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facedetect

A face detection, pupil/eyes localization and facial landmark points detection library.

Port of Pigo. Also uses some tweaks and thresholds from Photoprism (which is using pigo for detecting faces).

API Reference: https://denull.github.io/facedetect/

Installation

nimble install facedetect

Cascade files

This library uses the same cascade file format used by Pigo. Those files available at cascade directory, copy it to your app folder so they can be loaded at runtime. Including them statically will be supported later.

Usage

Although this is a direct port of Pigo, some APIs are a bit different. For example, instead of Pigo and PuplocCascade structs, this library uses FaceCascade and LandmarkCascade.

Also there're two APIs: low-level procs, which allow to separately detect faces, eyes and other landmark features, and a bit higher-level FaceDetector, which does everything in one go. Using FaceDetector is recommended, as it also applies some extra filtering on the results.

Example:

import facedetect

let fd = initFaceDetector() # This will load all cascade files from `cascade` directory
let image = readGrayscaleImage("sample.jpg") # Load image (using pixie) and convert it to grayscale
let people = fd.detect(image, minSize = 20, shiftFactor = 0.1, scaleFactor = 1.1)
for i, person in people:
  echo "Found face at ", person.face.x, ", ", person.face.y

There's also a demo app in cmd/demo.nim (but it uses wNim library, so it's Windows-only). For an example of cross-platform usage of lower-level (only face detection), see cmd/test.nim.

Benchmark

For benchmarking I replicated code from https://github.com/esimov/pigo-gocv-benchmark. It's available in demo/benchmark.nim Depending on compilation flags, it gives following results on my machine:

nim c -r cmd/benchmark (DEBUG build):                       180ms
nim c -d:release -r cmd/benchmark (RELEASE build):          52ms
nim c -d:danger --passC:"-O3 -flto -m64" -r cmd/benchmark:  50ms

For comparison, this is Go implementations:

cpu: AMD Ryzen 5 3600 6-Core Processor
BenchmarkGoCV-12              10         106933090 ns/op
BenchmarkPIGO-12              14          81145464 ns/op

So facedetect (with maximal optimisations enabled) seems to work about 38% faster than PIGO and 53% faster than GoCV. I should note, however, that this benchmark covers only the basic face detection (eyes/landmark detection is not included).