def detectImage(request):
userImage = request.FILES['userImage']
DATA_PATH = os.path.join(BASE_DIR, "data")
# MTCNN的模型
MTCNN_DATA_PATH = os.path.join(DATA_PATH, "mtcnn")
# FaceNet的模型
FACENET_DATA_PATH = os.path.join(DATA_PATH, "facenet", "20180402-114759",
"20180402-114759.pb")
# Classifier的模型
SVM_DATA_PATH = os.path.join(DATA_PATH, "serializer",
"lfw_svm_classifier.pkl")
# 訓練/驗證用的圖像資料目錄
IMG_OUT_PATH = os.path.join(DATA_PATH, "dataset")
#-------------------載入--------------------#
# 人臉特徵
with open(os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_features.pkl'),
'rb') as emb_features_file:
emb_features = pickle.load(emb_features_file)
# print(emb_features)
# 矩陣
with open(os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_labels.pkl'),
'rb') as emb_lables_file:
emb_labels = pickle.load(emb_lables_file)
# emb_labels
# user_ids
with open(
os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_labels_dict.pkl'),
'rb') as emb_lables_dict_file:
emb_labels_dict = pickle.load(emb_lables_dict_file)
# emb_labels_dict
emb_dict = {} # key 是label, value是embedding list
for feature, label in zip(emb_features, emb_labels):
# 檢查key有沒有存在
if label in emb_dict:
emb_dict[label].append(feature)
else:
emb_dict[label] = [feature]
# 計算兩個人臉特徵（Facenet Embedding 128 bytes vector)的歐式距離
def calc_dist(face1_emb, face2_emb):
return distance.euclidean(face1_emb, face2_emb)
face_distance_threshold = 1.1
def is_same_person(face_emb, face_label, threshold=1.1):
emb_distances = []
emb_features = emb_dict[face_label]
for i in range(len(emb_features)):
emb_distances.append(calc_dist(face_emb, emb_features[i]))
# 取得平均值
if np.mean(emb_distances) > threshold:
return False
else:
return True
#-------------------MTCNN相關變數-------------------#
minsize = 20
threshold = [0.6, 0.7, 0.7] # 三個網絡(P-Net, R-Net, O-Net)的閥值
factor = 0.709
margin = 44 # 在裁剪人臉時的邊框margin
image_size = 182
batch_size = 1000
input_image_size = 160
#--------------------載入模型---------------------#
# 創建Tensorflow Graph物件
tf.reset_default_graph()
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=0.8) # 將GPU的顯存設為60%
# 創建Tensorflow Session物件
tf_sess = tf.Session(
config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False)) # False 不打印設備分配紀錄
# 把session設為預設
tf_sess.as_default()
# 載入MTCNN模型 (偵測人臉位置)
pnet, rnet, onet = detect_face.create_mtcnn(tf_sess, MTCNN_DATA_PATH)
# 載入Facenet模型
print('Loading feature extraction model')
modeldir = FACENET_DATA_PATH
facenet.load_model(modeldir)
# 取得模型的輸入與輸出的佔位符
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(
"phase_train:0")
embedding_size = embeddings.get_shape()[1]
# 打印"人臉特徵向量"的向量大小
print("Face embedding size: ", embedding_size)
# 載入SVM分類器模型
classifier_filename = SVM_DATA_PATH
with open(classifier_filename, 'rb') as svm_model_file:
(face_svc_classifier,
face_identity_names) = pickle.load(svm_model_file)
HumanNames = face_identity_names #訓練時的人臉的身份
print('load classifier file-> %s' % classifier_filename)
#測試是否成功載入
# print(face_svc_classifier)
#-----------------------開始辨識---------------------------#
print('Start Recognition!')
im = Image.open(userImage)
im = im.convert("RGB")
#im.show()
imgPath = BASE_DIR + '/data/uploadedImages/' + str(userImage)
im.save(imgPath, 'JPEG')
face_input = imgPath
find_results = []
frame = cv2.imread(face_input) # 讀入圖像
draw = frame.copy() # 複製原圖像
frame = frame[:, :, ::-1] # 把BGR轉換成RGB
# 偵測人臉位置
# 偵測人臉的邊界框
bounding_boxes, _ = detect_face.detect_face(frame, minsize, pnet, rnet,
onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0] # 被偵測到的臉部總數
if nrof_faces > 0: # 如果有偵測到人臉
# 每一個 bounding_box包括了（x1,y1,x2,y2,confidence score)：
# 左上角座標 (x1,y1)
# 右下角座標 (x2,y2)
# 信心分數 confidence score
det = bounding_boxes[:, 0:4].astype(int) # 取出邊界框座標
img_size = np.asarray(frame.shape)[0:2] # 原圖像大小 (height, width)
print("Image: ", img_size)
# 人臉圖像前處理的暫存
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
# 擷取人臉特徵
for i in range(nrof_faces):
print("faces#{}".format(i))
emb_array = np.zeros((1, embedding_size))
x1 = bb[i][0] = det[i][0]
y1 = bb[i][1] = det[i][1]
x2 = bb[i][2] = det[i][2]
y2 = bb[i][3] = det[i][3]
print('({}, {}) : ({}, {})'.format(x1, y1, x2, y2)) #檢查人臉座標
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(
frame[0]) or bb[i][3] >= len(frame): #人臉超出範圍
print('face is out of range!')
continue
# **人臉圖像的前處理 **
# 根據邊界框的座標來進行人臉的裁剪
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(
misc.imresize(cropped[i], (image_size, image_size),
interp='bilinear'))
scaled[i] = cv2.resize(scaled[i],
(input_image_size, input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1, input_image_size,
input_image_size, 3))
feed_dict = {
images_placeholder: scaled_reshape[i],
phase_train_placeholder: False
}
# 進行臉部特徵擷取
emb_array[0, :] = tf_sess.run(embeddings, feed_dict=feed_dict)
# 進行人臉識別分類
face_id_idx = face_svc_classifier.predict(emb_array)
print(face_id_idx)
if is_same_person(emb_array, int(face_id_idx), 1.0):
face_id_name = HumanNames[int(face_id_idx)] # 取出人臉的名字
else:
print("No face detected, or image not recognized")
return redirect('/error_image') #檢測失敗
else:
print('Unable to align')
ids = face_id_name.strip('user')
return redirect('/records/details/' + str(ids))
def load_MTCNN(DATA_PATH):
with tf.Graph().as_default():
with tf.Session() as sess:
# DATA_PATH = os.path.join(BASE_DIR, "data") #data目錄
IMG_OUT_PATH = os.path.join(DATA_PATH, "dataset") #image目錄
# FACENET_DATA_PATH = os.path.join(DATA_PATH, "facenet","20180402-114759","20180402-114759.pb") #dacenet路徑
datadir = IMG_OUT_PATH # 經過偵測、對齊 & 裁剪後的人臉圖像目錄
dataset = facenet.get_dataset(
datadir) # 取得人臉類別(ImageClass)的列表與圖像路徑
paths, labels, labels_dict = facenet.get_image_paths_and_labels(
dataset) #取得每個人臉的圖像路徑跟ID標籤
#print (paths) #test
#print (labels) #test
#print (labels_dict) #test
print('Origin: Number of classes: %d' % len(labels_dict)) #人臉種類
print('Origin: Number of images: %d' % len(paths)) #人臉總數
#------------載入Facenet模型------------#
modeldir = '/content/gdrive/My Drive/Colab Notebooks/facenet/20180402-114759/20180402-114759.pb'
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
#------------計算人臉特徵向量------------#
batch_size = 5 # 一次輸入的樣本數量
image_size = 140 # 要做為Facenet的圖像輸入的大小
times_pohto = 10.0 # 每張照片看的次數
nrof_images = len(paths) # 總共要處理的人臉圖像
# 計算總共要跑的批次數
nrof_batches_per_epoch = int(
math.ceil(times_pohto * nrof_images / batch_size))
# 構建一個變數來保存"人臉特徵向量"
emb_array = np.zeros(
(nrof_images, embedding_size)) # <-- Face Embedding
for i in tqdm(range(nrof_batches_per_epoch)): # 實際訓練 facenet
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
return emb_array, labels, labels_dict
def trainer_photo(request):
DATA_PATH = os.path.join(BASE_DIR, "data") #data目錄
FACENET_DATA_PATH = os.path.join(DATA_PATH, "facenet", "20180402-114759",
"20180402-114759.pb") #dacenet路徑
SVM_DATA_PATH = os.path.join(DATA_PATH, "serializer",
"lfw_svm_classifier.pkl") #svm路徑
IMG_OUT_PATH = os.path.join(DATA_PATH, "dataset") #裁切後人臉目錄
#----------載入MTCNN-----------#
with tf.Graph().as_default():
with tf.Session() as sess:
datadir = IMG_OUT_PATH # 經過偵測、對齊 & 裁剪後的人臉圖像目錄
dataset = facenet.get_dataset(
datadir) # 取得人臉類別(ImageClass)的列表與圖像路徑
paths, labels, labels_dict = facenet.get_image_paths_and_labels(
dataset) #取得每個人臉的圖像路徑跟ID標籤
#print (paths) #test
#print (labels) #test
#print (labels_dict) #test
print('Origin: Number of classes: %d' % len(labels_dict)) #人臉種類
print('Origin: Number of images: %d' % len(paths)) #人臉總數
#------------載入Facenet模型------------#
modeldir = FACENET_DATA_PATH
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
#------------計算人臉特徵向量------------#
batch_size = 5 # 一次輸入的樣本數量
image_size = 140 # 要做為Facenet的圖像輸入的大小
times_pohto = 10.0 # 每張照片看的次數
nrof_images = len(paths) # 總共要處理的人臉圖像
# 計算總共要跑的批次數
nrof_batches_per_epoch = int(
math.ceil(times_pohto * nrof_images / batch_size))
# 構建一個變數來保存"人臉特徵向量"
emb_array = np.zeros(
(nrof_images, embedding_size)) # <-- Face Embedding
for i in tqdm(range(nrof_batches_per_epoch)): # 實際訓練 facenet
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
#--------------保存facenet.pkl-------------#
# 人臉特徵
emb_features_file = open(
os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_features.pkl'), 'wb')
pickle.dump(emb_array, emb_features_file)
emb_features_file.close()
# 矩陣
emb_lables_file = open(
os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_labels.pkl'), 'wb')
pickle.dump(labels, emb_lables_file)
emb_lables_file.close()
# user_ids
emb_lables_dict_file = open(
os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_labels_dict.pkl'),
'wb')
pickle.dump(labels_dict, emb_lables_dict_file)
emb_lables_dict_file.close()
#------------------載入pkl------------------#
# 人臉特徵
with open(os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_features.pkl'),
'rb') as emb_features_file:
emb_features = pickle.load(emb_features_file)
# 每一張的人臉標籤
with open(os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_labels.pkl'),
'rb') as emb_lables_file:
emb_labels = pickle.load(emb_lables_file)
# 總共的人臉標籤種類
with open(
os.path.join(DATA_PATH + '/recognizer', 'lfw_emb_labels_dict.pkl'),
'rb') as emb_lables_dict_file:
emb_labels_dict = pickle.load(emb_lables_dict_file)
#-------------------測試--------------------#
print("人臉特徵數量: {}, shape: {}, type: {}".format(len(emb_features),
emb_features.shape,
type(emb_features)))
print("人臉標籤數量: {}, type: {}".format(len(emb_labels), type(emb_labels)))
print("人臉標籤種類: {}, type: {}", len(emb_labels_dict), type(emb_labels_dict))
#-------------------準備相關變數-----------------#
# 訓練/測試變數
X_train = []
y_train = []
X_test = []
y_test = []
# 保存己經有處理過的人臉label
processed = set()
# 分割訓練資料集與驗證資料集
for (emb_feature, emb_label) in zip(emb_features, emb_labels):
if emb_label in processed:
X_train.append(emb_feature)
y_train.append(emb_label)
else:
X_test.append(emb_feature)
y_test.append(emb_label)
processed.add(emb_label)
# 結果
print('X_train: {}, y_train: {}'.format(len(X_train), len(y_train)))
print('X_test: {}, y_test: {}'.format(len(X_test), len(y_test)))
#----------------訓練人臉分類器(SVM Classifier)-----------------#
#使用scikit-learn的SVM分類器來進行訓練。
#使用linearSvc來訓練
# 訓練分類器
print('Training classifier')
linearsvc_classifier = LinearSVC(C=1, multi_class='crammer_singer')
# 進行訓練
linearsvc_classifier.fit(X_train, y_train)
classifier_filename = SVM_DATA_PATH
class_names = []
for key in sorted(emb_labels_dict.keys()):
class_names.append(emb_labels_dict[key].replace('_', ' '))
with open(classifier_filename, 'wb') as outfile:
pickle.dump((linearsvc_classifier, class_names), outfile)
print('Saved classifier model to file "%s"' % classifier_filename)
return redirect('/')