send to edge cloud

@@ -7,24 +7,33 @@ from keras.models import load_model
 from keras.preprocessing import image
 from keras.preprocessing import image
 
 
 # import queue
 # import queue
+import datetime
 import numpy as np
 import numpy as np
 from queue import Full, Empty
 from queue import Full, Empty
 from multiprocessing import Process, Queue
 from multiprocessing import Process, Queue
+import cv2
 
 
+fname = 'croppedimg/{}.png'
+save_imgs = False
+HOST = '192.168.35.87'
+PORT = 9999
 
 
 class LabelingModule:
 class LabelingModule:
     def __init__(self):
     def __init__(self):
-        # self.model1 = load_model('svhn_model.h5')
+        self.model1 = load_model('checker_model.h5')
         self.model2 = load_model('svhn_model.h5')
         self.model2 = load_model('svhn_model.h5')
         self.image_queue = Queue(maxsize=3000)
         self.image_queue = Queue(maxsize=3000)
         self.label_queue = Queue(maxsize=10)
         self.label_queue = Queue(maxsize=10)
         self.signal_queue = Queue()
         self.signal_queue = Queue()
-        self.predict_process = Process(target=_predict, args=(self.model2, self.image_queue, self.label_queue, self.signal_queue))
+        self.predict_process = Process(target=_predict, \
+                                       args=(
+                                       self.model1, self.model2, self.image_queue, self.label_queue, self.signal_queue))
 
 
     def run(self):
     def run(self):
         self.predict_process.start()
         self.predict_process.start()
 
 
     def close(self):
     def close(self):
+        self.signal_queue.put_nowait('stop')
         self.image_queue.close()
         self.image_queue.close()
         self.label_queue.close()
         self.label_queue.close()
 
 
@@ -49,9 +58,26 @@ class LabelingModule:
         img_tensor = img_tensor - img_tensor.mean()
         img_tensor = img_tensor - img_tensor.mean()
         return img_tensor
         return img_tensor
 
 
+def decode(output):
+    if(output[0]==0):
+        return 'Noise'
+    else:
+        if(output[1] == 3):
+            return str(output[2])+str(output[3])+str(output[4])
+        elif (output[1] == 4):
+            return str(output[2]) + str(output[3]) + str(output[4])+'-'+ str(output[5])
 
 
-def _predict(model, input_queue, output_queue, signal_queue):
+def send_predict_result(HOST, PORT,message):
+    # (address family) IPv4,  TCP
+    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    # raspberry pi addr
+    client_socket.connect((HOST, PORT))
+    client_socket.sendall('Door Plate Detected : '+message.encode('utf-8'))
+
+def _predict(model1, model2, input_queue, output_queue, signal_queue):
     print('predict process started.')
     print('predict process started.')
+
+    index = 0
     while True:
     while True:
         try:
         try:
             signal = signal_queue.get_nowait()
             signal = signal_queue.get_nowait()
@@ -60,13 +86,28 @@ def _predict(model, input_queue, output_queue, signal_queue):
         except Empty:
         except Empty:
             pass
             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])
+        try:
+            tensor = input_queue.get(timeout=-1)
+        except Empty:
+            continue
+        tensor = np.array([tensor])
+        has_number = model1.predict(tensor)[0]
+        if int(has_number[0]) == 1:
+            continue
+
+        if save_imgs:
+            img = cv2.cvtColor(tensor[0], cv2.COLOR_RGB2BGR)
+            cv2.imwrite(fname.format(index), img)
+            index += 1
+
+        label_data = model2.predict(tensor)
+        o1 = np.argmax(label_data[0])
+        o2 = np.argmax(label_data[1])
+        o3 = np.argmax(label_data[2])
+        o4 = np.argmax(label_data[3])
+        o5 = np.argmax(label_data[4])
+        o6 = np.argmax(label_data[5])
         output = [o1, o2, o3, o4, o5, o6]
         output = [o1, o2, o3, o4, o5, o6]
-        print('[LabelingModule] predict result :', output)
+        print('[LabelingModule] predict result :', decode(output))
+        send_predict_result(HOST,PORT)
+        print(decode(output)+" : Sended To Edge")