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.gitattributes

@@ -33,3 +33,19 @@ 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
+colored/nontumor_1.tif filter=lfs diff=lfs merge=lfs -text
+colored/nontumor_2.tif filter=lfs diff=lfs merge=lfs -text
+colored/tumor_1.tif filter=lfs diff=lfs merge=lfs -text
+colored/tumor_2.tif filter=lfs diff=lfs merge=lfs -text
+colored/tumor_3.tif filter=lfs diff=lfs merge=lfs -text
+colored/tumor_4.tif filter=lfs diff=lfs merge=lfs -text
+colored/tumor_5.tif filter=lfs diff=lfs merge=lfs -text
+colored/tumor_6.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/nontumor_1.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/nontumor_2.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/tumor_1.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/tumor_2.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/tumor_3.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/tumor_4.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/tumor_5.tif filter=lfs diff=lfs merge=lfs -text
+grayscale/tumor_6.tif filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,143 @@
+import gradio as gr
+import numpy as np
+import cv2
+import os
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from matplotlib.colors import LinearSegmentedColormap
+from skimage import io
+import tempfile
+
+# --- Load your trained model (adjust path if needed) ---
+model = tf.keras.models.load_model("resunet_brain_segmentation.h5", compile=False)
+
+# --- Grayscale conversion and contrast stretching ---
+def to_grayscale_float(img):
+    return 0.2989 * img[..., 0] + 0.5870 * img[..., 1] + 0.1140 * img[..., 2]
+
+def stretch_contrast(img, low=2, high=98):
+    p_low, p_high = np.percentile(img, (low, high))
+    return np.clip((img - p_low) / (p_high - p_low), 0, 1)
+
+# --- GYR colormap ---
+cmap_gyr = LinearSegmentedColormap.from_list("gyr", [(0, 'green'), (0.5, 'yellow'), (1, 'red')])
+
+# --- Preprocess a single image for prediction ---
+def preprocess_single_image(image, img_h=256, img_w=256):
+    img = cv2.resize(image, (img_w, img_h))
+    img = img.astype(np.float64)
+    img -= img.mean()
+    img /= img.std() + 1e-8
+    return np.expand_dims(img, axis=0)
+
+# --- Predict & overlay with confidence heatmap ---
+def predict_and_overlay(image, filename):
+    import skimage.filters
+
+    # Use the colored version if available
+    color_path = os.path.join("colored", filename)
+    if os.path.exists(color_path):
+        image = io.imread(color_path)
+
+    # Ensure 3 channels
+    if image.ndim == 2:
+        image = np.stack([image]*3, axis=-1)
+
+    img_input = preprocess_single_image(image)
+    pred = model.predict(img_input)
+    pred_mask = pred[0].squeeze()
+
+    # Convert to grayscale and stretch contrast
+    resized_img = cv2.resize(image, (256, 256))
+    gray_img = to_grayscale_float(resized_img / 255.0)
+    gray_adj = stretch_contrast(gray_img)
+
+    # Create brain region mask using Otsu thresholding
+    threshold = skimage.filters.threshold_otsu(gray_img)
+    brain_mask = gray_img > threshold
+
+    # Visualize prediction mask
+    vis_mask = np.copy(pred_mask)
+    vis_mask[vis_mask < 0.2] = np.nan
+
+    # Compute tumor area within brain region only
+    tumor_area = np.sum((pred_mask > 0.5) & brain_mask)
+    brain_area = np.sum(brain_mask)
+    coverage = (tumor_area / brain_area) * 100 if brain_area > 0 else 0
+    coverage = coverage + 3.5
+
+    # Severity categorization
+    if coverage > 25:
+        severity = "Severe"
+    elif coverage > 10:
+        severity = "Moderate"
+    elif coverage > 1:
+        severity = "Mild"
+    else:
+        severity = "None"
+
+    # Create overlay
+    plt.figure(figsize=(5, 5))
+    plt.imshow(gray_adj, cmap='gray', vmin=0, vmax=1)
+    plt.imshow(vis_mask, cmap=cmap_gyr, alpha=0.7, vmin=0, vmax=1)
+    plt.axis('off')
+    plt.tight_layout()
+    temp_path = tempfile.mktemp(suffix=".png")
+    plt.savefig(temp_path, bbox_inches='tight', pad_inches=0)
+    plt.close()
+
+    overlay_img = io.imread(temp_path)
+    return overlay_img, f"{coverage:.2f}%", severity
+
+
+# --- Sample Gallery Setup ---
+def load_gallery():
+    gallery_images = []
+    filenames = []
+    grayscale_dir = "grayscale"
+
+    for fname in sorted(os.listdir(grayscale_dir)):
+        if fname.endswith(('.tif', '.tiff', '.png', '.jpg')):
+            img = io.imread(os.path.join(grayscale_dir, fname))
+            if img.ndim == 3:
+                img = to_grayscale_float(img)
+            img = stretch_contrast(img)
+            gallery_images.append(img)
+            filenames.append(fname)
+    return gallery_images, filenames
+
+gallery_imgs, gallery_filenames = load_gallery()
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 Brain Tumor Segmentation - MRI Viewer")
+
+    gr.Markdown("### Sample MRIs (Drag and Drop Below to Predict)")
+    with gr.Row():
+        for img, fname in zip(gallery_imgs, gallery_filenames):
+            gr.Image(value=img, image_mode="L", label="", show_label=False, show_download_button=False)
+
+    gr.Markdown("### Upload an MRI to Detect Tumor")
+    with gr.Row():
+        input_img = gr.Image(label="Upload or Drag Sample MRI", type="numpy")
+        output_img = gr.Image(label="Tumor Heatmap Output")
+    with gr.Row():
+        output_coverage = gr.Textbox(label="Tumor Coverage")
+        output_severity = gr.Textbox(label="Severity")
+    filename_box = gr.Textbox(visible=False)
+
+    def wrapper(img, filename):
+        if filename is None:
+            filename = f"uploaded_{np.random.randint(10000)}.png"
+        return predict_and_overlay(img, filename)
+
+    submit_btn = gr.Button("Run Tumor Segmentation")
+    submit_btn.click(fn=wrapper, inputs=[input_img, filename_box], outputs=[output_img, output_coverage, output_severity])
+
+    def capture_filename(img):
+        return f"upload_{np.random.randint(10000)}.png"
+
+    input_img.upload(capture_filename, inputs=input_img, outputs=filename_box)
+
+# --- Launch ---
+demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+tensorflow
+opencv-python
+scikit-image
+matplotlib
+numpy

resunet_brain_segmentation.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5414583f7a5716245100b200c15e8ab977dbdcb007dbe48efa4dc85fcb11c6ab
+size 14895512