The Object Detector is a computer vision project that uses deep learning algorithms to detect and identify objects in images and videos. This project can be used for a variety of applications, such as security monitoring, autonomous vehicles, and smart home systems.
1.Supports detection of multiple object classes in a single image or video frame
2.Provides bounding boxes and class labels for each detected object
3.Utilizes a pre-trained deep learning model for fast and accurate object detection
4.Allows for custom training of the object detection model on new datasets
5.Provides an easy-to-use Python API for integrating the object detector into your own projects
Prerequisites
Python 3.6 or higher
TensorFlow 2.x or PyTorch 1.x
OpenCV
1.Clone the repository:
git clone https://github.com/NANDAGOPALNG/PyVerse/tree/main/Generative-AI/Object%20Detector
2.Install the required dependencies:
pip install -r requirements.txt
1.Import the object detector module: python
from object_detector import ObjectDetector
2.Create an instance of the object detector: python
detector = ObjectDetector()
3.Detect objects in an image: python
image = cv2.imread('image.jpg') detections = detector.detect(image)
4.Visualize the detected objects: python
for detection in detections:
x, y, w, h = detection['bbox']
label = detection['label']
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(image, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36,255,12), 2)
cv2.imshow('Object Detection', image)
cv2.waitKey(0)
We welcome contributions to the Object Detector project. If you would like to contribute, please follow these steps:
1.Fork the repository
2.Create a new branch for your feature or bug fix
3.Make your changes and commit them
4.Push your changes to your forked repository
5.Submit a pull request to the main repository
This project is licensed under the MIT License.