# ui2code-opencv

**Repository Path**: laver9/ui2code-opencv

## Basic Information

- **Project Name**: ui2code-opencv
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2019-02-28
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

ui2code-opencv

## Steps To Detect Component

Install following packages
 - tensorflow
 - imutils
 - numpy
 - opencv-python
 - matplotlib
 - pandas
 - pillow


2. Set Environment Variables<br>
```IMAGE_SIZE=224``` <br>
```ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"```

3. To Train <br>
``python -m scripts.retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=500 \
  --model_dir=tf_files/models/ \
  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  --output_graph=tf_files/retrained_graph.pb \
  --output_labels=tf_files/retrained_labels.txt \
  --architecture="${ARCHITECTURE}" \
  --image_dir=images/
  ``
4. To Predict Component

``` python detect_component.py --image=predict/web.png ```

### To Render on Webpage

1. cd to `myapp`
2. `npm install`
3. `npm start`