utils.py

# utils.py This file may be used for all utility functions
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
import math
from PIL import Image
import numpy as np
import cv2

'''
 Create a bijection betweeen int and object. May be used for reverse indexing
'''
class Indexer(object):
    def __init__(self):
        self.objs_to_ints = {}
        self.ints_to_objs = {}

    def __str__(self):
        return self.__repr__()

    def __len__(self):
        return len(self.ints_to_objs)

    def contains(self, obj):
        return self.index_of(obj) != -1
    
    def index_of(self, obj):
        if obj in self.objs_to_ints:
            return self.objs_to_ints[obj]
        return -1
    
    def get_object(self, idx):
        if idx in self.ints_to_objs:
            return self.ints_to_objs[idx]
        return -1

    # Get the index of the object, if add_object, add object to dict if not present
    def get_index(self, obj, add_object = True):
        if not add_object or obj in self.objs_to_ints:
            return self.index_of(obj)
        new_idx = len(self.ints_to_objs)
        self.objs_to_ints[obj] = new_idx
        self.ints_to_objs[new_idx] = obj
        return new_idx

'''
 Add features from feats to feature indexer
'''
def add_dataset_features(feats, feature_indexer):
    for i in range(len(feats)):
        feature_indexer.get_index(feats[i][0])

'''
 Concatenate image vectors
'''
def get_concatentated_images(indexes, image_database):
    result = []
    for idx in indexes:
        print(image_database[idx][0].shape)
        result.append(image_database[idx][0])
    return result

'''
 Find cosine similarity
'''
def find_cosine_distance(a, b):
    sim = cdist(a.reshape(1,-1), b.reshape(1,-1), 'cosine').reshape(-1)[0]
    return sim

'''
 Display similar image output
'''
def show_similar_images(query_img, retrieved_images):
    query = plt.figure(figsize=(2,2)).add_subplot(111)
    query.imshow(Image.open(query_img))
    query.set_xlabel("query image")
    retrieved = plt.figure()
    row, col = math.floor(len(retrieved_images)//5), 5
    for i in range(row*col):
        print(retrieved_images[i])
        img = Image.open(retrieved_images[i])
        subplot = retrieved.add_subplot(row,col,i+1)
        plt.axis('off')
        plt.imshow(img)
    plt.show()