Track image files using Git LFS

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,3 +1,86 @@
+# # +++++++++++++++ Fist, Version: 1.0.0 +++++++++++++++++++++
+# # Last Updated: 08 July 2025
+# # Fibril Segmentation with UNet++ using Gradio
+
+# import os
+# import torch
+# import numpy as np
+# from PIL import Image
+# import albumentations as A
+# from albumentations.pytorch import ToTensorV2
+# import segmentation_models_pytorch as smp
+# import gradio as gr
+
+# # ─── Configuration ─────────────────────────────────────────
+# CONFIG = {
+#     "model_path": "./model/encoder_resnet34_decoder_UnetPlusPlus_fibril_seg_model.pth",
+#     "img_size": 512
+# }
+
+# # ─── Device Setup ──────────────────────────────────────────
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# print(f"✅ Using device: {device}")
+
+# # ─── Load Model ────────────────────────────────────────────
+# model = smp.UnetPlusPlus(
+#     encoder_name='resnet34',
+#     encoder_depth=5,
+#     encoder_weights='imagenet',
+#     decoder_use_norm='batchnorm',
+#     decoder_channels=(256, 128, 64, 32, 16),
+#     decoder_attention_type=None,
+#     decoder_interpolation='nearest',
+#     in_channels=1,
+#     classes=1,
+#     activation=None
+# ).to(device)
+
+# model.load_state_dict(torch.load(CONFIG["model_path"], map_location=device))
+# model.eval()
+
+# # ─── Transform Function ────────────────────────────────────
+# def get_transform(size):
+#     return A.Compose([
+#         A.Resize(size, size),
+#         A.Normalize(mean=(0.5,), std=(0.5,)),
+#         ToTensorV2()
+#     ])
+
+# transform = get_transform(CONFIG["img_size"])
+
+# # ─── Prediction Function ───────────────────────────────────
+# def predict(image):
+#     image = image.convert("L")  # Convert to grayscale
+#     img_np = np.array(image)
+#     img_tensor = transform(image=img_np)["image"].unsqueeze(0).to(device)
+
+#     with torch.no_grad():
+#         pred = torch.sigmoid(model(img_tensor))
+#         mask = (pred > 0.5).float().cpu().squeeze().numpy()
+
+#     mask_img = Image.fromarray((mask * 255).astype(np.uint8))
+#     return mask_img
+
+# # ─── Gradio Interface ──────────────────────────────────────
+# demo = gr.Interface(
+#     fn=predict,
+#     inputs=gr.Image(type="pil", label="Upload Microscopy Image"),
+#     outputs=gr.Image(type="pil", label="Predicted Segmentation Mask"),
+#     title="Fibril Segmentation with Unet++",
+#     description="Upload a grayscale microscopy image to get its predicted segmentation mask.",
+#     allow_flagging="never",
+#     live=False
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+
+# #  +++++++++++++++ Second Version: 1.1.0 ++++++++++++++++++++
+# # Last Updated: 08 July 2025
+# # Improvements: Added examples, better UI, and device handling
+
 import os
 import torch
 import numpy as np
@@ -22,10 +105,7 @@ model = smp.UnetPlusPlus(
     encoder_name='resnet34',
     encoder_depth=5,
     encoder_weights='imagenet',
-    decoder_use_norm='batchnorm',
     decoder_channels=(256, 128, 64, 32, 16),
-    decoder_attention_type=None,
-    decoder_interpolation='nearest',
     in_channels=1,
     classes=1,
     activation=None
@@ -46,7 +126,7 @@ transform = get_transform(CONFIG["img_size"])
 
 # ─── Prediction Function ───────────────────────────────────
 def predict(image):
-    image = image.convert("L")  # Convert to grayscale
+    image = image.convert("L")  # Ensure grayscale
     img_np = np.array(image)
     img_tensor = transform(image=img_np)["image"].unsqueeze(0).to(device)
 
@@ -57,16 +137,51 @@ def predict(image):
     mask_img = Image.fromarray((mask * 255).astype(np.uint8))
     return mask_img
 
-# ─── Gradio Interface ──────────────────────────────────────
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Microscopy Image"),
-    outputs=gr.Image(type="pil", label="Predicted Segmentation Mask"),
-    title="Fibril Segmentation with Unet++",
-    description="Upload a grayscale microscopy image to get its predicted segmentation mask.",
-    allow_flagging="never",
-    live=False
-)
+# ─── Gradio UI (Improved) ──────────────────────────────────
+examples = [
+    ["examples/example1.jpg"],
+    ["examples/example2.jpg"],
+    ["examples/example3.jpg"],
+    ["examples/example4.jpg"],
+    ["examples/example5.jpg"],
+    ["examples/example6.jpg"],
+    ["examples/example7.jpg"]
+]
+
+css = """
+.gradio-container {
+    max-width: 950px;
+    margin: auto;
+}
+.gr-button {
+    background-color: #4a90e2;
+    color: white;
+    border-radius: 5px;
+}
+.gr-button:hover {
+    background-color: #357ABD;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("## 🧬 Fibril Segmentation with UNet++")
+    gr.Markdown("Upload a **grayscale microscopy image**, and this model will predict the **segmentation mask of fibrillar structures**.\n\nModel: ResNet34 encoder + UNet++ decoder")
+
+    with gr.Row():
+        input_img = gr.Image(label="Upload Microscopy Image", type="pil")
+        output_mask = gr.Image(label="Predicted Segmentation Mask", type="pil")
+
+    submit_btn = gr.Button("Segment Image")
+
+    submit_btn.click(fn=predict, inputs=input_img, outputs=output_mask)
+
+    gr.Examples(
+        examples=examples,
+        inputs=input_img,
+        label="Try with Example Images",
+        cache_examples=False
+    )
 
+# ─── Launch App ────────────────────────────────────────────
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()