Create app.py

app.py

@@ -0,0 +1,491 @@
+# Install required packages
+import subprocess
+import sys
+
+# Check if running in a standard environment (not Colab/Jupyter)
+# and install packages if needed
+if not os.path.exists("/.dockerenv") and not os.path.exists("/kaggle"):
+    try:
+        import gradio
+        import deepface
+        import cv2
+        import numpy as np
+        import matplotlib
+        import PIL
+    except ImportError:
+        print("Installing required packages...")
+        subprocess.check_call([sys.executable, "-m", "pip", "install",
+                              "gradio", "deepface", "opencv-python-headless", "numpy", "matplotlib", "pillow"])
+
+import gradio as gr
+import json
+import cv2
+import numpy as np
+from deepface import DeepFace
+import matplotlib.pyplot as plt
+from PIL import Image
+import tempfile
+import os
+import pandas as pd
+import shutil
+
+def verify_faces(img1, img2, threshold=0.70, model="VGG-Face"):
+    # Save uploaded images to temporary files
+    temp_dir = tempfile.mkdtemp()
+    img1_path = os.path.join(temp_dir, "image1.jpg")
+    img2_path = os.path.join(temp_dir, "image2.jpg")
+
+    # Convert to PIL Images and save
+    if isinstance(img1, np.ndarray):
+        Image.fromarray(img1).save(img1_path)
+    else:
+        img1.save(img1_path)
+
+    if isinstance(img2, np.ndarray):
+        Image.fromarray(img2).save(img2_path)
+    else:
+        img2.save(img2_path)
+
+    # Perform face verification
+    try:
+        result = DeepFace.verify(
+            img1_path=img1_path,
+            img2_path=img2_path,
+            model_name=model,
+            distance_metric="cosine",
+            threshold=threshold
+        )
+
+        # Create comparison visualization
+        fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+
+        # Display images
+        img1_display = cv2.imread(img1_path)
+        img1_display = cv2.cvtColor(img1_display, cv2.COLOR_BGR2RGB)
+        img2_display = cv2.imread(img2_path)
+        img2_display = cv2.cvtColor(img2_display, cv2.COLOR_BGR2RGB)
+
+        ax[0].imshow(img1_display)
+        ax[0].set_title("Image 1")
+        ax[0].axis("off")
+
+        ax[1].imshow(img2_display)
+        ax[1].set_title("Image 2")
+        ax[1].axis("off")
+
+        # Create result message
+        verification_result = "✅ FACE MATCHED" if result["verified"] else "❌ FACE NOT MATCHED"
+        confidence = round((1 - result["distance"]) * 100, 2)
+
+        plt.suptitle(f"{verification_result}\nConfidence: {confidence}%\nDistance: {result['distance']:.4f}",
+                    fontsize=16, fontweight='bold',
+                    color='green' if result["verified"] else 'red')
+
+        plt.tight_layout()
+
+        # Clean up temporary files
+        os.remove(img1_path)
+        os.remove(img2_path)
+        os.rmdir(temp_dir)
+
+        return fig, json.dumps(result, indent=2)
+
+    except Exception as e:
+        # Clean up temporary files
+        if os.path.exists(img1_path):
+            os.remove(img1_path)
+        if os.path.exists(img2_path):
+            os.remove(img2_path)
+        if os.path.exists(temp_dir):
+            os.rmdir(temp_dir)
+
+        error_msg = f"Error: {str(e)}"
+        if "No face detected" in str(e):
+            error_msg = "No face detected in one or both images. Please try different images."
+
+        return None, error_msg
+
+def find_faces(query_img, db_folder, threshold=0.70, model="VGG-Face"):
+    # Create temp directory
+    temp_dir = tempfile.mkdtemp()
+    query_path = os.path.join(temp_dir, "query.jpg")
+
+    # Save query image
+    if isinstance(query_img, np.ndarray):
+        Image.fromarray(query_img).save(query_path)
+    else:
+        query_img.save(query_path)
+
+    # If db_folder is just a string, assume it's a folder path the user entered
+    if isinstance(db_folder, str):
+        db_path = db_folder
+    else:
+        # Handling for folder upload (creates a temp directory with the images)
+        db_path = os.path.join(temp_dir, "db")
+        os.makedirs(db_path, exist_ok=True)
+
+        for i, file in enumerate(db_folder):
+            file_ext = os.path.splitext(file.name)[1]
+            shutil.copy(file.name, os.path.join(db_path, f"image_{i}{file_ext}"))
+
+    try:
+        # Find matching faces
+        dfs = DeepFace.find(
+            img_path=query_path,
+            db_path=db_path,
+            model_name=model,
+            distance_metric="cosine",
+            threshold=threshold
+        )
+
+        if isinstance(dfs, list):
+            # Handle case where multiple faces are found in query image
+            if len(dfs) == 0:
+                return None, "No matching faces found in the database."
+            df = dfs[0]  # Take first face results
+        else:
+            df = dfs
+
+        # Check if any matches were found
+        if df.empty:
+            return None, "No matching faces found in the database."
+
+        # Sort by similarity (lowest distance first)
+        df = df.sort_values(by=["distance"])
+
+        # Create visualization for top matches (up to 4)
+        num_matches = min(4, len(df))
+        fig, axes = plt.subplots(1, num_matches + 1, figsize=(15, 5))
+
+        # Display query image
+        query_display = cv2.imread(query_path)
+        query_display = cv2.cvtColor(query_display, cv2.COLOR_BGR2RGB)
+        axes[0].imshow(query_display)
+        axes[0].set_title("Query Image")
+        axes[0].axis("off")
+
+        # Display matches
+        for i in range(num_matches):
+            match_path = df.iloc[i]["identity"]
+            distance = df.iloc[i]["distance"]
+            confidence = round((1 - distance) * 100, 2)
+
+            match_img = cv2.imread(match_path)
+            match_img = cv2.cvtColor(match_img, cv2.COLOR_BGR2RGB)
+
+            axes[i+1].imshow(match_img)
+            axes[i+1].set_title(f"Match #{i+1}\nConfidence: {confidence}%")
+            axes[i+1].axis("off")
+
+        plt.suptitle(f"Found {len(df)} matching faces", fontsize=16, fontweight='bold')
+        plt.tight_layout()
+
+        # Format results for display
+        results = df[["identity", "distance"]].copy()
+        results["confidence"] = (1 - results["distance"]) * 100
+        results["confidence"] = results["confidence"].round(2)
+        results = results.rename(columns={"identity": "Image Path"})
+
+        # Clean up temp files
+        os.remove(query_path)
+        # Don't remove temp DB folder if it came from user input
+        if not isinstance(db_folder, str):
+            shutil.rmtree(db_path)
+
+        return fig, results.to_dict('records')
+
+    except Exception as e:
+        # Clean up temp files
+        if os.path.exists(query_path):
+            os.remove(query_path)
+
+        error_msg = f"Error: {str(e)}"
+        if "No face detected" in str(e):
+            error_msg = "No face detected in the query image. Please try a different image."
+
+        return None, error_msg
+
+def analyze_face(img, actions=['age', 'gender', 'race', 'emotion']):
+    # Create temp directory and save image
+    temp_dir = tempfile.mkdtemp()
+    img_path = os.path.join(temp_dir, "analyze.jpg")
+
+    if isinstance(img, np.ndarray):
+        Image.fromarray(img).save(img_path)
+    else:
+        img.save(img_path)
+
+    try:
+        # Analyze facial attributes
+        results = DeepFace.analyze(
+            img_path=img_path,
+            actions=actions,
+            enforce_detection=True,
+            detector_backend='opencv'
+        )
+
+        # Handle both single face and multiple faces results
+        if isinstance(results, list):
+            num_faces = len(results)
+        else:
+            num_faces = 1
+            results = [results]
+
+        # Create visualization
+        fig = plt.figure(figsize=(14, 7))
+
+        # Load the image for display
+        img_display = cv2.imread(img_path)
+        img_display = cv2.cvtColor(img_display, cv2.COLOR_BGR2RGB)
+
+        # Main image display
+        main_ax = plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2)
+        main_ax.imshow(img_display)
+        main_ax.set_title(f"Analyzed Image ({num_faces} face{'s' if num_faces > 1 else ''} detected)")
+        main_ax.axis('off')
+
+        # Create a results summary for each face
+        for i, face_result in enumerate(results):
+            if i >= 4:  # Limit to 4 faces for display
+                break
+
+            # Get main results
+            age = face_result.get('age', 'N/A')
+            gender = face_result.get('dominant_gender', 'N/A')
+            race = face_result.get('dominant_race', 'N/A')
+            emotion = face_result.get('dominant_emotion', 'N/A')
+
+            # Gender confidence
+            gender_conf = 'N/A'
+            if 'gender' in face_result and isinstance(face_result['gender'], dict):
+                for g, conf in face_result['gender'].items():
+                    if g.lower() == gender.lower():
+                        gender_conf = f"{conf:.1f}%"
+                        break
+
+            # Race confidence
+            race_conf = 'N/A'
+            if 'race' in face_result and isinstance(face_result['race'], dict):
+                for r, conf in face_result['race'].items():
+                    if r.lower() == race.lower():
+                        race_conf = f"{conf:.1f}%"
+                        break
+
+            # Emotion confidence
+            emotion_conf = 'N/A'
+            if 'emotion' in face_result and isinstance(face_result['emotion'], dict):
+                for e, conf in face_result['emotion'].items():
+                    if e.lower() == emotion.lower():
+                        emotion_conf = f"{conf:.1f}%"
+                        break
+
+            # Create subplot for this face's results
+            ax = plt.subplot2grid((2, 4), (0 if i < 2 else 1, 2 + (i % 2)))
+
+            # Format text for subplot
+            text = (
+                f"Face #{i+1}\n\n"
+                f"Age: {age}\n\n"
+                f"Gender: {gender} ({gender_conf})\n\n"
+                f"Race: {race} ({race_conf})\n\n"
+                f"Emotion: {emotion} ({emotion_conf})"
+            )
+
+            ax.text(0.5, 0.5, text, ha='center', va='center', fontsize=11)
+            ax.axis('off')
+
+        plt.tight_layout()
+
+        # Clean up temp files
+        os.remove(img_path)
+        os.rmdir(temp_dir)
+
+        # Format results for display in JSON
+        formatted_results = []
+        for i, res in enumerate(results[:8]):  # Limit to 8 faces for JSON display
+            face_data = {
+                "face_number": i+1,
+                "age": res.get("age", "N/A"),
+                "gender": {
+                    "dominant": res.get("dominant_gender", "N/A"),
+                    "confidence": res.get("gender", {})
+                },
+                "race": {
+                    "dominant": res.get("dominant_race", "N/A"),
+                    "confidence": res.get("race", {})
+                },
+                "emotion": {
+                    "dominant": res.get("dominant_emotion", "N/A"),
+                    "confidence": res.get("emotion", {})
+                }
+            }
+            formatted_results.append(face_data)
+
+        return fig, formatted_results
+
+    except Exception as e:
+        # Clean up temp files
+        if os.path.exists(img_path):
+            os.remove(img_path)
+        if os.path.exists(temp_dir):
+            os.rmdir(temp_dir)
+
+        error_msg = f"Error: {str(e)}"
+        if "No face detected" in str(e):
+            error_msg = "No face detected in the image. Please try a different image."
+
+        return None, error_msg
+
+# Create Gradio interface
+with gr.Blocks(title="Complete Face Recognition Tool", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🔍 Complete Face Recognition Tool
+
+    This tool provides three face recognition features:
+    - **Verify Faces**: Compare two specific images to check if they contain the same person
+    - **Find Faces**: Search for matching faces in a database/folder
+    - **Analyze Face**: Determine age, gender, race, and emotion from a facial image
+    """)
+
+    with gr.Tabs():
+        with gr.TabItem("Verify Faces"):
+            with gr.Row():
+                with gr.Column():
+                    img1_input = gr.Image(label="First Image", type="pil")
+                with gr.Column():
+                    img2_input = gr.Image(label="Second Image", type="pil")
+
+            with gr.Row():
+                with gr.Column():
+                    verify_threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.6, step=0.05,
+                                           label="Similarity Threshold (lower = stricter matching)")
+                with gr.Column():
+                    verify_model = gr.Dropdown(
+                        choices=["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"],
+                        value="VGG-Face",
+                        label="Face Recognition Model"
+                    )
+
+            verify_button = gr.Button("Verify Faces", variant="primary")
+
+            with gr.Row():
+                verify_result_plot = gr.Plot(label="Verification Result")
+
+            with gr.Row():
+                verify_json = gr.JSON(label="Technical Details")
+
+            # Set up the verification function
+            verify_button.click(
+                verify_faces,
+                inputs=[img1_input, img2_input, verify_threshold, verify_model],
+                outputs=[verify_result_plot, verify_json]
+            )
+
+            gr.Markdown("""
+            ### 📋 How to use Face Verification:
+            1. Upload two facial images
+            2. Adjust the similarity threshold if needed
+            3. Select a face recognition model
+            4. Click "Verify Faces" button
+            5. View the results below
+            """)
+
+        with gr.TabItem("Find Faces"):
+            with gr.Row():
+                query_img = gr.Image(label="Query Image (Face to find)", type="pil")
+
+            with gr.Row():
+                with gr.Column():
+                    db_path_input = gr.Textbox(label="Database Path (folder containing images to search in)")
+                    db_files_input = gr.File(label="Or upload images for database", file_count="multiple")
+
+            with gr.Row():
+                with gr.Column():
+                    find_threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.6, step=0.05,
+                                         label="Similarity Threshold (lower = stricter matching)")
+                with gr.Column():
+                    find_model = gr.Dropdown(
+                        choices=["VGG-Face", "Facenet", "OpenFace", "DeepFace", "ArcFace"],
+                        value="VGG-Face",
+                        label="Face Recognition Model"
+                    )
+
+            find_button = gr.Button("Find Matching Faces", variant="primary")
+
+            with gr.Row():
+                find_result_plot = gr.Plot(label="Search Results")
+
+            with gr.Row():
+                find_results_table = gr.JSON(label="Detailed Results")
+
+            # Connect function to button
+            find_button.click(
+                find_faces,
+                inputs=[query_img, db_path_input, find_threshold, find_model],
+                outputs=[find_result_plot, find_results_table]
+            )
+
+            # Also connect with files input
+            db_files_input.change(
+                lambda x: "",  # Clear the text input when files are uploaded
+                inputs=db_files_input,
+                outputs=db_path_input
+            )
+
+            gr.Markdown("""
+            ### 📋 How to use Face Finding:
+            1. Upload a query image containing the face you want to find
+            2. Either:
+               - Enter the path to a folder containing images to search through, or
+               - Upload multiple images to create a temporary database
+            3. Adjust the similarity threshold if needed
+            4. Select a face recognition model
+            5. Click "Find Matching Faces" button
+            6. View the results showing the most similar faces
+            """)
+
+        with gr.TabItem("Analyze Face"):
+            with gr.Row():
+                analyze_img = gr.Image(label="Upload Image for Analysis", type="pil")
+
+            with gr.Row():
+                actions_checkboxes = gr.CheckboxGroup(
+                    choices=["age", "gender", "race", "emotion"],
+                    value=["age", "gender", "race", "emotion"],
+                    label="Select Attributes to Analyze"
+                )
+
+            analyze_button = gr.Button("Analyze Face", variant="primary")
+
+            with gr.Row():
+                analyze_result_plot = gr.Plot(label="Analysis Results")
+
+            with gr.Row():
+                analyze_json = gr.JSON(label="Detailed Analysis")
+
+            # Connect function to button
+            analyze_button.click(
+                analyze_face,
+                inputs=[analyze_img, actions_checkboxes],
+                outputs=[analyze_result_plot, analyze_json]
+            )
+
+            gr.Markdown("""
+            ### 📋 How to use Facial Analysis:
+            1. Upload an image containing one or more faces
+            2. Select which attributes you want to analyze
+            3. Click "Analyze Face" button
+            4. View the visual results and detailed JSON data
+
+            ### 📊 Understanding the results:
+            - The tool can detect multiple faces in a single image
+            - For each face, it provides:
+              - Estimated age
+              - Predicted gender with confidence
+              - Predicted race/ethnicity with confidence
+              - Detected emotional expression with confidence
+            - The JSON output provides detailed confidence scores for all categories
+            """)
+
+# Launch the app
+demo.launch()