from pprecogg import gaborExtract, classifyFeatures

# path to the image you wish to classify
unknown_img_path = "/path/to/unknown/image"

# paths to the images whose class you know
adh_img_path = "/path/to/adh/image"
dcis_img_path = "/path/to/dcis/image"

# features are extracted into HDF5 files, and extract_gabor_features
# returns the path to said file
unknown_features_path = gaborExtract.extract_gabor_features(unknown_img_path)
adh_features_path = gaborExtract.extract_gabor_features(adh_img_path)
dcis_features_path = gaborExtract.extract_gabor_features(dcis_img_path)

# classify features from unknown image. 
# returns an array of class ID and an array of classified coordinates
# indexed by class (see: that array of class IDs)
class_names,classified_coords = classifyFeatures.classify_features(unknown_features_path,
known_features_paths)


# we can convert this into a dictionary where the key is the class name
# and the value are the coordinates that belong to it
classified_coords_dict = {class_names[class_num]: class_coords for class_num, class_coords in enumerate(classified_coords)}

# small ergonomic function to plot classified pixels on to the
# unknown image
classifyFeatures.plot_coords(classified_coords_dict,
                                 unknown_img_path)