Blind-Aid

Devpost Submission

How it Works

Installation

1. Clone the repository.
2. Navigate to Open-Detection-API directory.
3. Install Anaconda Navigator and run the following commands in Anaconda Prompt.

# Tensorflow CPU (If you don't have GPU)
conda env create -f conda-cpu.yml
conda activate yolov3-cpu
# TensorFlow CPU
pip install -r requirements.txt

If you have a GPU, run the following commands

# Tensorflow GPU
conda env create -f conda-gpu.yml
conda activate yolov3-gpu

# TensorFlow GPU
pip install -r requirements-gpu.txt

Downloading official pretrained weights

For Linux: Let's download official yolov3 weights pretrained on COCO dataset.

# yolov3
wget https://pjreddie.com/media/files/yolov3.weights -O weights/yolov3.weights

# yolov3-tiny
wget https://pjreddie.com/media/files/yolov3-tiny.weights -O weights/yolov3-tiny.weights

For Windows:

You can download the yolov3 weights by clicking here and yolov3-tiny here then save them to the weights folder.

Using Custom trained weights

Learn How To Train Custom YOLOV3 Weights Here: https://www.youtube.com/watch?v=zJDUhGL26iU Add your custom weights file to weights folder and your custom .names file into data/labels folder.

Saving your yolov3 weights as a TensorFlow model.

Load the weights using load_weights.py script. This will convert the yolov3 weights into TensorFlow .ckpt model files!

# yolov3
python load_weights.py

# yolov3-tiny
python load_weights.py --weights ./weights/yolov3-tiny.weights --output ./weights/yolov3-tiny.tf --tiny

After executing one of the above lines, you should see .tf files in your weights folder.

4. Running the Flask App
   

Initialize and run the Flask app on port 5000 of your local machine by running the following command from the root directory of this repo in a command prompt or shell.

python app.py

Navigate back to the repo and run the following command

python frontend/app.py

Boom! You are ready to go!

Images

Techstack

1. Tensorflow
2. OpenCV
3. gTTS
4. Flask

Live Working Demo : Click Me