Added Demo code files

Author: lorytony

demo-raspberry.py

@@ -0,0 +1,67 @@
+import numpy as np
+import random as rn
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers, models, optimizers
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.applications import VGG16
+import PIL
+from PIL import Image
+import cv2
+import os
+
+import classification
+import server_tcp
+from playsound import playsound
+
+
+if __name__ == "__main__":
+
+    count = 0
+    num_image = 0
+    flag_safe = 0
+    flag_dist = 0
+    flag_phone = 0
+
+    print("System is going to start----->\n")
+    
+
+    print(".......\n")
+    dest_folder = 'image/'
+    
+    vgg_model = tf.keras.models.load_model('model/model.h5')
+
+    while(True):
+
+        path_img = server_tcp.get_raspberry_image(dest_folder)
+        img = cv2.imread(path_img)
+        cv2.imshow('output',img)
+
+        if cv2.waitKey(20) & 0xFF == ord('q'):
+            break
+
+        if len(os.listdir("image")) == 5:
+            for image in os.listdir("image"):
+                result = classification.classification(vgg_model,"image/"+image)
+                print("Result: " + result)
+                if  result == "Phone":
+                   flag_phone = flag_phone + 1
+                elif result == "Distraction":
+                    flag_dist = flag_dist + 1
+                else:
+                    flag_safe = flag_safe + 1
+                os.remove("image/"+image)
+                num_image = 0
+     
+            if max(flag_phone, flag_dist, flag_safe) == flag_dist:
+                playsound('audio/dist.mp3')
+            elif max(flag_phone, flag_dist, flag_safe) == flag_phone:
+                playsound('audio/phone.mp3')
+            elif max(flag_phone, flag_dist, flag_safe) == flag_safe:
+                playsound('audio/safe.mp3')
+
+            flag_safe = 0
+            flag_phone = 0
+            flag_dist = 0
+    
+ 

demo-webcam.py

@@ -0,0 +1,71 @@
+import numpy as np
+import random as rn
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers, models, optimizers
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.applications import VGG16
+import PIL
+from PIL import Image
+import cv2
+import os
+
+import classification
+from playsound import playsound
+
+
+if __name__ == "__main__":
+
+    count = 0
+    num_image = 0
+    flag_safe = 0
+    flag_dist = 0
+    flag_phone = 0
+
+    print("System is going to start----->\n")
+    
+
+    print(".......\n")
+
+    mac_cam = cv2.VideoCapture(0)
+    vgg_model = tf.keras.models.load_model('model/model.h5')
+
+    while(True):
+
+        retrieve, frames = mac_cam.read()
+        cv2.imshow('Try2Catch', frames)
+        count = count + 1
+        if count == 20:
+            
+            cv2.imwrite("image/"+str(num_image)+".png",frames)
+            count = 0
+            num_image = num_image + 1
+
+        if cv2.waitKey(20) & 0xFF == ord('q'):
+            break
+
+        if len(os.listdir("image")) == 10:
+            for image in os.listdir("image"):
+                result = classification.classification(vgg_model,"image/"+image)
+                print("Result: " + result)
+                if  result == "Phone":
+                   flag_phone = flag_phone + 1
+                elif result == "Distraction":
+                    flag_dist = flag_dist + 1
+                else:
+                    flag_safe = flag_safe + 1
+                os.remove("image/"+image)
+                num_image = 0
+     
+            if max(flag_phone, flag_dist, flag_safe) == flag_dist:
+                playsound('audio/dist.mp3')
+            elif max(flag_phone, flag_dist, flag_safe) == flag_phone:
+                playsound('audio/phone.mp3')
+            elif max(flag_phone, flag_dist, flag_safe) == flag_safe:
+                playsound('audio/safe.mp3')
+
+            flag_safe = 0
+            flag_phone = 0
+            flag_dist = 0
+    
+ 

server_tcp.py

@@ -0,0 +1,63 @@
+import os
+import cv2
+import socket
+import numpy as np
+
+
+
+
+
+HOST = "169.254.108.159"  # Ethernet Interface Address PC
+PORT = 65432
+#HOST = "127.0.0.1"  # The server's hostname or IP address
+#PORT = 80  # Port to listen on (non-privileged ports are > 1023)
+
+#@dest_folder = 'image'
+def get_raspberry_image(dest_folder):
+    all_data = b''
+    i = len(os.listdir(dest_folder)) + 1
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind((HOST, PORT))
+        s.listen()
+        conn, addr = s.accept()
+        with conn:
+            print(f"Connected by {addr}")
+            while True:
+                data = conn.recv(8760)
+                #print(f"Received {data!r}")
+                #print("dimensione dati ricevuti: ",len(data))
+
+                if not data: #it is true when data == b'' 
+                    break
+                #all_data.extend(data) NOT WORKS
+                all_data = b"".join([all_data, data])
+                #print("dimensione temporanea all_data: ",len(all_data))
+                #conn.send(data)
+
+
+    #print("dimensione immagine: ",len(all_data))
+    image = np.asarray(bytearray(all_data))
+    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+    path_captured_image = dest_folder + 'img%d.jpg' %i
+    #print("percorso immagine: " + path_captured_image)
+    cv2.imwrite(path_captured_image, image)
+    
+
+    return path_captured_image
+
+
+
+
+ # convert to numpy array
+#image = np.asarray(bytearray(data))
+    
+# RGB to Grayscale
+#image = cv2.imdecode(image, 0)
+    
+# display image
+#cv2.imshow("output", image)
+
+#decimg=cv2.imdecode(data,0)
+#cv2.imshow('SERVER',decimg)
+#cv2.waitKey(0)
+#cv2.destroyAllWindows()