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Image Pipeline Playground

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# 🎨 Image Pipes

### Stop scripting. Start seeing.

**Build computer vision pipelines visually — inspect every pixel transformation in real time, then export clean, production-ready Python the moment you're happy with the result.**

**⚡ Build • Preview • Experiment • Export**

[Download](https://github.com/mrajaeim/image-pipes/releases) •
[Demo](#-demo) •
[Features](#-features) •
[Architecture](#-architecture) •
[Quick Start](#-quick-start) •
[Roadmap](#-roadmap)

</div>

## 🚀 Experience the Workflow

</td>

### Visual Editor

- 🎯 Drag & Drop Nodes
- ⚡ Instant Execution
- 👀 Live Image Preview
- 🧠 OpenCV & Albumentations
- 📦 Export Python Code

</td>
</tr>
</table>

---

### Dataset Augmentation

Create hundreds of augmentations visually.

</td>

### Pipeline Templates

Build reusable computer vision pipelines.

</td>
</tr>
</table>

---

## 💭 The Problem

If you've built a computer vision pipeline before, this loop probably feels familiar:

```text
tweak a parameter → run the script → save an image → open it → squint → repeat
```

Each iteration costs you a context switch. Multiply that by every blur kernel, every threshold value, every augmentation you're tuning, and "quick experiment" turns into an afternoon lost to `cv2.imshow()` windows and throwaway debug scripts.

**Image Pipes breaks the loop.** Assemble your pipeline as a graph, watch every node's output update live, and walk away with production Python — not a black box you're locked into forever.

```text
┌──────────────┐
│ Load Image   │
└──────┬───────┘
       ▼
┌──────────────┐
│ Resize       │
└──────┬───────┘
       ▼
┌──────────────┐
│ Grayscale    │
└──────┬───────┘
       ▼
┌──────────────┐
│ GaussianBlur │
└──────┬───────┘
       ▼
┌──────────────┐
│ Threshold    │
└──────┬───────┘
       ▼
┌──────────────┐
│ FindContours │
└──────┬───────┘
       ▼
 Production Python 🐍
```

---

## ✨ Features

### 🎨 A Canvas, Not a Console

Assemble pipelines by dragging nodes onto an infinite canvas powered by React Flow. Connect them, configure them through rich auto-generated property panels, and let built-in validation catch mistakes before you ever hit "run."

- Drag & drop nodes with smart, type-aware connections
- Infinite canvas with smooth zoom & pan
- Dynamic property panels generated straight from node metadata
- Real-time validation as you build

### ⚡ Real-Time Preview, Every Step

Every node executes independently, so you can inspect a transformation the instant it happens — no more mentally simulating what five chained `cv2` calls did to your image.

See, live, at every node:

- Intermediate image outputs
- Bounding boxes, segmentation masks, and keypoints
- Execution time per node
- Runtime logs

### 🧠 A Smart Execution Engine Under the Hood

Pipelines aren't run top-to-bottom blindly. Image Pipes compiles your graph into a **Directed Acyclic Graph (DAG)**, topologically sorts it, and executes only what actually needs to run.

- Incremental execution & partial recomputation
- Run-to-selected-node for fast, targeted debugging
- In-memory caching so large pipelines stay snappy
- Full DAG validation before execution

The result: pipelines with dozens of nodes stay responsive, not sluggish.

### 📦 Metadata-Driven Architecture

Adding a new backend node automatically makes it available in the editor — forms, controls, categories, and validation are all generated for free. **Zero frontend work required.** Implement the node, and the UI builds itself around it.

### 🧪 A Serious Computer Vision Toolbox

**OpenCV** gives you the full classical CV toolkit:

- Color conversions, blur & filtering, edge detection
- Histograms, morphology, geometric transforms
- Contours, thresholding, K-Means, and much more

**Albumentations powers first-class data augmentation** right inside the visual graph. Chain augmentations like flips, rotations, color jitter, noise, cutout, and elastic transforms alongside your preprocessing nodes — all while correctly propagating:

- Images
- Bounding boxes
- Segmentation masks
- Keypoints

Every run is deterministic and seedable, so your augmented datasets are fully reproducible across teammates and experiments — critical for anyone training a model on top of this pipeline.

### 🐍 Your Pipeline Is Never Trapped

Design visually, but never get locked in. Export readable, standalone Python built on OpenCV and Albumentations — no proprietary runtime, no generated framework, nothing that only works inside Image Pipes.

```python
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(blur, 50, 150)
```

That's it. That's the export. Drop it straight into your training pipeline, your ETL job, or a notebook.

---

## 🚀 Use Cases

Image Pipes fits naturally into workflows like:

- Dataset preprocessing for ML training
- Building and tuning data augmentation pipelines with Albumentations
- Teaching computer vision concepts visually
- Rapid OpenCV experimentation and prototyping
- Research reproducibility (deterministic, versionable pipelines)
- Debugging annotation pipelines (boxes, masks, keypoints)
- General image analysis and exploration

---

## 🏗 Architecture

```text
                     React Flow Editor
                             │
                    REST + WebSockets
                             │
                             ▼
                  DAG Execution Engine
               ┌─────────────┴─────────────┐
               │                           │
               ▼                           ▼
        OpenCV Processing          Albumentations
         (classical CV)          (data augmentation)
               │                           │
               └─────────────┬─────────────┘
                             │
                             ▼
                  Live Preview & Export
                             │
                             ▼
                    Standalone Python
```

---

## 🛠 Tech Stack

**Frontend**
- React 19 + TypeScript
- Vite
- React Flow
- Zustand
- TanStack Query
- Material UI
- Monaco Editor
- React Hook Form + Zod

</td>
<td valign="top">

**Backend**
- FastAPI
- OpenCV
- Albumentations
- NumPy
- Pillow
- WebSockets
- Pydantic v2

</td>
<td valign="top">

**Desktop**
- Electron

</td>
</tr>
</table>

---

## 🚀 Quick Start

### Download

Grab the latest desktop installer (Windows, macOS, or Linux) from the [Releases](https://github.com/mrajaeim/image-pipes/releases) page — no Python or Node required.

### From source

Clone the repository:

```bash
git clone https://github.com/mrajaeim/image-pipes.git
cd image-pipes
```

Install everything:

```bash
npm run install:all
```

Launch the desktop app:

```bash
npm run desktop
```

That's it — the Electron app automatically starts the backend and opens the editor. No separate servers to babysit.

---

## 🎯 Why Developers Love It

| | |
|---|---|
| ✅ Visual instead of script-based | ✅ Zero frontend work for custom nodes |
| ✅ Live preview at every step | ✅ Deterministic Albumentations augmentation |
| ✅ Fast, targeted experimentation | ✅ Clean, dependency-free Python export |
| ✅ Metadata-driven architecture | ✅ Open source & self-hosted desktop app |

---

## 🛣 Roadmap

- 🎥 Video pipelines & camera streaming
- 🧩 ONNX Runtime and PyTorch inference nodes
- ⚡ CUDA acceleration
- 📦 Batch processing
- 🔌 Plugin SDK
- ☁️ Cloud workspaces
- 📐 Pipeline templates
- 🤖 AI-assisted pipeline generation

---

## 🤝 Contributing

Contributions are welcome — bug fixes, performance improvements, new processing nodes, or entirely new features. If you're planning a large change, please open an issue first so we can discuss the design together.

---

## 📄 License

Distributed under the MIT License. See the **LICENSE** file for details.

</div>

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