def test_prediction_vs_tensorflow_inceptionV3(self): output_col = "prediction" image_df = image_utils.getSampleImageDF() # An example of how a pre-trained keras model can be used with TFImageTransformer with KSessionWrap() as (sess, g): with g.as_default(): K.set_learning_phase(0) # this is important but it's on the user to call it. # nChannels needed for input_tensor in the InceptionV3 call below image_string = utils.imageInputPlaceholder(nChannels=3) resized_images = tf.image.resize_images(image_string, InceptionV3Constants.INPUT_SHAPE) # keras expects array in RGB order, we get it from image schema in BGR => # need to flip preprocessed = preprocess_input(imageIO._reverseChannels(resized_images)) model = InceptionV3(input_tensor=preprocessed, weights="imagenet") graph = tfx.strip_and_freeze_until([model.output], g, sess, return_graph=True) transformer = TFImageTransformer(channelOrder='BGR', inputCol="image", outputCol=output_col, graph=graph, inputTensor=image_string, outputTensor=model.output, outputMode="vector") transformed_df = transformer.transform(image_df.limit(10)) self.assertDfHasCols(transformed_df, [output_col]) collected = transformed_df.collect() transformer_values, transformer_topK = self.transformOutputToComparables(collected, output_col, lambda row: row['image']['origin']) tf_values, tf_topK = self._executeTensorflow(graph, image_string.name, model.output.name, image_df) self.compareClassSets(tf_topK, transformer_topK) self.compareClassOrderings(tf_topK, transformer_topK) self.compareArrays(tf_values, transformer_values, decimal=5)
def test_prediction_vs_tensorflow_inceptionV3(self): output_col = "prediction" image_df = image_utils.getSampleImageDF() # An example of how a pre-trained keras model can be used with TFImageTransformer with KSessionWrap() as (sess, g): with g.as_default(): K.set_learning_phase(0) # this is important but it's on the user to call it. # nChannels needed for input_tensor in the InceptionV3 call below image_string = utils.imageInputPlaceholder(nChannels=3) resized_images = tf.image.resize_images(image_string, InceptionV3Constants.INPUT_SHAPE) # keras expects array in RGB order, we get it from image schema in BGR => need to flip preprocessed = preprocess_input(imageIO._reverseChannels(resized_images)) model = InceptionV3(input_tensor=preprocessed, weights="imagenet") graph = tfx.strip_and_freeze_until([model.output], g, sess, return_graph=True) transformer = TFImageTransformer(channelOrder='BGR', inputCol="image", outputCol=output_col, graph=graph, inputTensor=image_string, outputTensor=model.output, outputMode="vector") transformed_df = transformer.transform(image_df.limit(10)) self.assertDfHasCols(transformed_df, [output_col]) collected = transformed_df.collect() transformer_values, transformer_topK = self.transformOutputToComparables(collected, output_col, lambda row: row['image']['origin']) tf_values, tf_topK = self._executeTensorflow(graph, image_string.name, model.output.name, image_df) self.compareClassSets(tf_topK, transformer_topK) self.compareClassOrderings(tf_topK, transformer_topK) self.compareArrays(tf_values, transformer_values)
def test_load_image_vs_keras(self): g = tf.Graph() with g.as_default(): image_arr = utils.imageInputPlaceholder() # keras expects array in RGB order, we get it from image schema in BGR => need to flip preprocessed = preprocess_input( imageIO._reverseChannels(image_arr)) output_col = "transformed_image" transformer = TFImageTransformer(channelOrder='BGR', inputCol="image", outputCol=output_col, graph=g, inputTensor=image_arr, outputTensor=preprocessed.name, outputMode="vector") image_df = image_utils.getSampleImageDF() df = transformer.transform(image_df.limit(5)) for row in df.collect(): processed = np.array(row[output_col]).astype(np.float32) # compare to keras loading images = self._loadImageViaKeras(row["image"]['origin']) image = images[0] image.shape = (1, image.shape[0] * image.shape[1] * image.shape[2]) keras_processed = image[0] self.assertTrue((processed == keras_processed).all())
def test_resize(self): self.assertRaises(ValueError, imageIO.createResizeImageUDF, [1, 2, 3]) make_smaller = imageIO.createResizeImageUDF([4, 5]).func imgAsRow = imageIO.imageArrayToStruct(array) smallerImg = make_smaller(imgAsRow) self.assertEqual(smallerImg.height, 4) self.assertEqual(smallerImg.width, 5) # Compare to PIL resizing imgAsPIL = PIL.Image.fromarray(obj=imageIO._reverseChannels(array)).resize((5, 4)) smallerAry = imageIO._reverseChannels(np.asarray(imgAsPIL)) np.testing.assert_array_equal(smallerAry, imageIO.imageStructToArray(smallerImg)) # Test that resize with the same size is a no-op sameImage = imageIO.createResizeImageUDF((imgAsRow.height, imgAsRow.width)).func(imgAsRow) self.assertEqual(imgAsRow, sameImage) # Test that we have a valid image schema (all fields are in) for n in ImageSchema.imageSchema['image'].dataType.names: smallerImg[n]
def test_resize(self): self.assertRaises(ValueError, imageIO.createResizeImageUDF, [1, 2, 3]) make_smaller = imageIO.createResizeImageUDF([4, 5]).func imgAsRow = imageIO.imageArrayToStruct(array) smallerImg = make_smaller(imgAsRow) self.assertEqual(smallerImg.height, 4) self.assertEqual(smallerImg.width, 5) # Compare to PIL resizing imgAsPIL = PIL.Image.fromarray( obj=imageIO._reverseChannels(array)).resize((5, 4)) smallerAry = imageIO._reverseChannels(np.asarray(imgAsPIL)) np.testing.assert_array_equal(smallerAry, imageIO.imageStructToArray(smallerImg)) # Test that resize with the same size is a no-op sameImage = imageIO.createResizeImageUDF( (imgAsRow.height, imgAsRow.width)).func(imgAsRow) self.assertEqual(imgAsRow, sameImage) # Test that we have a valid image schema (all fields are in) for n in ImageSchema.imageSchema['image'].dataType.names: smallerImg[n]
def create_image_data(): # Random image-like data array = np.random.randint(0, 256, (10, 11, 3), 'uint8') # Compress as png imgFile = BytesIO() PIL.Image.fromarray(array).save(imgFile, 'png') imgFile.seek(0) # Get Png data as stream pngData = imgFile.read() # PIL is RGB but image schema is BGR => flip the channels return imageIO._reverseChannels(array), pngData
def _preprocessingInceptionV3Transformed(self, outputMode, outputCol): g = tf.Graph() with g.as_default(): image_arr = utils.imageInputPlaceholder() resized_images = tf.image.resize_images(image_arr, InceptionV3Constants.INPUT_SHAPE) # keras expects array in RGB order, we get it from image schema in BGR => need to flip processed_images = preprocess_input(imageIO._reverseChannels(resized_images)) self.assertEqual(processed_images.shape[1], InceptionV3Constants.INPUT_SHAPE[0]) self.assertEqual(processed_images.shape[2], InceptionV3Constants.INPUT_SHAPE[1]) transformer = TFImageTransformer(channelOrder='BGR', inputCol="image", outputCol=outputCol, graph=g, inputTensor=image_arr.name, outputTensor=processed_images, outputMode=outputMode) image_df = image_utils.getSampleImageDF() return transformer.transform(image_df.limit(5))
def test_load_image_vs_keras(self): g = tf.Graph() with g.as_default(): image_arr = utils.imageInputPlaceholder() # keras expects array in RGB order, we get it from image schema in BGR => need to flip preprocessed = preprocess_input(imageIO._reverseChannels(image_arr)) output_col = "transformed_image" transformer = TFImageTransformer(channelOrder='BGR', inputCol="image", outputCol=output_col, graph=g, inputTensor=image_arr, outputTensor=preprocessed.name, outputMode="vector") image_df = image_utils.getSampleImageDF() df = transformer.transform(image_df.limit(5)) for row in df.collect(): processed = np.array(row[output_col]).astype(np.float32) # compare to keras loading images = self._loadImageViaKeras(row["image"]['origin']) image = images[0] image.shape = (1, image.shape[0] * image.shape[1] * image.shape[2]) keras_processed = image[0] np.testing.assert_array_almost_equal(keras_processed, processed, decimal=6)
def load_image_uri_impl(uri): try: return imageArrayToStruct(_reverseChannels(loader(uri))) except BaseException: # pylint: disable=bare-except return None
def pil_load_spimg(fpath): from PIL import Image import numpy as np img_arr = np.array(Image.open(fpath), dtype=np.uint8) # PIL is RGB, image schema is BGR => need to flip the channels return imageArrayToStruct(_reverseChannels(img_arr))
def keras_load_spimg(fpath): # Keras loads image in RGB order, ImageSchema expects BGR => need to flip return imageArrayToStruct(_reverseChannels(keras_load_img(fpath)))
def preprocess(self, inputImage): # Keras expects RGB order return xception.preprocess_input(_reverseChannels(inputImage))