Getting Started with DIPLO
===========================

Welcome to DIPLO! This guide will walk you through your first workflow in 5 minutes.

Prerequisites
-------------

- DIPLO installed (see `README.md <../README.md>`_)
- A folder with microscopy images (PNG, JPEG, TIFF, etc.)

Step 1: Load Your Images
------------------------

1. Launch DIPLO:

   .. code-block:: bash

      python app.py

2. Click the **"Source"** tab at the top

3. Click **"Browse"** and select your images folder

4. You should see your first image in the viewer

5. Use the **Offset** slider to navigate through images

Step 2: Create a Workflow
-------------------------

1. Click the **"Workflow"** tab

2. Click **"+"** button to add your first node

3. Select **"SourceNode"** from the menu
   - This loads images from your Source folder

Step 3: Add Processing Nodes
-----------------------------

Let's build a simple deconvolution + segmentation workflow:

**Add a Deconvolution Node:**

1. Click **"+"** again
2. Go to **"Image Enhancement"** → **"DeconvolutionNode"**
3. This enhances raw microscopy images

**Add a Contour Detection Node:**

1. Click **"+"** again
2. Go to **"Detection and Classification"** → **"FindContoursNode"**
3. This detects object boundaries

Step 4: Connect Your Nodes
---------------------------

Connect the nodes together:

1. Click the **white output socket** (right side) of SourceNode
2. Drag to the **input socket** (left side) of DeconvolutionNode
3. Click the **white output socket** of DeconvolutionNode
4. Drag to the **input socket** of FindContoursNode

Your workflow should now look like:

.. code-block:: text

   SourceNode → DeconvolutionNode → FindContoursNode

Step 5: Run and Inspect Results
--------------------------------

1. Right-click on any node → **"Inspect Results"**

2. You'll see the output of that node:
   - **SourceNode**: Original image
   - **DeconvolutionNode**: Enhanced image
   - **FindContoursNode**: Detected contours

3. If everything looks good, proceed to Step 6!

Step 6: Add Classification (Optional)
--------------------------------------

To classify the detected objects:

1. Click **"+"** → **"Detection and Classification"** → **"ClassificationNode"**

2. Connect FindContoursNode → ClassificationNode

3. Right-click ClassificationNode → **"Inspect Results"**

4. See classification predictions!

Step 7: Save Your Workflow
---------------------------

1. Press **Ctrl+S** (or **Cmd+S** on Mac)

2. Give your workflow a name (e.g., "plankton_analysis.json")

3. It's saved in the **Workflows/** directory

You can now:
- **Reload it**: Click "Load Workflow"
- **Share it**: Send the JSON file to collaborators
- **Version control**: Add to Git

What's Next?
------------

Congratulations! You've created your first DIPLO workflow. Now explore:

- **Node Reference**: Learn all 20 available nodes (`nodes/reference`)
- **Workflow Guides**: See advanced examples (`usage/workflow_tab`)
- **Source Management**: Use the Source tab effectively (`usage/source_tab`)
- **Model Information**: Understand the pre-trained models

Tips & Tricks
-------------

✅ **Real-time Processing**: Workflows recompute automatically when you change connections

✅ **Inspect at Any Point**: Right-click any node to see intermediate results

✅ **Batch Processing**: Source tab can load 100+ images – workflow processes all

✅ **Export Results**: Results automatically save to the **Results/** directory

✅ **Parameter Tuning**: Double-click a node to adjust parameters (threshold, morphology, etc.)

Troubleshooting
---------------

**"Models are downloading"**
   First run takes 2-3 minutes to download pre-trained models. This happens only once.

**"No output shown"**
   Make sure nodes are connected properly. Nodes with no input connections produce no output.

**"Processing is slow"**
   For faster processing, enable GPU acceleration (see `README.md <../README.md#video-card-acceleration-optional>`_).

**"Where are my results?"**
   Check the **Results/** directory. Workflows automatically save outputs there.

**Need more help?**
   See the :doc:`Node Reference <nodes/reference>` for details on each node.