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Updated TensorFlow Object Detection API (markdown)

Dmitry Kurtaev 2018-01-29 18:35:20 +03:00
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TensorFlow-Object-Detection-API.md

@ -57,6 +57,60 @@ cv.waitKey()
```
![](https://user-images.githubusercontent.com/25801568/35504975-a5db962c-04f5-11e8-9a9f-1d803f86af7f.png)
## Run network in OpenCV
OpenCV needs an extra configuration file to import object detection models from TensorFlow. It's based on a text version of the same serialized graph in protocol buffers format (protobuf).
### Use existing config file for your model
You can use one of the configs that has been tested in OpenCV. Choose it depends on your model and TensorFlow version:
| Model | Version | ||
|-------|-------------|----|----|
| MobileNet-SSD | TensorFlow >= 1.4 | [weights](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz) | |
| Inception v2 SSD | TensorFlow >= 1.4 | [weights](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz) | [config](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/ssd_inception_v2_coco_2017_11_17.pbtxt) |
| MobileNet-SSD | TensorFlow < 1.4 | [weights](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz) | [config](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/ssd_mobilenet_v1_coco.pbtxt) |
### Get a text graph representation
You can use the following script to make a text graph representation. It removes weights nodes and some unused fields.
```python
import tensorflow as tf
# Read the graph.
with tf.gfile.FastGFile('graph.pb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Remove Const nodes.
for i in reversed(range(len(graph_def.node))):
if graph_def.node[i].op == 'Const':
del graph_def.node[i]
for attr in ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim',
'use_cudnn_on_gpu', 'Index', 'Tperm', 'is_training',
'Tpaddings']:
if attr in graph_def.node[i].attr:
del graph_def.node[i].attr[attr]
# Save as text.
tf.train.write_graph(graph_def, "", "graph.pbtxt", as_text=True)
```
### Generate a config file
Run `optimize_for_inference.py` tool to make your model simpler:
```bash
python ~/tensorflow/tensorflow/python/tools/optimize_for_inference.py \
--input frozen_inference_graph.pb \
--output opt_graph.pb \
--input_names image_tensor \
--output_names "num_detections,detection_scores,detection_boxes,detection_classes" \
--frozen_graph
```
Run a [graph transformation tool](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#using-the-graph-transform-tool) to fuse constant nodes.
Run [tf_text_graph.py]() script. If your model has different values of `num_classes`, `min_scale`, `max_scale`, `num_layers` or `aspect_ratios` comparing to [origin configuration files](https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs), specify it in the script arguments.
## References
* [TensorFlow library](https://www.tensorflow.org/)
* [COCO dataset](http://cocodataset.org/#home)
* [COCO dataset](http://cocodataset.org/#home)
* [Google Protobuf](https://developers.google.com/protocol-buffers/)