the Main Purpose of these Project is to Classifying images using deep learning and OpenCV . here i have implemented Python script that will use OpenCV dnn Module and GoogleLeNet (pre-trained on ImageNet) to classify images.

Realtime Object Recognition with Deep-Learning-OpenCV_3

Realtime object recognition using the OpenCV 3.3 dnn module + pretrained MobileNetSSD caffemodel.

Installation

All the dependencies can be installed using pip. Just use the following command from the root directory of the project.

pip3 install -r requirements.txt

How to run this script?

There are two options for video source:

• Webcam
• Android device running IP Camera (https://play.google.com/store/apps/details?id=com.pas.webcam&hl=en)

To run the script using webcam as source :

python3 real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel --source webcam

To run the script using IP Webcam as source, open the real_time_object_detection.py and edit the following line to match your host :

host = 'http://192.168.0.101:8080/'

Then to run the script using IP as source :

python3 real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel --source web

For any questions, create an issue in this repository.

Who made this

VirgileDjimgou

Contribute

1. Create an issue to discuss about your idea
2. [Fork it] (https://github.com/VirgileDjimgou/Deep-Learning-OpenCV_3)
3. Create your feature branch (git checkout -b my-new-feature)
4. Commit your changes (git commit -am 'Add some feature')
5. Push to the branch (git push origin my-new-feature)
6. Create a new Pull Request
7. Profit! ✅