Add dish classifier to python api.

Dish classifier uses seefood model, which can recognize 2023 different dishes.

Tested:
Test dish_classifier demo.

Change-Id: I128cfbb69be70ee670d96eb8eece72eb012d35ed

src/aiy/vision/models/dish_classifier.py

@@ -0,0 +1,57 @@
+# Copyright 2017 Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""API for Dish Classifier."""
+
+from aiy.vision.inference import ModelDescriptor
+from aiy.vision.models import utils
+from aiy.vision.models.dish_classifier_classes import CLASSES
+
+_COMPUTE_GRAPH_NAME = 'mobilenet_v1_192res_1.0_seefood.binaryproto'
+
+
+def model():
+  return ModelDescriptor(
+      name='dish_classifier',
+      input_shape=(1, 192, 192, 3),
+      input_normalizer=(128.0, 128.0),
+      compute_graph=utils.load_compute_graph(_COMPUTE_GRAPH_NAME))
+
+
+def get_classes(result, max_num_objects=None, object_prob_threshold=0.0):
+  """Converts dish classifier model output to list of detected objects.
+
+  Args:
+    result: output tensor from dish classifier model.
+    max_num_objects: int; max number of objects to return.
+    object_prob_threshold: float; min probability of each returned object.
+
+  Returns:
+    A list of (class_name: string, probability: float) pairs ordered by
+    probability from highest to lowest. The number of pairs is not greater than
+    max_num_objects. Each probability is greater than object_prob_threshold. For
+    example:
+
+    [('Ramen', 0.981934)
+     ('Yaka mein, 0.005497)]
+  """
+  assert len(result.tensors) == 1
+  tensor = result.tensors['MobilenetV1/Predictions/Softmax']
+  probs, shape = tensor.data, tensor.shape
+  assert (shape.batch, shape.height, shape.width, shape.depth) == (1, 1, 1,
+                                                                   2024)
+
+  pairs = [pair for pair in enumerate(probs) if pair[1] > object_prob_threshold]
+  pairs = sorted(pairs, key=lambda pair: pair[1], reverse=True)
+  pairs = pairs[0:max_num_objects]
+  return [('/'.join(CLASSES[index]), prob) for index, prob in pairs]