def predict_and_show(self, img_num, complete_df, width, height):
assert width * height == len(complete_df)
logger.info("Predicting neural network results on image %i ..." % img_num)
img_out_path = os.path.join(self.out_path, "prediction_image%i.jpg" % img_num)
array = np.zeros((width, height, 3), 'uint8')
complete_df = self._create_features(complete_df)
complete_df = complete_df[self.columns]
prediction = neural_networks.predict(self.theta, complete_df)
prediction = prediction.reshape((width, height))
array[..., 0] = 255*(1-prediction)
array[..., 1] = 255*prediction
img_num = Image.fromarray(array)
img_num.save(img_out_path)
def predict_and_show(self, img_num, complete_df, width, height):
assert width * height == len(complete_df)
logger.info("Predicting neural network results on image %i ..." %
img_num)
img_out_path = os.path.join(self.out_path,
"prediction_image%i.jpg" % img_num)
array = np.zeros((width, height, 3), 'uint8')
complete_df = self._create_features(complete_df)
complete_df = complete_df[self.columns]
prediction = neural_networks.predict(self.theta, complete_df)
prediction = prediction.reshape((width, height))
array[..., 0] = 255 * (1 - prediction)
array[..., 1] = 255 * prediction
img_num = Image.fromarray(array)
img_num.save(img_out_path)
def predict(self, imgs):
df = filter_df(self.df, img_nums=imgs, columns=self.columns)
return neural_networks.predict(self.theta, df)
def predict(self, imgs):
df = filter_df(self.df, img_nums=imgs, columns=self.columns)
return neural_networks.predict(self.theta, df)
