|
import gradio as gr |
|
import numpy as np |
|
import cv2 |
|
import torch |
|
|
|
|
|
|
|
from PIL import Image |
|
|
|
from src.plot_utils import show_masks |
|
from gradio_image_annotation import image_annotator |
|
|
|
|
|
from sam2.build_sam import build_sam2 |
|
from sam2.sam2_image_predictor import SAM2ImagePredictor |
|
|
|
choice_mapping = { |
|
"tiny": ["sam2_hiera_t.yaml", "assets/checkpoints/sam2_hiera_tiny.pt"], |
|
"small": ["sam2_hiera_s.yaml", "assets/checkpoints/sam2_hiera_small.pt"], |
|
"base_plus": ["sam2_hiera_b+.yaml", "assets/checkpoints/sam2_hiera_base_plus.pt"], |
|
"large": ["sam2_hiera_l.yaml", "assets/checkpoints/sam2_hiera_large.pt"], |
|
} |
|
|
|
|
|
def predict(model_choice: str, annotations, image): |
|
config_file, ckpt_path = choice_mapping[str(model_choice)] |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
sam2_model = build_sam2(config_file, ckpt_path, device=device) |
|
predictor = SAM2ImagePredictor(sam2_model) |
|
predictor.set_image(image) |
|
coordinates = np.array( |
|
[ |
|
int(annotations["boxes"][0]["xmin"]), |
|
int(annotations["boxes"][0]["ymin"]), |
|
int(annotations["boxes"][0]["xmax"]), |
|
int(annotations["boxes"][0]["ymax"]), |
|
] |
|
) |
|
masks, scores, _ = predictor.predict( |
|
point_coords=None, |
|
point_labels=None, |
|
box=coordinates[None, :], |
|
multimask_output=False, |
|
) |
|
mask = masks.transpose(1, 2, 0) |
|
mask_image = (mask * 255).astype(np.uint8) |
|
cv2.imwrite("mask.png", mask_image) |
|
|
|
return [ |
|
show_masks(image, masks, scores, box_coords=coordinates), |
|
gr.DownloadButton("Download Mask", value="mask.png", visible=True), |
|
] |
|
|
|
|
|
with gr.Blocks(delete_cache=(30, 30)) as demo: |
|
gr.Markdown( |
|
""" |
|
# 1. Choose Model Checkpoint |
|
""" |
|
) |
|
with gr.Row(): |
|
model = gr.Dropdown( |
|
choices=["tiny", "small", "base_plus", "large"], |
|
value="tiny", |
|
label="Model Checkpoint", |
|
info="Which model checkpoint to load?", |
|
) |
|
|
|
gr.Markdown( |
|
""" |
|
# 2. Upload an Image |
|
""" |
|
) |
|
|
|
with gr.Row(): |
|
img = gr.Image(value="./assets/img.png", type="numpy", label="Input Image") |
|
|
|
gr.Markdown( |
|
""" |
|
# 3. Draw Bounding Box |
|
""" |
|
) |
|
|
|
annotator = image_annotator( |
|
value={"image": img.value["path"]}, |
|
disable_edit_boxes=True, |
|
single_box=True, |
|
label="Draw a bounding box", |
|
) |
|
btn = gr.Button("Get Segmentation Mask") |
|
download_btn = gr.DownloadButton("Download Mask", value="mask.png", visible=False) |
|
btn.click( |
|
fn=predict, inputs=[model, annotator, img], outputs=[gr.Plot(), download_btn] |
|
) |
|
|
|
demo.launch() |
|
|
