indexes = [(-1,0), (1,0), 18 (0,-1),(0,1), 19 (1,-1),(-1,1), 20 (1, 1),(-1,-1), (0,0)] 21 22 var count = 0 23 var total = 0 24 total = 0 25 for index in indexes { 26 let x = indexX + index.0 27 let y = indexY + index.1 28 if x >= 0 && x < M.count && y >= 0 && y < M[0].count { 29 total += M[x][y] 30 count += 1 31 } 32 } 33 return total/count 34 } 35 }
996ms
1 class Solution { 2 func imageSmoother(_ M: [[Int]]) -> [[Int]] { 3 var smoothedM = M 4 5 for i in 0..<M.count { 6 for j in 0..<M[0].count { 7 let cells = [[i-1, j-1], [i-1, j], [i-1, j+1], [i, j-1], [i, j], [i, j+1], [i+1, j-1], [i+1, j], [i+1, j+1]] 8 var sum = 0 9 var count = 0 10 11 for cell in cells { 12 sum += getSum(M, cell[0], cell[1]) 13 count += getCount(M, cell[0], cell[1]) 14 } 15 16 smoothedM[i][j] = sum / count 17 } 18 } 19 20 return smoothedM 21 } 22 23 func getSum(_ M: [[Int]], _ i: Int, _ j: Int) -> Int { 24 if i < 0 || j < 0 || i >= M.count || j >= M[0].count { 25 return 0 26 } 27 28 return M[i][j] 29 } 30 31 func getCount(_ M: [[Int]], _ i: Int, _ j: Int) -> Int { 32 if i < 0 || j < 0 || i >= M.count || j >= M[0].count { 33 return 0 34 } 35 36 return 1 37 } 38 }
