Instance segementation using MaskRCNN for localization of document (object) images.
-
clone this repo
-
To use pretrained coco weights, download h5 file from here
-
pip3 install -r requirements.txt -
cd samples\My_docs
python3 doc.py train --dataset=/path/to/doc/dataset --weights=coco
The model generates bounding boxes and segmentation masks for each instance of an object in the image
- run 'Inspect_docs_model.ipynb' where masked outputs are tested.
We can get the masked output from this ipynb.
Click here to go to annotation tool link
1 Choose file directory
2 Annotate all the files with labels & polygons.
3 Export as Json and store it in train & val
Converts to gray scale and reduces noise using bilateral filter , smoothening color preserving the edges though! Uses adaptiveThreshold to calculate optimal threshold for small regions & to control lights. Median filter for removing sall details.
Edge detection doesn’t count with sides of the image, therefore in case that page touching a side of the image, the algorithm won’t produce a continuous, closed edge.
To prevent that we have to add small border, border 5 pixels wide works just fine. Finally, we can use Canny Edge Detection where we have to specify values of so called hysteresis thresholding, which separates edges into three groups: definitely an edge, definitely not an edge and something between.
Getting foreground images with contour