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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| from io import BytesIO | |
| import base64 | |
| import tempfile | |
| import os | |
| import time | |
| import urllib.request | |
| import matplotlib.pyplot as plt | |
| import pickle | |
| from sklearn.metrics.pairwise import cosine_similarity # type: ignore | |
| import pandas as pd | |
| # Importar DeepFace para reconocimiento facial avanzado | |
| try: | |
| from deepface import DeepFace | |
| DEEPFACE_AVAILABLE = True | |
| except ImportError: | |
| DEEPFACE_AVAILABLE = False | |
| # Import functions for face comparison | |
| try: | |
| from face_comparison import compare_faces, compare_faces_embeddings, generate_comparison_report_english, draw_face_matches, extract_face_embeddings, extract_face_embeddings_all_models | |
| FACE_COMPARISON_AVAILABLE = True | |
| except ImportError: | |
| FACE_COMPARISON_AVAILABLE = False | |
| st.warning("Face comparison functions are not available. Please check your installation.") | |
| # Función principal que encapsula toda la aplicación | |
| def main(): | |
| # Set page config with custom title and layout | |
| st.set_page_config( | |
| page_title="Advanced Face & Feature Detection", | |
| page_icon="👤", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Sidebar for navigation and controls | |
| st.sidebar.title("Controls & Settings") | |
| # Initialize session_state to store original image and camera state | |
| if 'original_image' not in st.session_state: | |
| st.session_state.original_image = None | |
| if 'camera_running' not in st.session_state: | |
| st.session_state.camera_running = False | |
| if 'feature_camera_running' not in st.session_state: | |
| st.session_state.feature_camera_running = False | |
| # Navigation menu | |
| app_mode = st.sidebar.selectbox( | |
| "Choose the app mode", | |
| ["About", "Face Detection", "Feature Detection", "Comparison Mode", "Face Recognition"] | |
| ) | |
| # Function to load DNN models with caching and auto-download | |
| def load_face_model(): | |
| # No need to create directory as we're using the root directory | |
| # | |
| # | |
| # Correct model file names | |
| modelFile = "res10_300x300_ssd_iter_140000.caffemodel" | |
| configFile = "deploy.prototxt.txt" | |
| # Check if files exist | |
| missing_files = [] | |
| if not os.path.exists(modelFile): | |
| missing_files.append(modelFile) | |
| if not os.path.exists(configFile): | |
| missing_files.append(configFile) | |
| if missing_files: | |
| st.error("Missing model files: " + ", ".join(missing_files)) | |
| st.error("Please manually download the following files:") | |
| st.code(""" | |
| 1. Download the model file: | |
| URL: https://raw.githubusercontent.com/sr6033/face-detection-with-OpenCV-and-DNN/master/res10_300x300_ssd_iter_140000.caffemodel | |
| Save as: res10_300x300_ssd_iter_140000.caffemodel | |
| 2. Download the configuration file: | |
| URL: https://raw.githubusercontent.com/sr6033/face-detection-with-OpenCV-and-DNN/master/deploy.prototxt.txt | |
| Save as: deploy.prototxt.txt | |
| """) | |
| st.stop() | |
| # Load model | |
| try: | |
| net = cv2.dnn.readNetFromCaffe(configFile, modelFile) | |
| return net | |
| except Exception as e: | |
| st.error(f"Error loading model: {e}") | |
| st.stop() | |
| def load_feature_models(): | |
| # Load pre-trained models for eye and smile detection | |
| eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') | |
| smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml') | |
| return eye_cascade, smile_cascade | |
| # Function for detecting faces in an image | |
| def detect_face_dnn(net, frame, conf_threshold=0.5): | |
| blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123], False, False) | |
| net.setInput(blob) | |
| detections = net.forward() | |
| # Procesar las detecciones para devolver una lista de bounding boxes | |
| bboxes = [] | |
| frame_h = frame.shape[0] | |
| frame_w = frame.shape[1] | |
| for i in range(detections.shape[2]): | |
| confidence = detections[0, 0, i, 2] | |
| if confidence > conf_threshold: | |
| x1 = int(detections[0, 0, i, 3] * frame_w) | |
| y1 = int(detections[0, 0, i, 4] * frame_h) | |
| x2 = int(detections[0, 0, i, 5] * frame_w) | |
| y2 = int(detections[0, 0, i, 6] * frame_h) | |
| # Asegurarse de que las coordenadas estén dentro de los límites de la imagen | |
| x1 = max(0, min(x1, frame_w - 1)) | |
| y1 = max(0, min(y1, frame_h - 1)) | |
| x2 = max(0, min(x2, frame_w - 1)) | |
| y2 = max(0, min(y2, frame_h - 1)) | |
| # Añadir el bounding box y la confianza | |
| bboxes.append([x1, y1, x2, y2, confidence]) | |
| return bboxes | |
| # Function for processing face detections | |
| def process_face_detections(frame, detections, conf_threshold=0.5, bbox_color=(0, 255, 0)): | |
| # Create a copy for drawing on | |
| result_frame = frame.copy() | |
| # Filtrar detecciones por umbral de confianza | |
| bboxes = [] | |
| for detection in detections: | |
| if len(detection) == 5: # Asegurarse de que la detección tiene el formato correcto | |
| x1, y1, x2, y2, confidence = detection | |
| if confidence >= conf_threshold: | |
| # Dibujar el bounding box | |
| cv2.rectangle(result_frame, (x1, y1), (x2, y2), bbox_color, 2) | |
| # Añadir texto con la confianza | |
| label = f"{confidence:.2f}" | |
| cv2.putText(result_frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, bbox_color, 2) | |
| # Añadir a la lista de bounding boxes | |
| bboxes.append([x1, y1, x2, y2, confidence]) | |
| return result_frame, bboxes | |
| # Function to detect facial features (eyes, smile) with improved profile face handling | |
| def detect_facial_features(frame, bboxes, eye_cascade, smile_cascade, detect_eyes=True, detect_smile=True, smile_sensitivity=15, eye_sensitivity=5): | |
| result_frame = frame.copy() | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| # Counters for detection summary | |
| eye_count = 0 | |
| smile_count = 0 | |
| for bbox in bboxes: | |
| x1, y1, x2, y2, _ = bbox | |
| roi_gray = gray[y1:y2, x1:x2] | |
| roi_color = result_frame[y1:y2, x1:x2] | |
| face_width = x2 - x1 | |
| face_height = y2 - y1 | |
| # Detect eyes if enabled | |
| if detect_eyes: | |
| # Adjust region of interest to focus on the upper part of the face | |
| upper_face_y1 = y1 | |
| upper_face_y2 = y1 + int(face_height * 0.55) # Slightly reduced to focus more on the eye area | |
| # For profile faces, we need to search the entire upper region | |
| # as well as the left and right sides separately | |
| # Full upper region for profile faces | |
| upper_face_roi_gray = gray[upper_face_y1:upper_face_y2, x1:x2] | |
| upper_face_roi_color = result_frame[upper_face_y1:upper_face_y2, x1:x2] | |
| # Split the upper region into two halves (left and right) to search for eyes individually | |
| mid_x = x1 + face_width // 2 | |
| left_eye_roi_gray = gray[upper_face_y1:upper_face_y2, x1:mid_x] | |
| right_eye_roi_gray = gray[upper_face_y1:upper_face_y2, mid_x:x2] | |
| left_eye_roi_color = result_frame[upper_face_y1:upper_face_y2, x1:mid_x] | |
| right_eye_roi_color = result_frame[upper_face_y1:upper_face_y2, mid_x:x2] | |
| # Apply histogram equalization and contrast enhancement for all regions | |
| if upper_face_roi_gray.size > 0: | |
| upper_face_roi_gray = cv2.equalizeHist(upper_face_roi_gray) | |
| # Enhance contrast | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) | |
| upper_face_roi_gray = clahe.apply(upper_face_roi_gray) | |
| # First try to detect eyes in the full upper region (for profile faces) | |
| full_eyes = eye_cascade.detectMultiScale( | |
| upper_face_roi_gray, | |
| scaleFactor=1.02, # More sensitive for profile faces | |
| minNeighbors=max(1, eye_sensitivity-3), # Even more sensitive | |
| minSize=(int(face_width * 0.07), int(face_width * 0.07)), | |
| maxSize=(int(face_width * 0.3), int(face_width * 0.3)) | |
| ) | |
| # If we found eyes in the full region, use those | |
| if len(full_eyes) > 0: | |
| # Sort by size (area) and take up to 2 largest | |
| full_eyes = sorted(full_eyes, key=lambda e: e[2] * e[3], reverse=True) | |
| full_eyes = full_eyes[:2] # Take at most 2 eyes | |
| for ex, ey, ew, eh in full_eyes: | |
| eye_count += 1 | |
| cv2.rectangle(upper_face_roi_color, (ex, ey), (ex+ew, ey+eh), (255, 0, 0), 2) | |
| cv2.putText(upper_face_roi_color, "Eye", (ex, ey-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) | |
| else: | |
| # If no eyes found in full region, try left and right separately | |
| if left_eye_roi_gray.size > 0: | |
| left_eye_roi_gray = cv2.equalizeHist(left_eye_roi_gray) | |
| left_eye_roi_gray = clahe.apply(left_eye_roi_gray) | |
| left_eyes = eye_cascade.detectMultiScale( | |
| left_eye_roi_gray, | |
| scaleFactor=1.03, | |
| minNeighbors=max(1, eye_sensitivity-2), | |
| minSize=(int(face_width * 0.08), int(face_width * 0.08)), | |
| maxSize=(int(face_width * 0.25), int(face_width * 0.25)) | |
| ) | |
| if len(left_eyes) > 0: | |
| # Sort by size and take the largest | |
| left_eyes = sorted(left_eyes, key=lambda e: e[2] * e[3], reverse=True) | |
| left_eye = left_eyes[0] | |
| eye_count += 1 | |
| # Draw rectangle for the left eye | |
| ex, ey, ew, eh = left_eye | |
| cv2.rectangle(left_eye_roi_color, (ex, ey), (ex+ew, ey+eh), (255, 0, 0), 2) | |
| cv2.putText(left_eye_roi_color, "Eye", (ex, ey-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) | |
| if right_eye_roi_gray.size > 0: | |
| right_eye_roi_gray = cv2.equalizeHist(right_eye_roi_gray) | |
| right_eye_roi_gray = clahe.apply(right_eye_roi_gray) | |
| right_eyes = eye_cascade.detectMultiScale( | |
| right_eye_roi_gray, | |
| scaleFactor=1.03, | |
| minNeighbors=max(1, eye_sensitivity-2), | |
| minSize=(int(face_width * 0.08), int(face_width * 0.08)), | |
| maxSize=(int(face_width * 0.25), int(face_width * 0.25)) | |
| ) | |
| if len(right_eyes) > 0: | |
| # Sort by size and take the largest | |
| right_eyes = sorted(right_eyes, key=lambda e: e[2] * e[3], reverse=True) | |
| right_eye = right_eyes[0] | |
| eye_count += 1 | |
| # Draw rectangle for the right eye | |
| ex, ey, ew, eh = right_eye | |
| cv2.rectangle(right_eye_roi_color, (ex, ey), (ex+ew, ey+eh), (255, 0, 0), 2) | |
| cv2.putText(right_eye_roi_color, "Eye", (ex, ey-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) | |
| # Detect smile if enabled | |
| if detect_smile: | |
| # For profile faces, we need to adjust the region of interest | |
| # Try multiple regions to improve detection | |
| # Standard region (middle to bottom) | |
| lower_face_y1 = y1 + int(face_height * 0.5) | |
| lower_face_roi_gray = gray[lower_face_y1:y2, x1:x2] | |
| lower_face_roi_color = result_frame[lower_face_y1:y2, x1:x2] | |
| # Alternative region (lower third) | |
| alt_lower_face_y1 = y1 + int(face_height * 0.65) | |
| alt_lower_face_roi_gray = gray[alt_lower_face_y1:y2, x1:x2] | |
| # Apply histogram equalization and enhance contrast | |
| smile_detected = False | |
| if lower_face_roi_gray.size > 0: | |
| lower_face_roi_gray = cv2.equalizeHist(lower_face_roi_gray) | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) | |
| lower_face_roi_gray = clahe.apply(lower_face_roi_gray) | |
| # Try with standard parameters | |
| smiles = smile_cascade.detectMultiScale( | |
| lower_face_roi_gray, | |
| scaleFactor=1.2, | |
| minNeighbors=smile_sensitivity, | |
| minSize=(int(face_width * 0.25), int(face_width * 0.15)), | |
| maxSize=(int(face_width * 0.7), int(face_width * 0.4)) | |
| ) | |
| if len(smiles) > 0: | |
| # Sort by size and take the largest | |
| smiles = sorted(smiles, key=lambda s: s[2] * s[3], reverse=True) | |
| sx, sy, sw, sh = smiles[0] | |
| # Increment smile counter | |
| smile_count += 1 | |
| smile_detected = True | |
| # Draw rectangle for the smile | |
| cv2.rectangle(lower_face_roi_color, (sx, sy), (sx+sw, sy+sh), (0, 0, 255), 2) | |
| cv2.putText(lower_face_roi_color, "Smile", (sx, sy-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) | |
| # If no smile detected in standard region, try alternative region | |
| if not smile_detected and alt_lower_face_roi_gray.size > 0: | |
| alt_lower_face_roi_gray = cv2.equalizeHist(alt_lower_face_roi_gray) | |
| alt_lower_face_roi_gray = clahe.apply(alt_lower_face_roi_gray) | |
| # Try with more sensitive parameters | |
| alt_smiles = smile_cascade.detectMultiScale( | |
| alt_lower_face_roi_gray, | |
| scaleFactor=1.1, | |
| minNeighbors=max(1, smile_sensitivity-5), # More sensitive | |
| minSize=(int(face_width * 0.2), int(face_width * 0.1)), | |
| maxSize=(int(face_width * 0.6), int(face_width * 0.3)) | |
| ) | |
| if len(alt_smiles) > 0: | |
| # Sort by size and take the largest | |
| alt_smiles = sorted(alt_smiles, key=lambda s: s[2] * s[3], reverse=True) | |
| sx, sy, sw, sh = alt_smiles[0] | |
| # Adjust coordinates for the alternative region | |
| adjusted_sy = sy + (alt_lower_face_y1 - lower_face_y1) | |
| # Increment smile counter | |
| smile_count += 1 | |
| # Draw rectangle for the smile (in the original lower face ROI) | |
| cv2.rectangle(lower_face_roi_color, (sx, adjusted_sy), (sx+sw, adjusted_sy+sh), (0, 0, 255), 2) | |
| cv2.putText(lower_face_roi_color, "Smile", (sx, adjusted_sy-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) | |
| return result_frame, eye_count, smile_count | |
| # Función para detectar atributos faciales (edad, género, emoción) | |
| def detect_face_attributes(image, bbox): | |
| """ | |
| Detecta atributos faciales como edad, género y emoción usando DeepFace. | |
| Args: | |
| image: Imagen en formato OpenCV (BGR) | |
| bbox: Bounding box de la cara [x1, y1, x2, y2, conf] | |
| Returns: | |
| Diccionario con los atributos detectados | |
| """ | |
| if not DEEPFACE_AVAILABLE: | |
| return None | |
| try: | |
| x1, y1, x2, y2, _ = bbox | |
| face_img = image[y1:y2, x1:x2] | |
| # Convertir de BGR a RGB para DeepFace | |
| face_img_rgb = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB) | |
| # Analyze atributos faciales | |
| attributes = DeepFace.analyze( | |
| img_path=face_img_rgb, | |
| actions=['age', 'gender', 'emotion'], | |
| enforce_detection=False, | |
| detector_backend="opencv" | |
| ) | |
| return attributes[0] | |
| except Exception as e: | |
| st.error(f"Error detecting facial attributes: {str(e)}") | |
| return None | |
| # Function to apply age and gender detection (placeholder - would need additional models) | |
| def detect_age_gender(frame, bboxes): | |
| # Versión mejorada que usa DeepFace si está disponible | |
| result_frame = frame.copy() | |
| for i, bbox in enumerate(bboxes): | |
| x1, y1, x2, y2, _ = bbox | |
| if DEEPFACE_AVAILABLE: | |
| # Intentar usar DeepFace para análisis facial | |
| attributes = detect_face_attributes(frame, bbox) | |
| if attributes: | |
| # Extraer información de atributos | |
| age = attributes.get('age', 'Unknown') | |
| gender = attributes.get('gender', 'Unknown') | |
| emotion = attributes.get('dominant_emotion', 'Unknown').capitalize() | |
| gender_prob = attributes.get('gender', {}).get('Woman', 0) | |
| # Determinar color basado en confianza | |
| if gender == 'Woman': | |
| gender_color = (255, 0, 255) # Magenta para mujer | |
| else: | |
| gender_color = (255, 0, 0) # Azul para hombre | |
| # Añadir texto con información | |
| cv2.putText(result_frame, f"Age: {age}", (x1, y2+20), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2) | |
| cv2.putText(result_frame, f"Gender: {gender}", (x1, y2+40), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, gender_color, 2) | |
| cv2.putText(result_frame, f"Emotion: {emotion}", (x1, y2+60), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2) | |
| else: | |
| # Fallback si DeepFace falla | |
| cv2.putText(result_frame, "Age: Unknown", (x1, y2+20), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2) | |
| cv2.putText(result_frame, "Gender: Unknown", (x1, y2+40), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2) | |
| else: | |
| # Usar texto placeholder si DeepFace no está disponible | |
| cv2.putText(result_frame, "Age: 25-35", (x1, y2+20), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2) | |
| cv2.putText(result_frame, "Gender: Unknown", (x1, y2+40), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2) | |
| return result_frame | |
| # Function to generate download link for processed image | |
| def get_image_download_link(img, filename, text): | |
| buffered = BytesIO() | |
| img.save(buffered, format="JPEG") | |
| img_str = base64.b64encode(buffered.getvalue()).decode() | |
| href = f'<a href="data:file/txt;base64,{img_str}" download="{filename}">{text}</a>' | |
| return href | |
| # Function to process video frames | |
| def process_video(video_path, face_net, eye_cascade, smile_cascade, conf_threshold=0.5, detect_eyes=True, detect_smile=True, bbox_color=(0, 255, 0), smile_sensitivity=15, eye_sensitivity=5): | |
| cap = cv2.VideoCapture(video_path) | |
| # Get video properties | |
| frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| # Create temporary output file | |
| temp_dir = tempfile.mkdtemp() | |
| temp_output_path = os.path.join(temp_dir, "processed_video.mp4") | |
| # Initialize video writer | |
| fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
| out = cv2.VideoWriter(temp_output_path, fourcc, fps, (frame_width, frame_height)) | |
| # Create a progress bar | |
| frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| progress_bar = st.progress(0) | |
| status_text = st.empty() | |
| # Process video frames | |
| current_frame = 0 | |
| processing_times = [] | |
| # Total counters for statistics | |
| total_faces = 0 | |
| total_eyes = 0 | |
| total_smiles = 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| # Start timing for performance metrics | |
| start_time = time.time() | |
| # Detect faces | |
| detections = detect_face_dnn(face_net, frame, conf_threshold) | |
| processed_frame, bboxes = process_face_detections(frame, detections, conf_threshold, bbox_color) | |
| # Update face counter | |
| total_faces += len(bboxes) | |
| # Detect facial features if enabled | |
| if detect_eyes or detect_smile: | |
| processed_frame, eye_count, smile_count = detect_facial_features( | |
| processed_frame, | |
| bboxes, | |
| eye_cascade, | |
| smile_cascade, | |
| detect_eyes, | |
| detect_smile, | |
| smile_sensitivity, | |
| eye_sensitivity | |
| ) | |
| # Update counters | |
| total_eyes += eye_count | |
| total_smiles += smile_count | |
| # End timing | |
| processing_times.append(time.time() - start_time) | |
| # Write the processed frame | |
| out.write(processed_frame) | |
| # Update progress | |
| current_frame += 1 | |
| progress_bar.progress(current_frame / frame_count) | |
| status_text.text(f"Processing frame {current_frame}/{frame_count}") | |
| # Release resources | |
| cap.release() | |
| out.release() | |
| # Calculate and display performance metrics | |
| if processing_times: | |
| avg_time = sum(processing_times) / len(processing_times) | |
| status_text.text(f"Processing complete! Average processing time: {avg_time:.4f}s per frame") | |
| # Return detection statistics | |
| detection_stats = { | |
| "faces": total_faces // max(1, current_frame), # Average per frame | |
| "eyes": total_eyes // max(1, current_frame), # Average per frame | |
| "smiles": total_smiles // max(1, current_frame) # Average per frame | |
| } | |
| return temp_output_path, temp_dir, detection_stats | |
| # Camera control functions | |
| def start_camera(): | |
| st.session_state.camera_running = True | |
| def stop_camera(): | |
| st.session_state.camera_running = False | |
| st.session_state.camera_stopped = True | |
| def start_feature_camera(): | |
| st.session_state.feature_camera_running = True | |
| def stop_feature_camera(): | |
| st.session_state.feature_camera_running = False | |
| st.session_state.feature_camera_stopped = True | |
| if app_mode == "About": | |
| st.markdown(""" | |
| ## About This App | |
| This application uses OpenCV's Deep Neural Network (DNN) module and Haar Cascade classifiers to detect faces and facial features in images and videos. | |
| ### Features: | |
| - Face detection using OpenCV DNN | |
| - Eye and smile detection using Haar Cascades | |
| - Support for both image and video processing | |
| - Adjustable confidence threshold | |
| - Download options for processed media | |
| - Performance metrics | |
| ### How to use: | |
| 1. Select a mode from the sidebar | |
| 2. Upload an image or video | |
| 3. Adjust settings as needed | |
| 4. View and download the results | |
| ### Technologies Used: | |
| - Streamlit for the web interface | |
| - OpenCV for computer vision operations | |
| - Python for backend processing | |
| ### Models: | |
| - SSD MobileNet for face detection | |
| - Haar Cascades for facial features | |
| """) | |
| # Display a sample image or GIF | |
| st.image("https://opencv.org/wp-content/uploads/2019/07/detection.gif", caption="Sample face detection", use_container_width=True) | |
| elif app_mode == "Face Detection": | |
| # Load the face detection model | |
| face_net = load_face_model() | |
| # Input type selection (Image or Video) | |
| input_type = st.sidebar.radio("Select Input Type", ["Image", "Video"]) | |
| # Confidence threshold slider | |
| conf_threshold = st.sidebar.slider( | |
| "Confidence Threshold", | |
| min_value=0.0, | |
| max_value=1.0, | |
| value=0.5, | |
| step=0.05, | |
| help="Adjust the threshold for face detection confidence (higher = fewer detections but more accurate)" | |
| ) | |
| # Style options | |
| bbox_color = st.sidebar.color_picker("Bounding Box Color", "#00FF00") | |
| # Convert hex color to BGR for OpenCV | |
| bbox_color_rgb = tuple(int(bbox_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)) | |
| bbox_color_bgr = (bbox_color_rgb[2], bbox_color_rgb[1], bbox_color_rgb[0]) # Convert RGB to BGR | |
| # Display processing metrics | |
| show_metrics = st.sidebar.checkbox("Show Processing Metrics", True) | |
| if input_type == "Image": | |
| # File uploader for images | |
| file_buffer = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png']) | |
| if file_buffer is not None: | |
| # Read the file and convert it to OpenCV format | |
| raw_bytes = np.asarray(bytearray(file_buffer.read()), dtype=np.uint8) | |
| image = cv2.imdecode(raw_bytes, cv2.IMREAD_COLOR) | |
| # Save la imagen original en session_state para reprocesarla cuando cambie el umbral | |
| # Usar un identificador único para cada archivo para detectar cambios | |
| file_id = file_buffer.name + str(file_buffer.size) | |
| if 'file_id' not in st.session_state or st.session_state.file_id != file_id: | |
| st.session_state.file_id = file_id | |
| st.session_state.original_image = image.copy() | |
| # Display original image | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Original Image") | |
| st.image(st.session_state.original_image, channels='BGR', use_container_width=True) | |
| # Start timing for performance metrics | |
| start_time = time.time() | |
| # Detect faces | |
| detections = detect_face_dnn(face_net, st.session_state.original_image, conf_threshold) | |
| processed_image, bboxes = process_face_detections(st.session_state.original_image, detections, conf_threshold, bbox_color_bgr) | |
| # Calculate processing time | |
| processing_time = time.time() - start_time | |
| # Display the processed image | |
| with col2: | |
| st.subheader("Processed Image") | |
| st.image(processed_image, channels='BGR', use_container_width=True) | |
| # Convert OpenCV image to PIL for download | |
| pil_img = Image.fromarray(processed_image[:, :, ::-1]) | |
| st.markdown( | |
| get_image_download_link(pil_img, "face_detection_result.jpg", "📥 Download Processed Image"), | |
| unsafe_allow_html=True | |
| ) | |
| # Show metrics if enabled | |
| if show_metrics: | |
| st.subheader("Processing Metrics") | |
| col1, col2, col3 = st.columns(3) | |
| col1.metric("Processing Time", f"{processing_time:.4f} seconds") | |
| col2.metric("Faces Detected", len(bboxes)) | |
| col3.metric("Confidence Threshold", f"{conf_threshold:.2f}") | |
| # Display detailed metrics in an expandable section | |
| with st.expander("Detailed Detection Information"): | |
| if bboxes: | |
| st.write("Detected faces with confidence scores:") | |
| for i, bbox in enumerate(bboxes): | |
| st.write(f"Face #{i+1}: Confidence = {bbox[4]:.4f}") | |
| else: | |
| st.write("No faces detected in the image.") | |
| else: # Video mode | |
| # Video mode options | |
| video_source = st.radio("Select video source", ["Upload video", "Use webcam"]) | |
| if video_source == "Upload video": | |
| # File uploader for videos | |
| file_buffer = st.file_uploader("Upload a video", type=['mp4', 'avi', 'mov']) | |
| if file_buffer is not None: | |
| # Save uploaded video to temporary file | |
| temp_dir = tempfile.mkdtemp() | |
| temp_path = os.path.join(temp_dir, "input_video.mp4") | |
| with open(temp_path, "wb") as f: | |
| f.write(file_buffer.read()) | |
| # Display original video | |
| st.subheader("Original Video") | |
| st.video(temp_path) | |
| # Load models for feature detection (will be used in the processing) | |
| eye_cascade, smile_cascade = load_feature_models() | |
| # Process video button | |
| if st.button("Process Video"): | |
| with st.spinner("Processing video... This may take a while depending on the video length."): | |
| # Process the video | |
| output_path, output_dir, detection_stats = process_video( | |
| temp_path, | |
| face_net, | |
| eye_cascade, | |
| smile_cascade, | |
| conf_threshold, | |
| detect_eyes=True, | |
| detect_smile=True, | |
| bbox_color=bbox_color_bgr, | |
| eye_sensitivity=5 | |
| ) | |
| # Display processed video | |
| st.subheader("Processed Video") | |
| st.video(output_path) | |
| # Mostrar estadísticas de detección | |
| st.subheader("Detection Summary") | |
| summary_col1, summary_col2, summary_col3 = st.columns(3) | |
| summary_col1.metric("Avg. Faces per Frame", detection_stats["faces"]) | |
| if detect_eyes: # type: ignore | |
| summary_col2.metric("Avg. Eyes per Frame", detection_stats["eyes"]) | |
| else: | |
| summary_col2.metric("Eyes Detected", "N/A") | |
| if detect_smile: # type: ignore | |
| summary_col3.metric("Avg. Smiles per Frame", detection_stats["smiles"]) | |
| else: | |
| summary_col3.metric("Smiles Detected", "N/A") | |
| # Provide download link | |
| with open(output_path, 'rb') as f: | |
| video_bytes = f.read() | |
| st.download_button( | |
| label="📥 Download Processed Video", | |
| data=video_bytes, | |
| file_name="processed_video.mp4", | |
| mime="video/mp4" | |
| ) | |
| # Clean up temporary files | |
| try: | |
| os.remove(temp_path) | |
| os.remove(output_path) | |
| os.rmdir(temp_dir) | |
| os.rmdir(output_dir) | |
| except: | |
| pass | |
| else: # Use webcam | |
| st.subheader("Real-time face detection") | |
| st.write("Click 'Start Camera' to begin real-time face detection.") | |
| # Placeholder for webcam video | |
| camera_placeholder = st.empty() | |
| # Buttons to control the camera | |
| col1, col2 = st.columns(2) | |
| start_button = col1.button("Start Camera", on_click=start_camera) | |
| stop_button = col2.button("Stop Camera", on_click=stop_camera) | |
| # Show message when camera is stopped | |
| if 'camera_stopped' in st.session_state and st.session_state.camera_stopped: | |
| st.info("Camera stopped. Click 'Start Camera' to activate it again.") | |
| st.session_state.camera_stopped = False | |
| if st.session_state.camera_running: | |
| st.info("Camera activated. Processing real-time video...") | |
| # Initialize webcam | |
| cap = cv2.VideoCapture(0) # 0 is typically the main webcam | |
| if not cap.isOpened(): | |
| st.error("Could not access webcam. Make sure it's connected and not being used by another application.") | |
| st.warning("⚠️ Note: If you're using this app on Hugging Face Spaces, webcam access is not supported. Try running this app locally for webcam features.") | |
| st.session_state.camera_running = False | |
| else: | |
| # Display real-time video with face detection | |
| try: | |
| while st.session_state.camera_running: | |
| ret, frame = cap.read() | |
| if not ret: | |
| st.error("Error reading frame from camera.") | |
| break | |
| # Detect faces | |
| detections = detect_face_dnn(face_net, frame, conf_threshold) | |
| processed_frame, bboxes = process_face_detections(frame, detections, conf_threshold, bbox_color_bgr) | |
| # Display the processed frame | |
| camera_placeholder.image(processed_frame, channels="BGR", use_container_width=True) | |
| # Small pause to avoid overloading the CPU | |
| time.sleep(0.01) | |
| finally: | |
| # Release the camera when stopped | |
| cap.release() | |
| elif app_mode == "Feature Detection": | |
| # Load all required models | |
| face_net = load_face_model() | |
| eye_cascade, smile_cascade = load_feature_models() | |
| # Feature selection checkboxes | |
| st.sidebar.subheader("Feature Detection Options") | |
| detect_eyes = st.sidebar.checkbox("Detect Eyes", True) | |
| # Add controls for eye detection sensitivity | |
| eye_sensitivity = 5 # Default value | |
| if detect_eyes: | |
| eye_sensitivity = st.sidebar.slider( | |
| "Eye Detection Sensitivity", | |
| min_value=1, | |
| max_value=10, | |
| value=5, | |
| step=1, | |
| help="Adjust the sensitivity of eye detection (lower value = more detections)" | |
| ) | |
| detect_smile = st.sidebar.checkbox("Detect Smile", True) | |
| # Add controls for smile detection sensitivity | |
| smile_sensitivity = 15 # Default value | |
| if detect_smile: | |
| smile_sensitivity = st.sidebar.slider( | |
| "Smile Detection Sensitivity", | |
| min_value=5, | |
| max_value=30, | |
| value=15, | |
| step=1, | |
| help="Adjust the sensitivity of smile detection (lower value = more detections)" | |
| ) | |
| detect_age_gender_option = st.sidebar.checkbox("Detect Age/Gender (Demo)", False) | |
| # Confidence threshold slider | |
| conf_threshold = st.sidebar.slider( | |
| "Face Detection Confidence", | |
| min_value=0.0, | |
| max_value=1.0, | |
| value=0.5, | |
| step=0.05 | |
| ) | |
| # Style options | |
| bbox_color = st.sidebar.color_picker("Bounding Box Color", "#00FF00") | |
| # Convert hex color to BGR for OpenCV | |
| bbox_color_rgb = tuple(int(bbox_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)) | |
| bbox_color_bgr = (bbox_color_rgb[2], bbox_color_rgb[1], bbox_color_rgb[0]) # Convert RGB to BGR | |
| # Input type selection | |
| input_type = st.sidebar.radio("Select Input Type", ["Image", "Video"]) | |
| if input_type == "Image": | |
| # File uploader for images | |
| file_buffer = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png']) | |
| if file_buffer is not None: | |
| # Read the file and convert it to OpenCV format | |
| raw_bytes = np.asarray(bytearray(file_buffer.read()), dtype=np.uint8) | |
| image = cv2.imdecode(raw_bytes, cv2.IMREAD_COLOR) | |
| # Save la imagen original en session_state para reprocesarla cuando cambie el umbral | |
| # Usar un identificador único para cada archivo para detectar cambios | |
| file_id = file_buffer.name + str(file_buffer.size) | |
| if 'feature_file_id' not in st.session_state or st.session_state.feature_file_id != file_id: | |
| st.session_state.feature_file_id = file_id | |
| st.session_state.feature_original_image = image.copy() | |
| # Display original image | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Original Image") | |
| st.image(st.session_state.feature_original_image, channels='BGR', use_container_width=True) | |
| # Start processing with face detection | |
| detections = detect_face_dnn(face_net, st.session_state.feature_original_image, conf_threshold) | |
| processed_image, bboxes = process_face_detections(st.session_state.feature_original_image, detections, conf_threshold, bbox_color_bgr) | |
| # Inicializar contadores | |
| eye_count = 0 | |
| smile_count = 0 | |
| # Detect facial features if any options are enabled | |
| if detect_eyes or detect_smile: | |
| processed_image, eye_count, smile_count = detect_facial_features( | |
| processed_image, | |
| bboxes, | |
| eye_cascade, | |
| smile_cascade, | |
| detect_eyes, | |
| detect_smile, | |
| smile_sensitivity, | |
| eye_sensitivity | |
| ) | |
| # Apply age/gender detection if enabled (demo purpose) | |
| if detect_age_gender_option: | |
| processed_image = detect_age_gender(processed_image, bboxes) | |
| # Display the processed image | |
| with col2: | |
| st.subheader("Processed Image") | |
| st.image(processed_image, channels='BGR', use_container_width=True) | |
| # Convert OpenCV image to PIL for download | |
| pil_img = Image.fromarray(processed_image[:, :, ::-1]) | |
| st.markdown( | |
| get_image_download_link(pil_img, "feature_detection_result.jpg", "📥 Download Processed Image"), | |
| unsafe_allow_html=True | |
| ) | |
| # Display detection summary | |
| st.subheader("Detection Summary") | |
| summary_col1, summary_col2, summary_col3 = st.columns(3) | |
| summary_col1.metric("Faces Detected", len(bboxes)) | |
| if detect_eyes: | |
| summary_col2.metric("Eyes Detected", eye_count) | |
| else: | |
| summary_col2.metric("Eyes Detected", "N/A") | |
| if detect_smile: | |
| summary_col3.metric("Smiles Detected", smile_count) | |
| else: | |
| summary_col3.metric("Smiles Detected", "N/A") | |
| else: # Video mode | |
| st.write("Facial feature detection in video") | |
| # Video mode options | |
| video_source = st.radio("Select video source", ["Upload video", "Use webcam"]) | |
| if video_source == "Upload video": | |
| st.write("Upload a video to process with facial feature detection.") | |
| # Similar implementation to Face Detection mode for uploaded videos | |
| file_buffer = st.file_uploader("Upload a video", type=['mp4', 'avi', 'mov']) | |
| if file_buffer is not None: | |
| # Save uploaded video to temporary file | |
| temp_dir = tempfile.mkdtemp() | |
| temp_path = os.path.join(temp_dir, "input_video.mp4") | |
| with open(temp_path, "wb") as f: | |
| f.write(file_buffer.read()) | |
| # Display original video | |
| st.subheader("Original Video") | |
| st.video(temp_path) | |
| # Process video button | |
| if st.button("Process Video"): | |
| with st.spinner("Processing video... This may take a while depending on the video length."): | |
| # Process the video with feature detection | |
| output_path, output_dir, detection_stats = process_video( | |
| temp_path, | |
| face_net, | |
| eye_cascade, | |
| smile_cascade, | |
| conf_threshold, | |
| detect_eyes=True, | |
| detect_smile=True, | |
| bbox_color=bbox_color_bgr, | |
| smile_sensitivity=smile_sensitivity, | |
| eye_sensitivity=eye_sensitivity | |
| ) | |
| # Display processed video | |
| st.subheader("Processed Video") | |
| st.video(output_path) | |
| # Mostrar estadísticas de detección | |
| st.subheader("Detection Summary") | |
| summary_col1, summary_col2, summary_col3 = st.columns(3) | |
| summary_col1.metric("Avg. Faces per Frame", detection_stats["faces"]) | |
| if detect_eyes: | |
| summary_col2.metric("Avg. Eyes per Frame", detection_stats["eyes"]) | |
| else: | |
| summary_col2.metric("Eyes Detected", "N/A") | |
| if detect_smile: | |
| summary_col3.metric("Avg. Smiles per Frame", detection_stats["smiles"]) | |
| else: | |
| summary_col3.metric("Smiles Detected", "N/A") | |
| # Provide download link | |
| with open(output_path, 'rb') as f: | |
| video_bytes = f.read() | |
| st.download_button( | |
| label="📥 Download Processed Video", | |
| data=video_bytes, | |
| file_name="feature_detection_video.mp4", | |
| mime="video/mp4" | |
| ) | |
| # Clean up temporary files | |
| try: | |
| os.remove(temp_path) | |
| os.remove(output_path) | |
| os.rmdir(temp_dir) | |
| os.rmdir(output_dir) | |
| except: | |
| pass | |
| else: # Usar cámara web | |
| st.subheader("Real-time facial feature detection") | |
| st.write("Click 'Start Camera' to begin real-time detection.") | |
| # Placeholder for webcam video | |
| camera_placeholder = st.empty() | |
| # Buttons to control the camera | |
| col1, col2 = st.columns(2) | |
| start_button = col1.button("Start Camera", on_click=start_feature_camera) | |
| stop_button = col2.button("Stop Camera", on_click=stop_feature_camera) | |
| # Show message when camera is stopped | |
| if 'feature_camera_stopped' in st.session_state and st.session_state.feature_camera_stopped: | |
| st.info("Camera stopped. Click 'Start Camera' to activate it again.") | |
| st.session_state.feature_camera_stopped = False | |
| if st.session_state.feature_camera_running: | |
| st.info("Camera activated. Processing real-time video with feature detection...") | |
| # Initialize webcam | |
| cap = cv2.VideoCapture(0) # 0 is typically the main webcam | |
| if not cap.isOpened(): | |
| st.error("Could not access webcam. Make sure it's connected and not being used by another application.") | |
| st.warning("⚠️ Note: If you're using this app on Hugging Face Spaces, webcam access is not supported. Try running this app locally for webcam features.") | |
| st.session_state.feature_camera_running = False | |
| else: | |
| # Display real-time video with face and feature detection | |
| try: | |
| # Create placeholders for metrics | |
| metrics_placeholder = st.empty() | |
| metrics_col1, metrics_col2, metrics_col3 = metrics_placeholder.columns(3) | |
| # Initialize counters | |
| face_count_total = 0 | |
| eye_count_total = 0 | |
| smile_count_total = 0 | |
| frame_count = 0 | |
| while st.session_state.feature_camera_running: | |
| ret, frame = cap.read() | |
| if not ret: | |
| st.error("Error reading frame from camera.") | |
| break | |
| # Detect faces | |
| detections = detect_face_dnn(face_net, frame, conf_threshold) | |
| processed_frame, bboxes = process_face_detections(frame, detections, conf_threshold, bbox_color_bgr) | |
| # Update face counter | |
| face_count = len(bboxes) | |
| face_count_total += face_count | |
| # Initialize counters for this frame | |
| eye_count = 0 | |
| smile_count = 0 | |
| # Detect facial features if enabled | |
| if detect_eyes or detect_smile: | |
| processed_frame, eye_count, smile_count = detect_facial_features( | |
| processed_frame, | |
| bboxes, | |
| eye_cascade, | |
| smile_cascade, | |
| detect_eyes, | |
| detect_smile, | |
| smile_sensitivity, | |
| eye_sensitivity | |
| ) | |
| # Update total counters | |
| eye_count_total += eye_count | |
| smile_count_total += smile_count | |
| # Apply age/gender detection if enabled | |
| if detect_age_gender_option: | |
| processed_frame = detect_age_gender(processed_frame, bboxes) | |
| # Display the processed frame | |
| camera_placeholder.image(processed_frame, channels="BGR", use_container_width=True) | |
| # Update frame counter | |
| frame_count += 1 | |
| # Update metrics every 5 frames to avoid overloading the interface | |
| if frame_count % 5 == 0: | |
| metrics_col1.metric("Faces Detected", face_count) | |
| if detect_eyes: | |
| metrics_col2.metric("Eyes Detected", eye_count) | |
| else: | |
| metrics_col2.metric("Eyes Detected", "N/A") | |
| if detect_smile: | |
| metrics_col3.metric("Smiles Detected", smile_count) | |
| else: | |
| metrics_col3.metric("Smiles Detected", "N/A") | |
| # Small pause to avoid overloading the CPU | |
| time.sleep(0.01) | |
| finally: | |
| # Release the camera when stopped | |
| cap.release() | |
| elif app_mode == "Comparison Mode": | |
| st.subheader("Face Comparison") | |
| st.write("Upload two images to compare faces between them.") | |
| # Añadir explicación sobre la interpretación de resultados | |
| with st.expander("📌 How to interpret similarity results"): | |
| st.markdown(""" | |
| ### Facial Similarity Interpretation Guide | |
| The system calculates similarity between faces based on multiple facial features and characteristics. | |
| **Similarity Ranges:** | |
| - **70-100%**: HIGH Similarity - Very likely to be the same person or identical twins | |
| - **50-70%**: MEDIUM Similarity - Possible match, requires verification | |
| - **30-50%**: LOW Similarity - Different people with some similar features | |
| - **0-30%**: VERY LOW Similarity - Completely different people | |
| **Enhanced Comparison System:** | |
| The system uses a sophisticated approach that: | |
| 1. Analyzes multiple facial characteristics with advanced precision | |
| 2. Evaluates hair style/color, facial structure, texture patterns, and expressions with improved accuracy | |
| 3. Applies a balanced differentiation between similar and different individuals | |
| 4. Creates a clear gap between similar and different people's scores | |
| 5. Reduces scores for people with different facial structures | |
| 6. Applies penalty factors for critical differences in facial features | |
| **Features Analyzed:** | |
| - Facial texture patterns (HOG features) | |
| - Eye region characteristics (highly weighted) | |
| - Nose bridge features | |
| - Hair style and color patterns (enhanced detection) | |
| - Precise facial proportions and structure | |
| - Texture and edge patterns | |
| - Facial expressions | |
| - Critical difference markers (aspect ratio, brightness patterns, texture variance) | |
| **Factors affecting similarity:** | |
| - Face angle and expression | |
| - Lighting conditions | |
| - Age differences | |
| - Image quality | |
| - Gender characteristics (with stronger weighting) | |
| - Critical facial structure differences | |
| **Important note:** This system is designed to provide highly accurate similarity scores that create a clear distinction between different individuals while still recognizing truly similar people. The algorithm now applies multiple reduction factors to ensure that different people receive appropriately low similarity scores. For official identification, always use certified systems. | |
| """) | |
| # Load face detection model | |
| face_net = load_face_model() | |
| # Side-by-side file uploaders | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write("First Image") | |
| file1 = st.file_uploader("Upload first image", type=['jpg', 'jpeg', 'png'], key="file1") | |
| with col2: | |
| st.write("Second Image") | |
| file2 = st.file_uploader("Upload second image", type=['jpg', 'jpeg', 'png'], key="file2") | |
| # Set confidence threshold | |
| conf_threshold = st.slider("Face Detection Confidence", min_value=0.0, max_value=1.0, value=0.5, step=0.05) | |
| # Similarity threshold for considering a match | |
| similarity_threshold = st.slider("Similarity Threshold (%)", min_value=35.0, max_value=95.0, value=45.0, step=5.0, | |
| help="Minimum percentage of similarity to consider two faces as a match") | |
| # Selección del método de comparación | |
| comparison_method = st.radio( | |
| "Facial Comparison Method", | |
| ["HOG (Fast, effective)", "Embeddings (Slow, more precise)"], | |
| help="HOG uses histograms of oriented gradients for quick comparison. Embeddings use deep neural networks for greater precision." | |
| ) | |
| # Si se selecciona embeddings, mostrar opciones de modelos y advertencia | |
| embedding_model = "VGG-Face" | |
| if comparison_method == "Embeddings (Slow, more precise)" and DEEPFACE_AVAILABLE: | |
| st.warning("WARNING: The current version of TensorFlow (2.19) may have incompatibilities with some models. It is recommended to use HOG if you experience problems.") | |
| embedding_model = st.selectbox( | |
| "Embedding model", | |
| ["VGG-Face", "Facenet", "OpenFace", "ArcFace"], # Eliminado "DeepFace" de la lista | |
| help="Select the neural network model to extract facial embeddings" | |
| ) | |
| elif comparison_method == "Embeddings (Slow, more precise)" and not DEEPFACE_AVAILABLE: | |
| st.warning("The DeepFace library is not available. Please install with 'pip install deepface' to use embeddings.") | |
| st.info("Using HOG method by default.") | |
| comparison_method = "HOG (Fast, effective)" | |
| # Style options | |
| bbox_color = st.color_picker("Bounding Box Color", "#00FF00") | |
| # Convert hex color to BGR for OpenCV | |
| bbox_color_rgb = tuple(int(bbox_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)) | |
| bbox_color_bgr = (bbox_color_rgb[2], bbox_color_rgb[1], bbox_color_rgb[0]) # Convert RGB to BGR | |
| # Process the images when both are uploaded | |
| if file1 is not None and file2 is not None: | |
| # Read both images | |
| raw_bytes1 = np.asarray(bytearray(file1.read()), dtype=np.uint8) | |
| image1 = cv2.imdecode(raw_bytes1, cv2.IMREAD_COLOR) | |
| raw_bytes2 = np.asarray(bytearray(file2.read()), dtype=np.uint8) | |
| image2 = cv2.imdecode(raw_bytes2, cv2.IMREAD_COLOR) | |
| # Save original images in session_state | |
| # Use a unique identifier for each file to detect changes | |
| file1_id = file1.name + str(file1.size) | |
| file2_id = file2.name + str(file2.size) | |
| if 'file1_id' not in st.session_state or st.session_state.file1_id != file1_id: | |
| st.session_state.file1_id = file1_id | |
| st.session_state.original_image1 = image1.copy() | |
| if 'file2_id' not in st.session_state or st.session_state.file2_id != file2_id: | |
| st.session_state.file2_id = file2_id | |
| st.session_state.original_image2 = image2.copy() | |
| # Display original images | |
| with col1: | |
| st.image(st.session_state.original_image1, channels='BGR', use_container_width=True, caption="Image 1") | |
| with col2: | |
| st.image(st.session_state.original_image2, channels='BGR', use_container_width=True, caption="Image 2") | |
| # Detect faces in both images | |
| detections1 = detect_face_dnn(face_net, st.session_state.original_image1, conf_threshold) | |
| processed_image1, bboxes1 = process_face_detections(st.session_state.original_image1, detections1, conf_threshold, bbox_color_bgr) | |
| detections2 = detect_face_dnn(face_net, st.session_state.original_image2, conf_threshold) | |
| processed_image2, bboxes2 = process_face_detections(st.session_state.original_image2, detections2, conf_threshold, bbox_color_bgr) | |
| # Display processed images | |
| st.subheader("Detected Faces") | |
| proc_col1, proc_col2 = st.columns(2) | |
| with proc_col1: | |
| st.image(processed_image1, channels='BGR', use_container_width=True, caption="Processed Image 1") | |
| st.write(f"Faces detected: {len(bboxes1)}") | |
| with proc_col2: | |
| st.image(processed_image2, channels='BGR', use_container_width=True, caption="Processed Image 2") | |
| st.write(f"Faces detected: {len(bboxes2)}") | |
| # Compare faces | |
| if len(bboxes1) == 0 or len(bboxes2) == 0: | |
| st.warning("Cannot compare: One or both images have no faces detected.") | |
| else: | |
| with st.spinner("Comparing faces..."): | |
| # Perform face comparison based on selected method | |
| if comparison_method == "Embeddings (Slow, more precise)" and DEEPFACE_AVAILABLE: | |
| try: | |
| st.info(f"Using embedding model: {embedding_model}") | |
| comparison_results = compare_faces_embeddings( | |
| st.session_state.original_image1, bboxes1, | |
| st.session_state.original_image2, bboxes2, | |
| model_name=embedding_model | |
| ) | |
| except Exception as e: | |
| st.error(f"Error using embeddings: {str(e)}") | |
| st.info("Automatically switching to HOG method...") | |
| comparison_results = compare_faces( | |
| st.session_state.original_image1, bboxes1, | |
| st.session_state.original_image2, bboxes2 | |
| ) | |
| else: | |
| # Usar método HOG tradicional | |
| if comparison_method == "Embeddings (Slow, more precise)": | |
| st.warning("Using HOG method because DeepFace is not available.") | |
| comparison_results = compare_faces( | |
| st.session_state.original_image1, bboxes1, | |
| st.session_state.original_image2, bboxes2 | |
| ) | |
| # Generate comparison report | |
| report = generate_comparison_report_english(comparison_results, bboxes1, bboxes2) | |
| # Create combined image with match lines | |
| combined_image = draw_face_matches( | |
| st.session_state.original_image1, bboxes1, | |
| st.session_state.original_image2, bboxes2, | |
| comparison_results, | |
| threshold=similarity_threshold | |
| ) | |
| # Show results | |
| st.subheader("Comparison Results") | |
| # Show combined image | |
| st.image(combined_image, channels='BGR', use_container_width=True, | |
| caption="Visual Comparison (red lines indicate matches above threshold)") | |
| # Show similarity statistics | |
| st.subheader("Similarity Statistics") | |
| # Calculate general statistics | |
| all_similarities = [] | |
| for face_comparisons in comparison_results: | |
| for comp in face_comparisons: | |
| all_similarities.append(float(comp["similarity"])) | |
| if all_similarities: | |
| avg_similarity = sum(all_similarities) / len(all_similarities) | |
| max_similarity = max(all_similarities) | |
| min_similarity = min(all_similarities) | |
| # Determinar el nivel de similitud promedio | |
| if avg_similarity >= 70: # Updated from 80 to 70 | |
| avg_level = "HIGH" | |
| avg_color = "normal" | |
| elif avg_similarity >= 50: # Updated from 65 to 50 | |
| avg_level = "MEDIUM" | |
| avg_color = "normal" | |
| elif avg_similarity >= 30: # Updated from 35 to 30 | |
| avg_level = "LOW" | |
| avg_color = "inverse" | |
| else: | |
| avg_level = "VERY LOW" | |
| avg_color = "inverse" | |
| # Determinar el nivel de similitud máxima | |
| if max_similarity >= 70: # Updated from 80 to 70 | |
| max_level = "HIGH" | |
| max_color = "normal" | |
| elif max_similarity >= 50: # Updated from 65 to 50 | |
| max_level = "MEDIUM" | |
| max_color = "normal" | |
| elif max_similarity >= 30: # Updated from 35 to 30 | |
| max_level = "LOW" | |
| max_color = "inverse" | |
| else: | |
| max_level = "VERY LOW" | |
| max_color = "inverse" | |
| # Show metrics with color coding | |
| col1, col2, col3 = st.columns(3) | |
| col1.metric("Average Similarity", f"{avg_similarity:.2f}%", | |
| delta=avg_level, delta_color=avg_color) | |
| col2.metric("Maximum Similarity", f"{max_similarity:.2f}%", | |
| delta=max_level, delta_color=max_color) | |
| col3.metric("Minimum Similarity", f"{min_similarity:.2f}%") | |
| # Count matches above threshold | |
| matches_above_threshold = sum(1 for s in all_similarities if s >= similarity_threshold) | |
| st.metric(f"Matches above threshold ({similarity_threshold}%)", matches_above_threshold) | |
| # Determine if there are significant matches | |
| best_matches = [face_comp[0] for face_comp in comparison_results if face_comp] | |
| if any(float(match["similarity"]) >= similarity_threshold for match in best_matches): | |
| if any(float(match["similarity"]) >= 70 for match in best_matches): # Updated from 80 to 70 | |
| st.success("CONCLUSION: HIGH similarity matches found between images.") | |
| elif any(float(match["similarity"]) >= 50 for match in best_matches): # Updated from 65 to 50 | |
| st.info("CONCLUSION: MEDIUM similarity matches found between images.") | |
| else: | |
| st.warning("CONCLUSION: LOW similarity matches found between images.") | |
| else: | |
| st.error("CONCLUSION: No significant matches found between images.") | |
| # Añadir gráfico de distribución de similitud | |
| st.subheader("Similarity Distribution") | |
| # Crear histograma de similitudes | |
| fig, ax = plt.subplots(figsize=(10, 4)) | |
| bins = [0, 30, 50, 70, 100] # Updated from [0, 35, 65, 80, 100] | |
| labels = ['Very Low', 'Low', 'Medium', 'High'] | |
| colors = ['darkred', 'red', 'orange', 'green'] | |
| # Contar cuántos valores caen en cada rango | |
| hist_data = [sum(1 for s in all_similarities if bins[i] <= s < bins[i+1]) for i in range(len(bins)-1)] | |
| # Crear gráfico de barras | |
| bars = ax.bar(labels, hist_data, color=colors) | |
| # Añadir etiquetas | |
| ax.set_xlabel('Similarity Level') | |
| ax.set_ylabel('Number of Comparisons') | |
| ax.set_title('Similarity Level Distribution') | |
| # Añadir valores sobre las barras | |
| for bar in bars: | |
| height = bar.get_height() | |
| ax.text(bar.get_x() + bar.get_width()/2., height + 0.1, | |
| f'{int(height)}', ha='center', va='bottom') | |
| st.pyplot(fig) | |
| # Show detailed report in an expandable section | |
| with st.expander("View Detailed Report"): | |
| st.write(report) | |
| # Provide option to download the report | |
| st.download_button( | |
| label="📥 Download Comparison Report", | |
| data=report, | |
| file_name="face_comparison_report.txt", | |
| mime="text/plain" | |
| ) | |
| # Provide option to download the combined image | |
| pil_combined_img = Image.fromarray(combined_image[:, :, ::-1]) | |
| buf = BytesIO() | |
| pil_combined_img.save(buf, format="JPEG") | |
| byte_im = buf.getvalue() | |
| st.download_button( | |
| label="📥 Download Comparison Image", | |
| data=byte_im, | |
| file_name="face_comparison.jpg", | |
| mime="image/jpeg" | |
| ) | |
| # Add a help text for eye detection sensitivity in the Feature Detection mode | |
| if app_mode == "Feature Detection": | |
| st.sidebar.markdown("**Eye Detection Settings**") | |
| st.sidebar.info("Adjust the slider to change the sensitivity of eye detection. A higher value will detect more eyes but may generate false positives.") | |
| elif app_mode == "Face Recognition": | |
| st.title("Face Recognition System") | |
| st.markdown(""" | |
| Este módulo permite registrar rostros y reconocerlos posteriormente en tiempo real o en imágenes. | |
| Utiliza embeddings faciales para una identificación precisa. | |
| """) | |
| # Verificar si DeepFace está disponible | |
| if not DEEPFACE_AVAILABLE: | |
| st.error("DeepFace is not available. Please install the library with 'pip install deepface'") | |
| st.stop() | |
| # Load el modelo de detección facial | |
| face_net = load_face_model() | |
| # Inicializar base de datos de rostros si no existe | |
| if 'face_database' not in st.session_state: | |
| st.session_state.face_database = {} | |
| # Crear pestañas para las diferentes funcionalidades | |
| tab1, tab2, tab3 = st.tabs(["Register Face", "Image Recognition", "Real-time Recognition"]) | |
| with tab1: | |
| st.header("Register New Face") | |
| # Añadir el file_uploader para la imagen | |
| uploaded_file = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'], key="register_face_image") | |
| # Formulario de registro | |
| with st.form("face_registration_form"): | |
| person_name = st.text_input("Person's name") | |
| # Selector de modelo | |
| model_choice = st.selectbox( | |
| "Embedding model", | |
| ["VGG-Face", "Facenet", "OpenFace", "ArcFace"], | |
| index=0 | |
| ) | |
| # Ajuste de umbral de confianza | |
| confidence_threshold = st.slider( | |
| "Detection Confidence", | |
| min_value=0.0, | |
| max_value=1.0, | |
| value=0.5, | |
| step=0.01 | |
| ) | |
| # Opción para añadir a persona existente | |
| add_to_existing = st.checkbox( | |
| "Add to existing person" | |
| ) | |
| # Botón de registro | |
| register_button = st.form_submit_button("Register Face") | |
| if register_button: | |
| # Validar que se haya proporcionado un nombre | |
| if not person_name: | |
| st.error("Person's name is required. Please enter a name.") | |
| elif uploaded_file is None: | |
| st.error("Please upload an image.") | |
| else: | |
| # Mostrar spinner durante el procesamiento | |
| with st.spinner('Processing image and extracting facial features...'): | |
| # Process imagen | |
| raw_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| image = cv2.imdecode(raw_bytes, cv2.IMREAD_COLOR) | |
| # Detect rostros | |
| face_net = load_face_model() | |
| detections = detect_face_dnn(face_net, image, conf_threshold=confidence_threshold) | |
| # Procesar detecciones y obtener bounding boxes | |
| processed_image, bboxes = process_face_detections(image, detections, confidence_threshold) | |
| if not bboxes: | |
| st.error("No faces detected in the image. Please upload another image.") | |
| elif len(bboxes) > 1: | |
| st.warning("Multiple faces detected. The first one will be used.") | |
| # Extraer embeddings del primer rostro | |
| if bboxes and len(bboxes) > 0 and len(bboxes[0]) == 5: | |
| embeddings_all_models = extract_face_embeddings_all_models(image, bboxes[0]) | |
| if embeddings_all_models: | |
| # Guardar en la base de datos | |
| if add_to_existing and person_name in st.session_state.face_database: | |
| # Añadir a persona existente | |
| if 'embeddings' in st.session_state.face_database[person_name]: | |
| # Formato nuevo con múltiples embeddings | |
| for embedding in embeddings_all_models: | |
| model_name = embedding['model'] | |
| model_idx = -1 | |
| # Buscar si ya existe un embedding de este modelo | |
| for i, model in enumerate(st.session_state.face_database[person_name]['models']): | |
| if model == model_name: | |
| model_idx = i | |
| break | |
| if model_idx >= 0: | |
| # Actualizar embedding existente | |
| st.session_state.face_database[person_name]['embeddings'][model_idx] = embedding['embedding'] | |
| else: | |
| # Añadir nuevo modelo | |
| st.session_state.face_database[person_name]['models'].append(model_name) | |
| st.session_state.face_database[person_name]['embeddings'].append(embedding['embedding']) | |
| # Incrementar contador | |
| st.session_state.face_database[person_name]['count'] += 1 | |
| else: | |
| st.error("Failed to extract embeddings. Please try again with a clearer image.") | |
| else: | |
| # Solo un rostro detectado | |
| embeddings_all_models = extract_face_embeddings_all_models(image, bboxes[0]) | |
| if embeddings_all_models: | |
| # Guardar en la base de datos | |
| if add_to_existing and person_name in st.session_state.face_database: | |
| # Añadir a persona existente | |
| if 'embeddings' in st.session_state.face_database[person_name]: | |
| # Formato nuevo con múltiples embeddings | |
| for embedding in embeddings_all_models: | |
| model_name = embedding['model'] | |
| model_idx = -1 | |
| # Buscar si ya existe un embedding de este modelo | |
| for i, model in enumerate(st.session_state.face_database[person_name]['models']): | |
| if model == model_name: | |
| model_idx = i | |
| break | |
| if model_idx >= 0: | |
| # Actualizar embedding existente | |
| st.session_state.face_database[person_name]['embeddings'][model_idx] = embedding['embedding'] | |
| else: | |
| # Añadir nuevo modelo | |
| st.session_state.face_database[person_name]['models'].append(model_name) | |
| st.session_state.face_database[person_name]['embeddings'].append(embedding['embedding']) | |
| st.success(f"Face registered successfully for {person_name}!") | |
| # Mostrar la imagen con el rostro detectado | |
| processed_image, _ = process_face_detections(image, [bboxes[0]], confidence_threshold) | |
| st.image(cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB), caption=f"Registered face: {person_name}") | |
| else: | |
| st.error("Failed to extract embeddings. Please try again with a clearer image.") | |
| # Mostrar tabla de rostros registrados | |
| st.subheader("Registered Faces") | |
| if 'face_database' in st.session_state and st.session_state.face_database: | |
| # Inicializar variables para la tabla | |
| data = [] | |
| # Preparar datos para la tabla | |
| for name, info in st.session_state.face_database.items(): | |
| # Determinar el número de embeddings | |
| if 'embeddings' in info: | |
| num_embeddings = len(info['embeddings']) | |
| models = ', '.join(info['models']) | |
| else: | |
| num_embeddings = 1 | |
| models = 'VGG-Face' # Modelo por defecto para formato antiguo | |
| # Determinar el número de imágenes | |
| num_images = info.get('count', 1) | |
| # Añadir a los datos | |
| data.append({ | |
| "Name": name, | |
| "Images": num_images, | |
| "Embeddings": num_embeddings, | |
| "Models": models | |
| }) | |
| # Crear DataFrame | |
| import pandas as pd | |
| df = pd.DataFrame(data) | |
| # Mostrar tabla con botones de eliminación | |
| for i, row in df.iterrows(): | |
| col1, col2, col3, col4, col5 = st.columns([3, 2, 2, 4, 2]) | |
| with col1: | |
| st.write(row["Name"]) | |
| with col2: | |
| st.write(row["Images"]) | |
| with col3: | |
| st.write(row["Embeddings"]) | |
| with col4: | |
| st.write(row["Models"]) | |
| with col5: | |
| if st.button("Delete", key=f"delete_{row['Name']}"): | |
| # Eliminar el registro | |
| if row["Name"] in st.session_state.face_database: | |
| del st.session_state.face_database[row["Name"]] | |
| st.success(f"Deleted {row['Name']} from the database.") | |
| st.experimental_rerun() | |
| # Botón para eliminar todos los registros | |
| if st.button("Delete All Registered Faces"): | |
| # Mostrar confirmación | |
| if 'confirm_delete_all' not in st.session_state: | |
| st.session_state.confirm_delete_all = False | |
| if not st.session_state.confirm_delete_all: | |
| st.warning("Are you sure you want to delete all registered faces? This action cannot be undone.") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.button("Yes, delete all"): | |
| st.session_state.face_database = {} | |
| st.session_state.confirm_delete_all = False | |
| st.success("All registered faces have been deleted.") | |
| st.experimental_rerun() | |
| with col2: | |
| if st.button("Cancel"): | |
| st.session_state.confirm_delete_all = False | |
| st.experimental_rerun() | |
| else: | |
| st.info("No faces registered yet. Use the form above to register faces.") | |
| with tab2: | |
| st.header("Image Recognition") | |
| # Verificar si hay rostros registrados | |
| if not st.session_state.face_database: | |
| st.warning("No faces registered. Please register at least one face first.") | |
| else: | |
| # Subir imagen para reconocimiento | |
| uploaded_file = st.file_uploader("Subir imagen para reconocimiento", type=['jpg', 'jpeg', 'png'], key="recognition_image") | |
| # Configuración avanzada | |
| with st.expander("Configuración avanzada", expanded=False): | |
| # Configuración de umbral de similitud | |
| similarity_threshold = st.slider( | |
| "Similarity threshold (%)", | |
| min_value=35.0, | |
| max_value=95.0, | |
| value=45.0, | |
| step=5.0, | |
| help="Porcentaje mínimo de similitud para considerar una coincidencia" | |
| ) | |
| confidence_threshold = st.slider( | |
| "Detection Confidence", | |
| min_value=0.3, | |
| max_value=0.9, | |
| value=0.5, | |
| step=0.05, | |
| help="Un valor más alto es más restrictivo pero más preciso" | |
| ) | |
| model_choice = st.selectbox( | |
| "Embedding model", | |
| ["VGG-Face", "Facenet", "OpenFace", "ArcFace"], | |
| help="Diferentes modelos pueden dar resultados distintos según las características faciales" | |
| ) | |
| voting_method = st.radio( | |
| "Método de votación para múltiples embeddings", | |
| ["Promedio", "Mejor coincidencia", "Votación ponderada"], | |
| help="Cómo combinar resultados cuando hay múltiples imágenes de una persona" | |
| ) | |
| show_all_matches = st.checkbox( | |
| "Mostrar todas las coincidencias", | |
| value=False, | |
| help="Mostrar las 3 mejores coincidencias para cada rostro" | |
| ) | |
| if uploaded_file is not None: | |
| # Mostrar spinner durante el procesamiento | |
| with st.spinner('Processing image and analyzing faces...'): | |
| # Process la imagen subida | |
| raw_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| image = cv2.imdecode(raw_bytes, cv2.IMREAD_COLOR) | |
| # Detect rostros | |
| detections = detect_face_dnn(face_net, image, confidence_threshold) | |
| processed_image, bboxes = process_face_detections(image, detections, confidence_threshold) | |
| if not bboxes: | |
| st.error("No se detectaron rostros en la imagen.") | |
| else: | |
| # Mostrar imagen con rostros detectados | |
| st.image(processed_image, channels='BGR', caption="Faces detected") | |
| # Reconocer cada rostro | |
| result_image = image.copy() | |
| # Crear columnas para mostrar estadísticas | |
| stats_cols = st.columns(len(bboxes) if len(bboxes) <= 3 else 3) | |
| for i, bbox in enumerate(bboxes): | |
| # Extraer embedding del rostro | |
| embedding = extract_face_embeddings(image, bbox, model_name=model_choice) | |
| if embedding is not None: | |
| # Compare con rostros registrados | |
| matches = [] | |
| for name, info in st.session_state.face_database.items(): | |
| if 'embeddings' in info: | |
| # Nuevo formato con múltiples embeddings | |
| similarities = [] | |
| for idx, registered_embedding in enumerate(info['embeddings']): | |
| # Usar el mismo modelo si es posible | |
| if info['models'][idx] == model_choice: | |
| weight = 1.0 # Dar más peso a embeddings del mismo modelo | |
| else: | |
| weight = 0.8 # Peso menor para embeddings de otros modelos | |
| # Asegurarse de que los embeddings sean compatibles | |
| try: | |
| similarity = cosine_similarity([embedding["embedding"]], [registered_embedding])[0][0] * 100 * weight | |
| similarities.append(similarity) | |
| except ValueError as e: | |
| # Si hay error de dimensiones incompatibles, omitir esta comparación | |
| # Modelos incompatibles: {info['models'][idx]} vs {embedding['model']} | |
| continue | |
| # Aplicar método de votación seleccionado | |
| if voting_method == "Promedio": | |
| if similarities: # Verificar que la lista no esté vacía | |
| final_similarity = sum(similarities) / len(similarities) | |
| else: | |
| final_similarity = 0.0 # Valor predeterminado si no hay similitudes | |
| elif voting_method == "Mejor coincidencia": | |
| if similarities: # Verificar que la lista no esté vacía | |
| final_similarity = max(similarities) | |
| else: | |
| final_similarity = 0.0 # Valor predeterminado si no hay similitudes | |
| else: # Votación ponderada | |
| if similarities: # Verificar que la lista no esté vacía | |
| # Dar más peso a similitudes más altas | |
| weighted_sum = sum(s * (i+1) for i, s in enumerate(sorted(similarities))) | |
| weights_sum = sum(i+1 for i in range(len(similarities))) | |
| final_similarity = weighted_sum / weights_sum | |
| else: | |
| final_similarity = 0.0 # Valor predeterminado si no hay similitudes | |
| matches.append({"name": name, "similarity": final_similarity, "count": info['count']}) | |
| else: | |
| # Formato antiguo con un solo embedding | |
| registered_embedding = info['embedding'] | |
| try: | |
| similarity = cosine_similarity([embedding["embedding"]], [registered_embedding])[0][0] * 100 | |
| matches.append({"name": name, "similarity": similarity, "count": 1}) | |
| except ValueError as e: | |
| # Si hay error de dimensiones incompatibles, omitir esta comparación | |
| # Modelos incompatibles: {embedding['model']} vs formato antiguo | |
| continue | |
| # Ordenar coincidencias por similitud | |
| matches.sort(key=lambda x: x["similarity"], reverse=True) | |
| # Dibujar resultado en la imagen | |
| x1, y1, x2, y2, _ = bbox | |
| if matches and matches[0]["similarity"] >= similarity_threshold: | |
| # Coincidencia encontrada | |
| best_match = matches[0] | |
| # Color basado en nivel de similitud | |
| if best_match["similarity"] >= 80: | |
| color = (0, 255, 0) # Verde para alta similitud | |
| elif best_match["similarity"] >= 65: | |
| color = (0, 255, 255) # Amarillo para media similitud | |
| else: | |
| color = (0, 165, 255) # Naranja para baja similitud | |
| # Dibujar rectángulo y etiqueta principal | |
| label = f"{best_match['name']}: {best_match['similarity']:.1f}%" | |
| cv2.rectangle(result_image, (x1, y1), (x2, y2), color, 2) | |
| cv2.putText(result_image, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) | |
| # Mostrar coincidencias adicionales si está activado | |
| if show_all_matches and len(matches) > 1: | |
| for j, match in enumerate(matches[1:3]): # Mostrar las siguientes 2 mejores coincidencias | |
| sub_label = f"#{j+2}: {match['name']}: {match['similarity']:.1f}%" | |
| cv2.putText(result_image, sub_label, (x1, y1-(j+2)*20), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (200, 200, 200), 1) | |
| # Mostrar estadísticas en columnas | |
| col_idx = i % 3 | |
| with stats_cols[col_idx]: | |
| st.metric( | |
| f"Rostro {i+1}", | |
| f"{best_match['name']}", | |
| f"{best_match['similarity']:.1f}%" | |
| ) | |
| if show_all_matches and len(matches) > 1: | |
| st.write("Otras coincidencias:") | |
| for j, match in enumerate(matches[1:3]): | |
| st.write(f"- {match['name']}: {match['similarity']:.1f}%") | |
| else: | |
| # No hay coincidencia | |
| label = "Desconocido" | |
| if matches: | |
| label += f": {matches[0]['similarity']:.1f}%" | |
| cv2.rectangle(result_image, (x1, y1), (x2, y2), (0, 0, 255), 2) | |
| cv2.putText(result_image, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) | |
| # Mostrar estadísticas en columnas | |
| col_idx = i % 3 | |
| with stats_cols[col_idx]: | |
| st.metric( | |
| f"Rostro {i+1}", | |
| "Desconocido", | |
| f"{matches[0]['similarity']:.1f}%" if matches else "N/A" | |
| ) | |
| # Mostrar resultado | |
| st.subheader("Recognition Result") | |
| st.image(result_image, channels='BGR', use_container_width=True) | |
| with tab3: | |
| st.header("Real-time Recognition") | |
| # Verificar si hay rostros registrados | |
| if not st.session_state.face_database: | |
| st.warning("No faces registered. Please register at least one face first.") | |
| else: | |
| # Configuración avanzada | |
| with st.expander("Configuración avanzada", expanded=False): | |
| # Configuración de umbral de similitud | |
| similarity_threshold = st.slider( | |
| "Similarity threshold (%)", | |
| min_value=35.0, | |
| max_value=95.0, | |
| value=45.0, | |
| step=5.0, | |
| key="realtime_threshold", | |
| help="Porcentaje mínimo de similitud para considerar una coincidencia" | |
| ) | |
| confidence_threshold = st.slider( | |
| "Detection Confidence", | |
| min_value=0.3, | |
| max_value=0.9, | |
| value=0.5, | |
| step=0.05, | |
| key="realtime_confidence", | |
| help="Un valor más alto es más restrictivo pero más preciso" | |
| ) | |
| model_choice = st.selectbox( | |
| "Embedding model", | |
| ["VGG-Face", "Facenet", "OpenFace", "ArcFace"], | |
| key="realtime_model", | |
| help="Diferentes modelos pueden dar resultados distintos según las características faciales" | |
| ) | |
| voting_method = st.radio( | |
| "Método de votación para múltiples embeddings", | |
| ["Promedio", "Mejor coincidencia", "Votación ponderada"], | |
| key="realtime_voting", | |
| help="Cómo combinar resultados cuando hay múltiples imágenes de una persona" | |
| ) | |
| show_confidence = st.checkbox( | |
| "Mostrar porcentaje de confianza", | |
| value=True, | |
| help="Mostrar el porcentaje de similitud junto al nombre" | |
| ) | |
| stabilize_results = st.checkbox( | |
| "Estabilizar resultados", | |
| value=True, | |
| help="Reduce fluctuaciones en la identificación usando un promedio temporal" | |
| ) | |
| fps_limit = st.slider( | |
| "Límite de FPS", | |
| min_value=5, | |
| max_value=30, | |
| value=15, | |
| step=1, | |
| help="Limitar los frames por segundo para reducir uso de CPU" | |
| ) | |
| # Inicializar estado de la cámara | |
| if 'recognition_camera_running' not in st.session_state: | |
| st.session_state.recognition_camera_running = False | |
| # Inicializar historial de reconocimiento para estabilización | |
| if 'recognition_history' not in st.session_state: | |
| st.session_state.recognition_history = {} | |
| # Botones para controlar la cámara | |
| col1, col2 = st.columns(2) | |
| start_button = col1.button("Iniciar Cámara", key="start_recognition_camera", | |
| on_click=lambda: setattr(st.session_state, 'recognition_camera_running', True)) | |
| stop_button = col2.button("Detener Cámara", key="stop_recognition_camera", | |
| on_click=lambda: setattr(st.session_state, 'recognition_camera_running', False)) | |
| # Placeholder para el video | |
| video_placeholder = st.empty() | |
| # Placeholder para métricas | |
| metrics_cols = st.columns(3) | |
| with metrics_cols[0]: | |
| faces_metric = st.empty() | |
| with metrics_cols[1]: | |
| fps_metric = st.empty() | |
| with metrics_cols[2]: | |
| time_metric = st.empty() | |
| if st.session_state.recognition_camera_running: | |
| st.info("Cámara activada. Procesando video en tiempo real...") | |
| # Inicializar webcam | |
| cap = cv2.VideoCapture(0) | |
| if not cap.isOpened(): | |
| st.error("No se pudo acceder a la cámara. Asegúrese de que esté conectada y no esté siendo utilizada por otra aplicación.") | |
| st.session_state.recognition_camera_running = False | |
| else: | |
| try: | |
| # Variables para métricas | |
| frame_count = 0 | |
| start_time = time.time() | |
| last_frame_time = start_time | |
| fps_history = [] | |
| while st.session_state.recognition_camera_running: | |
| # Control de FPS | |
| current_time = time.time() | |
| elapsed = current_time - last_frame_time | |
| if elapsed < 1.0/fps_limit: | |
| time.sleep(0.01) # Pequeña pausa para no sobrecargar la CPU | |
| continue | |
| last_frame_time = current_time | |
| # Leer frame | |
| ret, frame = cap.read() | |
| if not ret: | |
| st.error("Error al leer frame de la cámara.") | |
| break | |
| # Actualizar contador de frames | |
| frame_count += 1 | |
| # Calcular FPS | |
| if frame_count % 5 == 0: | |
| fps = 5 / (current_time - start_time) | |
| fps_history.append(fps) | |
| if len(fps_history) > 10: | |
| fps_history.pop(0) | |
| avg_fps = sum(fps_history) / len(fps_history) | |
| start_time = current_time | |
| # Actualizar métricas | |
| fps_metric.metric("FPS", f"{avg_fps:.1f}") | |
| time_metric.metric("Tiempo activo", f"{int(current_time - time.time() + st.session_state.get('camera_start_time', current_time))}s") | |
| # Detect rostros | |
| detections = detect_face_dnn(face_net, frame, confidence_threshold) | |
| _, bboxes = process_face_detections(frame, detections, confidence_threshold) | |
| # Actualizar métrica de rostros | |
| if frame_count % 5 == 0: | |
| faces_metric.metric("Faces detected", len(bboxes)) | |
| # Reconocer cada rostro | |
| result_frame = frame.copy() | |
| for i, bbox in enumerate(bboxes): | |
| face_id = f"face_{i}" | |
| # Extraer embedding del rostro | |
| embedding = extract_face_embeddings(frame, bbox, model_name=model_choice) | |
| if embedding is not None: | |
| # Compare con rostros registrados | |
| matches = [] | |
| for name, info in st.session_state.face_database.items(): | |
| if 'embeddings' in info: | |
| # Nuevo formato con múltiples embeddings | |
| similarities = [] | |
| for idx, registered_embedding in enumerate(info['embeddings']): | |
| # Usar el mismo modelo si es posible | |
| if info['models'][idx] == model_choice: | |
| weight = 1.0 # Dar más peso a embeddings del mismo modelo | |
| else: | |
| weight = 0.8 # Peso menor para embeddings de otros modelos | |
| # Asegurarse de que los embeddings sean compatibles | |
| try: | |
| similarity = cosine_similarity([embedding["embedding"]], [registered_embedding])[0][0] * 100 * weight | |
| similarities.append(similarity) | |
| except ValueError as e: | |
| # Si hay error de dimensiones incompatibles, omitir esta comparación | |
| continue | |
| # Aplicar método de votación seleccionado | |
| if voting_method == "Promedio": | |
| final_similarity = sum(similarities) / len(similarities) | |
| elif voting_method == "Mejor coincidencia": | |
| final_similarity = max(similarities) | |
| else: # Votación ponderada | |
| # Dar más peso a similitudes más altas | |
| weighted_sum = sum(s * (i+1) for i, s in enumerate(sorted(similarities))) | |
| weights_sum = sum(i+1 for i in range(len(similarities))) | |
| final_similarity = weighted_sum / weights_sum | |
| matches.append({"name": name, "similarity": final_similarity}) | |
| else: | |
| # Formato antiguo con un solo embedding | |
| registered_embedding = info['embedding'] | |
| try: | |
| similarity = cosine_similarity([embedding["embedding"]], [registered_embedding])[0][0] * 100 | |
| matches.append({"name": name, "similarity": similarity}) | |
| except ValueError as e: | |
| # Si hay error de dimensiones incompatibles, omitir esta comparación | |
| # Modelos incompatibles: {embedding['model']} vs formato antiguo | |
| continue | |
| # Ordenar coincidencias por similitud | |
| matches.sort(key=lambda x: x["similarity"], reverse=True) | |
| # Estabilizar resultados si está activado | |
| if stabilize_results and matches: | |
| best_match = matches[0] | |
| # Inicializar historial para este rostro si no existe | |
| if face_id not in st.session_state.recognition_history: | |
| st.session_state.recognition_history[face_id] = { | |
| "names": [], | |
| "similarities": [] | |
| } | |
| # Añadir al historial | |
| history = st.session_state.recognition_history[face_id] | |
| history["names"].append(best_match["name"]) | |
| history["similarities"].append(best_match["similarity"]) | |
| # Limitar historial a los últimos 10 frames | |
| if len(history["names"]) > 10: | |
| history["names"].pop(0) | |
| history["similarities"].pop(0) | |
| # Determinar el nombre más frecuente en el historial | |
| if len(history["names"]) >= 3: # Necesitamos al menos 3 frames para estabilizar | |
| name_counts = {} | |
| for name in history["names"]: | |
| if name not in name_counts: | |
| name_counts[name] = 0 | |
| name_counts[name] += 1 | |
| # Encontrar el nombre más frecuente | |
| stable_name = max(name_counts.items(), key=lambda x: x[1])[0] | |
| # Calcular similitud promedio para ese nombre | |
| stable_similarities = [ | |
| history["similarities"][i] | |
| for i in range(len(history["names"])) | |
| if history["names"][i] == stable_name | |
| ] | |
| stable_similarity = sum(stable_similarities) / len(stable_similarities) | |
| # Reemplazar la mejor coincidencia con el resultado estabilizado | |
| best_match = {"name": stable_name, "similarity": stable_similarity} | |
| else: | |
| best_match = matches[0] | |
| else: | |
| best_match = matches[0] if matches else None | |
| # Dibujar resultado en la imagen | |
| x1, y1, x2, y2, _ = bbox | |
| if best_match and best_match["similarity"] >= similarity_threshold: | |
| # Coincidencia encontrada | |
| # Color basado en nivel de similitud | |
| if best_match["similarity"] >= 80: | |
| color = (0, 255, 0) # Verde para alta similitud | |
| elif best_match["similarity"] >= 65: | |
| color = (0, 255, 255) # Amarillo para media similitud | |
| else: | |
| color = (0, 165, 255) # Naranja para baja similitud | |
| # Dibujar rectángulo y etiqueta | |
| cv2.rectangle(result_frame, (x1, y1), (x2, y2), color, 2) | |
| if show_confidence: | |
| label = f"{best_match['name']}: {best_match['similarity']:.1f}%" | |
| else: | |
| label = f"{best_match['name']}" | |
| cv2.putText(result_frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) | |
| else: | |
| # No hay coincidencia | |
| cv2.rectangle(result_frame, (x1, y1), (x2, y2), (0, 0, 255), 2) | |
| if best_match: | |
| label = f"Desconocido: {best_match['similarity']:.1f}%" if show_confidence else "Desconocido" | |
| else: | |
| label = "Desconocido" | |
| cv2.putText(result_frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) | |
| # Mostrar resultado | |
| video_placeholder.image(result_frame, channels="BGR", use_container_width=True) | |
| finally: | |
| # Liberar la cámara cuando se detenga | |
| cap.release() | |
| # Limpiar historial de reconocimiento | |
| st.session_state.recognition_history = {} | |
| else: | |
| st.info("Haga clic en 'Iniciar Cámara' para comenzar el reconocimiento en tiempo real.") | |
| # Si se ejecuta este archivo directamente, llamar a la función main | |
| if __name__ == "__main__": | |
| main() | |