Find similar images within a dataset.
Useful for removing duplicate images from a dataset after scraping images with google-images-download.
The Python API returns pairs, duplicates, where pairs are the (ordered) closest pairs and distances is the
corresponding embedding distance.
See the installation docs for all details.
pip install simagesor install from source:
git clone https://github.com/justinshenk/simages
cd simages
pip install .To install the interactive interface, install mongodb and use rather pip install "simages[all]".
- Minimal command-line interface with
simages-show:
- Interactive image deletion with
simages add/find:
Two interfaces exist:
- minimal interface which plots the duplicates for visual inspection
- mongodb + flask interface which allows interactive deletion [optional]
In your console, enter the directory with images and use simages-show:
$ simages-show --data-dir .usage: simages-show [-h] [--data-dir DATA_DIR] [--show-train]
[--epochs EPOCHS] [--num-channels NUM_CHANNELS]
[--pairs PAIRS] [--zdim ZDIM] [-s]
-h, --help show this help message and exit
--data-dir DATA_DIR, -d DATA_DIR
Folder containing image data
--show-train, -t Show training of embedding extractor every epoch
--epochs EPOCHS, -e EPOCHS
Number of passes of dataset through model for
training. More is better but takes more time.
--num-channels NUM_CHANNELS, -c NUM_CHANNELS
Number of channels for data (1 for grayscale, 3 for
color)
--pairs PAIRS, -p PAIRS
Number of pairs of images to show
--zdim ZDIM, -z ZDIM Compression bits (bigger generally performs better but
takes more time)
-s, --show Show closest pairs
Note: To install the web interface API, install and run mongodb and use pip install "simages[all]" to install optional dependencies.
Add your pictures to the database (this will take some time depending on the number of pictures)
simages add <images_folder_path>
A webpage will come up with all of the similar or duplicate pictures:
simages find <images_folder_path>
Usage:
simages add <path> ... [--db=<db_path>] [--parallel=<num_processes>]
simages remove <path> ... [--db=<db_path>]
simages clear [--db=<db_path>]
simages show [--db=<db_path>]
simages find <path> [--print] [--delete] [--match-time] [--trash=<trash_path>] [--db=<db_path>] [--epochs=<epochs>]
simages -h | --help
Options:
-h, --help Show this screen
--db=<db_path> The location of the database or a MongoDB URI. (default: ./db)
--parallel=<num_processes> The number of parallel processes to run to hash the image
files (default: number of CPUs).
find:
--print Only print duplicate files rather than displaying HTML file
--delete Move all found duplicate pictures to the trash. This option takes priority over --print.
--match-time Adds the extra constraint that duplicate images must have the
same capture times in order to be considered.
--trash=<trash_path> Where files will be put when they are deleted (default: ./Trash)
--epochs=<epochs> Epochs for training [default: 2]
from simages import find_duplicates
import numpy as np
array_data = np.random.random(100, 3, 48, 48)# N x C x H x W
pairs, distances = find_duplicates(array_data)
from simages import find_duplicates
data_dir = "my_images_folder"
pairs, distances = find_duplicates(data_dir)
Default options for find_duplicates are:
def find_duplicates(
input: Union[str or np.ndarray],
n: int = 5,
num_epochs: int = 2,
num_channels: int = 3,
show: bool = False,
show_train: bool = False,
**kwargs
):
"""Find duplicates in dataset. Either `array` or `data_dir` must be specified.
Args:
input (str or np.ndarray): folder directory or N x C x H x W array
n (int): number of closest pairs to identify
num_epochs (int): how long to train the autoencoder (more is generally better)
show (bool): display the closest pairs
show_train (bool): show output every
z_dim (int): size of compression (more is generally better, but slower)
kwargs (dict): etc, passed to `EmbeddingExtractor`
Returns:
pairs (np.ndarray): indices for closest pairs of images, n x 2 array
distances (np.ndarray): distances of each pair to each otherfrom simages import Embeddings
import numpy as np
N = 1000
data = np.random.random((N, 28, 28))
embeddings = Embeddings(data)
# Access the array
array = embeddings.array # N x z (compression size)
# Get 10 closest pairs of images
pairs, distances = embeddings.duplicates(n=5)In [0]: pairs
Out[0]: array([[912, 990], [716, 790], [907, 943], [483, 492], [806, 883]])
In [1]: distances
Out[1]: array([0.00148035, 0.00150703, 0.00158789, 0.00168699, 0.00168721])from simages import EmbeddingExtractor
import numpy as np
N = 1000
data = np.random.random((N, 28, 28))
extractor = EmbeddingExtractor(data, num_channels=1) # grayscale
# Show 10 closest pairs of images
pairs, distances = extractor.show_duplicates(n=10)Class attributes and parameters:
class EmbeddingExtractor:
"""Extract embeddings from data with models and allow visualization.
Attributes:
trainloader (torch loader)
evalloader (torch loader)
model (torch.nn.Module)
embeddings (np.ndarray)
"""
def __init__(
self,
input:Union[str, np.ndarray],
num_channels=None,
num_epochs=2,
batch_size=32,
show_train=True,
show=False,
z_dim=8,
**kwargs,
):
"""Inits EmbeddingExtractor with input, either `str` or `np.nd.array`, performs training and validation.
Args:
input (np.ndarray or str): data
num_channels (int): grayscale = 1, color = 3
num_epochs (int): more is better (generally)
batch_size (int): number of images per batch
show_train (bool): show intermediate training results
show (bool): show closest pairs
z_dim (int): compression size
kwargs (dict)
"""Specify tne number of pairs to identify with the parameter n.
simages uses a convolutional autoencoder with PyTorch and compares the latent representations with closely 📐.
simages depends on the following packages:
- closely
- torch
- torchvision
- scikit-learn
- matplotlib
The following dependencies are required for the interactive deleting interface:
- pymongodb
- fastcluster
- flask
- jinja2
- dnspython
- python-magic
- termcolor
If you use simages, please cite it:
@misc{justin_shenk_2019_3237830,
author = {Justin Shenk},
title = {justinshenk/simages: v19.0.1},
month = jun,
year = 2019,
doi = {10.5281/zenodo.3237830},
url = {https://doi.org/10.5281/zenodo.3237830}
}