# Real-World Evaluation Images for Articulated Objects Interaction Generation

This dataset contains the real-world images used in evaluating [DragAPart](https://dragapart.github.io/), a conditional image generator that models interaction with articulated objects.

## 📦 How to Use It?

Each sample consists of:
- `original_image_XXX.png`: The base image showing an articulated object.
- `arrow_locations_XXX.npy`: A NumPy file containing the arrow coordinates for interaction.

The `.npy` file stores one arrow as:

```python
[x0, y0, x1, y1]  # Normalized coordinates in [0, 1]
```

Where:
- `(x0, y0)` is the **starting point** of the interaction (e.g., where the user clicks),
- `(x1, y1)` is the **end point** indicating the direction or extent of the manipulation.

These coordinates are normalized relative to the image size.

---

## 🖼️ Visualization

You can visualize the interaction using the following Python script:

```python
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

# Load image and arrow data
image_path = "original_image_000.png"
arrow_path = "arrow_locations_000.npy"

image = Image.open(image_path)
arrow = np.load(arrow_path)[0]  # [x0, y0, x1, y1]

# Convert normalized coordinates to pixel values
width, height = image.size
x0, y0 = int(arrow[0] * width), int(arrow[1] * height)
x1, y1 = int(arrow[2] * width), int(arrow[3] * height)

# Plot the image and overlay the interaction arrow
plt.figure(figsize=(6, 6))
plt.imshow(image)
plt.arrow(x0, y0, x1 - x0, y1 - y0,
          color='red', width=2, head_width=10, length_includes_head=True)
plt.axis('off')
plt.title("Interactive Manipulation Arrow")
plt.show()
```

This will display the original image with a red arrow showing the suggested user interaction as below:


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6467cc17e7a6a374fd1a41e5/_lPDMDThiHi0RqxiFOF5a.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6467cc17e7a6a374fd1a41e5/Nzl3TaTRd99WyB6IxqXhb.png)