Core Cloud 전처리 프레임 수신 및 예측모듈 작성

+import socket
+import cv2
+import numpy as np
+from multiprocessing import Queue
+
+from labeling_module import LabelingModule
+#socket에서 수신한 버퍼를 반환함.
+def recvall(sock, count):
+    # 바이트 문자열
+    buf = b''
+    while count:
+        newbuf = sock.recv(count)
+        if not newbuf: return None
+        buf += newbuf
+        count -= len(newbuf)
+    return buf
+
+    
+if __name__ == "__main__":
+    lm.predict_process.start()
+    HOST='127.0.0.1'
+    PORT=9999
+    
+    #TCP 사용
+    s=socket.socket(socket.AF_INET,socket.SOCK_STREAM)
+    print('Socket created')
+    
+    #서버의 아이피와 포트번호 지정
+    s.bind((HOST,PORT))
+    print('Socket bind complete')
+    # 클라이언트의 접속을 기다린다. (클라이언트 연결을 10개까지 받는다)
+    s.listen(10)
+    print('Socket now listening')
+    
+    #연결, conn에는 소켓 객체, addr은 소켓에 바인드 된 주소
+    conn,addr=s.accept()
+    while True:
+        # client에서 받은 stringData의 크기 (==(str(len(stringData))).encode().ljust(16))
+        length = recvall(conn, 16)
+        stringData = recvall(conn, int(length))
+        data = np.fromstring(stringData, dtype = 'uint8')
+        
+        #data를 디코딩한다.
+        cropped = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        cropped = cv2.resize(cropped, (48,48)) #Crop Image Resize
+        lm.new_tensor(cropped) # Predict result
+        lm.predict_process.join() # thread join
\ No newline at end of file

+# plaidml
+# import plaidml.keras
+# plaidml.keras.install_backend()
+
+# packages
+from keras.models import load_model
+from keras.preprocessing import image
+
+# import queue
+import numpy as np
+from queue import Full, Empty
+from multiprocessing import Process, Queue
+
+class LabelingModule:
+    def __init__(self):
+        # self.model1 = load_model('svhn_model.h5')
+        self.model2 = load_model('svhn_model.h5')
+        self.image_queue = Queue(maxsize=3000)
+        self.label_queue = Queue(maxsize=10)
+        self.signal_queue = Queue()
+        self.predict_process = Process(target=_predict, \
+            args=(self.model2, self.image_queue, self.label_queue, self.signal_queue))
+
+    def run(self):
+        self.predict_process.start()
+
+    def close(self):
+        self.image_queue.close()
+        self.label_queue.close()
+
+    def new_tensor(self, tensor):
+        try:
+            self.image_queue.put(tensor)
+        except Full:
+            print('[LabelingModule] image_queue is full')
+
+    def new_image(self, filename):
+        tensor = self._img_to_tensor(filename)
+        try:
+            self.image_queue.put(tensor)
+        except Full:
+            print('[LabelingModule] image_queue is full')
+
+    def _img_to_tensor(self, filename):
+        img = image.load_img(filename, target_size=(48, 48))
+        img_tensor = image.img_to_array(img)
+        img_tensor = np.squeeze(img_tensor)
+        img_tensor /= 255.
+        img_tensor = img_tensor - img_tensor.mean()
+        return img_tensor
+
+def _predict(model, input_queue, output_queue, signal_queue):
+    print('predict process started.')
+    while True:
+        try:
+            signal = signal_queue.get_nowait()
+            if signal == 'stop':
+                break
+        except Empty:
+            pass
+        
+        tensor = input_queue.get(timeout=-1)
+        dat = model.predict(np.array([tensor]))
+        o1 = np.argmax(dat[0])
+        o2 = np.argmax(dat[1])
+        o3 = np.argmax(dat[2])
+        o4 = np.argmax(dat[3])
+        o5 = np.argmax(dat[4])
+        o6 = np.argmax(dat[5])
+        output = [o1, o2, o3, o4, o5, o6]
+        print('[LabelingModule] predict result :', output)