Created by Son Nguyen in 2024, this repository contains Python scripts for various AI-powered classifiers. These classifiers can be used for object detection, face detection, character recognition, and more. The classifiers are built using popular deep learning frameworks such as OpenCV
, TensorFlow
, and PyTorch
.
This repository contains 8 subdirectories: one for vehicle classification, one for human face classification, one for mood classification, one for flower classification , one for object classification, one for character classification, one for animal classification, and one for speech recognition, namely Vehicle-Classification
, Human-Face-Classification
, Mood-Classification
, Flowers-Classification
, Object-Classification
, Character-Recognition
, Animals-Classification
, and Speech-Recognition
. Refer to the information below for details on each classifier.
What's even more interesting is that all these classifiers can use your webcam for live testing, video files, or image files!
- Before You Begin
- Main Script - Entry Point
- Vehicle Classification
- Human Face Classification
- Mood Classification
- Flower Classification
- Object Classification
- Character Classification (OCR)
- Animal Classification
- Speech Recognition
- Contact Information
- Future Work
- License
Before you begin, ensure you have the following installed on your machine:
- Python 3.12 or higher
- OpenCV
- TensorFlow
- PyTorch
- NumPy
- Matplotlib
- Tesseract OCR
- Pytesseract
- SpeechRecognition
- MoviePy
- PyDub
- PyAudio
- Git LFS (for downloading large model weights files)
- A webcam (if you want to use live testing)
- A microphone (if you want to use speech recognition)
- A video file or image file for testing the classifiers
- A stable internet connection (for downloading model weights and dependencies)
- A working speaker or headphones (for speech recognition)
You are also recommended to use a virtual environment to use these classifiers. You can create a virtual environment using venv
or conda
.
python -m venv env
source env/bin/activate
If you prefer not to navigate through the subdirectories, you can run the main script main.py
to choose and run any of the classifiers. The main script will ask you to choose a classifier from the list of available classifiers. You can then select a classifier and run it.
To run the main script, use the following command:
python main.py
The main script will display a list of available classifiers. Enter the number corresponding to the classifier you want to run. The script will then run the selected classifier.
To stop the script, press Q
, ESC
, or otherwise close the window.
coco.names
: Class names used for vehicle detection.traffic.mp4
: Sample video for vehicle detection.india.jpg
: Sample image for vehicle detection.yolov3.cfg
: YOLOv3 model configuration file.yolov3.weights
: Pre-trained YOLOv3 model weights.vehicle_detection.py
: Python script for vehicle detection and classification.
-
Clone the Repository
git clone https://github.com/hoangsonww/AI-Classification.git cd AI-Classification/Vehicle-Classification
-
Download Model Weights Download the pre-trained YOLOv3 model weights (
yolov3.weights
) from the official YOLO website or another trusted source and place it in theVehicle-Classification
directory. -
Install Dependencies Install the required Python dependencies.
pip install -r requirements.txt
-
Install and Pull Git LFS Install Git LFS by following the instructions on the official Git LFS website. Then, pull the model weights using Git LFS.
git lfs install git lfs pull
Alternatively, you can download the weights file from the official YOLO website and place it in the
Vehicle-Classification
directory. However, using Git LFS is recommended.This is crucial for pulling the large model weights file. Without Git LFS, the weights file may not be downloaded correctly and the script may not work as expected.
-
Run Vehicle Detection
python vehicle_detection.py
You will then be asked to choose your input type (image, video, or webcam). Enter image
to classify the vehicles in the sample video provided (traffic.mp4
), or enter video
to classify vehicles in a video file. You can also use your webcam for live testing.
All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output video will display the detected vehicles along with their class labels. The class labels are based on the COCO dataset, which includes various classes such as car, truck, bus, motorcycle, and bicycle.
Example output:
deploy.prototxt
: Model configuration file for the face detector.res10_300x300_ssd_iter_140000.caffemodel
: Pre-trained model weights for face detection.age_deploy.prototxt
: Model configuration file for age prediction.age_net.caffemodel
: Pre-trained model weights for age prediction.gender_deploy.prototxt
: Model configuration file for gender prediction.gender_net.caffemodel
: Pre-trained model weights for gender prediction.faces_classification.py
: Python script for face detection, age, and gender classification.woman-30.mp4
: Sample video for face classificationman.jpg
: Sample image for face classification.
-
Clone the Repository
git clone https://github.com/hoangsonww/AI-Classification.git cd AI-Classification/Face-Classification
-
Download Model Weights: Ensure you have the model weights (
res10_300x300_ssd_iter_140000.caffemodel
,age_net.caffemodel
,gender_net.caffemodel
) in theHuman-Face-Classification
directory. -
Install Dependencies: Install the required Python dependencies.
pip install -r requirements.txt
-
Run Face Classification: Execute the face_classification.py script.
python face_classification.py
You will then be asked to choose your input type (image, video, or webcam). Enter image
to classify the faces in the sample image provided (woman-30.mp4
), or enter video
to classify faces in a video file. You can also use your webcam for live testing.
All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output will be a video displaying the detected faces along with their estimated age and gender.
Example output:
mood_classifier.py
: Python script for mood classification.angry.mp4
: Sample video for mood classification (angry).surprised.jpg
: Sample image for mood classification (surprised).
- Clone the Repository
git clone cd AI-Classification/Mood-Classification
- Install Dependencies
Install the required Python dependencies.
pip install -r requirements.txt
- Run Mood Classification
python mood_classifier.py
You will then be asked to choose your input type (image, video, or webcam). Enter image
to classify the mood in the sample image provided (surprised.jpg
), or enter video
to classify the mood in a video file. You can also use your webcam for live testing.
The script will then display the detected mood in the image, video, or webcam stream and in the console.
All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output will be displayed the detected mood in the image, video, or webcam stream and in the console.
Example output:
ocr.py
: Python script for character classification.OIP.jpg
: Sample JPEG image for character classification.chars.jpg
: Sample JPEG image for character classification.chars.mp4
: Sample video for character classification.letters.mp4
: Sample video for character classification.
-
Clone the Repository
git clone cd AI-Classification/Character-Recognition
-
Install the required Python dependencies.
pip install -r requirements.txt
-
Install Tessaract OCR
- For Windows: Download and install the Tesseract OCR executable from the official Tesseract OCR website.
- For Linux: Install Tesseract OCR using the package manager.
sudo apt-get install tesseract-ocr
- For macOS: Install Tesseract OCR using Homebrew.
brew install tesseract
- This is required for the OCR functionality to work. Also, when you install, note down the installation path of the Tesseract OCR executable. Replace the path in the
pytesseract.pytesseract.tesseract_cmd
variable in theocr.py
script with yours. - For example, if you installed Tesseract OCR in the default location on Windows, the path would be:
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
-
Run Character Classification
python ocr.py
You will then be asked to choose your input type (image, video, or webcam). Enter
image
to classify the characters in the sample image provided (OIP.jpg
), or entervideo
to classify characters in a video file. You can also use your webcam for live testing.image
The script will then display the detected characters in the image, video, or webcam stream.
All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output will display the class labels of the characters detected in the image along with the confidence scores.
Example output:
flower_classification.py
: Python script for flower classification.daisy.jpg
: Sample JPEG image for flower classification (Daisy).marigold.jpg
: Sample JPEG image for flower classification (Marigold).rose.mp4
: Sample video for flower classification (Rose).
-
Clone the Repository
git clone https://github.com/hoangsonww/AI-Classification.git cd AI-Classification/Flowers-Classification
-
Install Dependencies Install the required Python dependencies.
pip install -r requirements.txt
-
Run Object Classification
python flower_classification.py
You will then be asked to choose your input type (image, video, or webcam). Enter image
to classify the flowers in the sample image provided (daisy.jpg
), or enter video
to classify flowers in a video file. You can also use your webcam for live testing.
All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output will display the class label of the flower detected in the image along with the confidence score.
Example output: Here are the sample image of Daisy flowers.
object_classification.py
: Python script for object classification.objects.jpg
: Sample JPEG image for object classification.objects.png
: Sample PNG image for object classification.balls.mp4
: Sample video for object classification.OIP.jpg
: Sample image for object classification.
-
Clone the Repository
git clone https://github.com/hoangsonww/AI-Classification.git cd AI-Classification/object_classification
-
Install Dependencies Install the required Python dependencies.
pip install -r requirements.txt
-
Run Object Classification
python object_classification.py
You will then be asked to choose your input type (image, video, or webcam). Enter image
to classify the objects in the sample image provided (objects.jpg
), or enter video
to classify objects in a video file. You can also use your webcam for live testing.
Feel free to change the paths and other parameters in the script to suit your needs.
Note: All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output will display the class labels of the objects detected in the image along with the confidence scores. Or, if you choose to use your webcam, the output will display the class labels of the objects detected in the video stream. If you choose to use a video file, the output will be a video displaying the detected objects along with their class labels.
Example output:
animal_classification.py
: Python script for animal classification.cow.jpg
: Sample JPEG image for animal classification (Cow).ox.jpg
: Sample JPEG image for animal classification (Ox).
-
Clone the Repository
git clone https://github.com/hoangsonww/AI-Classification.git cd AI-Classification/Animals-Classification
-
Install Dependencies Install the required Python dependencies.
pip install -r requirements.txt
-
Run Object Classification
python animal_classification.py
The script will then ask you to choose your input type (image, video, or webcam). Enter image
to classify the animals in the sample image provided (cow.jpg
), or enter video
to classify animals in a video file. You can also use your webcam for live testing.
All our classifiers will only stop when you press Q
, ESC
, or otherwise close the window.
The output will display the class labels of the animals detected in the image along with the confidence scores.
Example output:
speech_classifier.py
: Python script for speech recognition.speech.mp4
: Sample video file for speech recognition in a video context.temp_audio.wav
: Temp audio file (used by our AI) for speech recognition.
-
Clone the Repository
git clone https://github.com/hoangsonww/AI-Classification.git cd AI-Classification/Speech-Recognition
-
Install Dependencies Install the required Python dependencies.
pip install -r requirements.txt
-
Run Speech Recognition
python speech_classifier.py
You will then be asked to choose your preferred input method (microphone or video). Enter microphone
to use your microphone for live speech recognition, or enter video
to use a video file for speech recognition.
You will see the output of the speech recognition process in the console. The script will display the recognized speech from the audio input. The audio is processed in chunks and recognized in real-time. All our classifiers will stop when you press Q
, ESC
, or otherwise close the window.
Example output:
For any questions or issues, please contact:
- Name: Son Nguyen
- Email: [email protected]
- Add more classifiers for various tasks such as emotion recognition, sentiment analysis, and more.
- Refine existing classifiers and improve their accuracy and performance.
- Add more sample images and videos for testing the classifiers.
This project is licensed under the MIT License - see the LICENSE file for details.
This repository is a work in progress and under active development. If you have any suggestions or improvements, feel free to contribute to this repository. Thank you for visiting!