https://github.com/shamiul5201/license_plate_detection
This project implements a vehicle registration plate detection system using Detectron2, a powerful object detection and segmentation framework built on PyTorch. The project involves training a model on a custom dataset to identify and localize registration plates in images.
https://github.com/shamiul5201/license_plate_detection
coco-api detectron2 object-detection python pytorch
Last synced: 3 months ago
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This project implements a vehicle registration plate detection system using Detectron2, a powerful object detection and segmentation framework built on PyTorch. The project involves training a model on a custom dataset to identify and localize registration plates in images.
- Host: GitHub
- URL: https://github.com/shamiul5201/license_plate_detection
- Owner: shamiul5201
- License: other
- Created: 2025-01-01T12:11:34.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-01T12:52:18.000Z (5 months ago)
- Last Synced: 2025-01-01T13:35:34.285Z (5 months ago)
- Topics: coco-api, detectron2, object-detection, python, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 6.67 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: license-plate-detection.ipynb
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README
# Vehicle Registration Plate Detection using Detectron2
This project implements a vehicle registration plate detection system using Detectron2, a powerful object detection and segmentation framework built on PyTorch. The project involves training a model on a custom dataset to identify and localize registration plates in images.
## Installation
1. Install Detectron2 and other dependencies:
```bash
!pip install pycocotools
```
2. Set up the Detectron2 environment:
```python
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
```
3. Import necessary libraries:
```python
import numpy as np
import cv2, random, os
import matplotlib.pyplot as plt
from detectron2 import model_zoo
from detectron2.engine import DefaultTrainer, DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.structures import BoxMode
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
```
---
## Dataset Preparation
1. **Directory Structure**
- Ensure the dataset is organized as follows:
```
/Dataset
|-- train
| |-- Vehicle registration plate
| |-- Images
| |-- Label
|-- validation
|-- Vehicle registration plate
|-- Images
|-- Label
```
2. **Annotations**
- Annotations are stored in `.txt` files, with bounding box coordinates for each image.
- Example of an annotation line:
```
class_id x_center y_center width height xmin ymin xmax ymax
```
3. **Dataset Registration**
- Register the datasets for training and validation using the following code:
```python
DatasetCatalog.register(name='train_regn_plate', func=lambda: get_regn_plate_dicts(data_root, train_txt))
DatasetCatalog.register(name='val_regn_plate', func=lambda: get_regn_plate_dicts(data_root, val_txt))
MetadataCatalog.get('train_regn_plate').set(thing_classes=['regn_plate'])
MetadataCatalog.get('val_regn_plate').set(thing_classes=['regn_plate'])
```
---
## Code Overview
### Dataset Registration
The function `get_regn_plate_dicts` processes the annotations and image metadata into a Detectron2-compatible format. It:
- Reads image dimensions and paths.
- Parses bounding box annotations.
- Outputs a list of dictionaries containing image and annotation data.
### Visualization
Visualize random samples from the dataset to verify correct registration:
```python
for d in random.sample(val_data_dict, 3):
img = cv2.imread(d["file_name"])
visualizer = Visualizer(img[:, :, ::-1], metadata=val_metadata, scale=0.5)
vis = visualizer.draw_dataset_dict(d)
plt.figure(figsize=(12, 12))
plt.imshow(vis.get_image())
plt.show()
```
### Training
1. Configure the model using a pre-trained RetinaNet from the Detectron2 model zoo.
2. Customize solver parameters and dataset settings:
```python
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file('COCO-Detection/retinanet_R_50_FPN_3x.yaml'))
cfg.DATASETS.TRAIN = ('train_regn_plate',)
cfg.DATASETS.TEST = ()
cfg.DATALOADER.NUM_WORKERS = 4
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url('COCO-Detection/retinanet_R_50_FPN_3x.yaml')
cfg.SOLVER.IMS_PER_BATCH = 8
cfg.SOLVER.BASE_LR = 0.0001
cfg.SOLVER.MAX_ITER = max_iter
cfg.MODEL.RETINANET.NUM_CLASSES = 1
cfg.OUTPUT_DIR = 'outputs'
```
3. Train the model:
```python
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
trainer.train()
```
### Inference
Perform predictions on validation data and visualize the results:
```python
predictor = DefaultPredictor(cfg)
for d in random.sample(val_data_dict, 5):
im = cv2.imread(d["file_name"])
outputs = predictor(im)
v = Visualizer(im[:, :, ::-1], metadata=val_metadata, scale=0.8)
v = v.draw_instance_predictions(outputs['instances'].to('cpu'))
plt.figure(figsize=(12, 12))
plt.imshow(v.get_image())
plt.show()
```
---
## Evaluation
1. Evaluate the model on the validation dataset using COCO metrics:
```python
evaluator = COCOEvaluator(dataset_name='val_regn_plate', output_dir=eval_dir, distributed=False)
val_loader = build_detection_test_loader(cfg, 'val_regn_plate')
inference_on_dataset(trainer.model, val_loader, evaluator)
```
## Acknowledgments
- **Detectron2**: A modular and scalable object detection library.
- **Dataset**: Custom dataset for vehicle registration plate detection.