Bavan dev

translation/trans.py

@@ -9,15 +9,20 @@ import tensorflow.keras
 from PIL import Image, ImageOps
 import numpy as np
 import os
-PATH = os.path.dirname(__file__)
+import string
 
+class Translation:
 
-def sign_predict():
+    PATH = os.path.dirname(__file__)
+
+
+    # def sign_predict():
     # Disable scientific notation for clarity
     np.set_printoptions(suppress=True)
 
     # Load the model
     model = tensorflow.keras.models.load_model(PATH + '/converted_keras/keras_model.h5')
+    #model = tensorflow.keras.models.load_model('C:/Users/user/Documents/A to Y 1000 dataset/keras_model.h5')
 
     # Create the array of the right shape to feed into the keras model
     # The 'length' or number of images you can put into the array is
@@ -26,30 +31,46 @@ def sign_predict():
     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
     #data = np.ndarray(shape=(1, 480, 640, 3), dtype=np.float32)
 
-    # Replace this with the path to your image
-    image = Image.open('C:/Users/user/Documents/GitHub/2020_077/opencv_frame_0.png')
+    def sign_predict(self, loc):
+
+        # Replace this with the path to your image
+        # 'C:/Users/user/Documents/GitHub/2020_077/opencv_frame_0.png'
+        image = Image.open(loc)
 
-    # resize the image to a 224x224 with the same strategy as in TM2:
-    # resizing the image to be at least 224x224 and then cropping from the center
+        # resize the image to a 224x224 with the same strategy as in TM2:
+        # resizing the image to be at least 224x224 and then cropping from the center
 
-    size = (224, 224)
-    #size = (640, 480)
-    image = ImageOps.fit(image, size, Image.ANTIALIAS)
+        size = (224, 224)
+        #size = (640, 480)
+        image = ImageOps.fit(image, size, Image.ANTIALIAS)
 
-    # turn the image into a numpy array
-    image_array = np.asarray(image)
-    print(image_array)
+        # turn the image into a numpy array
+        image_array = np.asarray(image)
+        #print(image_array)
 
-    # display the resized image
-    image.show()
+        # display the resized image
+        #image.show()
 
-    # Normalize the image
-    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+        # Normalize the image
+        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
 
-    # Load the image into the array
-    data[0] = normalized_image_array
+        # Load the image into the array
+        self.data[0] = normalized_image_array
 
-    # run the inference
-    prediction = model.predict(data)
-    print(prediction)
-    return
+        # run the inference
+        prediction = self.model.predict(self.data)
+        #print(prediction)
+        #print(prediction.shape)
+        #print(type(prediction))
+        #print(prediction[0, 2])
+        for alpha in range(26):
+            if prediction[0, alpha] >= 0.8:
+                #print(string.ascii_uppercase[alpha])
+                return string.ascii_uppercase[alpha]
+                #break
+        
+        # if os.path.exists(loc):
+        #     os.remove(loc)
+        # else:
+        #     print("The file does not exist")
+        # return

translation/transMain.py

+from trans import Translation
+import TextVoice
+
+if __name__ == '__main__':
+    t1 = Translation()
+    Tex = TextVoice()
+
+    location1 = 'C:/Users/user/Documents/GitHub/2020_077/test1.jpg'
+    location2 = 'C:/Users/user/Documents/GitHub/2020_077/test2.jpg'
+    location3 = 'C:/Users/user/Documents/GitHub/2020_077/test3.jpg'
+    location4 = 'C:/Users/user/Documents/GitHub/2020_077/test4.jpg'
+    location5 = 'C:/Users/user/Documents/GitHub/2020_077/test55.jpg'
+
+    Tex.getInput()
+    #getInput('t1.sign_predict(location1)')
+    # t1.sign_predict(location2)
+    # t1.sign_predict(location3)
+    # t1.sign_predict(location4)
+    # t1.sign_predict(location5)
+    # t1.sign_predict(location2)
+    # t1.sign_predict(location3)
+    # t1.sign_predict(location4)
+    # t1.sign_predict(location5)
+    # t1.sign_predict(location1)
+    # t1.sign_predict(location4)
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