---
license: mit
language:
- en
base_model:
- jameslahm/yolov10n
---


```markdown
Document Layout Detection

This script demonstrates how to use the document layout detection model on an image.
Below are the steps and code implementation.

---
## Step 1: Import Required Libraries
```
```python
import cv2
import matplotlib.pyplot as plt
import numpy as np
from ultralytics import YOLO
from google.colab.patches import cv2_imshow
```

- **cv2**: For image processing.
- **matplotlib.pyplot**: For plotting if needed.
- **numpy**: For numerical operations.
- **YOLOv10**: For object detection.
- **cv2_imshow**: For displaying images in Google Colab.

---

## Step 2: Load YOLOv10 Model
```python
model = YOLO('vprashant/doclayout_detector/weights')  
```

- Load the YOLOv10 model with the path to your trained weights.

---

## Step 3: Read and Prepare the Image
```python
img = cv2.imread('/content/yolov10/dataset/train/images/11.png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
```

- Read the image from the specified path.
- Convert the image from BGR to RGB color space.

---

## Step 4: Perform Object Detection
```python
results = model(img)
```

- Run the YOLOv10 model on the image to get detection results.

---

## Step 5: Extract and Process Detection Results
```python
results = results[0] 
boxes = results.boxes
data = boxes.data.cpu().numpy()  
```

- Extract the first result (image-level result).
- Access the detected bounding boxes.
- Convert detection data to a NumPy array for processing.

---

## Step 6: Visualize Results
```python
for i, detection in enumerate(data):
    x1, y1, x2, y2, conf, cls_id = detection
    x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])  # Convert coordinates to integers
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw bounding box
    
    class_name = model.names[int(cls_id)]  # Get class name
    label = f"{class_name}: {conf:.2f}"  # Create label with confidence score
    cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 
                0.9, (0, 255, 0), 2)  # Add text label
```

- Loop through all detections.
- Draw bounding boxes and labels on the image.

---

## Step 7: Display the Processed Image
```python
cv2_imshow(img) 
cv2.waitKey(0)
cv2.destroyAllWindows()
```

- Display the image with detections in Google Colab using `cv2_imshow`.
- Wait for a keypress and close any OpenCV windows.

---

## Note
- Ensure you have the trained YOLOv10 model and the dataset in the specified paths.
- Replace the paths with your local or Colab paths.
- Install necessary libraries like OpenCV, Matplotlib, and ultralytics if not already installed.
```