mp4保存

core/inference_client.py

@@ -5,15 +5,15 @@ Manages the server process and handles HTTP communication
 using standard library (avoiding requests dependency).
 """
 
-import subprocess
-import time
 import json
-import urllib.request
-import urllib.error
-import threading
 import os
 import signal
-from typing import Optional, Dict, Any, Tuple
+import subprocess
+import threading
+import time
+import urllib.error
+import urllib.request
+from typing import Any, Dict, Optional, Tuple
 
 
 class InferenceClient:
@@ -46,24 +46,24 @@ class InferenceClient:
             # Load environment variables from .env file if it exists
             env_file = os.path.join(root_dir, ".env")
             if os.path.exists(env_file):
-                with open(env_file, 'r') as f:
+                with open(env_file, "r") as f:
                     for line in f:
                         line = line.strip()
-                        if line and not line.startswith('#') and '=' in line:
-                            key, value = line.split('=', 1)
+                        if line and not line.startswith("#") and "=" in line:
+                            key, value = line.split("=", 1)
                             server_env[key] = value
                 print(f"[FaceMask] Loaded environment from: {env_file}")
 
             # Clean PYTHONPATH to avoid conflicts with Nix Python packages
             # Only include project root to allow local imports
-            server_env['PYTHONPATH'] = root_dir
+            server_env["PYTHONPATH"] = root_dir
 
             # Remove Python-related environment variables that might cause conflicts
             # These can cause venv to import packages from Nix instead of venv
             env_vars_to_remove = [
-                'PYTHONUNBUFFERED',
-                '__PYVENV_LAUNCHER__',  # macOS venv variable
-                'VIRTUAL_ENV',  # Will be set by venv's Python automatically
+                "PYTHONUNBUFFERED",
+                "__PYVENV_LAUNCHER__",  # macOS venv variable
+                "VIRTUAL_ENV",  # Will be set by venv's Python automatically
             ]
             for var in env_vars_to_remove:
                 server_env.pop(var, None)
@@ -73,25 +73,27 @@ class InferenceClient:
             if os.path.isdir(venv_bin):
                 # Build a clean PATH with venv first, then essential system paths
                 # Filter out any Nix Python-specific paths to avoid version conflicts
-                current_path = server_env.get('PATH', '')
-                path_entries = current_path.split(':')
+                current_path = server_env.get("PATH", "")
+                path_entries = current_path.split(":")
 
                 # Filter out Nix Python 3.11 paths
                 filtered_paths = [
-                    p for p in path_entries
-                    if not ('/python3.11/' in p.lower() or '/python3-3.11' in p.lower())
+                    p
+                    for p in path_entries
+                    if not ("/python3.11/" in p.lower() or "/python3-3.11" in p.lower())
                 ]
 
                 # Reconstruct PATH with venv first
-                clean_path = ':'.join([venv_bin] + filtered_paths)
-                server_env['PATH'] = clean_path
+                clean_path = ":".join([venv_bin] + filtered_paths)
+                server_env["PATH"] = clean_path
                 print(f"[FaceMask] Using venv from: {venv_bin}")
 
             # Prepare log file for server output
             import tempfile
+
             log_dir = tempfile.gettempdir()
             self.log_file_path = os.path.join(log_dir, "facemask_server.log")
-            self.log_file = open(self.log_file_path, 'w', buffering=1)  # Line buffered
+            self.log_file = open(self.log_file_path, "w", buffering=1)  # Line buffered
             print(f"[FaceMask] Server log: {self.log_file_path}")
 
             # Start process with 'python' command (will use venv if PATH is set correctly)
@@ -120,12 +122,12 @@ class InferenceClient:
                     try:
                         if self.log_file:
                             self.log_file.close()
-                        with open(self.log_file_path, 'r') as f:
+                        with open(self.log_file_path, "r") as f:
                             log_content = f.read()
                             if log_content.strip():
                                 print("[FaceMask] Server log:")
                                 # Show last 50 lines
-                                lines = log_content.strip().split('\n')
+                                lines = log_content.strip().split("\n")
                                 for line in lines[-50:]:
                                     print(line)
                     except Exception as e:
@@ -143,11 +145,11 @@ class InferenceClient:
             try:
                 if self.log_file:
                     self.log_file.close()
-                with open(self.log_file_path, 'r') as f:
+                with open(self.log_file_path, "r") as f:
                     log_content = f.read()
                     if log_content.strip():
                         print("[FaceMask] Server log (partial):")
-                        lines = log_content.strip().split('\n')
+                        lines = log_content.strip().split("\n")
                         for line in lines[-30:]:
                             print(line)
             except Exception:
@@ -179,7 +181,9 @@ class InferenceClient:
     def is_server_running(self) -> bool:
         """Check if server is responding."""
         try:
-            with urllib.request.urlopen(f"{self.SERVER_URL}/status", timeout=1) as response:
+            with urllib.request.urlopen(
+                f"{self.SERVER_URL}/status", timeout=1
+            ) as response:
                 return response.status == 200
         except (urllib.error.URLError, ConnectionRefusedError, TimeoutError):
             return False
@@ -215,23 +219,25 @@ class InferenceClient:
 
         req = urllib.request.Request(
             f"{self.SERVER_URL}/generate",
-            data=json.dumps(data).encode('utf-8'),
-            headers={'Content-Type': 'application/json'},
-            method='POST'
+            data=json.dumps(data).encode("utf-8"),
+            headers={"Content-Type": "application/json"},
+            method="POST",
         )
 
         try:
             with urllib.request.urlopen(req) as response:
-                result = json.loads(response.read().decode('utf-8'))
-                return result['id']
+                result = json.loads(response.read().decode("utf-8"))
+                return result["id"]
         except urllib.error.HTTPError as e:
             raise RuntimeError(f"Server error: {e.read().decode('utf-8')}")
 
     def get_task_status(self, task_id: str) -> Dict[str, Any]:
         """Get status of a task."""
         try:
-            with urllib.request.urlopen(f"{self.SERVER_URL}/tasks/{task_id}") as response:
-                return json.loads(response.read().decode('utf-8'))
+            with urllib.request.urlopen(
+                f"{self.SERVER_URL}/tasks/{task_id}"
+            ) as response:
+                return json.loads(response.read().decode("utf-8"))
         except urllib.error.HTTPError:
             return {"status": "unknown"}
 
@@ -239,8 +245,7 @@ class InferenceClient:
         """Cancel a task."""
         try:
             req = urllib.request.Request(
-                f"{self.SERVER_URL}/tasks/{task_id}/cancel",
-                method='POST'
+                f"{self.SERVER_URL}/tasks/{task_id}/cancel", method="POST"
             )
             with urllib.request.urlopen(req):
                 pass
@@ -251,6 +256,7 @@ class InferenceClient:
 # Singleton
 _client: Optional[InferenceClient] = None
 
+
 def get_client() -> InferenceClient:
     global _client
     if _client is None:

flake.nix

@@ -48,8 +48,8 @@
             export ROCM_PATH="${pkgs.rocmPackages.clr}"
             export HSA_OVERRIDE_GFX_VERSION="11.0.0"  # RX 7900 (RDNA 3 / gfx1100)
 
-            # LD_LIBRARY_PATH: ROCm、C++標準ライブラリ、その他必要なライブラリ
-            export LD_LIBRARY_PATH="${pkgs.stdenv.cc.cc.lib}/lib:${pkgs.zlib}/lib:${pkgs.zstd.out}/lib:${pkgs.rocmPackages.clr}/lib:${pkgs.rocmPackages.rocm-runtime}/lib:$LD_LIBRARY_PATH"
+            # LD_LIBRARY_PATH: ROCm libraries FIRST (critical for GPU inference)
+            export LD_LIBRARY_PATH="${pkgs.rocmPackages.clr}/lib:${pkgs.rocmPackages.rocm-runtime}/lib:${pkgs.stdenv.cc.cc.lib}/lib:${pkgs.zlib}/lib:${pkgs.zstd.out}/lib:$LD_LIBRARY_PATH"
 
             # venvのセットアップ
             VENV_DIR="$PWD/.venv"
@@ -89,12 +89,11 @@
             export BLENDER_USER_ADDONS="$BLENDER_USER_SCRIPTS/addons"
 
             # 環境変数をファイルに保存（サーバープロセス用）
+            # CRITICAL: ROCm library paths MUST come first for GPU inference
             cat > "$PWD/.env" << EOF
-LD_LIBRARY_PATH=${pkgs.stdenv.cc.cc.lib}/lib:${pkgs.zlib}/lib:${pkgs.zstd.out}/lib:${pkgs.rocmPackages.clr}/lib:${pkgs.rocmPackages.rocm-runtime}/lib
+LD_LIBRARY_PATH=${pkgs.rocmPackages.clr}/lib:${pkgs.rocmPackages.rocm-runtime}/lib:${pkgs.stdenv.cc.cc.lib}/lib:${pkgs.zlib}/lib:${pkgs.zstd.out}/lib
 ROCM_PATH=${pkgs.rocmPackages.clr}
 HSA_OVERRIDE_GFX_VERSION=11.0.0
-PYTORCH_ROCM_ARCH=gfx1100
-ROCBLAS_TENSILE_LIBPATH=${pkgs.rocmPackages.clr}/lib/rocblas/library
 EOF
 
             echo "[Setup] Environment ready with GPU support"

operators/generate_mask.py

@@ -149,20 +149,40 @@ class SEQUENCER_OT_generate_face_mask(Operator):
             expected_frames: Number of frames expected
 
         Returns:
-            True if cache exists and has at least 90% of expected frames
+            True if cache exists and is valid
         """
         if not os.path.exists(cache_dir):
             return False
 
+        # Check for MP4 video (new format)
+        mask_video = os.path.join(cache_dir, "mask.mp4")
+        if os.path.exists(mask_video):
+            # Verify video has expected number of frames
+            import cv2
+            cap = cv2.VideoCapture(mask_video)
+            if cap.isOpened():
+                frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+                cap.release()
+                # Accept cache if at least 90% of frames exist
+                return frame_count >= expected_frames * 0.9
+            cap.release()
+            return False
+
+        # Fallback: check for PNG sequence (backward compatibility)
         mask_files = [f for f in os.listdir(cache_dir)
                       if f.startswith("mask_") and f.endswith(".png")]
 
         # Accept cache if at least 90% of frames exist
-        # (some frames may have been skipped due to read errors)
         return len(mask_files) >= expected_frames * 0.9
     
-    def _add_mask_strip(self, context, source_strip_name: str, mask_dir: str):
-        """Add mask image sequence as a new strip."""
+    def _add_mask_strip(self, context, source_strip_name: str, mask_path: str):
+        """Add mask video as a new strip.
+
+        Args:
+            context: Blender context
+            source_strip_name: Name of the source video strip
+            mask_path: Path to mask video file or directory (for backward compatibility)
+        """
         scene = context.scene
         seq_editor = scene.sequence_editor
 
@@ -174,17 +194,52 @@ class SEQUENCER_OT_generate_face_mask(Operator):
         if not source_strip:
             return
 
-        # Get first mask image
-        mask_files = sorted([
-            f for f in os.listdir(mask_dir)
-            if f.startswith("mask_") and f.endswith(".png")
-        ])
+        # Check if mask_path is a video file or directory (backward compatibility)
+        if os.path.isfile(mask_path):
+            # New format: single MP4 file
+            mask_video = mask_path
+        else:
+            # Old format: directory with PNG sequence (backward compatibility)
+            mask_video = os.path.join(mask_path, "mask.mp4")
+            if not os.path.exists(mask_video):
+                # Fallback to PNG sequence
+                mask_files = sorted([
+                    f for f in os.listdir(mask_path)
+                    if f.startswith("mask_") and f.endswith(".png")
+                ])
+                if not mask_files:
+                    return
+                first_mask = os.path.join(mask_path, mask_files[0])
+                self._add_mask_strip_png_sequence(context, source_strip_name, mask_path, mask_files, first_mask)
+                return
 
-        if not mask_files:
+        # Find an empty channel
+        used_channels = {s.channel for s in seq_editor.strips}
+        new_channel = source_strip.channel + 1
+        while new_channel in used_channels:
+            new_channel += 1
+
+        # Add movie strip (Blender 5.0 API)
+        mask_strip = seq_editor.strips.new_movie(
+            name=f"{source_strip_name}_mask",
+            filepath=mask_video,
+            channel=new_channel,
+            frame_start=source_strip.frame_final_start,
+        )
+
+        # Set blend mode for mask
+        mask_strip.blend_type = 'ALPHA_OVER'
+        mask_strip.blend_alpha = 0.5
+
+    def _add_mask_strip_png_sequence(self, context, source_strip_name, mask_dir, mask_files, first_mask):
+        """Backward compatibility: Add PNG sequence as mask strip."""
+        scene = context.scene
+        seq_editor = scene.sequence_editor
+        source_strip = seq_editor.strips.get(source_strip_name)
+
+        if not source_strip:
             return
 
-        first_mask = os.path.join(mask_dir, mask_files[0])
-        
         # Find an empty channel
         used_channels = {s.channel for s in seq_editor.strips}
         new_channel = source_strip.channel + 1

server/main.py

@@ -8,6 +8,29 @@ GPU-accelerated face detection using ONNX Runtime.
 import os
 import sys
 import platform
+
+# CRITICAL: Fix LD_LIBRARY_PATH before importing cv2 or torch
+# cv2 adds its own lib path to the front, which can override ROCm libraries
+def fix_library_path():
+    """Ensure ROCm libraries are loaded before cv2's bundled libraries."""
+    ld_path = os.environ.get('LD_LIBRARY_PATH', '')
+
+    # Split and filter paths
+    paths = [p for p in ld_path.split(':') if p]
+
+    # Separate ROCm/GPU paths from other paths
+    rocm_paths = [p for p in paths if 'rocm' in p.lower() or 'clr-' in p or 'hip' in p.lower()]
+    other_paths = [p for p in paths if p not in rocm_paths]
+
+    # Rebuild with ROCm paths first
+    if rocm_paths:
+        new_ld_path = ':'.join(rocm_paths + other_paths)
+        os.environ['LD_LIBRARY_PATH'] = new_ld_path
+        print(f"[FaceMask] Fixed LD_LIBRARY_PATH to prioritize ROCm libraries")
+
+# Fix library path BEFORE any other imports
+fix_library_path()
+
 import threading
 import uuid
 import queue
@@ -61,7 +84,11 @@ class GenerateRequest(BaseModel):
     mask_scale: float = 1.5
 
 def process_video_task(task_id: str, req: GenerateRequest):
-    """Background task to process video."""
+    """Background task to process video with async MP4 output."""
+    writer = None
+    write_queue = None
+    writer_thread = None
+
     try:
         tasks[task_id].status = TaskStatus.PROCESSING
         cancel_event = cancel_events.get(task_id)
@@ -78,7 +105,6 @@ def process_video_task(task_id: str, req: GenerateRequest):
             conf_threshold=req.conf_threshold,
             iou_threshold=req.iou_threshold
         )
-        # Ensure model is loaded
         _ = detector.model
 
         # Open video
@@ -88,7 +114,10 @@ def process_video_task(task_id: str, req: GenerateRequest):
             tasks[task_id].message = "Failed to open video"
             return
 
-        # Determine frame range
+        # Get video properties
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
         total_video_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
         end_frame = min(req.end_frame, total_video_frames - 1)
         frames_to_process = end_frame - req.start_frame + 1
@@ -98,7 +127,37 @@ def process_video_task(task_id: str, req: GenerateRequest):
         # Ensure output directory exists
         os.makedirs(req.output_dir, exist_ok=True)
 
-        print(f"Starting processing: {req.video_path} ({frames_to_process} frames)")
+        # Setup MP4 writer (grayscale)
+        output_video_path = os.path.join(req.output_dir, "mask.mp4")
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        writer = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height), isColor=False)
+
+        if not writer.isOpened():
+            tasks[task_id].status = TaskStatus.FAILED
+            tasks[task_id].message = "Failed to create video writer"
+            cap.release()
+            return
+
+        # Async writer setup
+        write_queue = queue.Queue(maxsize=30)  # Buffer up to 30 frames
+        writer_running = threading.Event()
+        writer_running.set()
+
+        def async_writer():
+            """Background thread for writing frames to video."""
+            while writer_running.is_set() or not write_queue.empty():
+                try:
+                    mask = write_queue.get(timeout=0.1)
+                    if mask is not None:
+                        writer.write(mask)
+                    write_queue.task_done()
+                except queue.Empty:
+                    continue
+
+        writer_thread = threading.Thread(target=async_writer, daemon=True)
+        writer_thread.start()
+
+        print(f"Starting processing: {req.video_path} ({frames_to_process} frames) -> {output_video_path}")
 
         # Process loop
         current_count = 0
@@ -123,22 +182,28 @@ def process_video_task(task_id: str, req: GenerateRequest):
                     mask_scale=req.mask_scale
                 )
 
-                # Save
-                mask_filename = f"mask_{current_count:06d}.png" # Note: using relative index for filename
-                mask_path = os.path.join(req.output_dir, mask_filename)
-                cv2.imwrite(mask_path, mask)
+                # Async write to queue
+                write_queue.put(mask)
 
             # Update progress
             current_count += 1
             tasks[task_id].progress = current_count
 
+        # Cleanup
+        writer_running.clear()
+        write_queue.join()  # Wait for all frames to be written
+        if writer_thread:
+            writer_thread.join(timeout=5)
+
         cap.release()
+        if writer:
+            writer.release()
 
         if tasks[task_id].status == TaskStatus.PROCESSING:
             tasks[task_id].status = TaskStatus.COMPLETED
-            tasks[task_id].result_path = req.output_dir
+            tasks[task_id].result_path = output_video_path  # Return video path
             tasks[task_id].message = "Processing completed successfully"
-            print(f"Task {task_id} completed.")
+            print(f"Task {task_id} completed: {output_video_path}")
             
     except Exception as e:
         tasks[task_id].status = TaskStatus.FAILED
@@ -230,12 +295,21 @@ def log_startup_diagnostics():
     for var in rocm_vars:
         value = os.environ.get(var)
         if value:
-            # Truncate very long values
-            if len(value) > 200:
-                display_value = value[:200] + "... (truncated)"
+            # For LD_LIBRARY_PATH, show if ROCm paths are included
+            if var == 'LD_LIBRARY_PATH':
+                has_rocm = 'rocm' in value.lower() or 'clr-' in value
+                has_hip = 'hip' in value.lower()
+                print(f"  {var}: {value[:100]}...")
+                print(f"    Contains ROCm paths: {has_rocm}")
+                print(f"    Contains HIP paths: {has_hip}")
+                if not has_rocm:
+                    print(f"    ⚠️  WARNING: ROCm library paths not found!")
             else:
-                display_value = value
-            print(f"  {var}: {display_value}")
+                if len(value) > 200:
+                    display_value = value[:200] + "... (truncated)"
+                else:
+                    display_value = value
+                print(f"  {var}: {display_value}")
         else:
             print(f"  {var}: (not set)")