Create app.py

app.py

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+import streamlit as st
+import cv2
+import open3d as o3d
+import numpy as np
+import tempfile
+import os
+
+# Title of the App
+st.title("3D Reconstruction Tool from Video 📹 → 🛠️ → 🧊")
+
+# Sidebar: Information
+st.sidebar.write("""
+## About the App
+Upload a video file, extract frames, reconstruct a 3D point cloud using Structure from Motion (SfM), and visualize or download the 3D mesh.
+""")
+
+# Step 1: Upload Video File
+uploaded_file = st.file_uploader("Upload a Video File (MP4, AVI)", type=["mp4", "avi"])
+
+# Function to extract frames from video
+def extract_frames(video_path, frame_rate=10):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    count = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if count % frame_rate == 0:
+            frames.append(frame)
+        count += 1
+    cap.release()
+    return frames
+
+# Function to save frames as images
+def save_frames_as_images(frames, output_dir):
+    os.makedirs(output_dir, exist_ok=True)
+    for i, frame in enumerate(frames):
+        filename = os.path.join(output_dir, f"frame_{i:04d}.png")
+        cv2.imwrite(filename, frame)
+
+# Step 2: Process Uploaded Video
+if uploaded_file:
+    st.video(uploaded_file)
+    st.write("Extracting frames...")
+
+    # Save the uploaded video temporarily
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_video:
+        tmp_video.write(uploaded_file.read())
+        video_path = tmp_video.name
+
+    # Extract frames
+    frames = extract_frames(video_path, frame_rate=10)
+    st.write(f"✅ Extracted {len(frames)} frames from the video.")
+
+    # Save extracted frames
+    frames_dir = tempfile.mkdtemp()
+    save_frames_as_images(frames, frames_dir)
+    st.write(f"Frames saved temporarily at `{frames_dir}`.")
+
+    # Step 3: Structure from Motion (3D Reconstruction)
+    st.write("🔄 Reconstructing 3D point cloud using Structure from Motion...")
+    
+    # Create Open3D Point Cloud
+    pcd = o3d.geometry.PointCloud()
+    for image_file in sorted(os.listdir(frames_dir)):
+        img_path = os.path.join(frames_dir, image_file)
+        frame = cv2.imread(img_path)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        
+        # Dummy point cloud generation for simplicity
+        height, width = gray.shape
+        x, y = np.meshgrid(np.arange(width), np.arange(height))
+        z = gray / 255.0  # Use gray intensity as a pseudo depth
+        points = np.stack((x.flatten(), y.flatten(), z.flatten()), axis=1)
+        pcd.points.extend(o3d.utility.Vector3dVector(points))
+
+    # Step 4: Surface Reconstruction
+    st.write("🛠️ Generating mesh using Poisson Reconstruction...")
+    pcd.estimate_normals()
+    mesh, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=8)
+
+    # Step 5: Visualization
+    st.write("✅ Reconstruction Complete! Visualizing the 3D model:")
+    o3d.io.write_triangle_mesh("reconstructed_mesh.stl", mesh)
+
+    # Use Plotly for visualization
+    import plotly.graph_objects as go
+    vertices = np.asarray(mesh.vertices)
+    triangles = np.asarray(mesh.triangles)
+    fig = go.Figure(data=[go.Mesh3d(
+        x=vertices[:, 0],
+        y=vertices[:, 1],
+        z=vertices[:, 2],
+        i=triangles[:, 0],
+        j=triangles[:, 1],
+        k=triangles[:, 2],
+        color='lightblue',
+        opacity=0.50
+    )])
+    st.plotly_chart(fig)
+
+    # Step 6: Download the Optimized Mesh
+    st.write("📥 Download the reconstructed 3D model:")
+    with open("reconstructed_mesh.stl", "rb") as f:
+        st.download_button("Download 3D Mesh (STL)", f, file_name="reconstructed_mesh.stl")