diff --git a/testing.py b/testing.py
new file mode 100644
index 0000000..74d996d
--- /dev/null
+++ b/testing.py
@@ -0,0 +1,70 @@
+from keras.models import load_model
+import numpy as np
+import keras
+import cv2
+
+
+# This method cuts top and bottom portions of the frame (which are only black areas of the car's dashboard or sky)
+def cutTopAndBottom(img):
+    height, width = img.shape
+    heightBeginning = 20
+    heightEnd = height - 30
+    crop_img = img[heightBeginning : heightEnd, 0 : width]
+    return crop_img
+
+
+# Loading the Keras trained model
+model = load_model('speed_model.h5')
+
+# Opening testing video
+videoFeed = cv2.VideoCapture('./sourceData/test.mp4')
+videoLengthInFrames = int(videoFeed.get(cv2.CAP_PROP_FRAME_COUNT))
+print(videoLengthInFrames)
+
+# Reading the first frame
+coupleCounter = 0
+frameCoupleArray = []
+ret1, oldFrame = videoFeed.read()
+oldFrameGrey = cv2.cvtColor(oldFrame, cv2.COLOR_BGR2GRAY)
+
+# Saving the size of the flow
+oldFrameGrey = cutTopAndBottom(oldFrameGrey)
+oldFrameGrey = cv2.equalizeHist(oldFrameGrey)
+dummyFlow = cv2.calcOpticalFlowFarneback(oldFrameGrey , oldFrameGrey, 0.5, 0.5, 5, 20, 3, 5, 1.2, 0)
+flowShape = dummyFlow.shape  # Original non cropped size is (480, 640, 2)
+
+# Iterating through all couples of frames of the video
+frameToPredict = [ 0 ]
+while(coupleCounter < videoLengthInFrames-20):
+
+    # Read a couple of new frames from the video feed
+    ret2, newFrame = videoFeed.read()
+
+    # Convert to greyscale
+    newFrameGrey = cv2.cvtColor(newFrame, cv2.COLOR_BGR2GRAY)
+
+    # Cut top and bottom portions of the image
+    newFrameGrey = cutTopAndBottom(newFrameGrey)
+
+    # Apply Histogram Equalization to increase contrast
+    newFrameGrey = cv2.equalizeHist(newFrameGrey)
+
+    # Calculate flow for this couple
+    flow = cv2.calcOpticalFlowFarneback(oldFrameGrey , newFrameGrey, 0.5, 0.5, 5, 20, 3, 5, 1.2, 0)
+    frameToPredict[0] = flow    # This format is required by how the model was trained through np arrays
+
+    # Making speed prediction
+    X = np.array(frameToPredict)
+    speedPrediction = model.predict(X)
+    speedText = str(round(speedPrediction[0,0], 2))
+
+    # Drawing speed text on image
+    cv2.putText(newFrame, speedText, (50, 50), cv2.FONT_HERSHEY_DUPLEX, 1.0, color=(51, 153, 255),lineType=cv2.LINE_AA)
+
+    # Incrementing couples counter and swapping frames
+    coupleCounter = coupleCounter + 1
+    oldFrameGrey = newFrameGrey
+    #print(str(coupleCounter))
+    cv2.imshow('frame',newFrame)
+    cv2.waitKey(1)
+