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https://github.com/20kaushik02/CSE515_MWDB_Project.git
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69 lines
1.4 KiB
Python
69 lines
1.4 KiB
Python
# All imports
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# Math
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import math
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import random
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import cv2
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import numpy as np
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from scipy.stats import pearsonr
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# from scipy.sparse.linalg import svds
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# from sklearn.decomposition import NMF
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from sklearn.decomposition import LatentDirichletAllocation
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# from sklearn.cluster import KMeans
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# Torch
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import torch
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import torchvision.transforms as transforms
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from torchvision.datasets import Caltech101
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from torchvision.models import resnet50, ResNet50_Weights
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import tensorly as tl
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# OS and env
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import json
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import os
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from os import getenv
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from dotenv import load_dotenv
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import warnings
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from joblib import dump, load
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load_dotenv()
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# MongoDB
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from pymongo import MongoClient
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# Visualizing
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import matplotlib.pyplot as plt
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valid_classification_methods = {
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"m-nn": 1,
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"decision tree": 2,
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"ppr": 3,
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}
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def getCollection(db, collection):
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"""Load feature descriptor collection from MongoDB"""
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client = MongoClient("mongodb://localhost:27017")
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return client[db][collection]
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def euclidean_distance_measure(img_1_fd, img_2_fd):
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img_1_fd_reshaped = img_1_fd.flatten()
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img_2_fd_reshaped = img_2_fd.flatten()
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# Calculate Euclidean distance
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return math.dist(img_1_fd_reshaped, img_2_fd_reshaped)
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valid_feature_models = {
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"cm": "cm_fd",
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"hog": "hog_fd",
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"avgpool": "avgpool_fd",
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"layer3": "layer3_fd",
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"fc": "fc_fd",
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"resnet": "resnet_fd",
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}
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