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| import streamlit as st | |
| from PIL import Image, ImageDraw | |
| import io | |
| import os | |
| import numpy as np | |
| import tempfile | |
| # Set page config | |
| st.set_page_config( | |
| page_title="BCCD Object Detection with YOLOv10", | |
| page_icon="🔍", | |
| layout="wide" | |
| ) | |
| # Initialize session state variables if they don't exist | |
| if 'model' not in st.session_state: | |
| st.session_state.model = None | |
| if 'class_names' not in st.session_state: | |
| st.session_state.class_names = ['RBC', 'WBC', 'Platelets'] # Classes in BCCD dataset | |
| # Mock function to demo the app without dependencies | |
| def get_model(): | |
| """This is a mock function to demonstrate the UI without actual model loading.""" | |
| return "mock_model" | |
| def main(): | |
| st.title("Blood Cell Object Detection with YOLOv10") | |
| st.markdown(""" | |
| This application uses a YOLOv10 model fine-tuned on the BCCD (Blood Cell Count Dataset) | |
| to detect three types of blood cells: Red Blood Cells (RBC), White Blood Cells (WBC), and Platelets. | |
| """) | |
| # Sidebar for model information and controls | |
| with st.sidebar: | |
| st.header("About") | |
| st.markdown(""" | |
| - **Model**: YOLOv10 | |
| - **Dataset**: BCCD (Blood Cell Count Dataset) | |
| - **Classes**: RBC, WBC, Platelets | |
| """) | |
| st.header("Instructions") | |
| st.markdown(""" | |
| 1. Upload an image of blood cells | |
| 2. The model will detect and classify blood cells | |
| 3. Results will show bounding boxes and detection metrics | |
| """) | |
| st.header("Model Confidence Threshold") | |
| confidence_threshold = st.slider("Confidence Threshold", 0.1, 0.9, 0.5, 0.05) | |
| st.header("Model File (Optional)") | |
| model_file = st.file_uploader("Upload custom model file (*.pt)", type=["pt"]) | |
| if model_file: | |
| st.success("Custom model loaded successfully!") | |
| # Set mock model for demo | |
| st.session_state.model = get_model() | |
| # File upload | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Read and display the uploaded image | |
| image_bytes = uploaded_file.read() | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Original Image") | |
| st.image(image, use_column_width=True) | |
| # Mock detection process for demo purposes | |
| with col2: | |
| st.subheader("Detection Results (Demo)") | |
| # Generate a demo image with bounding boxes | |
| # In a real implementation, this would use actual detection results | |
| draw_image = image.copy() | |
| draw = ImageDraw.Draw(draw_image) | |
| # Mock bounding boxes for demo (simulated detections) | |
| # Format: [x1, y1, x2, y2, class_id, confidence] | |
| mock_detections = [ | |
| [50, 50, 100, 100, 0, 0.92], # RBC | |
| [150, 75, 200, 125, 0, 0.88], # RBC | |
| [120, 200, 220, 300, 1, 0.94], # WBC | |
| [300, 150, 320, 170, 2, 0.85], # Platelet | |
| [250, 220, 270, 240, 2, 0.79] # Platelet | |
| ] | |
| # Draw bounding boxes | |
| class_colors = { | |
| 0: (255, 0, 0, 128), # RBC - Red (semi-transparent) | |
| 1: (0, 0, 255, 128), # WBC - Blue (semi-transparent) | |
| 2: (0, 255, 0, 128) # Platelets - Green (semi-transparent) | |
| } | |
| class_names = { | |
| 0: "RBC", | |
| 1: "WBC", | |
| 2: "Platelet" | |
| } | |
| # Draw each detection | |
| for det in mock_detections: | |
| x1, y1, x2, y2, class_id, conf = det | |
| # Draw rectangle | |
| draw.rectangle([x1, y1, x2, y2], outline=class_colors[class_id][:3], width=2) | |
| # Add label with confidence | |
| label = f"{class_names[class_id]} {conf:.2f}" | |
| draw.text((x1, y1-15), label, fill=class_colors[class_id][:3]) | |
| st.image(draw_image, use_column_width=True) | |
| st.caption("Demo visualization with simulated detections") | |
| # Show mock statistics | |
| st.subheader("Detection Statistics (Sample Data)") | |
| # Mock detection counts | |
| st.markdown("### Detection Counts") | |
| st.markdown("- **RBC**: 120") | |
| st.markdown("- **WBC**: 8") | |
| st.markdown("- **Platelets**: 30") | |
| # Display mock confidence metrics | |
| st.markdown("### Confidence Metrics") | |
| metrics_data = [ | |
| { | |
| "Class": "RBC", | |
| "Count": 120, | |
| "Avg Confidence": "0.85", | |
| "Max Confidence": "0.95", | |
| "Min Confidence": "0.72" | |
| }, | |
| { | |
| "Class": "WBC", | |
| "Count": 8, | |
| "Avg Confidence": "0.91", | |
| "Max Confidence": "0.98", | |
| "Min Confidence": "0.82" | |
| }, | |
| { | |
| "Class": "Platelets", | |
| "Count": 30, | |
| "Avg Confidence": "0.78", | |
| "Max Confidence": "0.89", | |
| "Min Confidence": "0.65" | |
| } | |
| ] | |
| st.table(metrics_data) | |
| # Add precision and recall table | |
| st.markdown("### Precision and Recall Metrics") | |
| precision_recall_data = [ | |
| { | |
| "Class": "All Classes", | |
| "Precision": "0.89", | |
| "Recall": "0.91", | |
| "F1-Score": "0.90", | |
| "IoU": "0.82" | |
| }, | |
| { | |
| "Class": "RBC", | |
| "Precision": "0.92", | |
| "Recall": "0.94", | |
| "F1-Score": "0.93", | |
| "IoU": "0.86" | |
| }, | |
| { | |
| "Class": "WBC", | |
| "Precision": "0.87", | |
| "Recall": "0.85", | |
| "F1-Score": "0.86", | |
| "IoU": "0.79" | |
| }, | |
| { | |
| "Class": "Platelets", | |
| "Precision": "0.84", | |
| "Recall": "0.81", | |
| "F1-Score": "0.82", | |
| "IoU": "0.75" | |
| } | |
| ] | |
| st.table(precision_recall_data) | |
| # Add explanation of metrics | |
| with st.expander("About Precision and Recall Metrics"): | |
| st.markdown(""" | |
| - **Precision**: The proportion of positive identifications that were actually correct. Formula: TP/(TP+FP) | |
| - **Recall**: The proportion of actual positives that were identified correctly. Formula: TP/(TP+FN) | |
| - **F1-Score**: The harmonic mean of precision and recall, providing a balance between the two. Formula: 2*(Precision*Recall)/(Precision+Recall) | |
| - **IoU (Intersection over Union)**: Measures the overlap between the predicted bounding box and the ground truth bounding box. | |
| *These metrics are crucial for evaluating the performance of object detection models. Higher values indicate better performance.* | |
| """) | |
| # Add information about training | |
| st.markdown("---") | |
| st.subheader("Model Training Information") | |
| st.markdown(""" | |
| The YOLOv10 model used in this application was fine-tuned on the BCCD dataset. | |
| To see the fine-tuning process or train your own model, check the `train_yolov10.py` file | |
| included in the repository. | |
| The BCCD dataset contains images of blood cells with annotations for: | |
| - Red Blood Cells (RBC) | |
| - White Blood Cells (WBC) | |
| - Platelets | |
| """) | |
| if __name__ == "__main__": | |
| main() | |