You are given a 0-indexed m x n grid image which represents a grayscale image, where image[i][j] represents a pixel with intensity in the range[0..255]. You are also given a non-negative integer threshold.
Two pixels image[a][b] and image[c][d] are said to be adjacent if |a - c| + |b - d| == 1.
A region is a 3 x 3 subgrid where the absolute difference in intensity between any two adjacent pixels is less than or equal to threshold.
All pixels in a region belong to that region, note that a pixel can belong to multiple regions.
\n\nYou need to calculate a 0-indexed m x n grid result, where result[i][j] is the average intensity of the region to which image[i][j] belongs, rounded down to the nearest integer. If image[i][j] belongs to multiple regions, result[i][j] is the average of the rounded down average intensities of these regions, rounded down to the nearest integer. If image[i][j] does not belong to any region, result[i][j] is equal to image[i][j].
Return the grid result.
\n
Example 1:
\n![\"\"](\"https://assets.leetcode.com/uploads/2023/12/21/example0corrected.png\")
\n\nInput: image = [[5,6,7,10],[8,9,10,10],[11,12,13,10]], threshold = 3\nOutput: [[9,9,9,9],[9,9,9,9],[9,9,9,9]]\nExplanation: There exist two regions in the image, which are shown as the shaded areas in the picture. The average intensity of the first region is 9, while the average intensity of the second region is 9.67 which is rounded down to 9. The average intensity of both of the regions is (9 + 9) / 2 = 9. As all the pixels belong to either region 1, region 2, or both of them, the intensity of every pixel in the result is 9. \nPlease note that the rounded-down values are used when calculating the average of multiple regions, hence the calculation is done using 9 as the average intensity of region 2, not 9.67.\n\n\n
Example 2:
\n![\"\"](\"https://assets.leetcode.com/uploads/2023/12/21/example1corrected.png\")
\n\nInput: image = [[10,20,30],[15,25,35],[20,30,40],[25,35,45]], threshold = 12\nOutput: [[25,25,25],[27,27,27],[27,27,27],[30,30,30]]\nExplanation: There exist two regions in the image, which are shown as the shaded areas in the picture. The average intensity of the first region is 25, while the average intensity of the second region is 30. The average intensity of both of the regions is (25 + 30) / 2 = 27.5 which is rounded down to 27. All the pixels in row 0 of the image belong to region 1, hence all the pixels in row 0 in the result are 25. Similarly, all the pixels in row 3 in the result are 30. The pixels in rows 1 and 2 of the image belong to region 1 and region 2, hence their assigned value is 27 in the result.\n\n\n
Example 3:
\n\n\nInput: image = [[5,6,7],[8,9,10],[11,12,13]], threshold = 1\nOutput: [[5,6,7],[8,9,10],[11,12,13]]\nExplanation: There does not exist any region in image, hence result[i][j] == image[i][j] for all the pixels.\n\n\n
\n
Constraints:
\n\n3 <= n, m <= 5000 <= image[i][j] <= 2550 <= threshold <= 2553 * 3 sub-grids to find all the regions.",
"Keep two 2-D arrays sum and num, for each position (x, y) in a region, increase sum[x][y] by the average sum of the region and increase num[x][y] by 1.",
"For each position (x, y), sum[x][y] / num[x][y] is the answer. Note when num[x][y] == 0, we use the original value in image instead."
],
"solution": null,
"status": null,
"sampleTestCase": "[[5,6,7,10],[8,9,10,10],[11,12,13,10]]\n3",
"metaData": "{\n \"name\": \"resultGrid\",\n \"params\": [\n {\n \"name\": \"image\",\n \"type\": \"integer[][]\"\n },\n {\n \"type\": \"integer\",\n \"name\": \"threshold\"\n }\n ],\n \"return\": {\n \"type\": \"integer[][]\"\n }\n}",
"judgerAvailable": true,
"judgeType": "large",
"mysqlSchemas": [],
"enableRunCode": true,
"enableTestMode": false,
"enableDebugger": true,
"envInfo": "{\"cpp\": [\"C++\", \"Compiled with clang 17 using the latest C++ 20 standard.
Your code is compiled with level two optimization (-O2). AddressSanitizer is also enabled to help detect out-of-bounds and use-after-free bugs.
Most standard library headers are already included automatically for your convenience.
\"], \"java\": [\"Java\", \"OpenJDK 21. Using compile arguments: --enable-preview --release 21
Most standard library headers are already included automatically for your convenience.
\\r\\nIncludes Pair class from https://docs.oracle.com/javase/8/javafx/api/javafx/util/Pair.html.
Python 2.7.12.
Most libraries are already imported automatically for your convenience, such as array, bisect, collections. If you need more libraries, you can import it yourself.
\\r\\n\\r\\nFor Map/TreeMap data structure, you may use sortedcontainers library.
\\r\\n\\r\\nNote that Python 2.7 will not be maintained past 2020. For the latest Python, please choose Python3 instead.
\"], \"c\": [\"C\", \"Compiled with gcc 11 using the gnu11 standard.
Your code is compiled with level one optimization (-O2). AddressSanitizer is also enabled to help detect out-of-bounds and use-after-free bugs.
Most standard library headers are already included automatically for your convenience.
\\r\\n\\r\\nFor hash table operations, you may use uthash. \\\"uthash.h\\\" is included by default. Below are some examples:
\\r\\n\\r\\n1. Adding an item to a hash.\\r\\n
\\r\\nstruct hash_entry {\\r\\n int id; /* we'll use this field as the key */\\r\\n char name[10];\\r\\n UT_hash_handle hh; /* makes this structure hashable */\\r\\n};\\r\\n\\r\\nstruct hash_entry *users = NULL;\\r\\n\\r\\nvoid add_user(struct hash_entry *s) {\\r\\n HASH_ADD_INT(users, id, s);\\r\\n}\\r\\n\\r\\n\\r\\n\\r\\n2. Looking up an item in a hash:\\r\\n
\\r\\nstruct hash_entry *find_user(int user_id) {\\r\\n struct hash_entry *s;\\r\\n HASH_FIND_INT(users, &user_id, s);\\r\\n return s;\\r\\n}\\r\\n\\r\\n\\r\\n\\r\\n3. Deleting an item in a hash:\\r\\n
\\r\\nvoid delete_user(struct hash_entry *user) {\\r\\n HASH_DEL(users, user); \\r\\n}\\r\\n\\r\\n\"], \"csharp\": [\"C#\", \"C# 12 with .NET 8 runtime
\"], \"javascript\": [\"JavaScript\", \"Node.js 20.10.0.
Your code is run with --harmony flag, enabling new ES6 features.
lodash.js library is included by default.
\\r\\n\\r\\nFor Priority Queue / Queue data structures, you may use 5.4.0 version of datastructures-js/priority-queue and 4.2.3 version of datastructures-js/queue.
\"], \"ruby\": [\"Ruby\", \"Ruby 3.2
Some common data structure implementations are provided in the Algorithms module: https://www.rubydoc.info/github/kanwei/algorithms/Algorithms
\"], \"swift\": [\"Swift\", \"Swift 5.9.
You may use swift-algorithms 1.2.0 and swift-collections 1.0.6.
\"], \"golang\": [\"Go\", \"Go 1.21
Support https://godoc.org/github.com/emirpasic/gods@v1.18.1 library.
\"], \"python3\": [\"Python3\", \"Python 3.11.
Most libraries are already imported automatically for your convenience, such as array, bisect, collections. If you need more libraries, you can import it yourself.
\\r\\n\\r\\nFor Map/TreeMap data structure, you may use sortedcontainers library.
\"], \"scala\": [\"Scala\", \"Scala 2.13.7.
Kotlin 1.9.0.
We are using an experimental compiler provided by JetBrains.
\"], \"rust\": [\"Rust\", \"Rust 1.74.1
Supports rand v0.6\\u00a0from crates.io
\"], \"php\": [\"PHP\", \"PHP 8.2.
With bcmath module
\"], \"typescript\": [\"Typescript\", \"TypeScript 5.1.6, Node.js 20.10.0.
Compile Options: --alwaysStrict --strictBindCallApply --strictFunctionTypes --target ES2022
Your code is run with --harmony flag, enabling new ES2022 features.
lodash.js library is included by default.
\"], \"racket\": [\"Racket\", \"Racket CS v8.11
\\r\\n\\r\\nUsing #lang racket
Required data/gvector data/queue data/order data/heap automatically for your convenience
Dart 3.2
\\r\\n\\r\\nYour code will be run directly without compiling
\"]}", "libraryUrl": null, "adminUrl": null, "challengeQuestion": null, "__typename": "QuestionNode" } } }