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main.py

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+import streamlit as st
+import torch
+import segmentation_models_pytorch as smp
+import numpy as np
+import cv2
+from PIL import Image
+import os
+import matplotlib.pyplot as plt
+from io import BytesIO
+
+# Set locale to Thai
+# (There is no direct i18n module in Streamlit for localization, manual translation is required)
+# st.set_locale("th")
+
+# Define the model architecture
+model = smp.Unet(
+    encoder_name="resnet34",
+    encoder_weights=None,  # We will load our own weights
+    in_channels=1,
+    classes=1,
+)
+
+# Load the model weights
+model_path = './unet_model_statedict_resnet34andimagenet_best.pth'
+model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+
+# Set the device to GPU if available, otherwise CPU
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+
+model.eval()
+st.sidebar.image("./smte_logo.png")
+
+# Function to preprocess the uploaded image
+def preprocess_image(image):
+    image = image.convert("L")  # Convert to grayscale
+    image = np.array(image)
+    image = cv2.resize(image, (256, 256))  # Resize to 256x256
+    image = image / 255.0  # Normalize to [0,1]
+    image = image[np.newaxis, np.newaxis, :, :]  # Add batch and channel dimensions
+    image = torch.tensor(image, dtype=torch.float32)
+    return image.to(device)
+
+# Function to apply the model and get the segmentation mask
+def get_segmentation_mask(image, threshold):
+    with torch.no_grad():
+        output = model(image)
+        output = output.squeeze().cpu().numpy()
+        output = (output > threshold).astype(np.uint8) * 255
+    return output
+
+# Function to list sample images in a directory
+def list_sample_images(directory):
+    valid_extensions = ['jpg', 'jpeg', 'png']
+    return [f for f in os.listdir(directory) if any(f.lower().endswith(ext) for ext in valid_extensions)]
+
+# Streamlit app
+st.title("Brainstroke segmentation from CT-SCAN")
+
+# Sidebar controls
+st.sidebar.title("ตัวควบคุม")
+
+# Sample image selection in sidebar
+sample_images_directory = './sample'  # Directory containing sample images
+sample_images = list_sample_images(sample_images_directory)
+selected_sample_image = st.sidebar.selectbox("เลือกรูปภาพตัวอย่าง", ["ไม่มี"] + sample_images)
+
+# Image upload in sidebar
+uploaded_file = st.sidebar.file_uploader("หรืออัปโหลดรูปภาพ MRI สมอง", type=["jpg", "jpeg", "png"])
+
+
+# Main content area
+if uploaded_file is not None:
+    original_image = Image.open(uploaded_file)
+    # st.image(original_image, caption='รูปภาพที่อัปโหลด', use_column_width=True)
+elif selected_sample_image != "ไม่มี":
+    image_path = os.path.join(sample_images_directory, selected_sample_image)
+    original_image = Image.open(image_path)
+    # st.image(original_image, caption=f'รูปภาพตัวอย่างที่เลือก: {selected_sample_image}', use_column_width=True)
+
+if 'original_image' in locals():
+    # Preprocess the image
+    input_image = preprocess_image(original_image)
+    
+    # Threshold slider in sidebar
+    threshold = st.sidebar.slider('ความมั่นใจของโมเดล', 0.0, 1.0, 0.5, 0.01)
+
+
+
+    # Display original and predicted images side by side
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(original_image, caption='รูปภาพต้นฉบับ', use_column_width=True)
+    with col2:
+                # Get segmentation mask
+        mask = get_segmentation_mask(input_image, threshold)
+        
+        # Convert the original image to grayscale and resize it
+        brain_image = original_image.convert("L")
+        brain_image = np.array(brain_image)
+        brain_image = cv2.resize(brain_image, (256, 256))
+        # Colormap selector and alpha slider in sidebar
+        colormap = st.sidebar.selectbox("เลือกสีการแยกส่วน", ["Blues", "viridis", "plasma", "inferno", "magma", "cividis"])
+        alpha = st.sidebar.slider("ค่าความโปร่งใสของการแยกส่วน", 0.0, 1.0, 0.7, 0.01)
+        
+        # Create a plot of the brain image and overlay the mask
+        fig, ax = plt.subplots()
+        ax.imshow(brain_image, cmap='gray')
+        ax.imshow(mask, cmap=colormap, alpha=alpha)
+        ax.axis('off')  # Hide axes
+
+        # Save the figure to a BytesIO object
+        buf = BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
+        buf.seek(0)
+
+        # Convert the BytesIO object to an image and display it
+        overlay_image = Image.open(buf)
+        st.image(overlay_image, caption='ภาพซ้อนกันของการแยกส่วน', use_column_width=True)
+        # Download button for the segmented image
+        st.sidebar.download_button(
+            label="ดาวน์โหลดผลวินิฉัย",
+            data=buf,
+            file_name='segmented_image.png',
+            mime='image/png',
+            
+        )

requirements.txt

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+altair==5.3.0
+attrs==23.2.0
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.6.2
+charset-normalizer==3.3.2
+click==8.1.7
+efficientnet-pytorch==0.7.1
+filelock==3.15.4
+fsspec==2024.6.1
+gitdb==4.0.11
+gitpython==3.1.43
+huggingface-hub==0.23.4
+idna==3.7
+jinja2==3.1.4
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+markdown-it-py==3.0.0
+markupsafe==2.1.5
+mdurl==0.1.2
+mpmath==1.3.0
+munch==4.0.0
+networkx==3.3
+numpy==2.0.0
+opencv-python-headless==4.10.0.84
+packaging==24.1
+pandas==2.2.2
+pillow==10.4.0
+pretrainedmodels==0.7.4
+protobuf==5.27.2
+pyarrow==16.1.0
+pydeck==0.9.1
+pygments==2.18.0
+python-dateutil==2.9.0.post0
+pytz==2024.1
+pyyaml==6.0.1
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.18.1
+safetensors==0.4.3
+segmentation-models-pytorch==0.3.3
+six==1.16.0
+smmap==5.0.1
+streamlit==1.36.0
+sympy==1.12.1
+tenacity==8.4.2
+timm==0.9.2
+toml==0.10.2
+toolz==0.12.1
+torch==2.3.1
+torchvision==0.18.1
+tornado==6.4.1
+tqdm==4.66.4
+matplotlib == 3.8.4

unet_model_statedict_resnet34andimagenet_best.pth

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