安装
- opencv 和 包模块opencv-contrib-python
- 安装失败的解决方案
pip uninstall opencv-python
pip uninstall opencv-contrib-python
pip install opencv-python
pip install opencv-contrib-python
- 完成后重启就可以了
实现
- 先对人脸进行识别
- 获取人脸特征图片并截取 灰度图保存
- 进行人脸模型训练
- 测试运行
人脸识别
- 运用了opencv 的人脸识别框架
face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
- 截取800 帧的人脸图片 并保存数据图片
face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
VIDEO_PATH = 'video/hero2.mp4'
face_id = 2
#sampleNum用来计数样本数目
count = 0
SAVE_PATH = 'data/'
cap = cv.VideoCapture(VIDEO_PATH)
count = 0
while cap.isOpened():
ret, img = cap.read()
if ret is not None:
if img is None:
continue
img = imutils.resize(img, width=600)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
face = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in face:
cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0))
count += 1
if not os.path.exists(SAVE_PATH + 'user.' + str(face_id)):
os.mkdir(SAVE_PATH + 'user.' + str(face_id))
cv.imwrite(SAVE_PATH + 'user.' + str(face_id) + "/count_" + str(count) + ".jpg", gray[y: y + h, x: x + w])
if count >= 800:
break
cv.imshow('h', img)
key = cv.waitKey(1)
if key == 27:
break
else:
break
cap.release()
cv.destroyAllWindows()
测试用一个宝藏up主的人脸测试，打扰了，你要火
进行人脸模型训练
- 运用opencv 的face mok
- recog = cv.face.LBPHFaceRecognizer_create()
# 人脸识别器
import time
recog = cv.face.LBPHFaceRecognizer_create()
recog.read('trainner/face.yaml')
#创建一个函数，用于从数据集文件夹中获取训练图片,并获取id
time_start = time.process_time()
def get_imgs_labels():
face_id = 0
face_arr = []
face_ids = []
for user_id in os.listdir(SAVE_PATH):
face_id = user_id.split('.')[1]
user_path = SAVE_PATH + user_id
image_paths = [os.path.join(user_path, key) for key in os.listdir(user_path)]
for path in image_paths:
face_ids.append(int(face_id))
img = cv.imread(path, 0)
# img_arr = np.array(img, dtype="uint8")
face_arr.append(img)
return face_arr, face_ids
face_arr, face_ids = get_imgs_labels()
time_end = time.process_time ()
print('runTime' + str((time_end - time_start)))
recog.train(train_img_gen)
print('train' + str((time.process_time () - time_end)))
recog.save('trainner/face.yaml')
- 训练完保存了一个模型文件
识别人脸测试
- 先用人脸识别出人脸区域，截取出人脸区域，进行灰度化
- 输入到模型中预测
- 图片展示
VIDEO_PATH = 'video/hero3.mp4'
font = cv.FONT_HERSHEY_SIMPLEX
idNum = 0
names = ['unknow', 'cc', 'dm']
cap = cv.VideoCapture(VIDEO_PATH)
while cap.isOpened():
ret, img = cap.read()
img = imutils.resize(img, width=600)
if ret is not None:
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
face = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in face:
cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0))
id, conf = recog.predict(gray[y: y+h, x: x+w])
user = ''
if conf < 100:
user = names[id]
conf = "{0}%".format(round(100-conf))
else:
user = "unknown"
conf = "{0}%".format(round(100-conf))
cv.putText(img, user, (x + 5, y - 5), font, 1, (0,255, 0), 1)
cv.putText(img, str(conf), (x + 50, y - 5), font, 1, (0,255, 0), 1)
cv.imshow('face', img)
key = cv.waitKey(1)
if key == 27:
break
cap.release()
cv.destroyAllWindows()
效果展示
- 下面这个是训练的视频，上面是测试的视频
完整代码
# %%
import cv2 as cv
import numpy as np
import imutils
import os
from PIL import Image
# %%
face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
VIDEO_PATH = 'video/hero2.mp4'
face_id = 2
#sampleNum用来计数样本数目
count = 0
SAVE_PATH = 'data/'
cap = cv.VideoCapture(VIDEO_PATH)
count = 0
while cap.isOpened():
ret, img = cap.read()
if ret is not None:
if img is None:
continue
img = imutils.resize(img, width=600)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
face = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in face:
cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0))
count += 1
if not os.path.exists(SAVE_PATH + 'user.' + str(face_id)):
os.mkdir(SAVE_PATH + 'user.' + str(face_id))
cv.imwrite(SAVE_PATH + 'user.' + str(face_id) + "/count_" + str(count) + ".jpg", gray[y: y + h, x: x + w])
if count >= 800:
break
cv.imshow('h', img)
key = cv.waitKey(1)
if key == 27:
break
else:
break
cap.release()
cv.destroyAllWindows()
# %%
import tensorflow.keras as keras
from keras.preprocessing.image import ImageDataGenerator
train_gen = ImageDataGenerator(rescale= 1./255)
train_img_gen = train_gen.flow_from_directory('./data/')
# %%
# 人脸识别器
import time
recog = cv.face.LBPHFaceRecognizer_create()
recog.read('trainner/face.yaml')
#创建一个函数，用于从数据集文件夹中获取训练图片,并获取id
time_start = time.process_time()
def get_imgs_labels():
face_id = 0
face_arr = []
face_ids = []
for user_id in os.listdir(SAVE_PATH):
face_id = user_id.split('.')[1]
user_path = SAVE_PATH + user_id
image_paths = [os.path.join(user_path, key) for key in os.listdir(user_path)]
for path in image_paths:
face_ids.append(int(face_id))
img = cv.imread(path, 0)
# img_arr = np.array(img, dtype="uint8")
face_arr.append(img)
return face_arr, face_ids
face_arr, face_ids = get_imgs_labels()
time_end = time.process_time ()
print('runTime' + str((time_end - time_start)))
recog.train(train_img_gen)
print('train' + str((time.process_time () - time_end)))
recog.save('trainner/face.yaml')
# %%
VIDEO_PATH = 'video/hero2.mp4'
font = cv.FONT_HERSHEY_SIMPLEX
idNum = 0
names = ['unknow', 'cc', 'dm']
cap = cv.VideoCapture(VIDEO_PATH)
while cap.isOpened():
ret, img = cap.read()
img = imutils.resize(img, width=600)
if ret is not None:
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
face = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in face:
cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0))
id, conf = recog.predict(gray[y: y+h, x: x+w])
user = ''
if conf < 100:
user = names[id]
conf = "{0}%".format(round(100-conf))
else:
user = "unknown"
conf = "{0}%".format(round(100-conf))
cv.putText(img, user, (x + 5, y - 5), font, 1, (0,255, 0), 1)
cv.putText(img, str(conf), (x + 50, y - 5), font, 1, (0,255, 0), 1)
cv.imshow('face', img)
key = cv.waitKey(1)
if key == 27:
break
cap.release()
cv.destroyAllWindows()
# %%
转载自CSDN-专业IT技术社区
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