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import gradio as gr
import numpy as np
from torchvision import transforms as T
from transformers import MaskFormerForInstanceSegmentation, MaskFormerImageProcessor
ade_mean=[0.485, 0.456, 0.406]
ade_std=[0.229, 0.224, 0.225]
palette = [
[120, 120, 120], [4, 200, 4], [180, 120, 120], [6, 230, 230],
[80, 50, 50], [120, 120, 80], [140, 140, 140], [204, 5, 255]
]
model_id = f"thiagohersan/maskformer-satellite-trees"
# preprocessor = MaskFormerImageProcessor.from_pretrained(model_id)
preprocessor = MaskFormerImageProcessor(
do_resize=False,
do_normalize=False,
do_rescale=False,
ignore_index=255,
reduce_labels=False
)
model = MaskFormerForInstanceSegmentation.from_pretrained(model_id)
test_transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=ade_mean, std=ade_std)
])
def visualize_instance_seg_mask(img_in, mask, id2label):
img_out = np.zeros((mask.shape[0], mask.shape[1], 3))
image_total_pixels = mask.shape[0] * mask.shape[1]
label_ids = np.unique(mask)
id2color = {id: palette[id] for id in label_ids}
id2count = {id: 0 for id in label_ids}
for i in range(img_out.shape[0]):
for j in range(img_out.shape[1]):
img_out[i, j, :] = id2color[mask[i, j]]
id2count[mask[i, j]] = id2count[mask[i, j]] + 1
image_res = (0.5 * img_in + 0.5 * img_out) / 255
dataframe = [[
f"{id2label[id]}",
f"{(100 * id2count[id] / image_total_pixels):.2f} %",
f"{np.sqrt(id2count[id] / image_total_pixels):.2f} m"
] for id in label_ids if 'tree' in id2label[id]]
return image_res, dataframe
def query_image(img):
img_size = (img.shape[0], img.shape[1])
inputs = preprocessor(images=test_transform(np.array(img)), return_tensors="pt")
outputs = model(**inputs)
results = preprocessor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[img_size])[0]
results = visualize_instance_seg_mask(img, results.numpy(), model.config.id2label)
return results
demo = gr.Interface(
query_image,
inputs=[gr.Image(label="Input Image")],
outputs=[gr.Image(label="Trees"), gr.DataFrame(headers=None, label="Area Info")],
title="maskformer-satellite-trees",
allow_flagging="never",
analytics_enabled=None
)
demo.launch(show_api=False)
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