Create app.py

app.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import gradio as gr
+
+# Initialize Mediapipe solutions
+mp_hands = mp.solutions.hands
+mp_face_mesh = mp.solutions.face_mesh
+mp_drawing = mp.solutions.drawing_utils
+
+# Function to process video frames and draw landmarks
+def process_video(input_video):
+    # Create a VideoCapture object from the input video
+    cap = cv2.VideoCapture(input_video)
+    processed_frames = []
+
+    # Initialize Hands and Face Mesh models
+    with mp_hands.Hands(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5) as hands, \
+         mp_face_mesh.FaceMesh(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            # Convert frame to RGB for Mediapipe
+            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+            # Process the frame for hands
+            hand_results = hands.process(rgb_frame)
+            # Process the frame for face mesh
+            face_results = face_mesh.process(rgb_frame)
+
+            # Draw Hand Landmarks
+            if hand_results.multi_hand_landmarks:
+                for hand_landmarks in hand_results.multi_hand_landmarks:
+                    mp_drawing.draw_landmarks(
+                        frame, hand_landmarks, mp_hands.HAND_CONNECTIONS,
+                        mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),
+                        mp_drawing.DrawingSpec(color=(250, 44, 250), thickness=2, circle_radius=2)
+                    )
+
+            # Draw Face Mesh Landmarks
+            if face_results.multi_face_landmarks:
+                for face_landmarks in face_results.multi_face_landmarks:
+                    mp_drawing.draw_landmarks(
+                        frame, face_landmarks, mp_face_mesh.FACEMESH_TESSELATION,
+                        mp_drawing.DrawingSpec(color=(80, 110, 10), thickness=1, circle_radius=1),
+                        mp_drawing.DrawingSpec(color=(80, 256, 121), thickness=1, circle_radius=1)
+                    )
+
+            # Store processed frame
+            processed_frames.append(frame)
+    
+    cap.release()
+
+    # Compile processed frames into a video
+    height, width, _ = processed_frames[0].shape
+    output_video_path = "processed_video.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(output_video_path, fourcc, 30.0, (width, height))
+    for frame in processed_frames:
+        out.write(frame)
+    out.release()
+
+    return output_video_path
+
+# Define Gradio interface
+def gradio_video_interface(input_video):
+    # Process the video to detect landmarks
+    output_video = process_video(input_video)
+    return output_video
+
+# Gradio interface for video input and output
+iface = gr.Interface(
+    fn=gradio_video_interface,
+    inputs=gr.Video(type="file"),
+    outputs=gr.Video(type="file"),
+    title="Face and Hand Landmarks Detection - Video",
+    description="Upload a video to detect face and hand landmarks using Mediapipe and OpenCV."
+)
+
+# Launch Gradio app
+if __name__ == "__main__":
+    iface.launch()