initial commit

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import mediapipe as mp
+
+# Initialize MediaPipe Pose
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(static_image_mode=True)
+mp_drawing = mp.solutions.drawing_utils
+mp_pose_landmark = mp_pose.PoseLandmark
+
+def detect_pose(image):
+    # Convert to RGB
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Run pose detection
+    result = pose.process(image_rgb)
+
+    keypoints = {}
+
+    if result.pose_landmarks:
+        # Draw landmarks on image
+        mp_drawing.draw_landmarks(image, result.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+
+        # Get image dimensions
+        height, width, _ = image.shape
+
+        # Extract specific landmarks
+        landmark_indices = {
+            'left_shoulder': mp_pose_landmark.LEFT_SHOULDER,
+            'right_shoulder': mp_pose_landmark.RIGHT_SHOULDER,
+            'left_hip': mp_pose_landmark.LEFT_HIP,
+            'right_hip': mp_pose_landmark.RIGHT_HIP
+        }
+
+        for name, index in landmark_indices.items():
+            lm = result.pose_landmarks.landmark[index]
+            x, y = int(lm.x * width), int(lm.y * height)
+            keypoints[name] = (x, y)
+
+            # Draw a circle + label for debug
+            cv2.circle(image, (x, y), 5, (0, 255, 0), -1)
+            cv2.putText(image, name, (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+    return image, keypoints
+
+# Gradio interface
+iface = gr.Interface(
+    fn=detect_pose,
+    inputs=gr.Image(type="numpy", label="Upload Full-Body Image"),
+    outputs=[
+        gr.Image(type="numpy", label="Pose Visualization"),
+        gr.JSON(label="Extracted Keypoints")
+    ],
+    title="Virtual Try-On - Pose Detection",
+    description="Detects body keypoints using MediaPipe Pose and visualizes them. Shoulders and hips are labeled."
+)
+
+if __name__ == "__main__":
+    iface.launch()