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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-03-09 13:19:48 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-03-09 13:20:02 +0000 |
commit | 58daab21cd043e1dc37024a7f99b396788372918 (patch) | |
tree | 96771e43bb69f7c1c2b0b4f7374cb74d7866d0cb /ml/dlib/python_examples/face_clustering.py | |
parent | Releasing debian version 1.43.2-1. (diff) | |
download | netdata-58daab21cd043e1dc37024a7f99b396788372918.tar.xz netdata-58daab21cd043e1dc37024a7f99b396788372918.zip |
Merging upstream version 1.44.3.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'ml/dlib/python_examples/face_clustering.py')
-rwxr-xr-x | ml/dlib/python_examples/face_clustering.py | 127 |
1 files changed, 127 insertions, 0 deletions
diff --git a/ml/dlib/python_examples/face_clustering.py b/ml/dlib/python_examples/face_clustering.py new file mode 100755 index 000000000..362613871 --- /dev/null +++ b/ml/dlib/python_examples/face_clustering.py @@ -0,0 +1,127 @@ +#!/usr/bin/python +# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt +# +# This example shows how to use dlib's face recognition tool for clustering using chinese_whispers. +# This is useful when you have a collection of photographs which you know are linked to +# a particular person, but the person may be photographed with multiple other people. +# In this example, we assume the largest cluster will contain photos of the common person in the +# collection of photographs. Then, we save extracted images of the face in the largest cluster in +# a 150x150 px format which is suitable for jittering and loading to perform metric learning (as shown +# in the dnn_metric_learning_on_images_ex.cpp example. +# https://github.com/davisking/dlib/blob/master/examples/dnn_metric_learning_on_images_ex.cpp +# +# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE +# You can install dlib using the command: +# pip install dlib +# +# Alternatively, if you want to compile dlib yourself then go into the dlib +# root folder and run: +# python setup.py install +# or +# python setup.py install --yes USE_AVX_INSTRUCTIONS +# if you have a CPU that supports AVX instructions, since this makes some +# things run faster. This code will also use CUDA if you have CUDA and cuDNN +# installed. +# +# Compiling dlib should work on any operating system so long as you have +# CMake installed. On Ubuntu, this can be done easily by running the +# command: +# sudo apt-get install cmake +# +# Also note that this example requires scikit-image which can be installed +# via the command: +# pip install scikit-image +# Or downloaded from http://scikit-image.org/download.html. + +import sys +import os +import dlib +import glob +from skimage import io + +if len(sys.argv) != 5: + print( + "Call this program like this:\n" + " ./face_clustering.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces output_folder\n" + "You can download a trained facial shape predictor and recognition model from:\n" + " http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n" + " http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2") + exit() + +predictor_path = sys.argv[1] +face_rec_model_path = sys.argv[2] +faces_folder_path = sys.argv[3] +output_folder_path = sys.argv[4] + +# Load all the models we need: a detector to find the faces, a shape predictor +# to find face landmarks so we can precisely localize the face, and finally the +# face recognition model. +detector = dlib.get_frontal_face_detector() +sp = dlib.shape_predictor(predictor_path) +facerec = dlib.face_recognition_model_v1(face_rec_model_path) + +descriptors = [] +images = [] + +# Now find all the faces and compute 128D face descriptors for each face. +for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")): + print("Processing file: {}".format(f)) + img = io.imread(f) + + # Ask the detector to find the bounding boxes of each face. The 1 in the + # second argument indicates that we should upsample the image 1 time. This + # will make everything bigger and allow us to detect more faces. + dets = detector(img, 1) + print("Number of faces detected: {}".format(len(dets))) + + # Now process each face we found. + for k, d in enumerate(dets): + # Get the landmarks/parts for the face in box d. + shape = sp(img, d) + + # Compute the 128D vector that describes the face in img identified by + # shape. + face_descriptor = facerec.compute_face_descriptor(img, shape) + descriptors.append(face_descriptor) + images.append((img, shape)) + +# Now let's cluster the faces. +labels = dlib.chinese_whispers_clustering(descriptors, 0.5) +num_classes = len(set(labels)) +print("Number of clusters: {}".format(num_classes)) + +# Find biggest class +biggest_class = None +biggest_class_length = 0 +for i in range(0, num_classes): + class_length = len([label for label in labels if label == i]) + if class_length > biggest_class_length: + biggest_class_length = class_length + biggest_class = i + +print("Biggest cluster id number: {}".format(biggest_class)) +print("Number of faces in biggest cluster: {}".format(biggest_class_length)) + +# Find the indices for the biggest class +indices = [] +for i, label in enumerate(labels): + if label == biggest_class: + indices.append(i) + +print("Indices of images in the biggest cluster: {}".format(str(indices))) + +# Ensure output directory exists +if not os.path.isdir(output_folder_path): + os.makedirs(output_folder_path) + +# Save the extracted faces +print("Saving faces in largest cluster to output folder...") +for i, index in enumerate(indices): + img, shape = images[index] + file_path = os.path.join(output_folder_path, "face_" + str(i)) + # The size and padding arguments are optional with default size=150x150 and padding=0.25 + dlib.save_face_chip(img, shape, file_path, size=150, padding=0.25) + + + + |