def face_recognition_image(model_path, dataset_path, filename, image_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
image = image_processing.read_image_gbk(image_path)
# 获取 判断标识 bounding_box crop_image
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")
exit(0)
# print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(image, bboxes,
resize_height,
resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb,
names_list)
# 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)]
print(show_info)
def createFeature(self, photos):
"""
创建人脸特征，返回人脸特征向量列表
:param photos:图片列表
:type photos:list
:return:embeddings列表
:rtype:list
"""
embeddings = [] # 用于保存人脸特征数据库
for photo in photos:
image = image_processing.change_image(photo)
# 进行人脸检测，获得bounding_box
bboxes, landmarks = face_detect.detect_face(image, False)
# 返回人脸框和5个关键点
bboxes = face_detect.get_square_bboxes(bboxes, landmarks)
# 获得人脸区域
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
# 人脸预处理，归一化
face_images = image_processing.get_prewhiten_images(
face_images, normalization=True)
# 获得人脸特征
pred_emb = face_net.get_embedding(face_images)
embeddings.append(pred_emb)
embeddings = np.asarray(embeddings) # 将元组转为列表
print(embeddings)
return embeddings
def face_recognition_image(model_path, dataset_path, filename, image_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.FaceDetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
# 读取待检图片
image = image_processing.read_image_gbk(image_path)
print("image_processing.read_image_gbk:", type(image),
image.shape) # <class 'numpy.ndarray'>, (616, 922, 3)，（高，宽，通道）
# 获取 判断标识 bounding_box crop_image
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(
bboxes, landmarks, fixed="height") # 以高为基准，获得等宽的举行
if bboxes == [] or landmarks == []:
print("-----no face")
exit(0)
print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width) # 按照bboxes截取矩形框
face_images = image_processing.get_prewhiten_images(face_images) # 图像归一化
pred_emb = face_net.get_embedding(face_images) # 获取facenet特征
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb,
names_list)
# 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_bboxes_text("face_reco", image, bboxes,
show_info)
def get_face_embedding(model_path,files_list, names_list):
# 获得embedding数据
colorSpace="RGB"
face_detect = face_rec.FaceDetection()
face_net = face_rec.FacenetEmbedding(model_path)
embeddings=[]
label_list=[]
for image_path, name in zip(files_list, names_list):
print("processing image :{}".format(image_path))
image = image_processing.read_image_gbk(image_path, colorSpace=colorSpace)
if not isinstance(image, np.ndarray): continue
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
face_images = image_processing.get_bboxes_image(image, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images,normalization=True)
pred_emb = face_net.get_embedding(face_images)
embeddings.append(pred_emb)
label_list.append(name)
return embeddings,label_list
def face_recognition_video(model_path,dataset_path, filename,video_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.Facedetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
cap = cv2.VideoCapture(video_path) # 获取视频数据
face_cnt = 0 # 控制视频读取时候，每秒截取指定数量图片进行一次识别
while cap.isOpened():
ok, frame = cap.read()
face_cnt += 1
if not ok:
break
if(face_cnt % 5 == 0):
frame = image_processing.read_image1(frame)
bboxes, landmarks = face_detect.detect_face(frame)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes, landmarks, fixed="height")
if bboxes == [] or landmarks == []:
print("-----no face")
continue
print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(frame, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb, names_list)
# 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)]
print("showinfo:", show_info)
image_processing.show_image_bboxes_text("face_recognition", frame, bboxes, show_info)
cap.release()
cv2.destroyWindow("face_recognition")
def faceRecognition(photo):
"""
:param photo: 前端发来的照片(原生照片，没有经过任何处理)
:type photo: numpy.ndarray
:return: 识别结果
:rtype:
"""
# 人脸检测、对齐
# bboxes人脸框信息（左上角横纵坐标、右下角横纵坐标）
# landmarks人脸五个关键点坐标（左右眼、鼻尖、左右嘴角）
bboxes, landmarks, image = mtcnnDetection.photo_calculation_and_processing(
photo_path)
# 算法要求，将照片调整为160*160，输入到facenet中
face_images = image_processing.get_bboxes_image(image, bboxes, 160, 160)
face_images = image_processing.get_prewhiten_images(face_images)
# 生成face_images的128维的人脸特征向量
pred_emb = face_net.get_embedding(face_images)
# 调用数据库模块,返回名字列表names_list,人脸特征向量数组dataset_emb(未实现)
# 字典形式{name:[feature0,feature1,feature2]}
db = database.Dao()
dic = db.nameFeature_dic()
fF = feature.Feature()
show_info = fF.calculateDistance(pred_emb, dic, threshold)
print(show_info)
if show_info is not "unkonw":
"""签到成功"""
pass
else:
"""签到失败"""
def get_pair_scores(faces_data, issames_data, model_path, save_path=None):
'''
计算分数
:param faces_data:
:param issames_data:
:param model_path: insightFace模型路径
:param save_path:
:return:
'''
faces_list1, faces_list2 = split_data(faces_data)
face_net = face_recognition.facenetEmbedding(model_path)
pred_score = []
i = 0
for face1, face2, issame in zip(faces_list1, faces_list2, issames_data):
# pred_id, pred_scores = faceRec.predict(faces)
# 或者使用get_faces_embedding()获得embedding，再比较compare_embedding()
face_images1 = image_processing.get_prewhiten_images(
[face1], normalization=False)
face_images2 = image_processing.get_prewhiten_images(
[face2], normalization=False)
face_emb1 = face_net.get_embedding(face_images1)
face_emb2 = face_net.get_embedding(face_images2)
# face_emb1 = face_net.get_faces_embedding([face1])
# face_emb2 = face_net.get_faces_embedding([face2])
dist = np.sqrt(np.sum(np.square(np.subtract(face_emb1, face_emb2))))
pred_score.append(dist)
i += 1
if i % 100 == 0:
print('processing data :', i)
pred_score = np.array(pred_score).reshape(-1)
issames_data = issames_data + 0 # 将true和false转为1/0
if save_path is not None:
issames_path = os.path.join(save_path, "issames.npy")
pred_score_path = os.path.join(save_path, "pred_score.npy")
np.save(issames_path, issames_data)
np.save(pred_score_path, pred_score)
return pred_score, issames_data
def face_recognition_image(face_detect, face_net, compared_face_path, idcard_face_path):
# 初始化mtcnn人脸检测
# face_detect = face_recognition.Facedetection()
# 初始化facenet
# face_net = face_recognition.facenetEmbedding(model_path)
# 人脸数据是否有效
valid = False
compared_face = image_processing.read_image(compared_face_path)
# 获取 判断标识 bounding_box crop_image
bboxes, landmarks = face_detect.detect_face(compared_face)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes, landmarks, fixed="height")
if bboxes == [] or landmarks == []:
return valid, '未检测到人脸'
elif len(bboxes) > 1:
return valid, '检测到多张人脸'
compared_face_images = image_processing.get_bboxes_image(compared_face, bboxes, resize_height, resize_width)
compared_face_images = image_processing.get_prewhiten_images(compared_face_images)
compared_face_emb = face_net.get_embedding(compared_face_images)
idcard_face = image_processing.read_image(idcard_face_path)
# 获取 判断标识 bounding_box crop_image
bboxes, landmarks = face_detect.detect_face(idcard_face)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes, landmarks, fixed="height")
if bboxes == [] or landmarks == []:
return valid, '证件人脸识别错误，请重新上传'
elif len(bboxes) > 1:
return valid, '证件人脸识别错误，请重新上传'
valid = True
idcard_face_images = image_processing.get_bboxes_image(idcard_face, bboxes, resize_height, resize_width)
idcard_face_images = image_processing.get_prewhiten_images(idcard_face_images)
idcard_face_emb = face_net.get_embedding(idcard_face_images)
compare_res = compare_embadding(compared_face_emb, idcard_face_emb)
return valid, compare_res
def get_face_embedding(model_path, files_list, names_list):
'''
获得embedding数据
:param files_list: 图像列表
:param names_list: 与files_list一一的名称列表
:return:
'''
# 转换颜色空间RGB or BGR
colorSpace = "RGB"
# 初始化mtcnn人脸检测
face_detect = face_recognition.FaceDetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
embeddings = [] # 用于保存人脸特征数据库
label_list = [] # 保存人脸label的名称，与embeddings一一对应
for image_path, name in zip(files_list, names_list):
print("processing image: {}".format(image_path))
image = image_processing.read_image_gbk(image_path,
colorSpace=colorSpace)
# 进行人脸检测，获得bounding_box
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed="height")
# image_processing.show_image_boxes("image",image,bboxes)
if bboxes == [] or landmarks == []:
print("-----no face")
continue
if len(bboxes) >= 2 or len(landmarks) >= 2:
print("-----image total {} faces".format(len(bboxes)))
continue
# 获得人脸区域
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
# 人脸预处理，归一化
face_images = image_processing.get_prewhiten_images(face_images,
normalization=True)
# 获得人脸特征
pred_emb = face_net.get_embedding(face_images)
embeddings.append(pred_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
# label_list.append(image_path)
label_list.append(name)
return embeddings, label_list
def image_fix(image):
# 处理图像,主逻辑
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed='height')
if bboxes and landmarks:
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = file_processing.compare_embedding(
pred_emb, dataset_embedding, name_list)
show_info = [
n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)
]
image = image_processing.get_image_bboxes_text_han(
image, bboxes, show_info)
else:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def face_recognition_image_nn(dataset_emb, names_list, face_detect, face_net,
image):
# 获取 判断标识 bounding_box crop_image
image = image_processing.read_image_gbk_nn(image)
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks, nn_bboxes = face_detect.get_square_bboxes_nn(
bboxes, landmarks, fixed="height")
if bboxes == [] or landmarks == []:
# print("-----no face")
return None, None
# print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(image, bboxes,
resize_height,
resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb,
names_list)
# 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)]
# print(show_info)
return pred_name, nn_bboxes
def face_recognition_image():
"""
Args:
model_path (): facenet预训练模型的路径
dataset_path (): 存放人脸特征数据库的路径
filename (): 存放每张图片信息的txt文档路径
"""
global model_path, dataset_path, filename
# 加载人脸特征数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
bboxes, landmarks, image = photo_calculation_and_processing()
if bboxes == [] or landmarks == []: # bboxes保存人脸框信息，左上角横纵坐标、右下角横纵坐标
print("-----no face")
exit(0)
print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height,
resize_width) # 将照片调整为160*160，输入到facenet中
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images) # 生成face_images的人脸特征向量
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb,
names_list)
def faceRecognition(photo_path):
"""
Args:
model_path (): facenet预训练模型的路径
dataset_path (): 存放人脸特征数据库的路径
filename (): 存放每张图片信息的txt文档路径
photo_path:进行识别的图片的路径
分别检测到照片
"""
# 人脸检测、对齐
# bboxes人脸框信息（左上角横纵坐标、右下角横纵坐标）
# landmarks人脸五个关键点坐标（左右眼、鼻尖、左右嘴角）
bboxes, landmarks, image = mtcnnDetection.photo_calculation_and_processing(
photo_path)
if bboxes == [] or landmarks == []:
# 如果图片中没有人脸
print("-----no face")
print("-----image have {} faces".format(len(bboxes)))
# 算法要求，将照片调整为160*160，输入到facenet中
face_images = image_processing.get_bboxes_image(image, bboxes, 160, 160)
face_images = image_processing.get_prewhiten_images(face_images)
# 生成face_images的128维人脸特征向量
pred_emb = face_net.get_embedding(face_images)
print(pred_emb)
# if (frame_count % frame_interval) == 0: # 跳帧处理，解决算法和采集速度不匹配
if frame_count > -1:
frame = np.asanyarray(frame)
if normalization:
frame = image_processing.image_normalization(frame)
# print("frame:", type(frame), frame.shape) # <class 'numpy.ndarray'> (480, 640, 3)，（高，宽，通道）
bboxes, landmarks = face_detect.detect_face(frame)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes, landmarks, fixed="height") # 以高为基准，获得等宽的矩形
if bboxes == [] or landmarks == []:
print("-----no face")
else:
print("-----now have {} faces".format(len(bboxes)))
# print("bboxes:", bboxes)
face_images = image_processing.get_bboxes_image(frame, bboxes, resize_height, resize_width) # 按照bboxes截取矩形框
face_images = image_processing.get_prewhiten_images(face_images) # 图像归一化
pred_emb = face_net.get_embedding(face_images) # 获取facenet特征
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb, names_list)
# 在图像上绘制人脸边框和识别的结果
boxes_name = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
for name, box in zip(boxes_name, bboxes):
box = [int(b) for b in box]
cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2, 8, 0)
# cv2.putText(frame, name, (box[0], box[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), thickness=2)
zh_cn_nums = general_util.get_zhcn_number(name) # 中文的字数（一个中文字20个像素宽，一个英文字10个像素宽）
t_size = (20 * zh_cn_nums + 10 * (len(name) - zh_cn_nums), 22)
c2 = box[0] + t_size[0], box[1] - t_size[1] - 3 # 纵坐标，多减3目的是字上方稍留空
cv2.rectangle(frame, (box[0], box[1]), c2, (0, 0, 255), -1) # filled
# print("t_size:", t_size, " c1:", c1, " c2:", c2)
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))
def face_512_vector(self, face_images=None):
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = self.face_net.get_embedding(face_images)
return pred_emb
