主に2つの記事を参照してください。
https://blog.csdn.net/liuxiao214/article/details/83411820
https://www.jianshu.com/p/577af31ced74
0.環境
windows
python3.6
Dlib
numpy==1.14.5
glob
opencv-python==3.4.3.18
Dlibリファレンスをインストールします:https://blog.csdn.net/qq_35975447/article/details/109802787
0.1ファイル構造
│ .gitignore
│ faceAlignment.py
│ faceDetect.py
│ faceLandmarks.py
│ faceRecognition.py
│ file.txt
│
├─data
│ │ test_1988.jpg
│ │
│ ├─candidate-faces
│ │ liushishi.jpg
│ │ liuyifei.jpg
│ │ tangyan.jpg
│ │ tongliya.jpg
│ │ yangzi.jpg
│ │ zhaoliying.jpg
│ │
│ └─faces
│ tangyan.jpg
│ zhaoliying.jpg
│
├─models
│ dlib_face_recognition_resnet_model_v1.dat
│ mmod_human_face_detector.dat
│ shape_predictor_5_face_landmarks.dat
│ shape_predictor_68_face_landmarks.dat
│
└─results
├─alignment
│ test_1988_0_Align68.jpg
│ test_1988_1_Align68.jpg
│ test_1988_2_Align68.jpg
│ test_1988_3_Align68.jpg
│ test_1988_4_Align68.jpg
│ test_1988_5_Align68.jpg
│
├─detect
│ test_1988_HOG.jpg
│ test_1988_MMOD.jpg
│
├─landmarks
│ test_1988_5Landmarks.jpg
│ test_1988_68Landmarks.jpg
│
└─recongnition
recognition_reslut.txt
0.2モデルのダウンロード
https://github.com/davisking/dlib-models
0.3私のコード
https://download.csdn.net/download/qq_35975447/13129563
1.2つの顔検出方法の比較
2つの方法は主に次のとおりです。Dlibにはディープラーニングモデルが付属しており、これを呼び出します。
1.1時間
豚 |
MMOD |
1.437422513961792s
|
106.82666826248169s |
1.2効果
元の画像:
HOG効果:
MMOD効果:
1.2コード
ここでのコードは主に最初のリンクを参照し、次にDlib独自の方法に従って深層学習モデルを呼び出し、テスト時間がループで実行され、画像がresults / detect /ディレクトリに保存されます。
# encoding:utf-8
import dlib
import cv2
import os
import time
def rect_to_bb(rect): # 获得人脸矩形的坐标信息
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
return (x, y, w, h)
def resize(image, width=1200): # 将待检测的image进行resize
r = width * 1.0 / image.shape[1]
dim = (width, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
return resized
def detect(isHOG=False):
image_path = "./data/"
image_file = "test_1988.jpg"
startTime = time.time()
if isHOG:
detector = dlib.get_frontal_face_detector() # 基于HOG+SVM分类
else:
model_path = "./models/mmod_human_face_detector.dat" # 基于 Maximum-Margin Object Detector 的深度学习人脸检测方案
detector = dlib.cnn_face_detection_model_v1(model_path)
image = cv2.imread(image_path + image_file)
image = resize(image, width=1200)
# image = resize(image, width=600)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)
print("{} method, detect spend {}s ".format(("HOG" if isHOG else "MMOD"), time.time()-startTime))
for (i, rect) in enumerate(rects):
if isHOG:
(x, y, w, h) = rect_to_bb(rect)
else:
(x, y, w, h) = rect_to_bb(rect.rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(image, "Face: {}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow("Output", image)
savePath = "./results/detect/"
if not os.path.exists(savePath):
os.makedirs(savePath)
if isHOG:
saveName = image_file[:-4] + "_HOG.jpg"
else:
saveName = image_file[:-4] + "_MMOD.jpg"
cv2.imwrite(savePath + saveName, image)
cv2.waitKey(10)
if __name__ == "__main__":
isHOG = True
detect(isHOG)
if isHOG:
isHOG = not isHOG
detect(isHOG)
2.顔のキーポイント検出の2つの比較
2つの主なタイプは次のとおりです。5つのキーポイントと68のキーポイント。両方ともモデルを呼び出す必要があります。
2.1時間
68ランドマーク |
5ランドマーク |
0.011994600296020508s |
0.0030002593994140625s |
2.2パフォーマンス効果
5つのランドマーク
68のランドマーク
2.3コード
ここでのコードは主に最初のリンクを参照し、2つのメソッドをそれぞれ実行してから、結果を./results/landmarks/に保存します。
# encoding:utf-8
import dlib
import numpy as np
import cv2
import os
import time
def rect_to_bb(rect): # 获得人脸矩形的坐标信息
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
return (x, y, w, h)
def shape_to_np(shape, is68Landmarks=True, dtype="int"): # 将包含68个特征的的shape转换为numpy array格式
if is68Landmarks:
landmarkNum = 68
else:
landmarkNum = 5
coords = np.zeros((landmarkNum, 2), dtype=dtype)
for i in range(0, landmarkNum):
coords[i] = (shape.part(i).x, shape.part(i).y)
return coords
def resize(image, width=1200): # 将待检测的image进行resize
r = width * 1.0 / image.shape[1]
dim = (width, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
return resized
def feature(is68Landmarks=True):
image_path = "./data/"
image_file = "test_1988.jpg"
detector = dlib.get_frontal_face_detector()
if is68Landmarks:
predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat")
else:
predictor = dlib.shape_predictor("./models/shape_predictor_5_face_landmarks.dat")
image = cv2.imread(image_path + image_file)
image = resize(image, width=1200)# 1200
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)
shapes = []
startTime = time.time()
for (i, rect) in enumerate(rects):
shape = predictor(gray, rect)
shape = shape_to_np(shape, is68Landmarks)
shapes.append(shape)
(x, y, w, h) = rect_to_bb(rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(image, "Face: {}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
print("{} method, detect spend {}s ".format(("68Landmarks" if is68Landmarks else "5Landmarks"), time.time()-startTime))
for shape in shapes:
for (x, y) in shape:
cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
cv2.imshow("Output", image)
savePath = "./results/landmarks/"
if not os.path.exists(savePath):
os.makedirs(savePath)
if is68Landmarks:
saveName = image_file[:-4] + "_68Landmarks.jpg"
else:
saveName = image_file[:-4] + "_5Landmarks.jpg"
cv2.imwrite(savePath + saveName, image)
cv2.waitKey(10)
if __name__ == "__main__":
is68Landmarks = True
feature(is68Landmarks)
if is68Landmarks:
is68Landmarks = not is68Landmarks
feature(is68Landmarks)
3.顔の位置合わせ
ここで2つの方法をテストする必要がありましたが、5つの重要なポイントを変更するのは簡単ではなかったので、あきらめましたが、参照コードはまだ残っているので、自分で削除できます。
3.1時間
配置 |
0.04295229911804199s |
3.2効果
写真が曖昧すぎるので、いくつか選んでください。
3.3コード
ここでの重要なポイントは、以下に基づいています。
結果は./results/alignment/に保存されます:
# encoding:utf-8
import dlib
import cv2
import numpy as np
import math
import os
import time
def rect_to_bb(rect): # 获得人脸矩形的坐标信息
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
return (x, y, w, h)
def resize(image, width=1200): # 将待检测的image进行resize
r = width * 1.0 / image.shape[1]
dim = (width, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
return resized
def face_alignment_68(faces):
# 使用68点关键点模型,根据关键点信息求解变换矩阵,然后把变换矩阵应用到整个图像上。
predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat") # 用来预测关键点
faces_aligned = []
global startTime
startTime = time.time()
for face in faces:
rec = dlib.rectangle(0,0,face.shape[0],face.shape[1])
shape = predictor(np.uint8(face),rec) # 注意输入的必须是uint8类型
order = [36,45,30,48,54] # left eye, right eye, nose, left mouth, right mouth 注意关键点的顺序,这个在网上可以找
for j in order:
x = shape.part(j).x
y = shape.part(j).y
cv2.circle(face, (x, y), 2, (0, 0, 255), -1)
eye_center =((shape.part(36).x + shape.part(45).x) * 1./2, # 计算两眼的中心坐标
(shape.part(36).y + shape.part(45).y) * 1./2)
dx = (shape.part(45).x - shape.part(36).x) # note: right - right
dy = (shape.part(45).y - shape.part(36).y)
angle = math.atan2(dy,dx) * 180. / math.pi # 计算角度
RotateMatrix = cv2.getRotationMatrix2D(eye_center, angle, scale=1) # 计算仿射矩阵
RotImg = cv2.warpAffine(face, RotateMatrix, (face.shape[0], face.shape[1])) # 进行仿射变换,即旋转
faces_aligned.append(RotImg)
return faces_aligned
def face_alignment_5(rgb_img, faces):
startTime = time.time()
faces_aligned = []
for face in faces:
# RotImg = dlib.get_face_chip(rgb_img, face)
RotImg = dlib.get_face_chip(np.uint8(rgb_img), np.uint8(face))
# RotImg = dlib.get_face_chip(rgb_img, face, size=224, padding=0.25)
faces_aligned.append(RotImg)
return faces_aligned
def demo(isAlignment_5=True):
image_path = "./data/"
image_file = "test_1988.jpg"
im_raw = cv2.imread(image_path + image_file).astype('uint8')
# detector = dlib.get_frontal_face_detector()
model_path = "./models/mmod_human_face_detector.dat" # 基于 Maximum-Margin Object Detector 的深度学习人脸检测方案
detector = dlib.cnn_face_detection_model_v1(model_path)
im_raw = resize(im_raw, width=1200)
gray = cv2.cvtColor(im_raw, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)
src_faces = []
for (i, rect) in enumerate(rects):
(x, y, w, h) = rect_to_bb(rect.rect)
detect_face = im_raw[y:y+h,x:x+w]
src_faces.append(detect_face)
cv2.rectangle(im_raw, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(im_raw, "Face: {}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
if isAlignment_5:
faces_aligned = face_alignment_5(im_raw, src_faces)
else:
faces_aligned = face_alignment_68(src_faces)
print("{} method, detect spend {}s ".format(("Alignment_5" if isAlignment_5 else "Alignment_68"), time.time()-startTime))
cv2.imshow("src", im_raw)
savePath = "./results/alignment/"
if not os.path.exists(savePath):
os.makedirs(savePath)
if isAlignment_5:
saveName = "_Align5.jpg"
else:
saveName = "_Align68.jpg"
i = 0
for face in faces_aligned:
cv2.imshow("det_{}".format(i), face)
cv2.imwrite(savePath + image_file[:-4] + "_{}".format(i) + saveName, face)
i = i + 1
cv2.waitKey(10)
if __name__ == "__main__":
isAlignment_5 = False
demo(isAlignment_5)
if isAlignment_5:
isAlignment_5 = not isAlignment_5
demo(isAlignment_5)
4.顔認識
この場所は、主に認識が良くないときに多くの時間を費やします、私はコードを分析するために私の心を沈めませんでした、ここでは参照1のコードに従って変更されています、候補リストを手動で設定する必要はありません、私たちだけが必要です候補者の顔を自分で設定するフォルダ内の候補者ライブラリは、区別できる人にちなんで名付けられています。
4.1準備プロセス
(1)候補データベース内の候補面。このデータベースにアクセスしてクエリを実行します。これらは既知であり、正しいIDを持っています。
(2)候補者の顔の写真の命名:
(3)照会する面:
(4)結果
Processing file: ./data/candidate-faces\liushishi.jpg
Number of faces detected: 1
Processing file: ./data/candidate-faces\liuyifei.jpg
Number of faces detected: 1
Processing file: ./data/candidate-faces\tangyan.jpg
Number of faces detected: 1
Processing file: ./data/candidate-faces\tongliya.jpg
Number of faces detected: 1
Processing file: ./data/candidate-faces\yangzi.jpg
Number of faces detected: 1
Processing file: ./data/candidate-faces\zhaoliying.jpg
Number of faces detected: 1
c_d :[('tangyan', 0.45614611065543303), ('liushishi', 0.4777414300544273), ('yangzi', 0.520176500668875), ('tongliya', 0.547071465533885), ('zhaoliying', 0.64414064895386), ('liuyifei', 0.669962308077882)]
The person_test--./data/faces/tangyan.jpg is: tangyan
c_d :[('zhaoliying', 0.4041512584817519), ('liushishi', 0.4681194204229278), ('tangyan', 0.4728928349513442), ('yangzi', 0.47474913579746303), ('tongliya', 0.5446001882500634), ('liuyifei', 0.6104574640831666)]
The person_test--./data/faces/zhaoliying.jpg is: zhaoliying
4.2コード
結果はファイル./results/recongnition/recognition_reslut.txtに保存されます。
# encoding:utf-8
import dlib
import cv2
import numpy as np
import os, glob
def resize(image, width=1200): # 将待检测的image进行resize
r = width * 1.0 / image.shape[1]
dim = (width, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
return resized
def rect_to_bb(rect): # 获得人脸矩形的坐标信息
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
return (x, y, w, h)
def create_face_space():
# 对文件夹下的每一个人脸进行:
# 1.人脸检测
# 2.关键点检测
# 3.描述子提取
# 候选人脸文件夹
faces_folder_path = "./data/candidate-faces/"
# 候选人脸描述子list
descriptors = []
candidates = []
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
print("Processing file: {}".format(f))
img = cv2.imread(f)
# img = resize(img, width=300)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 1.人脸检测
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
candidate = f.split('\\')[-1][:-4]
for k, d in enumerate(dets):
# 2.关键点检测
shape = sp(img, d)
# 3.描述子提取,128D向量
face_descriptor = facerec.compute_face_descriptor(img, shape)
# 转换为numpy array
v = np.array(face_descriptor)
descriptors.append(v)
candidates.append(candidate)
return descriptors, candidates
def predict(descriptors, path):
# 对需识别人脸进行同样处理
# 提取描述子
img = cv2.imread(path)
# img = io.imread(path)
# img = resize(img, width=300)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dets = detector(gray, 1)
dist = []
if len(dets) == 0:
pass
for k, d in enumerate(dets):
shape = sp(img, d)
face_descriptor = facerec.compute_face_descriptor(img, shape)
d_test = np.array(face_descriptor)
# 计算欧式距离
for i in descriptors:
dist_ = np.linalg.norm(i-d_test)
dist.append(dist_)
# print(dist)
return dist
def demo():
global detector, sp, facerec
# 加载正脸检测器
detector = dlib.get_frontal_face_detector()
# 加载人脸关键点检测器
sp = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat")
# 3. 加载人脸识别模型
facerec = dlib.face_recognition_model_v1("./models/dlib_face_recognition_resnet_model_v1.dat")
# 提取候选人特征与候选人名单
descriptors, candidates = create_face_space()
savePath = "./results/recongnition/"
if not os.path.exists(savePath):
os.makedirs(savePath)
fp = open(savePath + 'recognition_reslut.txt', 'a')
predict_path = "./data/faces/*.jpg"
for f in glob.glob(predict_path):
f = f.replace("\\", '/')
# print("f :{}".format(f))
dist = predict(descriptors, f)
# 候选人和距离组成一个dict
c_d = dict(zip(candidates, dist))
if not c_d:
print(str(c_d) + " is None")
continue
cd_sorted = sorted(c_d.items(), key=lambda d:d[1])
print("c_d :{}".format(cd_sorted))
print("The person_test--{} is: ".format(f), cd_sorted[0][0])
fp.write("\nThe person_test--{} is: with similar : {}".format(f, cd_sorted[0][0]))
fp.close()
if __name__ == "__main__":
demo()
参照
2. [ツール] Dlibインターフェースの学習と共通機能の導入
5. Dlibは顔の特徴点を抽出します(68点、opencv描画)