Image-Recognition

Supervised Machine Learning Model to recognise images. In this example we will use it to detect flowers. It can be reused to identify other object images by retraining the model.

How to use this model to detect image of sunflower/daisy/dandelion/rose/tulips?

Activate the virtual environment:

source venv/bin/activate

Predicting the flower category. Place the image to be detected in the root project directory(this folder):

python label_image.py testimage.jpg 

Note: In this case testimage.jpg is the image to be predicted and it is placed in the root folder directory

##Reusing the model for some other images! You don't need to clone the whole project! Just download the retrain.py and label_image.py in your project directory

Go to the project directory. Create a new virtual environment for your application:

virtualenv venv

Activate the virtual environment:

source venv/bin/activate

Install tensorflow library:

pip install tensorflow

Download the data set for images you want to recognise/classify and place it in the project directory. In above case(flower recognition) flower_photos is the data-set directory. Within the dataset directory keep images of each kind of object in different directries specifying their name.

Train the classifier using inception v3 neural network:

python retrain.py \
--bottleneck_dir=bottlenecks \
--how_many_training_steps 4000 \
--model_dir=inception \
--output_graph=retrained_graph.pb \
--output_labels=retrained_labels.txt \
--image_dir [image data-set directry name/path]

Predicting the image category. Place the image to be detected in the root project directory(this folder):

python label_image.py testimage.jpg 

Note: In this case testimage.jpg is the image to be predicted and it is placed in the root folder directory