Create app.py

app.py

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+import gradio as gr
+from deepface import DeepFace
+import cv2
+import mediapipe as mp
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+
+# Initialize MediaPipe Pose for body bounding box detection
+mp_pose = mp.solutions.pose
+
+def analyze_images(img1, img2):
+    # Face comparison with DeepFace
+    face_result = DeepFace.verify(img1, img2, model_name='VGG-Face')
+    is_same_person = face_result['verified']
+    similarity_score = face_result['distance']
+
+    # Convert images to OpenCV format
+    img1_cv = cv2.cvtColor(np.array(img1), cv2.COLOR_RGB2BGR)
+    img2_cv = cv2.cvtColor(np.array(img2), cv2.COLOR_RGB2BGR)
+
+    # Body analysis with MediaPipe
+    def get_body_info(image):
+        with mp_pose.Pose(static_image_mode=True) as pose:
+            results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+            if results.pose_landmarks:
+                landmarks = results.pose_landmarks.landmark
+                # Calculate bounding box height and width
+                x_values = [landmark.x for landmark in landmarks]
+                y_values = [landmark.y for landmark in landmarks]
+                width = max(x_values) - min(x_values)
+                height = max(y_values) - min(y_values)
+                aspect_ratio = height / width if width > 0 else 0
+                return aspect_ratio, width, height
+            return None, None, None
+
+    aspect_ratio1, width1, height1 = get_body_info(img1_cv)
+    aspect_ratio2, width2, height2 = get_body_info(img2_cv)
+
+    # Create a chart to display face and body similarities
+    labels = ['Face Similarity', 'Body Aspect Ratio', 'Body Width', 'Body Height']
+    values1 = [similarity_score, aspect_ratio1, width1, height1]
+    values2 = [similarity_score, aspect_ratio2, width2, height2]
+
+    x = np.arange(len(labels))
+    width = 0.35
+
+    fig, ax = plt.subplots()
+    ax.bar(x - width/2, values1, width, label='Image 1')
+    ax.bar(x + width/2, values2, width, label='Image 2')
+
+    ax.set_ylabel('Scores')
+    ax.set_title('Comparison of Face and Body Features')
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels)
+    ax.legend()
+
+    # Save and return the chart
+    plt.tight_layout()
+    plt_path = "comparison_chart.png"
+    plt.savefig(plt_path)
+    plt.close(fig)
+
+    return is_same_person, plt_path
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=analyze_images,
+    inputs=[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],
+    outputs=[gr.outputs.Textbox(label="Same Person?"), gr.outputs.Image(label="Comparison Chart")],
+    title="Face and Body Similarity Analyzer",
+    description="Upload two images to analyze if they contain the same person and compare body features."
+)
+
+iface.launch()