This is a submission to the Iris Challenge. Taken from the challenge instructions:
There are 31 images in the dataset. They are all taken from drones in a range of different environments.Provide us with as much contextual awareness about these environments as you possibly can. We want to automatically understand everything about the scene in the way a human pilot might.
This repo contains two parts:
- Object detection model for clouds, sun, houses, and trees
- Horizon line detector
Detector
The detector (detector/*) uses the TF Object Detection API. The procedure is as follows:
- Find images on the internet, use LabelImg to label them (I used a tiny dataset of 20 images)
- Generate TFRecords files for training using
detector/generate_dataset.ipynb - Train a model from a pre-trained coco OD model using
detector/train_model.ipynb - Run the saved model on the the challenge test_images using
detector/run_model.ipynb
Horizon Angle
The horizon angle regression model (horizon/*) uses OpenCV built-ins. The procedure is as follows:
- Preprocess image (gray, blurring)
- Use Canny Edge Detection on image, dilate the resulting edges
- Use Hough Line Transform to get lines, fine tune parameters as needed
- Average the resulting Hough Lines to get the horizon
Below are some hand-picked results. (Find more in output_images/*)
- Python 3.5
- TensorFlow 0.14
- Jupyter Notebooks
- OpenCV 3.3
Hugo Ponte