# EAST Text Detection

Scene-text detection with the EAST (Efficient and Accurate Scene Text) detector.
The model was originally distributed as a frozen TensorFlow graph
(`frozen_east_text_detection.pb`) and converted to ONNX for use with OpenCV's DNN module.

## Model Details
- **Architecture**: EAST with a ResNet-50 backbone and a feature-fusion head
- **Input**: RGB image, 320×320, raw 0–255 float, mean `(123.68, 116.78, 103.94)`, swapRB,
  NCHW layout (`input_images:0`, shape `[1, 3, 320, 320]`)
- **Outputs**:
  - `feature_fusion/Conv_7/Sigmoid:0` — score map, shape `[1, 1, 80, 80]`
  - `feature_fusion/concat_3:0` — RBOX geometry, shape `[1, 5, 80, 80]`
- **Post-processing**: OpenCV's `TextDetectionModel_EAST` decodes the score/geometry maps
  into rotated boxes (confidence threshold + rotated-NMS)
- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 15)
- **Original weights**: https://github.com/argman/EAST

Both input and outputs are emitted in NCHW so OpenCV consumes them directly.

## Usage

### Python
```bash
python demo.py --model east_text_detection_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
```

Or import directly:
```python
import cv2

model = cv2.dnn.TextDetectionModel_EAST("east_text_detection_2026jul.onnx")
# see demo.py for the full inference pipeline
```

### C++
The C++ demo runs inference with OpenCV's DNN module (default engine — no ONNX Runtime
needed). Adjust the OpenCV paths to your setup:
```bash
OCV=/path/to/opencv                          # OpenCV source tree
OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
g++ -std=c++17 demo.cpp -o demo \
  -I$OCV/include \
  -I$OCV/modules/core/include \
  -I$OCV/modules/dnn/include \
  -I$OCV/modules/imgproc/include \
  -I$OCV/modules/imgcodecs/include \
  -I$OCVBUILD \
  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
./demo --model east_text_detection_2026jul.onnx --image example_outputs/input_image.png
```

## Conversion
The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 15)
via [convert_to_onnx.py](./convert_to_onnx.py) — input `input_images:0`, outputs
`feature_fusion/Conv_7/Sigmoid:0` and `feature_fusion/concat_3:0`. Both the input and the
outputs are forced to NCHW (`inputs_as_nchw` / `outputs_as_nchw`) so the tensors match
OpenCV's layout; without `outputs_as_nchw` the score/geometry maps come out as NHWC and the
EAST decoder rejects them. Requires `tensorflow`, `tf2onnx`, and `onnx`.

```bash
python convert_to_onnx.py --pb ../pb/frozen_east_text_detection.pb
```

## License
See [LICENSE](./LICENSE) — the model originates from [argman/EAST](https://github.com/argman/EAST),
released under the GNU General Public License v3.0.