import streamlit as st import subprocess import sys import os import time import shutil import tempfile import threading import queue from datetime import datetime from pathlib import Path def run_command(command, working_dir, progress_bar, progress_text, step_start_progress, step_weight, show_progress=True): try: env = os.environ.copy() env["PYTHONUNBUFFERED"] = "1" process = subprocess.Popen( command, cwd=working_dir, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1, universal_newlines=True, env=env ) stdout_queue = queue.Queue() stderr_queue = queue.Queue() def read_output(pipe, q, source): for line in iter(pipe.readline, ''): q.put((source, line)) pipe.close() stdout_thread = threading.Thread(target=read_output, args=(process.stdout, stdout_queue, 'stdout')) stderr_thread = threading.Thread(target=read_output, args=(process.stderr, stderr_queue, 'stderr')) stdout_thread.daemon = True stderr_thread.daemon = True stdout_thread.start() stderr_thread.start() total_progress = step_start_progress stderr_lines = [] while process.poll() is None or not (stdout_queue.empty() and stderr_queue.empty()): try: source, line = next((q.get_nowait() for q in [stdout_queue, stderr_queue] if not q.empty()), (None, None)) if line: if source == 'stdout': if show_progress and line.startswith("PROGRESS:"): try: progress_str = line.strip().split("PROGRESS:")[1].replace("%", "") # Remove '%' progress = float(progress_str) # Convert to float after removing '%' # Debug output if Path(command[1]).name == 'gen_skes.py': if progress <= 100.0: # 2D Keypoint generation (0-100% maps to 0-60%) adjusted_progress = step_start_progress + (progress / 100.0 * 0.6) else: # 3D Pose generation (100-200% maps to 60-80%) adjusted_progress = step_start_progress + 0.6 + ((progress - 100.0) / 100.0 * 0.2) total_progress = min(adjusted_progress, step_start_progress + step_weight) else: # For conver_bvh.py or others with 0-100% progress adjusted_progress = step_start_progress + (progress / 100.0 * step_weight) total_progress = min(adjusted_progress, step_start_progress + step_weight) progress_bar.progress(total_progress) progress_text.text(f"Progress: {int(total_progress * 100)}%") except ValueError as e: print(f"DEBUG: Error parsing progress: {e}") pass elif source == 'stderr': stderr_lines.append(line.strip()) except queue.Empty: time.sleep(0.01) stdout_thread.join() stderr_thread.join() if process.returncode != 0: stderr_output = '\n'.join(stderr_lines) st.error(f"Error in {Path(command[1]).name}:\n{stderr_output}") return False if show_progress: progress_bar.progress(step_start_progress + step_weight) progress_text.text(f"Progress: {int((step_start_progress + step_weight) * 100)}%") return True except Exception as e: st.error(f"Exception in {Path(command[1]).name}: {str(e)}") return False def cleanup_output_folder(output_dir, delay=1800): time.sleep(delay) if os.path.exists(output_dir): shutil.rmtree(output_dir, ignore_errors=True) print(f"Deleted temporary output folder after timeout: {output_dir}") def process_video(video_file): base_dir = Path(__file__).parent.resolve() gen_skes_path = base_dir / "VideoToNPZ" / "gen_skes.py" convert_obj_path = base_dir / "convertNPZtoBVH" / "conver_obj.py" convert_bvh_path = base_dir / "convertNPZtoBVH" / "conver_bvh.py" for script_path in [gen_skes_path, convert_obj_path, convert_bvh_path]: if not script_path.exists(): st.error(f"Required script not found: {script_path}") return None with tempfile.TemporaryDirectory() as tmp_dir: video_path = Path(tmp_dir) / "input_video.mp4" with open(video_path, "wb") as f: f.write(video_file.read()) if not video_path.exists(): st.error(f"Video file not found at: {video_path}") return None timestamp = datetime.now().strftime("%Y%m%d%H%M%S") output_dir = base_dir / f"outputs_{timestamp}" output_dir.mkdir(exist_ok=True) if not os.access(output_dir, os.W_OK): st.error(f"Cannot write to output directory: {output_dir}") return None default_output_dir = base_dir / "outputs" pipeline_steps = [ {"command": [sys.executable, str(gen_skes_path), "-v", str(video_path)], "working_dir": gen_skes_path.parent, "weight": 0.8, "show_progress": True}, {"command": [sys.executable, str(convert_obj_path), "--output-dir", str(output_dir)], "working_dir": convert_obj_path.parent, "weight": 0.0, "show_progress": False}, {"command": [sys.executable, str(convert_bvh_path), "--output-dir", str(output_dir)], "working_dir": convert_bvh_path.parent, "weight": 0.2, "show_progress": True} ] progress_bar = st.progress(0.0) progress_text = st.empty() total_progress = 0.0 for i, step in enumerate(pipeline_steps): success = run_command( step["command"], step["working_dir"], progress_bar, progress_text, total_progress, step["weight"], show_progress=step["show_progress"] ) if not success: st.error(f"Failed at step: {' '.join(map(str, step['command']))}") if default_output_dir.exists(): shutil.rmtree(default_output_dir, ignore_errors=True) return None if i == 0 and default_output_dir.exists(): npz_dir = default_output_dir / "npz" if npz_dir.exists(): target_npz_dir = output_dir / "npz" shutil.move(str(npz_dir), str(target_npz_dir)) if default_output_dir.exists(): shutil.rmtree(default_output_dir, ignore_errors=True) total_progress += step["weight"] if step["show_progress"]: progress_bar.progress(min(total_progress, 1.0)) progress_text.text(f"Progress: {int(total_progress * 100)}%") bvh_output_dir = output_dir / "bvh" bvh_file = bvh_output_dir / "output.bvh" if bvh_file.exists(): cleanup_thread = threading.Thread(target=cleanup_output_folder, args=(output_dir,)) cleanup_thread.daemon = True cleanup_thread.start() return { 'bvh_file': bvh_file, 'output_dir': output_dir } else: st.error(f"Failed to generate BVH file at: {bvh_file}") if default_output_dir.exists(): shutil.rmtree(default_output_dir, ignore_errors=True) return None def cleanup_immediate(output_dir): if output_dir and os.path.exists(output_dir): shutil.rmtree(output_dir, ignore_errors=True) st.success("Output folder cleaned up successfully.") else: st.warning("No output folder to clean up.") def main(): st.set_page_config( page_title="Motion Capture Studio | Video to BVH Converter", page_icon="🎬", layout="wide", initial_sidebar_state="collapsed" ) st.markdown(""" """, unsafe_allow_html=True) # Header st.markdown('

Motion Capture Studio

', unsafe_allow_html=True) st.markdown('

Transform your videos into professional BVH motion files with cutting-edge AI

', unsafe_allow_html=True) # Main content st.markdown('
', unsafe_allow_html=True) col1, col2 = st.columns([1, 1], gap="medium") with col1: st.markdown('

Upload Your Video

', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) uploaded_file = st.file_uploader( "Drop your video here or click to browse", type=['mp4', 'avi', 'mov'], help="For best results, use clear full-body motion videos with good lighting", key="file_uploader" ) if uploaded_file: st.session_state['uploaded_file'] = uploaded_file st.markdown('
', unsafe_allow_html=True) if not st.session_state.get('uploaded_file'): st.markdown("""
💡 Pro Tip: Use MP4, AVI, or MOV files with clear, well-lit full-body motion
""", unsafe_allow_html=True) with col2: st.markdown('

Video Preview

', unsafe_allow_html=True) if uploaded_file := st.session_state.get('uploaded_file', None): st.markdown('
', unsafe_allow_html=True) st.video(uploaded_file) st.markdown('
', unsafe_allow_html=True) else: st.markdown("""
Your video preview will appear here
""", unsafe_allow_html=True) # Control Section (Button and Progress Bar) if st.session_state.get('uploaded_file'): st.markdown('
', unsafe_allow_html=True) if st.button("âš¡ Start Motion Capture", key="convert_btn"): with st.spinner("Processing your video..."): progress_bar = st.progress(0.0) progress_text = st.empty() st.markdown('
🔄 Analyzing motion patterns...
', unsafe_allow_html=True) result = process_video(st.session_state['uploaded_file']) if result: st.markdown('
✅ Motion capture complete!
', unsafe_allow_html=True) with open(result['bvh_file'], "rb") as f: st.download_button( label="📥 Download BVH File", data=f, file_name="motion_capture.bvh", mime="application/octet-stream", on_click=cleanup_immediate, args=(result['output_dir'],), key="download_btn" ) st.markdown('
', unsafe_allow_html=True) # Pipeline Info st.markdown('
', unsafe_allow_html=True) st.markdown('

Processing Pipeline

', unsafe_allow_html=True) st.markdown("""
1
Pose Estimation
AI detects and tracks human movements frame-by-frame
2
3D Conversion
Converts 2D poses into 3D spatial data
3
BVH Generation
Formats motion data into industry-standard BVH files
""", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) # Footer st.markdown("""
© 2025 Motion Capture Studio | Powered by Streamlit & Advanced AI Technology
""", unsafe_allow_html=True) if __name__ == "__main__": main()