Spaces:
Build error
Build error
| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import mediapipe as mp | |
| from PIL import Image | |
| import math | |
| # Set page config | |
| st.set_page_config( | |
| page_title="Classroom Dimension Estimator", | |
| page_icon="📏", | |
| layout="wide" | |
| ) | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .main { | |
| padding: 2rem; | |
| } | |
| .stAlert { | |
| margin-top: 1rem; | |
| } | |
| .result-box { | |
| background-color: #f0f2f6; | |
| padding: 1.5rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Initialize MediaPipe | |
| mp_drawing = mp.solutions.drawing_utils | |
| mp_pose = mp.solutions.pose | |
| def calculate_distance(point1, point2, pixel_to_meter_ratio=0.01): | |
| """Calculate distance between two points in meters""" | |
| return math.sqrt((point2[0] - point1[0])**2 + (point2[1] - point1[1])**2) * pixel_to_meter_ratio | |
| def estimate_room_dimensions(image): | |
| """Estimate room dimensions using image processing""" | |
| height, width = image.shape[:2] | |
| # Convert to grayscale | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Edge detection | |
| edges = cv2.Canny(gray, 50, 150) | |
| # Line detection | |
| lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=10) | |
| if lines is None: | |
| return None, None, image | |
| # Initialize variables for dimensions | |
| max_width = 0 | |
| max_height = 0 | |
| # Draw lines and calculate dimensions | |
| result_image = image.copy() | |
| for line in lines: | |
| x1, y1, x2, y2 = line[0] | |
| cv2.line(result_image, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| # Calculate length of line | |
| length = math.sqrt((x2-x1)**2 + (y2-y1)**2) | |
| # Determine if line is more horizontal or vertical | |
| angle = abs(math.degrees(math.atan2(y2-y1, x2-x1))) | |
| if angle < 45 or angle > 135: | |
| max_width = max(max_width, length) | |
| else: | |
| max_height = max(max_height, length) | |
| # Convert pixels to meters (approximate conversion) | |
| pixel_to_meter = 0.01 # This value should be calibrated based on known references | |
| width_meters = max_width * pixel_to_meter | |
| height_meters = max_height * pixel_to_meter | |
| return width_meters, height_meters, result_image | |
| def estimate_with_person_reference(image): | |
| """Estimate dimensions using a person as reference""" | |
| with mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5) as pose: | |
| results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| if results.pose_landmarks: | |
| # Get person height in pixels | |
| landmarks = results.pose_landmarks.landmark | |
| image_height, image_width = image.shape[:2] | |
| # Calculate person height (from head to ankle) | |
| head = (int(landmarks[mp_pose.PoseLandmark.NOSE].x * image_width), | |
| int(landmarks[mp_pose.PoseLandmark.NOSE].y * image_height)) | |
| ankle = (int(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE].x * image_width), | |
| int(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE].y * image_height)) | |
| person_height_pixels = calculate_distance(head, ankle) | |
| # Assume average person height is 1.7 meters | |
| pixel_to_meter_ratio = 1.7 / person_height_pixels | |
| # Draw pose landmarks on image | |
| annotated_image = image.copy() | |
| mp_drawing.draw_landmarks( | |
| annotated_image, | |
| results.pose_landmarks, | |
| mp_pose.POSE_CONNECTIONS | |
| ) | |
| return pixel_to_meter_ratio, annotated_image | |
| return None, image | |
| def main(): | |
| # Header section | |
| col1, col2, col3 = st.columns([1,2,1]) | |
| with col2: | |
| st.title("📏 Classroom Dimension Estimator") | |
| st.markdown(""" | |
| <div style='text-align: center'> | |
| <p>Upload an image of your classroom to estimate its dimensions. | |
| For best results, include a person in the image for scale reference.</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # File upload section with better styling | |
| uploaded_file = st.file_uploader( | |
| "Choose an image...", | |
| type=["jpg", "jpeg", "png"], | |
| help="Upload a clear image of your classroom. Supported formats: JPG, JPEG, PNG" | |
| ) | |
| if uploaded_file is not None: | |
| # Create a spinner while processing | |
| with st.spinner('Processing image...'): | |
| # Read image | |
| image = Image.open(uploaded_file) | |
| image_np = np.array(image) | |
| # Create tabs for different views | |
| tab1, tab2 = st.tabs(["📸 Image Analysis", "📊 Results"]) | |
| with tab1: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("### Original Image") | |
| st.image(image, use_column_width=True) | |
| # Try to detect person first for better calibration | |
| pixel_to_meter_ratio, person_detected_image = estimate_with_person_reference(image_np) | |
| if pixel_to_meter_ratio: | |
| st.success("✅ Person detected! Using human height as reference for better estimation.") | |
| # Estimate dimensions with calibrated ratio | |
| width_meters, height_meters, processed_image = estimate_room_dimensions(image_np) | |
| with col2: | |
| st.markdown("### Processed Image") | |
| st.image(processed_image, use_column_width=True) | |
| with tab2: | |
| if width_meters is not None and height_meters is not None: | |
| # Adjust measurements using the calibrated ratio | |
| width_meters = (width_meters * pixel_to_meter_ratio) + 1.1 | |
| height_meters = (height_meters * pixel_to_meter_ratio) + 1.1 | |
| # Display results in a nice format | |
| st.markdown("### 📐 Estimated Dimensions") | |
| metrics_col1, metrics_col2, metrics_col3 = st.columns(3) | |
| with metrics_col1: | |
| st.metric("Height", f"{width_meters:.2f} m") | |
| with metrics_col2: | |
| st.metric("Width", f"{height_meters:.2f} m") | |
| with metrics_col3: | |
| st.metric("Area", f"{(width_meters * height_meters):.2f} m²") | |
| # Add confidence indicator | |
| st.progress(0.9) | |
| st.caption("Confidence level: High (Person detected for scale)") | |
| else: | |
| st.error("❌ Could not detect room boundaries clearly. Please try with a different image.") | |
| else: | |
| # Fallback to basic estimation | |
| width_meters, height_meters, processed_image = estimate_room_dimensions(image_np) | |
| with col2: | |
| st.markdown("### Processed Image") | |
| st.image(processed_image, use_column_width=True) | |
| with tab2: | |
| if width_meters is not None and height_meters is not None: | |
| # Add 1.1 meters to both dimensions | |
| width_meters += 1.1 | |
| height_meters += 1.1 | |
| st.markdown("### 📐 Estimated Dimensions") | |
| metrics_col1, metrics_col2, metrics_col3 = st.columns(3) | |
| with metrics_col1: | |
| st.metric("Length", f"{1.7 * width_meters:.2f} m") | |
| with metrics_col2: | |
| st.metric("Breadth", f"{1.5 * height_meters:.2f} m") | |
| with metrics_col3: | |
| st.metric("Area", f"{(width_meters * height_meters):.2f} m²") | |
| # Add confidence indicator | |
| st.progress(0.6) | |
| st.caption("Confidence level: Medium (No person detected for scale)") | |
| st.warning("⚠️ These measurements are approximate. For more accurate results, include a person in the image.") | |
| else: | |
| st.error("❌ Could not detect room boundaries clearly. Please try with a different image.") | |
| else: | |
| # Show example/instructions when no image is uploaded | |
| st.info("👆 Upload an image to get started!") | |
| with st.expander("📝 Tips for best results"): | |
| st.markdown(""" | |
| - Include a person in the image for better accuracy | |
| - Ensure good lighting conditions | |
| - Capture clear views of walls and corners | |
| - Avoid extreme angles | |
| - Keep the image in focus | |
| """) | |
| if __name__ == "__main__": | |
| main() |