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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import json | |
| from PIL import Image | |
| import io | |
| import tempfile | |
| import os | |
| from datetime import datetime | |
| from seat_depth_analysis import process_seat_depth_analysis | |
| st.set_page_config( | |
| page_title="Seat Depth Analyzer", | |
| page_icon="🪑", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Custom CSS for background gradient and styling | |
| st.markdown(""" | |
| <style> | |
| /* Gradient background for the whole app */ | |
| .stApp { | |
| background: linear-gradient(135deg, #e0f7fa, #ffffff, #fce4ec); | |
| background-attachment: fixed; | |
| } | |
| /* Make metric cards look modern */ | |
| div[data-testid="metric-container"] { | |
| background-color: rgba(255, 255, 255, 0.7); | |
| padding: 15px; | |
| border-radius: 12px; | |
| box-shadow: 0 2px 10px rgba(0,0,0,0.1); | |
| } | |
| /* Beautify sidebar */ | |
| section[data-testid="stSidebar"] { | |
| background: linear-gradient(180deg, #f1f8e9, #ffffff); | |
| } | |
| /* Make headers and titles prettier */ | |
| h1, h2, h3 { | |
| font-family: 'Segoe UI', sans-serif; | |
| color: #2c3e50; | |
| } | |
| /* Button tweaks */ | |
| button[kind="primary"] { | |
| background-color: #00796b; | |
| color: white; | |
| border-radius: 8px; | |
| padding: 8px 16px; | |
| } | |
| button[kind="primary"]:hover { | |
| background-color: #004d40; | |
| color: white; | |
| } | |
| /* Download button */ | |
| div.stDownloadButton > button { | |
| background-color: #3949ab; | |
| color: white; | |
| border-radius: 8px; | |
| } | |
| div.stDownloadButton > button:hover { | |
| background-color: #1a237e; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def main(): | |
| st.title("🪑✨ SitSmart") | |
| st.subheader("Analyze your seat — because not all thrones are ergonomic :)") | |
| st.markdown("---") | |
| # Sidebar for configuration | |
| st.sidebar.header("📐 Configuration: Anthropometric Assumption") | |
| st.sidebar.markdown( | |
| "We assume a default **ear-to-eye distance of 7 cm**, based on average adult anatomy. " | |
| "You may change this value if the subject in the image deviates significantly." | |
| ) | |
| st.sidebar.caption("Don’t worry, no need to measure your face with a ruler. 📏👂") | |
| # Eye-to-ear distance setting | |
| eye_to_ear_cm = st.sidebar.slider( | |
| "Eye-to-Ear Distance (cm)", | |
| min_value=5.0, | |
| max_value=10.0, | |
| value=7.0, | |
| step=0.1, | |
| help="Average distance from eye to ear for scaling reference (default: 7.0 cm)" | |
| ) | |
| sam_checkpoint = "sam_vit_b_01ec64.pth" | |
| # Information section | |
| st.sidebar.markdown("---") | |
| st.sidebar.header("📋 Classification Guide") | |
| st.sidebar.markdown(""" | |
| **Optimal**: 2-6 cm clearance from seat front to back of knee | |
| **Too Deep**: Less than 2 cm clearance (circulation risk) | |
| **Too Short**: More than 6 cm clearance (poor thigh support) | |
| """) | |
| st.header("📤 Choose Image") | |
| # Image source selection | |
| image_source = st.radio( | |
| "Select image source:", | |
| options=["Upload your own", "Choose from samples"], | |
| horizontal=True | |
| ) | |
| selected_image_path = None | |
| uploaded_file = None | |
| if image_source == "Upload your own": | |
| # File uploader | |
| uploaded_file = st.file_uploader( | |
| "Choose a side-profile image of person seated on chair", | |
| type=['png', 'jpg', 'jpeg', 'webp'], | |
| help="Upload a clear side-profile image showing the person seated with their back against the chair" | |
| ) | |
| if uploaded_file is not None: | |
| # Display uploaded image | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded Image", width=500) | |
| else: # Choose from samples | |
| sample_category = st.selectbox( | |
| "Select sample category:", | |
| options=["optimal", "too_deep", "too_short"], | |
| format_func=lambda x: x.replace("_", " ").title() | |
| ) | |
| # Get available sample images | |
| sample_images = get_sample_images(sample_category) | |
| if sample_images: | |
| selected_image = st.selectbox( | |
| "Select sample image:", | |
| options=sample_images, | |
| format_func=lambda x: x.replace("_", " ").replace(".png", "").replace(".jpg", "").replace(".jpeg", "").title() | |
| ) | |
| selected_image_path = os.path.join("sample_images", sample_category, selected_image) | |
| if os.path.exists(selected_image_path): | |
| # Display selected sample image | |
| image = Image.open(selected_image_path) | |
| st.image(image, caption=f"Sample: {selected_image}", width=500) | |
| else: | |
| st.error(f"Sample image not found: {selected_image_path}") | |
| selected_image_path = None | |
| else: | |
| st.warning(f"No sample images found in sample_images/{sample_category}/") | |
| # Process button | |
| if (uploaded_file is not None or selected_image_path is not None): | |
| if st.button("🔍 Analyze Seat Depth", type="primary"): | |
| if image_source == "Upload your own": | |
| process_uploaded_image(uploaded_file, eye_to_ear_cm, sam_checkpoint) | |
| else: | |
| process_sample_image(selected_image_path, eye_to_ear_cm, sam_checkpoint) | |
| st.info("Upload an image and click 'Analyze Seat Depth' to see results here.") | |
| def get_sample_images(category): | |
| """Get list of sample images for a given category""" | |
| sample_dir = os.path.join("sample_images", category) | |
| if not os.path.exists(sample_dir): | |
| return [] | |
| # Get all image files | |
| valid_extensions = ['.png', '.jpg', '.jpeg', '.webp'] | |
| sample_images = [] | |
| try: | |
| for file in os.listdir(sample_dir): | |
| if any(file.lower().endswith(ext) for ext in valid_extensions): | |
| sample_images.append(file) | |
| return sorted(sample_images) # Sort alphabetically | |
| except Exception: | |
| return [] | |
| def process_uploaded_image(uploaded_file, eye_to_ear_cm, sam_checkpoint): | |
| """Process the uploaded image and display results""" | |
| with st.spinner("🔄 Processing uploaded image..."): | |
| try: | |
| # Save uploaded file temporarily | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as tmp_file: | |
| tmp_file.write(uploaded_file.getbuffer()) | |
| temp_path = tmp_file.name | |
| # Process the image using your main function | |
| output_json, pose_image, seat_band_image, final_image = process_seat_depth_analysis( | |
| temp_path, eye_to_ear_cm, sam_checkpoint | |
| ) | |
| # Display results | |
| display_results(output_json, pose_image, seat_band_image, final_image) | |
| # Clean up temporary file | |
| os.unlink(temp_path) | |
| except Exception as e: | |
| st.error(f"❌ Error processing image: {str(e)}") | |
| st.error("Please ensure the image shows a clear side profile of a person seated on a chair.") | |
| def process_sample_image(image_path, eye_to_ear_cm, sam_checkpoint): | |
| """Process the sample image and display results""" | |
| with st.spinner("🔄 Processing sample image..."): | |
| try: | |
| # Process the image using your main function | |
| output_json, pose_image, seat_band_image, final_image = process_seat_depth_analysis( | |
| image_path, eye_to_ear_cm, sam_checkpoint | |
| ) | |
| # Display results with sample info | |
| st.info(f"📁 **Sample Image**: {os.path.basename(image_path)} from {os.path.basename(os.path.dirname(image_path))} category") | |
| display_results(output_json, pose_image, seat_band_image, final_image) | |
| except Exception as e: | |
| st.error(f"❌ Error processing sample image: {str(e)}") | |
| st.error(f"Could not process: {image_path}") | |
| def display_results(output_json, pose_image, seat_band_image, final_image): | |
| """Display the analysis results in the Streamlit interface""" | |
| st.header("📊 Analysis Results") | |
| # Classification result with color coding | |
| category = output_json['classification']['category'] | |
| if category == "Optimal": | |
| st.success(f"✅ **Classification: {category}**") | |
| elif category == "Too Deep": | |
| st.error(f"🔴 **Classification: {category}**") | |
| else: # Too Short | |
| st.warning(f"⚠️ **Classification: {category}**") | |
| # Key measurements | |
| st.markdown("### 📏 Key Measurements") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| clearance_cm = output_json['measurements']['knee_clearance_cm'] | |
| st.metric( | |
| "Knee Clearance", | |
| f"{clearance_cm:.2f} cm", | |
| help="Distance between seat front and back of knee" | |
| ) | |
| with col2: | |
| facing = output_json['pose_detection']['facing_direction'] | |
| st.metric( | |
| "Facing Direction", | |
| facing.title(), | |
| help="Direction the person is facing in the image" | |
| ) | |
| with col3: | |
| pixels_per_cm = output_json['measurements']['pixels_per_cm'] | |
| st.metric( | |
| "Scale Factor", | |
| f"{pixels_per_cm:.2f} px/cm", | |
| help="Pixels per centimeter for measurements" | |
| ) | |
| # Reasoning | |
| st.markdown("### 🤔 Analysis Reasoning") | |
| st.info(output_json['classification']['reasoning']) | |
| # Image results tabs | |
| st.markdown("### 🖼️ Analysis Visualization") | |
| tab1, tab2, tab3 = st.tabs(["Final Result", "Pose Detection", "Seat Band Analysis"]) | |
| with tab1: | |
| st.image( | |
| final_image, | |
| caption="Final Analysis - Knee Clearance Measurement", | |
| width = 500 | |
| ) | |
| st.markdown("**Blue dot**: Seat front edge | **Red dot**: Back of knee position") | |
| with tab2: | |
| st.image( | |
| pose_image, | |
| caption="Pose Detection Overlay", | |
| width = 500 | |
| ) | |
| st.markdown("Shows detected pose landmarks and connections") | |
| with tab3: | |
| st.image( | |
| seat_band_image, | |
| caption="Seat Front Detection Band", | |
| width = 500 | |
| ) | |
| st.markdown("**Green lines**: Analysis band | **Blue dot**: Detected seat front") | |
| # Detailed measurements (expandable) | |
| with st.expander("📐 Detailed Measurements"): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.json({ | |
| "Measurements": { | |
| "Knee Clearance (px)": f"{output_json['measurements']['knee_clearance_px']:.1f}", | |
| "Knee Clearance (cm)": f"{output_json['measurements']['knee_clearance_cm']:.2f}", | |
| "Eye-to-Ear Distance (px)": f"{output_json['measurements']['eye_to_ear_distance_px']:.1f}", | |
| "Thigh Length (px)": f"{output_json['measurements']['thigh_length_px']:.1f}", | |
| "Seat Front Position": output_json['measurements']['seat_front_position'], | |
| "Back of Knee Position": output_json['measurements']['back_of_knee_position'] | |
| } | |
| }) | |
| with col2: | |
| st.json({ | |
| "Detection Info": { | |
| "Chair Detected": output_json['chair_detection']['chair_detected'], | |
| "Chair Confidence": f"{output_json['chair_detection']['chair_confidence']:.3f}", | |
| "Pose Detected": output_json['pose_detection']['pose_detected'], | |
| "Processing Time": f"{output_json['processing_time_ms']} ms" | |
| } | |
| }) | |
| # Warnings | |
| if output_json['warnings']: | |
| st.markdown("### ⚠️ Warnings") | |
| for warning in output_json['warnings']: | |
| st.warning(warning) | |
| # Download JSON results | |
| st.markdown("### 💾 Download Results") | |
| json_str = json.dumps(output_json, indent=2) | |
| st.download_button( | |
| label="📄 Download JSON Report", | |
| data=json_str, | |
| file_name=f"seat_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", | |
| mime="application/json", help="Yes, a whole JSON just for your seat." | |
| ) | |
| if __name__ == "__main__": | |
| # Add sample images section at the bottom | |
| # Footer | |
| st.markdown( | |
| "<div style='text-align: center; color: gray;'>" | |
| "Ergonomic Seat Depth Analyzer | Built with Streamlit" | |
| "</div>", | |
| unsafe_allow_html=True | |
| ) | |
| main() |