def run(self):
self._socket.bind('tcp://*:5505')
self._socket.setsockopt_string(zmq.SUBSCRIBE, np.unicode(''))
while self.active:
start = time.time()
frame = self._socket.recv_string()
img = base64.b64decode(frame)
npimg = np.fromstring(img, dtype=np.uint8)
source = cv2.imdecode(npimg, 1)
bounding_boxes = []
sess = tf.Session(graph=graph)
emb_array = np.zeros((1, embedding_size))
bounding_boxes, _ = mtcnn_detect.detect_face(
source, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
end = time.time()
print("receive time: %f", 1 / (end - start))
def process():
sess = tf.Session()
# read pnet, rnet, onet models from align directory and files are det1.npy, det2.npy, det3.npy
pnet, rnet, onet = mtcnn_detect.create_mtcnn(sess, 'models')
cropped_im = []
save_im = []
directory = "./dataImg/" + inputValue + "/"
if not os.path.exists(directory):
os.makedirs(directory)
for i in range(len(im_arr)):
frame = im_arr[i]
bounding_boxes, _ = mtcnn_detect.detect_face(frame, minsize, pnet,
rnet, onet, threshold,
factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces == 1:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
cropped = frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :]
cropped = cv2.resize(cropped, (image_size, image_size),
interpolation=cv2.INTER_CUBIC)
save_im.append(cropped)
#cropped = prewhiten(cropped)
#cropped = flip(cropped, False)
cropped_im.append(cropped)
print(directory)
for i in range(len(save_im)):
cv2.imwrite(directory + str(i) + '.jpg', save_im[i])
print('Extracted: %d' % len(cropped_im))
calSVM(cropped_im, inputValue)
with tf.Graph().as_default():
with tf.Session() as sess:
np.random.seed(seed=1324)
facenet.load_model('./models')
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]
emb_array = np.zeros((1, embedding_size))
(model, class_names) = pickle.load(open('svm_classifier.pkl', 'rb'))
frame = cv2.imread('.datas/1.jpg')
bounding_boxes, _ = mtcnn_detect.detect_face(frame, minsize, pnet,
rnet, onet, threshold,
factor)
nrof_faces = bounding_boxes.shape[0]
bounding_boxes, _ = mtcnn_detect.detect_face(frame, minsize, pnet,
rnet, onet, threshold,
factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
names = []
scaled_reshape = []
def train():
data = request.get_json()
data_image = data['data']
# cv2.imwrite("image_sent.jpg", np.array(data_image[0]))
#read image file string data
# print(np.array(data_image[0]).shape)
# filestr = request.files.getlist("file")
# namestr = request.files.get("name")
# name = []
# # print(namestr.read())
person_data = data['name']
person_address = data['address']
scaled = []
img = []
data_person = []
bounding_boxes = []
sess = tf.Session(graph=graph)
emb_array = np.zeros((len(data_image), embedding_size))
#convert string data to numpy array
for img in data_image:
# npimg = np.fromstring(i.read(), np.uint8)
# # convert numpy array to image
# img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)
img = np.asarray(img, np.uint8)
print(img.shape)
# cv2.imwrite("image_sent.jpg", img)
emb_array = np.zeros((len(data_image), embedding_size))
bounding_boxes, _ = mtcnn_detect.detect_face(img, minsize, pnet, rnet,
onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
cropped = []
names = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
#print(nrof_faces)
for i in range(nrof_faces):
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(
img[0]) or bb[i][3] >= len(img):
print('face is inner of range!')
continue
cropped.append(img[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
# for i in range(len(cropped)):
# if cropped[i].shape[0] >70 and cropped[i].shape[1]>70:
scaled.append(cropped[i])
for i in range(len(scaled)):
scaled[i] = cv2.resize(scaled[i], (image_size, image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i])
#Feed forward
feed_dict = {
images_placeholder: scaled_reshape,
phase_train_placeholder: False
}
#Đưa vector emb vào classifier
emb_array = sess.run(embeddings, feed_dict=feed_dict)
data_person.append(emb_array)
labels = []
embs = []
class_names = []
with open('class.txt') as file:
for l in file.readlines():
class_names.append(l.replace('\n', ''))
file.close()
print(class_names)
with open('data.txt') as json_file:
data = json.load(json_file)
for p in data['person']:
embs.append(p['emb'])
labels.append(p['name'])
name = person_data
if name in class_names:
person_label = class_names.index(name)
print('This person is already in database')
else:
person_label = len(class_names)
file = open('class.txt', 'w')
class_names.append(str(name))
for name in class_names:
file.write(str(name) + '\n')
file.close()
for i in range(len(emb_array)):
data['person'].append({
'name': person_label,
'emb': emb_array[i].tolist()
})
labels.append(person_label)
embs.append(emb_array[i])
with open('data.txt', 'w') as outfile:
json.dump(data, outfile)
X_train, X_test, y_train, y_test = train_test_split(np.array(embs),
np.array(labels),
test_size=0.33,
random_state=42)
print('Training SVM classifier')
model = SVC(kernel='linear', probability=True)
model.fit(X_train, y_train)
predictions = model.predict_proba(X_test)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[np.arange(len(best_class_indices)),
best_class_indices]
accuracy = np.mean(np.equal(best_class_indices, y_test))
print('Accuracy: %.3f' % accuracy)
with open('svm_classifier1.pkl', 'wb') as outfile:
pickle.dump((model, class_names), outfile)
print('Saved svm classifier')
# encode response using jsonpickle
sql = "INSERT INTO Student(name, address) VALUES (%s,%s)"
val = (name, person_address)
mycursor.execute(sql, val)
db_1.commit()
response_pickled = jsonpickle.encode(bounding_boxes.tolist())
return send_file('img.jpg', attachment_filename='img.jpg')
def test():
# #read image file string data
# filestr = request.files['file'].read()
# #convert string data to numpy array
# npimg = np.fromstring(filestr, np.uint8)
# # convert numpy array to image
# img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)
r = request
# convert string of image data to uint8
nparr = np.fromstring(r.data, np.uint8)
# decode image
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
response_list = []
bounding_boxes = []
sess = tf.Session(graph=graph)
emb_array = np.zeros((1, embedding_size))
bounding_boxes, _ = mtcnn_detect.detect_face(img, minsize, pnet, rnet,
onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
cropped = []
scaled = []
names = []
scaled_reshape = []
bb = np.zeros((nrof_faces, 4), dtype=np.int32)
#print(nrof_faces)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(
img[0]) or bb[i][3] >= len(img):
print('face is inner of range!')
continue
cropped.append(img[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
# cv2.rectangle(img,(bb[i][0],bb[i][1]),(bb[i][2],bb[i][3]),(0,0,255), thickness=2) #draw bounding box for the face
# for i in range(len(cropped)):
# if cropped[i].shape[0] >70 and cropped[i].shape[1]>70:
#
scaled.append(cropped[i])
response_list.append(nrof_faces)
for i in range(len(scaled)):
scaled[i] = cv2.resize(scaled[i], (image_size, image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1, image_size, image_size,
3))
#Feed forward
feed_dict = {
images_placeholder: scaled_reshape[i],
phase_train_placeholder: False
}
#Đưa vector emb vào classifier
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(
predictions, axis=1) #Class đạt độ chính xác cao nhất
best_class_probabilities = predictions[np.arange(
len(best_class_indices)), best_class_indices] #Độ chính xác
for j in range(len(best_class_indices)):
#print('%4d %s: %.3f' % (i, class_names[best_class_indices[i]], best_class_probabilities[i]))
if (best_class_probabilities[j] >= 0.3):
cv2.putText(img,
class_names[best_class_indices[j]],
(bb[i][0], bb[i][1] - 10),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 255, 0),
thickness=1,
lineType=2)
names.append(class_names[best_class_indices[j]])
time_stamp = time.strftime('%Y-%m-%d %H:%M:%S')
# print("Current Time =", current_time)
sql_check = "SELECT * FROM Checkin" + str(
time.strftime(
'%Y%m%d')) + " WHERE name = " + "'" + class_names[
best_class_indices[j]] + "'"
mycursor.execute(sql_check)
myresult = mycursor.fetchall()
if (len(myresult) == 0):
sql = "INSERT INTO Checkin" + str(
time.strftime(
'%Y%m%d')) + "(name, time) VALUES (%s,%s)"
val = (class_names[best_class_indices[j]], time_stamp)
mycursor.execute(sql, val)
db_1.commit()
response_list.append({
"name":
class_names[best_class_indices[j]],
"bb":
bb[i, :].tolist(),
"check":
"N",
"prob":
str(best_class_probabilities[j])
})
else:
response_list.append({
"name":
class_names[best_class_indices[j]],
"bb":
bb[i, :].tolist(),
"check":
"Y",
"prob":
str(best_class_probabilities[j])
})
else:
response_list.append({
"name":
"Unknown",
"bb":
bb[i, :].tolist(),
"check":
"Y",
"prob":
str(best_class_probabilities[j])
})
else:
response_list.append(0)
time_stamp = time.strftime('%Y-%m-%d %H:%M:%S')
# response_list.append(myresult)
response_list.append(time_stamp)
# encode response using jsonpickle
response_pickled = jsonpickle.encode(response_list)
return response_pickled