1. 檢測人臉
2.提取人臉
3.計算人臉特徵向量（用於1：N或1:1比對）,使用的是shape predictor
import sys
import os
import dlib
import glob
if len(sys.argv) != 4:
print(
"Call this program like this:\n"
" ./face_recognition.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces\n"
"You can download a trained facial shape predictor and recognition model from:\n"
" dlib.net/files/shape…\n"
" dlib.net/files/dlib_…")
exit()
predictor_path = sys.argv[1]
face_rec_model_path = sys.argv[2]
faces_folder_path = sys.argv[3]
# 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)
win = dlib.image_window()
# Now process all the images
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
print("Processing file: {}".format(f))
img = dlib.load_rgb_image(f)
win.clear_overlay()
win.set_image(img)
# 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):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
# Get the landmarks/parts for the face in box d.
shape = sp(img, d)
# Draw the face landmarks on the screen so we can see what face is currently being processed.
win.clear_overlay()
win.add_overlay(d)
win.add_overlay(shape)
# Compute the 128D vector that describes the face in img identified by
# shape. In general, if two face descriptor vectors have a Euclidean
# distance between them less than 0.6 then they are from the same
# person, otherwise they are from different people. Here we just print
# the vector to the screen.
face_descriptor = facerec.compute_face_descriptor(img, shape)
print(face_descriptor)
# It should also be noted that you can also call this function like this:
# face_descriptor = facerec.compute_face_descriptor(img, shape, 100)
# The version of the call without the 100 gets 99.13% accuracy on LFW
# while the version with 100 gets 99.38%. However, the 100 makes the
# call 100x slower to execute, so choose whatever version you like. To
# explain a little, the 3rd argument tells the code how many times to
# jitter/resample the image. When you set it to 100 it executes the
# face descriptor extraction 100 times on slightly modified versions of
# the face and returns the average result. You could also pick a more
# middle value, such as 10, which is only 10x slower but still gets an
# LFW accuracy of 99.3%.
dlib.hit_enter_to_continue()
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作者：_iorilan
來源：CSDN
原文：blog.csdn.net/lan_liang/a…
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