facetools
Easy-to-use face related tools, including face detection, landmark localization, alignment & recognition, based on PyTorch.
Do face detection and landmark localization using MTCNN
from PIL import Imagefrom align.detector import detect_facesfrom align.visualization_utils import show_results img = Image.open('imgs/single.jpg') # modify the image path to yoursbounding_boxes, landmarks = detect_faces(img) # detect bboxes and landmarks for all faces in the imageshow_results(img, bounding_boxes, landmarks) # visualize the results
Do alignment
from align.face_align import align res = align('imgs/single.jpg', save_path='./result', vis=False) res.show()
Do face encoding using IR50 model (download pretrained model)
from PIL import Imagefrom util.extract_feature import extract_featurefrom backbone.model_irse import IR_50image_1 = Image.open('imgs/align.jpg') # modify the image path to yoursmodel = IR_50([112, 112]) model_cp = 'checkpoint/backbone_ir50_ms1m_epoch120.pth'features = extract_feature(image_1, model, model_cp)print(features.size()) # output : torch.Size([1, 512])
Calculate the distance between two images
import numpy as npfrom PIL import Imagefrom util.extract_feature import extract_featurefrom backbone.model_irse import IR_50from scipy.spatial.distance import pdist face_1 = Image.open('imgs/person_1/17.jpg') face_2 = Image.open('imgs/person_1/18.jpg') # face_1 and face_2 belong to the same oneface_3 = Image.open('imgs/person_2/151.jpg') face_4 = Image.open('imgs/person_2/152.jpg') # face_3 and face_4 belong to the same onemodel = IR_50([112, 112]) model_cp = 'checkpoint/backbone_ir50_ms1m_epoch120.pth'data = [face_1, face_2, face_3, face_4] features = extract_feature(data, model, model_cp) features = [i.numpy() for i in features] # embeddings for face_1, face_2, face_3 and face_4diff = np.subtract(features[0], features[1]) dist = np.sum(np.square(diff), 1)print(dist) # output : 1984.6016diff = np.subtract(features[2], features[3]) dist = np.sum(np.square(diff), 1)print(dist) # output : 1921.2222diff = np.subtract(features[0], features[2]) dist = np.sum(np.square(diff), 1)print(dist) # output : 16876.32diff = np.subtract(features[1], features[3]) dist = np.sum(np.square(diff), 1)print(dist) # output : 17107.396dist = pdist(np.vstack([features[0], features[1]]), 'cosine')print(dist) # output : 0.12932935dist = pdist(np.vstack([features[2], features[3]]), 'cosine')print(dist) # output : 0.11706942dist = pdist(np.vstack([features[0], features[2]]), 'cosine')print(dist) # output : 1.09022914dist = pdist(np.vstack([features[1], features[3]]), 'cosine')print(dist) # output : 1.07447068
Do face parsing
from PIL import Imagefrom parsing.face_parsing import parsing, vis_parsing_maps image = Image.open('imgs/9.jpg') res = parsing(image) vis_parsing_maps(image, res, show=True, save_im=True)
It is easy to use facetools in your project.
Your project │ README.md │ ... │ foo.py │ └───facetools │ └───directory1 │ └───...
In foo.py
, you can easily import facetools by adding:
from facetools import detect_faces, show_resultsfrom PIL import Imagedef foo(): img = Image.open('/path/to/your/image') bounding_boxes, landmarks = detect_faces(img) show_results(img, bounding_boxes, landmarks)
This repo is based on face.evoLVe.PyTorch and face-parsing.PyTorch. Many thanks to the excellent repo.
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