Upload 2 files

main.py

@@ -0,0 +1,101 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+from transformers import pipeline
+import torch
+import tifffile
+import gradio as gr
+import os
+
+# Step 1: Setup
+print("Step 1: Setting up the environment...")
+device = 0 if torch.cuda.is_available() else -1
+print(f"    > Device selected: {'GPU' if device == 0 else 'CPU'}")
+
+# Step 2: Load SAM Model
+print("Step 2: Loading SAM Model...")
+generator = pipeline("mask-generation", model="facebook/sam-vit-huge", device=device)
+print("    > SAM Model loaded successfully.")
+
+
+def segment_image(image):
+    print("Step 3: Starting image segmentation...")
+
+    # Resize Image
+    print("    > Resizing image...")
+    raw_image = image.convert("RGB")
+    original_size = raw_image.size
+    resized_size = (original_size[0] // 3, original_size[1] // 3)
+    raw_image = raw_image.resize(resized_size)
+    print(f"    > Original size: {original_size}, Resized size: {resized_size}")
+
+    # Run SAM Segmentation
+    print("    > Running SAM segmentation...")
+    outputs = generator(raw_image, points_per_batch=64)
+    masks = outputs["masks"]
+    print(f"    > {len(masks)} masks generated.")
+
+    # Create Labeled Mask
+    print("    > Creating labeled mask...")
+    h, w = masks[0].shape
+    labeled_mask = np.zeros((h, w), dtype=np.uint16)
+    for i, mask in enumerate(masks):
+        labeled_mask[mask] = i + 1
+    print("    > Labeled mask created.")
+
+    # Generate Overlay
+    print("    > Generating overlay...")
+    overlay = np.zeros((h, w, 4))  # RGBA
+    np.random.seed(42)
+    for label in np.unique(labeled_mask):
+        if label == 0:
+            continue
+        color = np.random.rand(3)
+        overlay[labeled_mask == label] = np.append(color, 0.5)
+    print("    > Overlay generated.")
+
+    # Save the labeled mask as TIFF
+    output_path = "labeled_mask.tif"
+    print("    > Saving labeled mask as TIFF...")
+    tifffile.imwrite(output_path, labeled_mask)
+    print(f"    > Mask saved to: {output_path}")
+
+    # Plotting results
+    print("Step 4: Plotting results...")
+    plt.figure(figsize=(15, 5))
+
+    # Original Image
+    plt.subplot(1, 2, 1)
+    plt.imshow(image)
+    plt.title("Original Image")
+    plt.axis("off")
+
+    # Segmented Overlay
+    plt.subplot(1, 2, 2)
+    plt.imshow(raw_image)
+    plt.imshow(overlay)
+    plt.title("Segmented Overlay")
+    plt.axis("off")
+
+    plt.tight_layout()
+    plt.savefig("segmented_overlay.png")  # Save the overlay plot
+    plt.close()  # Close the plot to avoid display issues
+    print("    > Results plotted.")
+
+    return output_path  # Return path to the saved mask
+
+
+# Step 5: Gradio Interface
+print("Step 5: Setting up Gradio interface...")
+iface = gr.Interface(
+    fn=segment_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.File(label="Download Mask"),
+    title="Image Segmentation with SAM",
+    description="Upload an image to segment it and visualize the results."
+)
+
+# Step 6: Launch the interface
+print("Step 6: Launching the interface...")
+iface.launch()
+print("    > Interface launched successfully.")

requirements.txt

@@ -0,0 +1,72 @@
+aiofiles==24.1.0
+annotated-types==0.7.0
+anyio==4.9.0
+certifi==2025.6.15
+charset-normalizer==3.4.2
+click==8.2.1
+contourpy==1.3.2
+cycler==0.12.1
+exceptiongroup==1.3.0
+fastapi==0.115.12
+ffmpy==0.6.0
+filelock==3.18.0
+fonttools==4.58.4
+fsspec==2025.5.1
+gradio==5.34.0
+gradio_client==1.10.3
+groovy==0.1.2
+h11==0.16.0
+hf-xet==1.1.4
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.33.0
+idna==3.10
+Jinja2==3.1.6
+kiwisolver==1.4.8
+laspy==2.5.4
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.3
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.2.6
+orjson==3.10.18
+packaging==25.0
+pandas==2.3.0
+pillow==11.2.1
+pydantic==2.11.7
+pydantic_core==2.33.2
+pydub==0.25.1
+Pygments==2.19.1
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.4
+rich==14.0.0
+ruff==0.11.13
+safehttpx==0.1.6
+safetensors==0.5.3
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.46.2
+sympy==1.14.0
+tifffile==2025.5.10
+tokenizers==0.21.1
+tomlkit==0.13.3
+torch==2.7.1
+torchvision==0.22.1
+tqdm==4.67.1
+transformers==4.52.4
+typer==0.16.0
+typing-inspection==0.4.1
+typing_extensions==4.14.0
+tzdata==2025.2
+urllib3==2.4.0
+uvicorn==0.34.3
+websockets==15.0.1