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### Prepare SAM
```
pip install git+https://github.com/facebookresearch/segment-anything.git
```
or
```
git clone [email protected]:facebookresearch/segment-anything.git
cd segment-anything; pip install -e .
```
```
pip install opencv-python pycocotools matplotlib onnxruntime onnx
```
### Download the checkpoint:
https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
### Inference
The prompts are in json format:
```
prompts = [
{
"prompt_type":["click"],
"input_point":[[500, 375]],
"input_label":[1],
"multimask_output":"True",
},
{
"prompt_type":["click"],
"input_point":[[500, 375], [1125, 625]],
"input_label":[1, 0],
},
{
"prompt_type":["click", "box"],
"input_box":[425, 600, 700, 875],
"input_point":[[575, 750]],
"input_label": [0]
},
{
"prompt_type":["box"],
"input_boxes": [
[75, 275, 1725, 850],
[425, 600, 700, 875],
[1375, 550, 1650, 800],
[1240, 675, 1400, 750],
]
},
{
"prompt_type":["everything"]
},
]
```
In `base_segmenter.py`:
```
segmenter = BaseSegmenter(
device='cuda',
checkpoint='sam_vit_h_4b8939.pth',
model_type='vit_h'
)
for i, prompt in enumerate(prompts):
masks = segmenter.inference(image_path, prompt)
```
Outputs are masks (True and False numpy Matrix), shape: (num of masks, height, weight) |