Tasks 1 and 2 (upd: and 3)

.github/workflows/ci.yml

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+name: Kotlin CI
+
+on: [push, pull_request]
+
+jobs:
+  build-test-lint:
+    runs-on: ubuntu-latest
+    env:
+      _JAVA_OPTIONS: "-Xmx6g"
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up JDK 20
+        uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          java-version: 20
+
+      - name: Cache Gradle packages
+        uses: actions/cache@v4
+        with:
+          path: |
+            ~/.gradle/caches
+            ~/.gradle/wrapper
+          key: ${{ runner.os }}-gradle-${{ hashFiles('**/*.gradle*', '**/gradle-wrapper.properties') }}
+          restore-keys: |
+            ${{ runner.os }}-gradle-
+
+      - name: Grant execute permission to Gradle wrapper
+        run: chmod +x ./gradlew
+
+      - name: Run tests
+        run: ./gradlew test
+
+      - name: Run ktlint
+        run: ./gradlew ktlintCheck

LICENSE

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+   Copyright 2025 Sonia Kozyreva
+
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README.md

@@ -0,0 +1,141 @@
+# Image Convolution in Kotlin
+
+This project implements and benchmarks sequential and several parallel approaches to applying 2D filters to grayscale images.
+
+## ✨ Features
+
+- Sequential and parallel convolution
+- Interactive CLI tool for applying filters to images
+- Performance benchmarking and analysis
+- Predefined filters
+- Support for user-supplied or built-in test images
+- Convolve several images at once using sequential or asynchronous pipeline
+
+## Built-in Filters
+
+All filters are defined as 2D matrices (kernels), with optional normalization (`factor`) and offset (`bias`).
+
+* `blur_3x3`, `blur_5x5`
+* `gaussian_blur_3x3`, `gaussian_blur_5x5`
+* `sharpen`
+* `edge_detect`
+* `motion_blur`
+* `identity`
+* `emboss`
+
+## Modes of Convolution
+
+| Mode     | Description                      |
+| -------- | -------------------------------- |
+| `seq`    | Standard single-threaded version |
+| `pixels` | Parallelized per-pixel           |
+| `rows`   | Rows processed in parallel   |
+| `cols`   | Columns processed in parallel          |
+| `tiles`  | Blocks (tiles) of the image      |
+
+## Types of Pipelines
+
+| Mode     | Description                      |
+| -------- | -------------------------------- |
+| `seq`    | Convolve one image at a time using any mode |
+| `async` | Convolve several images simultaneously         |
+
+
+## 🧪 Requirements
+
+- JDK 17+
+- Kotlin
+- Gradle
+- OpenCV via [JavaCPP](https://github.com/bytedeco/javacpp)
+
+## Getting started
+Clone the repo:
+```bash
+git clone git@github.com:sofyak0zyreva/convolution.git
+```
+Run the following command to install the dependencies:
+
+```bash
+./gradlew build
+```
+
+## ▶️ Usage
+
+To apply a filter to an image via CLI:
+
+```bash
+./gradlew run --quiet --console=plain
+```
+
+You'll be prompted to:
+
+1. Enter an image path (or use defaults from _resources/images/_). You can enter path from the repository root or absolute path
+2. Select a mode (with optional batch/tile sizes -- they are responsible for how many pixels will be allocated per coroutine)
+3. Choose a filter
+
+The result will be saved as a new `.bmp` file in the project directory's `output` folder.
+
+
+## 🧵 Performance Benchmarking
+You can simply run `main()` in the specified file (simplest for `BenchmarkPipelines.kt`).
+
+To measure the performance of a specific mode (`BenchmarkSizes.kt`):
+
+```kotlin
+val result = benchmarkSingleMode(inputImage, filter, ConvolutionMode.ParallelRows(8))
+println(result)
+```
+Here, you can also use randomly generated images of a chosen size.
+
+To compare all modes (`BenchmarkAllModes.kt`):
+
+```kotlin
+val results = benchmarkAllModes(image, filter)
+results.forEach(::println)
+```
+
+To benchmark scalability (e.g., for rows/cols/tiles) (`EfficiencyAnalysis.kt`):
+
+```kotlin
+val sizes = listOf(1, 4, 8, 16, 32, 64, 128)
+benchmarkSizes(image, filter, { ConvolutionMode.ParallelRows(8) }, sizes) 
+```
+
+
+
+## ✅ Testing & Correctness
+
+* Sequential implementation serves as the reference with key points preserved:
+    - Compositionality: applying filters sequentially should equal applying their composition  
+      (e.g., `apply(filter1, apply(filter2, img)) == apply(filter1 ⊕ filter2, img)`)
+    - Identity: some filters compose to identity (e.g., _shift-left _then_ shift-right_)
+    - Zero-padding: expanding filters with zeros shouldn't change results
+    - Known-output filters: test with trivial filters (_zero filter, identity filter_)
+* All modes are tested against the sequential implementation for numerical accuracy
+* Standard Error of the Mean (SEM) is reported in benchmarks. See [Performance Analysis](./src/test/kotlin/benchmarks/EfficiencyAnalysis.md),
+  [Plots](./src/test/kotlin/benchmarks/plots), and [Results](./src/test/kotlin/benchmarks/results) for more
+
+Run:
+
+```bash
+./gradlew test
+```
+
+## 📂 Directory Structure
+
+```
+src/
+├── main/
+│   ├── kotlin/           ← Core logic, filters, modes, pipelines and CLI
+│   └── resources/
+│       └── images/       ← Sample input images
+└── test/
+    └── kotlin/
+        ├── benchmarks/    ← Performance analysis, plots, results
+        └── ...           ← Tests
+
+```
+
+## License
+This project uses JavaCPP Presets for OpenCV and OpenCV, both licensed under Apache License 2.0.
+See [`LICENSE`](LICENSE) for details.