Bavan dev

translation/Readme.txt

-Translating the signs.
\ No newline at end of file

translation/cam.py

+import cv2
+
+#def to take webcam pics
+
+def myCam():
+    cam = cv2.VideoCapture(0)
+
+    cv2.namedWindow("test")
+
+    img_counter = 0
+
+    while True:
+        ret, frame = cam.read()
+        if not ret:
+            print("failed to grab frame")
+            break
+        cv2.imshow("test", frame)
+
+        k = cv2.waitKey(1)
+        if k % 256 == 27:
+            # ESC pressed
+            print("Escape hit, closing...")
+            break
+        elif k % 256 == 32:
+            # SPACE pressed
+            img_name = "opencv_frame_{}.png".format(img_counter)
+            cv2.imwrite(img_name, frame)
+            print("{} written!".format(img_name))
+            img_counter += 1
+
+    cam.release()
+
+    cv2.destroyAllWindows()
+    return

translation/path_test.py

-import os
-import os
-PATH = os.path.dirname(__file__)
-print(PATH + '/converted_keras/keras_model.h5')
\ No newline at end of file

translation/trans.py

@@ -6,20 +6,23 @@ import numpy as np
 np.set_printoptions(suppress=True)
 
 # Load the model
-model = tensorflow.keras.models.load_model('C:/Users/user/Documents/GitHub/2020_077/translation/converted_keras (2)/keras_model.h5')
+model = tensorflow.keras.models.load_model('C:/Users/user/Documents/GitHub/2020_077/translation/converted_keras/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
 # determined by the first position in the shape tuple, in this case 1.
+
 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/Pictures/Camera Roll/test55.jpg')
+image = Image.open('C:/Users/user/Documents/GitHub/2020_077/opencv_frame_0.png')
 
 # 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)
 
 # turn the image into a numpy array