Add face comparison functionality

face_comparison.py

@@ -0,0 +1,252 @@
+import cv2
+import numpy as np
+import streamlit as st
+from sklearn.metrics.pairwise import cosine_similarity
+
+def compare_faces(image1, bboxes1, image2, bboxes2):
+    """
+    Compare faces using HOG features
+    """
+    # Convert images to grayscale
+    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
+    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+    
+    # Initialize list to store comparison results
+    comparison_results = []
+    
+    # Calculate HOG parameters based on face size
+    win_size = (64, 64)
+    block_size = (16, 16)
+    block_stride = (8, 8)
+    cell_size = (8, 8)
+    nbins = 9
+    
+    # Iterate over each face in the first image
+    for bbox1 in bboxes1:
+        x1_1, y1_1, x2_1, y2_1, _ = bbox1
+        
+        # Check if the face region is valid
+        if x1_1 >= x2_1 or y1_1 >= y2_1:
+            continue
+            
+        # Resize face to a standard size for HOG
+        face1_roi = image1[y1_1:y2_1, x1_1:x2_1]
+        face1_resized = cv2.resize(face1_roi, win_size)
+        face1_gray = cv2.cvtColor(face1_resized, cv2.COLOR_BGR2GRAY)
+        
+        # Calculate HOG features
+        hog = cv2.HOGDescriptor(win_size, block_size, block_stride, cell_size, nbins)
+        h1 = hog.compute(face1_gray)
+        
+        # Normalize the feature vector
+        h1_norm = h1 / np.linalg.norm(h1)
+        
+        # Store results for this face
+        face_comparisons = []
+        
+        # Compare with each face in the second image
+        for bbox2 in bboxes2:
+            x1_2, y1_2, x2_2, y2_2, _ = bbox2
+            
+            # Check if the face region is valid
+            if x1_2 >= x2_2 or y1_2 >= y2_2:
+                continue
+                
+            # Resize face to a standard size for HOG
+            face2_roi = image2[y1_2:y2_2, x1_2:x2_2]
+            face2_resized = cv2.resize(face2_roi, win_size)
+            face2_gray = cv2.cvtColor(face2_resized, cv2.COLOR_BGR2GRAY)
+            
+            # Calculate HOG features
+            h2 = hog.compute(face2_gray)
+            
+            # Normalize the feature vector
+            h2_norm = h2 / np.linalg.norm(h2)
+            
+            # Calculate cosine similarity
+            similarity = np.dot(h1_norm.flatten(), h2_norm.flatten()) * 100
+            
+            # Add result
+            face_comparisons.append({
+                "similarity": similarity
+            })
+        
+        comparison_results.append(face_comparisons)
+    
+    return comparison_results
+
+def compare_faces_embeddings(image1, bboxes1, image2, bboxes2, model_name="VGG-Face"):
+    """
+    Compare faces using facial embeddings from DeepFace
+    """
+    try:
+        from deepface import DeepFace
+        import numpy as np
+    except ImportError:
+        # Fallback to HOG if DeepFace is not available
+        return compare_faces(image1, bboxes1, image2, bboxes2)
+    
+    # Initialize list to store comparison results
+    comparison_results = []
+    
+    # Iterate over each face in the first image
+    for bbox1 in bboxes1:
+        x1_1, y1_1, x2_1, y2_1, _ = bbox1
+        
+        # Check if the face region is valid
+        if x1_1 >= x2_1 or y1_1 >= y2_1:
+            continue
+            
+        # Extract face region
+        face1_roi = image1[y1_1:y2_1, x1_1:x2_1]
+        
+        # Get embedding for the face
+        try:
+            embedding1 = DeepFace.represent(face1_roi, model_name=model_name, enforce_detection=False)[0]["embedding"]
+        except Exception as e:
+            st.warning(f"Error extracting embedding from face 1: {str(e)}")
+            # Try with a fallback model
+            try:
+                embedding1 = DeepFace.represent(face1_roi, model_name="OpenFace", enforce_detection=False)[0]["embedding"]
+            except:
+                # If still fails, use HOG
+                face_comparisons = []
+                for bbox2 in bboxes2:
+                    face_comparisons.append({"similarity": 0})
+                comparison_results.append(face_comparisons)
+                continue
+        
+        # Store results for this face
+        face_comparisons = []
+        
+        # Compare with each face in the second image
+        for bbox2 in bboxes2:
+            x1_2, y1_2, x2_2, y2_2, _ = bbox2
+            
+            # Check if the face region is valid
+            if x1_2 >= x2_2 or y1_2 >= y2_2:
+                continue
+                
+            # Extract face region
+            face2_roi = image2[y1_2:y2_2, x1_2:x2_2]
+            
+            # Get embedding for the face
+            try:
+                embedding2 = DeepFace.represent(face2_roi, model_name=model_name, enforce_detection=False)[0]["embedding"]
+            except Exception as e:
+                st.warning(f"Error extracting embedding from face 2: {str(e)}")
+                # Try with a fallback model
+                try:
+                    embedding2 = DeepFace.represent(face2_roi, model_name="OpenFace", enforce_detection=False)[0]["embedding"]
+                except:
+                    # If still fails, add a 0 similarity
+                    face_comparisons.append({"similarity": 0})
+                    continue
+            
+            # Calculate cosine similarity between embeddings
+            embedding1_array = np.array(embedding1).reshape(1, -1)
+            embedding2_array = np.array(embedding2).reshape(1, -1)
+            similarity = cosine_similarity(embedding1_array, embedding2_array)[0][0] * 100
+            
+            # Add result
+            face_comparisons.append({
+                "similarity": similarity
+            })
+        
+        comparison_results.append(face_comparisons)
+    
+    return comparison_results
+
+def generate_comparison_report_english(comparison_results, bboxes1, bboxes2, threshold=50.0):
+    """
+    Generate a text report of the face comparison results
+    """
+    report = []
+    
+    # Skip if no comparison results
+    if not comparison_results:
+        return ["No face comparisons were performed."]
+    
+    # Add header
+    report.append(f"Face Comparison Report:")
+    
+    # Add comparison results
+    for i, face_comparisons in enumerate(comparison_results):
+        report.append(f"\nFace {i+1} from Image 1:")
+        
+        # Skip if no comparisons for this face
+        if not face_comparisons:
+            report.append("  No comparisons available for this face.")
+            continue
+        
+        # Find best match
+        best_match_idx = max(range(len(face_comparisons)), key=lambda j: face_comparisons[j]["similarity"])
+        best_match_similarity = face_comparisons[best_match_idx]["similarity"]
+        
+        # Add best match info
+        if best_match_similarity >= threshold:
+            report.append(f"  Best match: Face {best_match_idx+1} from Image 2 (Similarity: {best_match_similarity:.2f}%)")
+        else:
+            report.append(f"  No strong matches found. Best similarity is with Face {best_match_idx+1} ({best_match_similarity:.2f}%)")
+        
+        # Add all comparisons
+        report.append("  All comparisons:")
+        for j, comp in enumerate(face_comparisons):
+            report.append(f"    Face {j+1}: Similarity {comp['similarity']:.2f}%")
+    
+    return report
+
+def draw_face_matches(image1, bboxes1, image2, bboxes2, comparison_results, threshold=50.0):
+    """
+    Create a combined image showing the two input images side by side with lines connecting matching faces
+    """
+    # Get dimensions
+    h1, w1 = image1.shape[:2]
+    h2, w2 = image2.shape[:2]
+    
+    # Create a combined image
+    combined_h = max(h1, h2)
+    combined_w = w1 + w2
+    combined_img = np.zeros((combined_h, combined_w, 3), dtype=np.uint8)
+    
+    # Copy images
+    combined_img[:h1, :w1] = image1
+    combined_img[:h2, w1:w1+w2] = image2
+    
+    # Draw lines between matching faces
+    for i, face_comparisons in enumerate(comparison_results):
+        # Skip if no comparisons for this face
+        if not face_comparisons:
+            continue
+            
+        # Get bbox for this face
+        x1_1, y1_1, x2_1, y2_1, _ = bboxes1[i]
+        center1_x = (x1_1 + x2_1) // 2
+        center1_y = (y1_1 + y2_1) // 2
+        
+        # For each comparison
+        for j, comp in enumerate(face_comparisons):
+            similarity = comp["similarity"]
+            
+            # Only draw lines for matches above threshold
+            if similarity >= threshold:
+                # Get bbox for the other face
+                x1_2, y1_2, x2_2, y2_2, _ = bboxes2[j]
+                center2_x = (x1_2 + x2_2) // 2 + w1  # Adjust for offset
+                center2_y = (y1_2 + y2_2) // 2
+                
+                # Calculate color based on similarity (green for high, red for low)
+                # Map 50-100% to color scale
+                color_val = min(255, max(0, int((similarity - threshold) * 255 / (100 - threshold))))
+                line_color = (0, 0, 255)  # Red for all matches
+                
+                # Draw line
+                cv2.line(combined_img, (center1_x, center1_y), (center2_x, center2_y), line_color, 2)
+                
+                # Add similarity text
+                text_x = (center1_x + center2_x) // 2 - 20
+                text_y = (center1_y + center2_y) // 2 - 10
+                cv2.putText(combined_img, f"{similarity:.1f}%", (text_x, text_y), 
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+    
+    return combined_img 

streamlit_app.py

@@ -20,6 +20,14 @@ try:
 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
+    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