By Annie and Yanisa 2019
Here are the scripts used in our RE-SEARCH project. Note: we did not include our physical dataset, as that would probably blow up the repository space limit.
DEMO FOLDER: contains the web application used during demo/presentation.
localhost:5000 ---> HOME PAGE /customer ---> CUSTOMER SIDE /manager ---> MANAGER SIDE
Customer side search engine which queries a LOCAL db to obtain search results and return images corresponding to key word search.
Manager side allows upload of images that get preprocessed and ran through saved models to return predicted labels.
DEMO/CUSTOMER.PY:
calls customer_helpers.py with inputted search keywords to obtain the paths of the image results.
DEMO/CUSTOMER_HELPERS.PY:
stores functions called by customer.py that generates query based on inputted search keywords and returns the paths of the image results.
MAIN.PY:
trains a NN model based on specified parameters and dataset specified through datafolder path. Will show plots, confusion matrices and save model.
MODEL.PY:
contains models called on by main file and the architecture of final sleeves, necklines, buttons and colour models
PREPROCESS.PY:
preprocesses all images scraped from web stored in webscraping/data folder, resizes and accumulates images into new test folders for main.py
INTEGRATE.PY:
runs all images stored in the input folder location through the model stored and outputs the label and confidence of the images in a list.
PROCESS.PY:
for images in a specified folder, calls integrate.py to generate predictions and labels for each clothing feature type and stores the results in a local database in a standardized form.
TEST.PY:
stores functions that generate queries to store and search through a local database.
MODELS FOLDER:
contains all the saved best models so far...
WEBSCRAPING FOLDER
contains scripts for scraping stores