Faster R-CNN with Inception v2 for auto annotation (#541)

utils/open_model_zoo/faster_rcnn_inception_v2_coco/README.md

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+# Faster R-CNN with Inception v2 (https://arxiv.org/pdf/1801.04381.pdf) pre-trained on the COCO dataset
+
+### What is it?
+* This application allows you automatically to annotate many various objects on images.
+* It uses [Faster RCNN Inception Resnet v2 Atrous Coco Model](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz) from [tensorflow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)
+* It can work on CPU (with Tensorflow or OpenVINO) or GPU (with Tensorflow GPU).

utils/open_model_zoo/faster_rcnn_inception_v2_coco/interp.py

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+threshold = .5
+
+for detection in detections:
+    frame_number = detection['frame_id']
+    height = detection['frame_height']
+    width = detection['frame_width']
+    detection = detection['detections']
+
+    prediction = detection[0][0]
+    for obj in prediction:
+        obj_class = int(obj[1])
+        obj_value = obj[2]
+        if obj_value >= threshold:
+            x = obj[3] * width
+            y = obj[4] * height
+            right = obj[5] * width
+            bottom = obj[6] * height
+
+            results.add_box(x, y, right, bottom, obj_class, frame_number)

utils/open_model_zoo/faster_rcnn_inception_v2_coco/mapping.json

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+{
+    "label_map": {
+        "1": "person",
+        "2": "bicycle",
+        "3": "car",
+        "4": "motorcycle",
+        "5": "airplane",
+        "6": "bus",
+        "7": "train",
+        "8": "truck",
+        "9": "boat",
+        "10": "traffic_light",
+        "11": "fire_hydrant",
+        "13": "stop_sign",
+        "14": "parking_meter",
+        "15": "bench",
+        "16": "bird",
+        "17": "cat",
+        "18": "dog",
+        "19": "horse",
+        "20": "sheep",
+        "21": "cow",
+        "22": "elephant",
+        "23": "bear",
+        "24": "zebra",
+        "25": "giraffe",
+        "27": "backpack",
+        "28": "umbrella",
+        "31": "handbag",
+        "32": "tie",
+        "33": "suitcase",
+        "34": "frisbee",
+        "35": "skis",
+        "36": "snowboard",
+        "37": "sports_ball",
+        "38": "kite",
+        "39": "baseball_bat",
+        "40": "baseball_glove",
+        "41": "skateboard",
+        "42": "surfboard",
+        "43": "tennis_racket",
+        "44": "bottle",
+        "46": "wine_glass",
+        "47": "cup",
+        "48": "fork",
+        "49": "knife",
+        "50": "spoon",
+        "51": "bowl",
+        "52": "banana",
+        "53": "apple",
+        "54": "sandwich",
+        "55": "orange",
+        "56": "broccoli",
+        "57": "carrot",
+        "58": "hot_dog",
+        "59": "pizza",
+        "60": "donut",
+        "61": "cake",
+        "62": "chair",
+        "63": "couch",
+        "64": "potted_plant",
+        "65": "bed",
+        "67": "dining_table",
+        "70": "toilet",
+        "72": "tv",
+        "73": "laptop",
+        "74": "mouse",
+        "75": "remote",
+        "76": "keyboard",
+        "77": "cell_phone",
+        "78": "microwave",
+        "79": "oven",
+        "80": "toaster",
+        "81": "sink",
+        "83": "refrigerator",
+        "84": "book",
+        "85": "clock",
+        "86": "vase",
+        "87": "scissors",
+        "88": "teddy_bear",
+        "89": "hair_drier",
+        "90": "toothbrush"
+    }
+}