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from transformers import SegformerForSemanticSegmentation
from transformers import SegformerImageProcessor
from PIL import Image
import gradio as gr
import numpy as np
import random
import cv2
import torch
image_list = [
"data/1.png",
"data/2.png",
"data/3.png",
"data/4.png",
]
model_path = ['deprem-ml/deprem_satellite_semantic_whu']
def visualize_instance_seg_mask(mask):
# Initialize image with zeros with the image resolution
# of the segmentation mask and 3 channels
image = np.zeros((mask.shape[0], mask.shape[1], 3))
# Create labels
labels = np.unique(mask)
label2color = {
label: (
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255),
)
for label in labels
}
for height in range(image.shape[0]):
for width in range(image.shape[1]):
image[height, width, :] = label2color[mask[height, width]]
image = image / 255
return image
def Segformer_Segmentation(image_path, model_id):
output_save = "output.png"
test_image = Image.open(image_path)
model = SegformerForSemanticSegmentation.from_pretrained(model_id)
proccessor = SegformerImageProcessor(model_id)
inputs = proccessor(images=test_image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
result = proccessor.post_process_semantic_segmentation(outputs)[0]
result = np.array(result)
result = visualize_instance_seg_mask(result)
cv2.imwrite(output_save, result*255)
return image_path, output_save
examples = [[image_list[0], "deprem-ml/deprem_satellite_semantic_whu"],
[image_list[1], "deprem-ml/deprem_satellite_semantic_whu"],
[image_list[2], "deprem-ml/deprem_satellite_semantic_whu"],
[image_list[3], "deprem-ml/deprem_satellite_semantic_whu"]]
title = "Deprem ML - Segformer Semantic Segmentation"
app = gr.Blocks()
with app:
gr.HTML("<h1 style='text-align: center'>{}</h1>".format(title))
with gr.Row():
with gr.Column():
gr.Markdown("Video")
input_video = gr.Image(type='filepath')
model_id = gr.Dropdown(value=model_path[0], choices=model_path)
input_video_button = gr.Button(value="Predict")
with gr.Column():
output_orijinal_image = gr.Image(type='filepath')
with gr.Column():
output_mask_image = gr.Image(type='filepath')
gr.Examples(examples, inputs=[input_video, model_id], outputs=[output_orijinal_image, output_mask_image], fn=Segformer_Segmentation, cache_examples=True)
input_video_button.click(Segformer_Segmentation, inputs=[input_video, model_id], outputs=[output_orijinal_image, output_mask_image])
app.launch()
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