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import gradio as gr
import cv2
import torch
import numpy as np
import matplotlib.pyplot as plt
from celldetection import fetch_model, to_tensor
# ✅ Load the model
device = 'cpu'
model = fetch_model('ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c').to(device).eval()
# ✅ Inference function
def segment(image):
img_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
x = to_tensor(img_rgb, transpose=True, device=device, dtype=torch.float32)[None]
with torch.no_grad():
output = model(x)
contours = output['contours'][0]
original = (img_rgb * 255).astype(np.uint8).copy()
segmented = original.copy()
for contour in contours:
contour = np.array(contour.cpu(), dtype=np.int32)
cv2.drawContours(segmented, [contour], -1, (255, 0, 0), 2)
h, w, c = original.shape
gap = 60
canvas = np.zeros((h, w * 2 + gap, c), dtype=np.uint8)
canvas[:, :w, :] = original
canvas[:, w + gap:, :] = segmented
return cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR)
# ✅ Example images list
examples = [
["1.png"],
["2.png"],
["3.png"]
]
# ✅ Launch the Gradio interface
gr.Interface(
fn=segment,
inputs=gr.Image(type="numpy"),
outputs="image",
title="Cell Segmentation Demo (FZJ-INM1)",
description="Upload a microscopy image to see side-by-side segmentation.",
examples=examples
).launch()
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