Update app.py

app.py

@@ -1,93 +1,79 @@
 import streamlit as st
-import cv2
 import open3d as o3d
 import numpy as np
 import tempfile
 import os
+from PIL import Image
 
 # Title of the App
-st.title("3D Reconstruction Tool from Video 📹 → 🛠️ → 🧊")
+st.title("3D Reconstruction Tool from Images 📷 → 🛠️ → 🧊")
 
 # 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.
+Upload multiple images of an object taken from different angles.  
+The app will reconstruct a 3D point cloud, generate a surface mesh, and allow you to visualize and download the 3D model.
 """)
 
-# 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
+# Step 1: Upload Images
+uploaded_images = st.file_uploader(
+    "Upload Multiple Images (PNG, JPG, JPEG)", 
+    type=["png", "jpg", "jpeg"], 
+    accept_multiple_files=True
+)
+
+# Check if images are uploaded
+if uploaded_images:
+    st.write(f"✅ {len(uploaded_images)} images uploaded successfully!")
+
+    # Step 2: Process Uploaded Images
+    st.write("🔄 Generating 3D Point Cloud from the images...")
+
+    # Save uploaded images temporarily
+    images_dir = tempfile.mkdtemp()
+    image_paths = []
+    for idx, uploaded_file in enumerate(uploaded_images):
+        image_path = os.path.join(images_dir, f"image_{idx:04d}.png")
+        image = Image.open(uploaded_file)
+        image.save(image_path)
+        image_paths.append(image_path)
+
+    # Step 3: Generate Point Cloud (Simplified Approach)
+    # Combine grayscale data into a pseudo-3D 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
+
+    for image_path in image_paths:
+        # Load image and convert to grayscale
+        img = np.array(Image.open(image_path).convert("L"))
+        height, width = img.shape
+
+        # Generate x, y, z coordinates (z = intensity as pseudo-depth)
         x, y = np.meshgrid(np.arange(width), np.arange(height))
-        z = gray / 255.0  # Use gray intensity as a pseudo depth
+        z = img / 255.0  # Normalize grayscale intensity to pseudo-depth
         points = np.stack((x.flatten(), y.flatten(), z.flatten()), axis=1)
+
+        # Add points to the point cloud
         pcd.points.extend(o3d.utility.Vector3dVector(points))
 
-    # Step 4: Surface Reconstruction
-    st.write("🛠️ Generating mesh using Poisson Reconstruction...")
+    # Step 4: Surface Reconstruction using Poisson Reconstruction
+    st.write("🛠️ Generating mesh using Poisson Surface Reconstruction...")
+
+    # Estimate normals for the point cloud
     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
+    # Save the mesh temporarily for visualization
+    output_mesh_path = "reconstructed_mesh.stl"
+    o3d.io.write_triangle_mesh(output_mesh_path, mesh)
+
+    # Use Plotly to visualize the 3D mesh
     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],
@@ -102,5 +88,5 @@ if uploaded_file:
 
     # 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")
+    with open(output_mesh_path, "rb") as f:
+        st.download_button("Download 3D Mesh (STL)", f, file_name="3D_Model.stl")