An application to predict and map attention heatpoints using pretrained VGG16 model for UI/UX development and A/B testing.
#Installing the required modules for running the project To install the requirements from requirements.txt please run:
pip install -r requirements.txt
Alternatively the requirements could also be installed by running seperate pip commands for each entry in the requirements.txt file. This can be done as shown below.
pip install numpy
pip install opencv-python
pip install imutils
pip install tensorflow
The project primarily contains a custom module named gradcammodule and a script named apply_gradcam.py which is the primary script where the program execution starts. The output heatmap generated are stored in the root folder GRAD-CAM having filenames in the format Result_NameofFile.jpg.The directory images contains the testing images I used to generate the output heatmaps.
To get the vision output heatmap for a particular image fire up a terminal and run the following command.
python apply_gradcam.py -i images/amazon.jpg
Here the command line argument i or --image specifies the path to the input image.
After the script fires up, it open's up the open-cv window where the generated heatmap is vertically stacked along with the original image and the output heatmap.
A demo of the output produced is shown below.
