def ElaborateImagesAndMakePredition(self, inp_img):
# crop a good percentage of the image in order to gain performances. found a good tradeoff with those values
start_time = time.time()
img_data_pipelined = FaceExtractionPipeline.SingletonPipeline(
).FaceExtractionPipelineImage(
inp_img, math.ceil(np.shape(inp_img)[0] * 20 / 100),
math.ceil(np.shape(inp_img)[0] * 40 / 100))
if img_data_pipelined is not None:
# plt.imshow(img_data_pipelined, 'gray')
# plt.show()
inp = LoadData.MergeImages(self.ref_img, img_data_pipelined)
inp = np.expand_dims(inp, axis=0)
#with self.graph.as_default():
predicted_label = self.model.predict(inp)
print(('same' if predicted_label[0, 1] > 0.975 else 'wrong') +
str(predicted_label))
return True, predicted_label[0, 1]
else:
return False, 0
print("--- %s seconds for a frame---" % (time.time() - start_time))
