def create_face(images_dir, out_face_dir):
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
:param images_dir:
:param out_face_dir:
:return:
'''
# image_list=file_processing.get_files_list(images_dir, postfix='jpg')
image_list, names_list = file_processing.gen_files_labels(images_dir,
postfix='jpg')
face_detect = face_recognition.Facedetection()
for image_path, name in zip(image_list, names_list):
image = image_processing.read_image(image_path,
resize_height=0,
resize_width=0,
normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:, 0:4].astype(int)
bounding_box = bounding_box[0, :]
print("face box:{}".format(bounding_box))
face_image = image_processing.crop_image(image, bounding_box)
# image_processing.show_image("face", face_image)
# image_processing.show_image_box("face",image,bounding_box)
out_path = os.path.join(out_face_dir, name)
face_image = image_processing.resize_image(face_image, resize_height,
resize_width)
if not os.path.exists(out_path):
os.mkdir(out_path)
basename = os.path.basename(image_path)
out_path = os.path.join(out_path, basename)
image_processing.save_image(out_path, face_image)
def create_face_embedding(model_path,dataset_path,out_emb_path,out_filename):
# 建立npy文件
files_list,names_list=file_processing.gen_files_labels(dataset_path,postfix=['*.jpg'])
embeddings,label_list=get_face_embedding(model_path,files_list, names_list)
print("label_list:{}".format(label_list))
print("have {} label".format(len(label_list)))
embeddings=np.asarray(embeddings)
np.save(out_emb_path, embeddings)
file_processing.write_list_data(out_filename, label_list, mode='w')
def create_embedding(model_path, emb_face_dir):
face_net = face_recognition.facenetEmbedding(model_path)
image_list, names_list = file_processing.gen_files_labels(emb_face_dir,
postfix='jpg')
images = image_processing.get_images(image_list, 160, 160, whiten=True)
compare_emb = face_net.get_embedding(images)
h5file = h5py.File('face.h5', 'w')
h5file['X_train'] = compare_emb
h5file.close()
file_processing.write_data('name.txt', image_list, model='w')
def create_face(images_dir, out_face_dir):
print(images_dir)
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
:param images_dir:
:param out_face_dir:
:return:
'''
# image_list=file_processing.get_files_list(images_dir, postfix='jpg')
image_list, names_list = file_processing.gen_files_labels(images_dir)
face_detect = face_recognition.Facedetection()
for image_path, name in zip(image_list, names_list):
print("图片的路径", str(image_path))
if image_path == 'dataset/images/.DS_Store':
continue
if image_path == 'dataset/images/face1/.DS_Store':
continue
if image_path == 'dataset/images/face2/.DS_Store':
continue
if image_path == 'dataset/images/face3/.DS_Store':
continue
if image_path == 'dataset/images/face4/.DS_Store':
continue
if image_path == './dataset/emb_face/.DS_Store':
continue
image = image_processing.read_image(image_path,
resize_height=0,
resize_width=0,
normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:, 0:4].astype(int)
if (len(bounding_box) < 1):
continue
bounding_box = bounding_box[0, :]
print("face box:{}".format(bounding_box))
face_image = image_processing.crop_image(image, bounding_box)
# image_processing.show_image("face", face_image)
# image_processing.show_image_box("face",image,bounding_box)
out_path = os.path.join(out_face_dir, name)
if (len(face_image) > 0):
face_image = image_processing.resize_image(face_image,
resize_height,
resize_width)
else:
continue
if not os.path.exists(out_path):
os.mkdir(out_path)
basename = os.path.basename(image_path)
out_path = os.path.join(out_path, basename)
image_processing.save_image(out_path, face_image)
def create_face(images_dir, out_face_dir):
'''
生成人脸数据图库，保存在out_face_dir中，这些数据库将用于生成embedding数据库
'''
#此处引用自定义函数 : file_processing.gen_files_labels(images_dir,postfix='jpg')
#返回值: 图像数组: images_dir下所有文件(是的,你没想错!包括子目录下的所有文件)的路径(Path)会被存储到image_list
# 标签数组: names_list就是相应的的"文件名或子目录名"，在本项目中,子目录名 将作为 作为样本的标签(即ID,e.g.姓名 或 学号之类的)
print('#2nd--Mtcnn人脸库')
image_list,names_list=file_processing.gen_files_labels(images_dir,postfix='jpg')
face_detect=face_recognition.Facedetection()
for image_path ,name in zip(image_list,names_list):
# try:
image=image_processing.read_image(image_path, resize_height=0, resize_width=0, normalization=False)
# 获取 判断标识 bounding_box crop_image
bounding_box, points = face_detect.detect_face(image)
bounding_box = bounding_box[:,0:4].astype(int)
# try:
bounding_box=bounding_box[0,:]
# except:
# print("跳过当前图片")
# continue
print("矩形框坐标为:{}".format(bounding_box))
face_image = image_processing.crop_image(image,bounding_box)
# except:
# print("当前图像帧格式非法，将被忽略")
out_path=os.path.join(out_face_dir,name)
face_image=image_processing.resize_image(face_image, resize_height, resize_width)
if not os.path.exists(out_path):
os.mkdir(out_path)
basename=os.path.basename(image_path)
out_path=os.path.join(out_path,basename)
image_processing.save_image(out_path,face_image)
def main():
conn = pymysql.connect(host="localhost",
port=3306,
user="******",
password="******",
db="test")
cursor = conn.cursor()
# 1.激活
ret = fun.Activate(Appkey, SDKey)
if ret == 0 or ret == 90114:
print('激活成功:', ret)
else:
print('激活失败:', ret)
pass
# 2.初始化
ret = fun.initAll()
if ret[0] == 0:
print('初始化成功:', ret, '句柄', fun.Handle)
else:
print('初始化失败:', ret)
pass
files_list, names_list = file_processing.gen_files_labels(
asf_dataset_path, postfix=['*.jpg'])
asf_embeddings, asf_label_list = fun.Feature_extract_batch(
fun, files_list, names_list) # 特征，标签
print("label_list:{}".format(asf_label_list))
print("have {} label".format(len(asf_label_list)))
for f, name in zip(asf_embeddings, asf_label_list):
# print(f.feature, f.featureSize)
f_bytes = BytesIO(string_at(f.feature, f.featureSize))
# print("f.getvalue():", len(f.getvalue()), f.getvalue())
# a.write(f.getvalue())
sql = '''
insert into asf_user_face_info(name, feature_size, feature, create_time)
values ("%s", %d, "%s", "%s")
''' % (str(name), f.featureSize,
str(base64.b64encode(f_bytes.getvalue())), str(time.time()))
# print(sql)
cursor.execute(sql)
cursor.execute("commit")
cursor.close()
def create_embedding(model_path, emb_face_dir, out_emb_path, out_filename):
'''
产生embedding数据库，这些embedding其实就是人脸特征
'''
print('#3rd--FaceNet人脸库')
face_net = face_recognition.facenetEmbedding(model_path)
image_list,names_list=file_processing.gen_files_labels(emb_face_dir,postfix='jpg')
images= image_processing.get_images(image_list,resize_height,resize_width,whiten=True)
compare_emb = face_net.get_embedding(images)
np.save(out_emb_path, compare_emb)
file_processing.write_data(out_filename, names_list, model='w')
def create_face_embedding(model_path, dataset_path, out_emb_path,
out_filename):
'''
:param model_path: faceNet模型路径
:param dataset_path: 人脸数据库路径，每一类单独一个文件夹
:param out_emb_path: 输出embeddings的路径
:param out_filename: 输出与embeddings一一对应的标签
:return: None
'''
files_list, names_list = file_processing.gen_files_labels(
dataset_path, postfix=['*.jpg', '*jpeg'])
embeddings, label_list = get_face_embedding(model_path, files_list,
names_list)
print("label_list:{}".format(label_list))
print("have {} label".format(len(label_list)))
embeddings = np.asarray(embeddings)
np.save(out_emb_path, embeddings)
file_processing.write_list_data(out_filename, label_list, mode='w')
def create_embedding(model_path, emb_face_dir, out_emb_path, out_filename):
'''
产生embedding数据库，保存在out_data_path中，这些embedding其实就是人脸的特征
:param model_path:
:param emb_face_dir:
:param out_emb_path:
:param out_filename:
:return:
'''
face_net = face_recognition.facenetEmbedding(model_path)
# image_list=file_processing.get_files_list(emb_face_dir,postfix='jpg')
image_list, names_list = file_processing.gen_files_labels(emb_face_dir,
postfix='jpg')
images = image_processing.get_images(image_list,
resize_height,
resize_width,
whiten=True)
compare_emb = face_net.get_embedding(images)
np.save(out_emb_path, compare_emb)
# 可以选择保存image_list或者names_list作为人脸的标签
# 测试时建议保存image_list，这样方便知道被检测人脸与哪一张图片相似
file_processing.write_data(out_filename, image_list, model='w')
def face_recognition_for_bzl(model_path, test_dataset, filename):
# 加载数据库的数据
dataset_emb, names_list = predict.load_dataset(dataset_path, filename)
print("loadind dataset...\n names_list:{}".format(names_list))
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
#获得测试图片的路径和label
filePath_list, label_list = file_processing.gen_files_labels(test_dataset)
label_list = [name.split('_')[0] for name in label_list]
print("filePath_list:{},label_list{}".format(len(filePath_list),
len(label_list)))
right_num = 0
wrong_num = 0
detection_num = 0
test_num = len(filePath_list)
for image_path, label_name in zip(filePath_list, label_list):
print("image_path:{}".format(image_path))
# 读取图片
image = image_processing.read_image_gbk(image_path)
# 人脸检测
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed="height")
if bboxes == [] or landmarks == []:
print("-----no face")
continue
if len(bboxes) >= 2 or len(landmarks) >= 2:
print("-----image have {} faces".format(len(bboxes)))
continue
# 获得人脸框区域
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images,
normalization=True)
# face_images = image_processing.get_prewhiten_images(face_images,normalization=True)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = predict.compare_embadding(pred_emb,
dataset_emb,
names_list,
threshold=1.3)
# 在图像上绘制人脸边框和识别的结果
# show_info = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
# image_processing.show_image_text("face_recognition", image, bboxes, show_info)
index = 0
pred_name = pred_name[index]
pred_score = pred_score[index]
if pred_name == label_name:
right_num += 1
else:
wrong_num += 1
detection_num += 1
print(
"-------------label_name:{},pred_name:{},score:{:3.4f},status:{}".
format(label_name, pred_name, pred_score,
(label_name == pred_name)))
# 准确率
accuracy = right_num / detection_num
# 漏检率
misdetection = (test_num - detection_num) / test_num
print("-------------right_num/detection_num:{}/{},accuracy rate:{}".format(
right_num, detection_num, accuracy))
print(
"-------------misdetection/all_num:{}/{},misdetection rate:{}".format(
(test_num - detection_num), test_num, misdetection))
SDKey = b'D5QB8ARVCxWsTLAeWi2SqAmXkVToqWCVAto6UNce3mXd'
ret = fun.Activate(Appkey, SDKey) # 激活
if ret == 0 or ret == 90114:
log.logger.info("激活成功: %s" % (ret))
else:
log.logger.warn("激活失败: %s" % (ret))
pass
ret = fun.initAll() # 初始化
if ret[0] == 0:
log.logger.info("初始化成功: %s, 句柄: %s" % (ret, fun.Handle))
else:
log.logger.warn("初始化失败: %s" % (ret))
# 3.拿到files_list和names_list
files_list, names_list = file_processing.gen_files_labels(
data_path, postfix=['*.jpg'])
# 4.数据预处理，过滤掉有多个人的图片，确保每个人只有一张图片
start = time.time()
emb_file_list = []
emb_name_list = []
already_existing_list = [] # 已经存在于nameList的人，不用再往特征集准备数据里放
for file, name in zip(files_list, names_list):
if name not in already_existing_list: # 如果该人照片没有被处理过，则进行人脸检测，过滤掉图中多个人脸的
frame = cv2.imdecode(np.fromfile(file, dtype=np.uint8),
cv2.IMREAD_COLOR)
bboxes, landmarks = face_detect.detect_face(frame)
if len(bboxes) == 1: # 只有一个人
emb_file_list.append(file) # 文件列表
emb_name_list.append(name) # 标签列表
already_existing_list.append(name) # 该人已被处理
def face_recognition_for_bzl(model_path,dataset_path,test_dataset, filename,threshold):
# 获得测试图片的路径和label
filePath_list, label_list=file_processing.gen_files_labels(test_dataset,postfix=None)
label_list=[name.split('_')[0] for name in label_list]
print("filePath_list:{},label_list{}".format(len(filePath_list),len(label_list)))
face_face_recognition_batch(model_path,dataset_path,filename,filePath_list,label_list,threshold)
