scrubbbbs / cbird

Command-line program for managing a media collection, with focus on Content-Based Image Retrieval (Computer Vision) methods for finding duplicates.
GNU General Public License v2.0
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command-line-interface computer-vision content-based-image-retrieval duplicate-detection duplicate-files duplicates ffmpeg opencv qt6 similarity-search

About cbird

cbird is a command-line program for finding duplicate images and videos that cannot be found by general methods such as file hashing. Content-Based Image Recognition (CBIR) is used, which examines the pixels of files to get comparable features and "perceptual" hash codes.

The main features are:

Installing

Compile

Compile it yourself using my detailed notes.

Download

Download binaries on Github

Linux AppImage

Add execute permission and run

chmod +x cbird-0.7.0-x86_64.AppImage
./cbird-0.7.0-x86_64.AppImage -install # optional install helper
cbird [...]
AppImage Issues

Mac OS X 11.0+ x86

cbird/cbird-mac [...]

Windows 10

Windows PowerShell

Optional: create shortcuts for cbird

Getting Started

Get Help

cbird -help is very detailed

Index the files in <path>, caching into <path>/_index

cbird -use <path> -update

Index files in cwd

cbird -update

MD5 Checksums

cbird -dups -show

Search cwd, default threshold

cbird -similar -show

Search cwd, lowest threshold

cbird -p.dht 1 -similar -show

Using the GUI

This is lacking documentation at the moment. But for now...

Use Cases

File Formats

Common formats are supported, as well as many obscure formats. The available formats will ultimately vary based on the configuration of Qt and FFmpeg.

cbird -about lists the image and video extensions. Note that video extensions are not checked against FFmpeg at runtime, so they could be unavailable.

Additionally, zip files are supported for images.

To get the most formats you will need to compile FFmpeg and Qt with the necessary options. Additional image formats are also available with kimageformats.

Link Handling

Links are ignored by default. To follow links, use the index option -i.links 1

If the search path contains links, they are only considered when scanning for changes (-update), otherwise there is no special treatment. For example, deleting a link is the same as any other deletion operation.

Duplicate inodes are not followed by default. If there are duplicate inodes in the tree, the first inode in breadth-first traversal is indexed. To follow all inodes, for example to find duplicate hard links, use -i.dups 1.

The index stores relative paths (to the indexed/root path), this makes the index stable if the parent directory changes. However, if a path contains links, or is a link itself, it is stored as-is; which may be less stable than the storing the link target. To store the resolved links instead, use i.resolve 1. This is only possible if the link target is a child of the index root.

Note that cbird does not not prevent broken links from occurring, the link check is temporary during the index update.

Using Weeds

The "weed" feature allows fast deletion of deleted files that reappear in the future. A weed record is a pair of file hashes, one is the weed/deleted file, the other is the original/retained file. When the weed shows up again, it can be deleted without inspection (-nuke-weeds)

How weeds are recorded

  1. Two files are examined (matching pair) -- use -p.mm 1 or -p.eg 1 to force pairs
  2. Neither file is a zip member
  3. When one of the two files is deleted, it is marked as a weed of the first one

Broken weeds

There is nothing to prevent deletion of the original/retained file, so the weed record can become invalidated. If the original is no longer present, the association can be unset with the "Forget Weed" command.

cbird -weeds -show # show all weeds
cbird -nuke-weeds  # delete all weeds
cbird -similar -with isWeed true # isolate weeds in search results

Environment Variables

There are a few for power users.

Wish List, Bugs, Etc

Check the development notes for known bugs and feature ideas.

Report bugs or request features on github

Search Algorithms

There are several algorithms, some are better than others depending on the situation.

Discrete Cosine Transform (DCT) Hash (-p.alg dct)

Uses one 64-bit hash per image, similar to pHash. Very fast, good for rescaled images and lightly cropped images.

DCT Features -p.alg fdct

Uses DCT hashes centered on scale/rotation invariant features, up to 400 per image. Good for heavily cropped images, much faster than ORB.

Oriented Rotated Brief (ORB) Descriptors -p.alg orb

Uses 256-bit scale/rotation invariant feature descriptors, up to 400 per image. Good for rotated and cropped images, but slow.

Color Histogram -p.alg color

Uses histogram of up to 32 colors (256-byte) per image. Sometimes works when all else fails. This is the only algorithm that finds reflected images, others require -p.refl and must rehash the reflected image (very slow)

DCT Video Index -p.alg video

Uses DCT hashes of video frames. Frames are preprocessed to remove letterboxing. Can also find video thumbnails in the source video since they have the same hash type.

Template Matcher -p.tm 1

Filters results with a high resolution secondary matcher that finds the exact overlap of an image pair. This is most useful to drop poor matches from fdct and orb. Since it requires decompressing the source/destination image it is extremely slow. It can help to reduce the maximum number of matches per image with -p.mm #

How it Performs

Indexing

Indexing happens when -update is used. It can take a while the first time, however subsequent updates only consider changes.

Unused algorithms can be disabled to speed up indexing. If you have large images, you may as well enable all algorithms because image decompression dominates the process.

Table 1: Indexing 1000 6000px images, 8 GB, SSD

Arguments Note Time (seconds)
-update all enabled 46
-i.algos 0 -update md5 only 2
-i.algos 1 -update +dct 41
-i.algos 3 -update +dct features 44
-i.algos 7 -update +orb features 44
-i.algos 15 -update +color hist 46

Searching

Search speed varies with algorithm. The OpenCV search tree for ORB is quite slow compared to others. It is better suited for -similar-to to search a smaller subset suspected to have duplicates.

Table 2: Searching 1000 images

Arguments Note Time (milliseconds)
-similar dct 54
-p.alg fdct -similar dct features 200
-p.alg orb -similar orb features 9000
-p.alg color -similar histograms 450

Large Datasets

Indexing large sets of smaller images benefits from disabling algorithms.

Table 3: Indexing 500k 400px images in 100 zip files, 39GB, NAS

Arguments Note Rate (Img/s) Time (minutes)
-i.algos 0 -update md5 only 861 9:41
-i.algos 1 -update +dct 683 12:11
-i.algos 3 -update +dct features 377 22:04
-i.algos 7 -update +orb features 348 23:56
-i.algos 15 -update +colors 227 36:39

For N^2 search (-similar) only DCT hash is normally practical.

Table 4: Searching 500k images

Arguments Note Time (s)
-p.dht 1 -similar dct, threshold 1 5.5
-p.dht 2 -similar dct, threshold 2 5.6
-p.dht 3 -similar dct, threshold 3 5.9
-p.dht 4 -similar dct, threshold 4 7.1
-p.dht 5 -similar dct, threshold 5 8.9

For K*N (K needle images, N haystack images) the slower algorithms can be practical even for large datasets. For a quick test we can select and search for the first 10 items:

cbird -p.alg fdct -select-type i -head 10 -similar-to @

Table 5: Searching for 10 images in 500k

Arguments Note Time (s)
-p.alg dct -p.dht 2 dct, threshold 2 1.3
-p.alg fdct -p.dht 7 dct-features, threshold 7 1.5
-p.alg orb orb-features 84.4[^1]
-p.alg color colors dnf[^2]

Release Notes

v0.7

v0.6.0

[^1]: OpenCV search tree only partially cached on disk, slow to start [^2]: Color search lacks a search tree, not suitable for large sets