From 3ae8a720282b620f021567362a3162ce9664e836 Mon Sep 17 00:00:00 2001
From: Ben Hoff <bhoff@nciinc.com>
Date: Thu, 11 Jul 2019 10:38:57 -0400
Subject: [PATCH] added in handeling for openvino 2019 (#545)

---
 cvat/apps/auto_annotation/model_loader.py | 23 +++++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/cvat/apps/auto_annotation/model_loader.py b/cvat/apps/auto_annotation/model_loader.py
index a933190ec955..09b67f417f0b 100644
--- a/cvat/apps/auto_annotation/model_loader.py
+++ b/cvat/apps/auto_annotation/model_loader.py
@@ -7,6 +7,7 @@
 import cv2
 import os
 import subprocess
+import numpy as np
 
 from cvat.apps.auto_annotation.inference_engine import make_plugin, make_network
 
@@ -28,9 +29,18 @@ def __init__(self, model, weights):
             raise Exception("Following layers are not supported by the plugin for specified device {}:\n {}".
                       format(plugin.device, ", ".join(not_supported_layers)))
 
-        self._input_blob_name = next(iter(network.inputs))
+        iter_inputs = iter(network.inputs)
+        self._input_blob_name = next(iter_inputs)
         self._output_blob_name = next(iter(network.outputs))
 
+        self._require_image_info = False
+
+        # NOTE: handeling for the inclusion of `image_info` in OpenVino2019
+        if 'image_info' in network.inputs:
+            self._require_image_info = True
+        if self._input_blob_name == 'image_info':
+            self._input_blob_name = next(iter_inputs)
+
         self._net = plugin.load(network=network, num_requests=2)
         input_type = network.inputs[self._input_blob_name]
         self._input_layout = input_type if isinstance(input_type, list) else input_type.shape
@@ -39,7 +49,16 @@ def infer(self, image):
         _, _, h, w = self._input_layout
         in_frame = image if image.shape[:-1] == (h, w) else cv2.resize(image, (w, h))
         in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
-        results = self._net.infer(inputs={self._input_blob_name: in_frame})
+        inputs = {self._input_blob_name: in_frame}
+        if self._require_image_info:
+            info = np.zeros([1, 3])
+            info[0, 0] = h
+            info[0, 1] = w
+            # frame number
+            info[0, 2] = 1
+            inputs['image_info'] = info
+
+        results = self._net.infer(inputs)
         if len(results) == 1:
             return results[self._output_blob_name].copy()
         else: