Merge pull request #1845 from maxwbuckley/ruff-code-health

Add ruff CI gate and fix deterministic lint issues

.github/workflows/ruff.yml

@@ -0,0 +1,16 @@
+name: ruff
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v4.0.0
+        with:
+          version: "0.15.7"
+          args: "check --output-format=github"

README.md

@@ -30,7 +30,7 @@ By using this software, you agree to these terms and commit to using it in a man
 
 Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
 
-## Exclusive v2.7 beta Quick Start - Pre-built (Windows/Mac Silicon/CPU)
+## Exclusive v2.7 RC2 Quick Start - Pre-built (Windows/Mac Silicon/CPU)
 
   <a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
 

benchmark_pipeline.py

@@ -14,7 +14,6 @@ if sys.platform == "win32":
 
 import insightface
 from insightface.app import FaceAnalysis
-from insightface.utils import face_align
 from modules.processors.frame.face_swapper import _fast_paste_back
 from modules import platform_info
 
@@ -81,10 +80,14 @@ def capture_thread():
         try:
             capture_queue.put_nowait(frame)
         except queue.Full:
-            try: capture_queue.get_nowait()
-            except queue.Empty: pass
-            try: capture_queue.put_nowait(frame)
-            except queue.Full: pass
+            try:
+                capture_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                capture_queue.put_nowait(frame)
+            except queue.Full:
+                pass
 
 cap_t = threading.Thread(target=capture_thread, daemon=True)
 cap_t.start()

modules/__init__.py

@@ -11,8 +11,8 @@ def imwrite_unicode(path, img, params=None):
     root, ext = os.path.splitext(path)
     if not ext:
         ext = ".png"
-        result, encoded_img = cv2.imencode(ext, img, params if params else [])
-        result, encoded_img = cv2.imencode(f".{ext}", img, params if params is not None else [])
-        encoded_img.tofile(path)
-        return True
-    return False
+    result, encoded_img = cv2.imencode(ext, img, params if params is not None else [])
+    if not result:
+        return False
+    encoded_img.tofile(path)
+    return True

modules/cluster_analysis.py

@@ -1,6 +1,5 @@
 import numpy as np
 from sklearn.cluster import KMeans
-from sklearn.metrics import silhouette_score
 from typing import Any
 
 

modules/core.py

@@ -171,8 +171,6 @@ def limit_resources() -> None:
     # limit memory usage
     if modules.globals.max_memory:
         memory = modules.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = modules.globals.max_memory * 1024 ** 6
         if platform.system().lower() == 'windows':
             import ctypes
             kernel32 = ctypes.windll.kernel32
@@ -324,7 +322,8 @@ def start() -> None:
 def destroy(to_quit=True) -> None:
     if modules.globals.target_path:
         clean_temp(modules.globals.target_path)
-    if to_quit: quit()
+    if to_quit:
+        quit()
 
 
 def run() -> None:

modules/face_analyser.py

@@ -5,7 +5,6 @@ import insightface
 import threading
 
 import cv2
-import numpy as np
 import modules.globals
 from tqdm import tqdm
 from modules.typing import Frame
@@ -257,6 +256,8 @@ def get_unique_faces_from_target_image() -> Any:
         modules.globals.source_target_map = []
         target_frame = cv2.imread(modules.globals.target_path)
         many_faces = get_many_faces(target_frame)
+        if many_faces is None:
+            return None
         i = 0
 
         for face in many_faces:
@@ -291,6 +292,8 @@ def get_unique_faces_from_target_video() -> Any:
         for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
             temp_frame = cv2.imread(temp_frame_path)
             many_faces = get_many_faces(temp_frame)
+            if many_faces is None:
+                continue
 
             for face in many_faces:
                 face_embeddings.append(face.normed_embedding)

modules/gpu_processing.py

@@ -21,7 +21,7 @@ from __future__ import annotations
 import os
 import cv2
 import numpy as np
-from typing import Tuple, Optional
+from typing import Tuple
 
 # ---------------------------------------------------------------------------
 # CUDA availability detection (evaluated once at import time)

modules/onnx_optimize.py

@@ -392,7 +392,7 @@ def _decompose_split(model) -> bool:
 
     # Collect all needed boundary constants
     for _, (a, b) in splits:
-        ensure_const(f"_sp_s0", [0])
+        ensure_const("_sp_s0", [0])
         ensure_const(f"_sp_s{a}", [a])
         ensure_const(f"_sp_s{a + b}", [a + b])
 

modules/processors/frame/core.py

@@ -37,6 +37,7 @@ def load_frame_processor_module(frame_processor: str) -> Any:
         frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
         for method_name in FRAME_PROCESSORS_INTERFACE:
             if not hasattr(frame_processor_module, method_name):
+                print(f"Frame processor {frame_processor} is missing required method {method_name}")
                 sys.exit()
     except ImportError:
         print(f"Frame processor {frame_processor} not found")
@@ -59,7 +60,7 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
     current_processor_names = [proc.__name__.split('.')[-1] for proc in FRAME_PROCESSORS_MODULES]
 
     for frame_processor, state in modules.globals.fp_ui.items():
-        if state == True and frame_processor not in current_processor_names:
+        if state and frame_processor not in current_processor_names:
             try:
                 frame_processor_module = load_frame_processor_module(frame_processor)
                 FRAME_PROCESSORS_MODULES.append(frame_processor_module)
@@ -70,7 +71,7 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
             except Exception as e:
                  print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
 
-        elif state == False and frame_processor in current_processor_names:
+        elif not state and frame_processor in current_processor_names:
             try:
                 module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
                 if module_to_remove:

modules/processors/frame/face_enhancer.py

@@ -11,7 +11,7 @@ import onnxruntime
 import modules.globals
 import modules.processors.frame.core
 from modules.core import update_status
-from modules.face_analyser import get_one_face, get_many_faces
+from modules.face_analyser import get_many_faces
 from modules.typing import Frame, Face
 from modules.utilities import (
     is_image,

modules/processors/frame/face_enhancer_gpen256.py

@@ -5,7 +5,6 @@ import os
 import threading
 
 import cv2
-import numpy as np
 
 import modules.globals
 import modules.processors.frame.core

modules/processors/frame/face_enhancer_gpen512.py

@@ -5,7 +5,6 @@ import os
 import threading
 
 import cv2
-import numpy as np
 
 import modules.globals
 import modules.processors.frame.core

modules/processors/frame/face_masking.py

@@ -2,7 +2,7 @@ import cv2
 import numpy as np
 from modules.typing import Face, Frame
 import modules.globals
-from modules.gpu_processing import gpu_gaussian_blur, gpu_resize, gpu_cvt_color
+from modules.gpu_processing import gpu_gaussian_blur, gpu_resize
 
 def apply_color_transfer(source, target):
     """

modules/processors/frame/face_swapper.py

@@ -16,7 +16,7 @@ from modules.utilities import (
     is_video,
 )
 from modules.cluster_analysis import find_closest_centroid
-from modules.gpu_processing import gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted, gpu_resize, gpu_cvt_color
+from modules.gpu_processing import gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted, gpu_resize
 import os
 from collections import deque
 import time
@@ -680,7 +680,8 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
                 continue
 
             face_region = processed_frame[y1:y2, x1:x2]
-            if face_region.size == 0: continue
+            if face_region.size == 0:
+                continue
 
             # Apply sharpening (GPU-accelerated when CUDA OpenCV is available)
             try:
@@ -815,9 +816,11 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
             else: # Single face or specific mapping
                  for map_data in source_target_map:
                     source_info = map_data.get("source", {})
-                    if not source_info: continue # Skip if no source info
+                    if not source_info:
+                        continue # Skip if no source info
                     source_face = source_info.get("face")
-                    if not source_face: continue # Skip if no source defined for this map entry
+                    if not source_face:
+                        continue # Skip if no source defined for this map entry
 
                     if is_image(modules.globals.target_path):
                         target_info = map_data.get("target", {})
@@ -854,7 +857,8 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                      if len(detected_faces) <= len(target_embeddings):
                           # More targets defined than detected - match each detected face
                           for detected_face in detected_faces:
-                              if detected_face.normed_embedding is None: continue
+                              if detected_face.normed_embedding is None:
+                                  continue
                               closest_idx, _ = find_closest_centroid(target_embeddings, detected_face.normed_embedding)
                               if 0 <= closest_idx < len(source_faces):
                                   source_target_pairs.append((source_faces[closest_idx], detected_face))
@@ -862,7 +866,8 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                           # More faces detected than targets defined - match each target embedding to closest detected face
                           detected_embeddings = [f.normed_embedding for f in detected_faces if f.normed_embedding is not None]
                           detected_faces_with_embedding = [f for f in detected_faces if f.normed_embedding is not None]
-                          if not detected_embeddings: return processed_frame # No embeddings to match
+                          if not detected_embeddings:
+                              return processed_frame # No embeddings to match
 
                           for i, target_embedding in enumerate(target_embeddings):
                               if 0 <= i < len(source_faces): # Ensure source face exists for this embedding
@@ -936,7 +941,7 @@ def process_frames(
 
     # --- Stop processing entirely if in Simple Mode and source face is invalid ---
     if not use_v2 and source_face is None:
-        update_status(f"Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
+        update_status("Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
         if progress:
             # Ensure the progress bar completes if it was started
             remaining_updates = total_frames - progress.n if hasattr(progress, 'n') else total_frames
@@ -955,11 +960,13 @@ def process_frames(
             temp_frame = cv2.imread(temp_frame_path)
             if temp_frame is None:
                 print(f"{NAME}: Error: Could not read frame: {temp_frame_path}, skipping.")
-                if progress: progress.update(1)
+                if progress:
+                    progress.update(1)
                 continue # Skip this frame if read fails
         except Exception as read_e:
             print(f"{NAME}: Error reading frame {temp_frame_path}: {read_e}, skipping.")
-            if progress: progress.update(1)
+            if progress:
+                progress.update(1)
             continue
 
         # Select processing function and execute
@@ -1496,7 +1503,8 @@ def apply_color_transfer(source, target):
             if len(source.shape) == 2: # Grayscale
                 source = cv2.cvtColor(source, cv2.COLOR_GRAY2BGR)
             source = np.clip(source, 0, 255).astype(np.uint8)
-            if len(source.shape)!= 3 or source.shape[2]!= 3: raise ValueError("Conversion failed")
+            if len(source.shape) != 3 or source.shape[2] != 3:
+                raise ValueError("Conversion failed")
         except Exception:
             return source
     if len(target.shape) != 3 or target.shape[2] != 3 or target.dtype != np.uint8:
@@ -1505,7 +1513,8 @@ def apply_color_transfer(source, target):
             if len(target.shape) == 2: # Grayscale
                 target = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
             target = np.clip(target, 0, 255).astype(np.uint8)
-            if len(target.shape)!= 3 or target.shape[2]!= 3: raise ValueError("Conversion failed")
+            if len(target.shape) != 3 or target.shape[2] != 3:
+                raise ValueError("Conversion failed")
         except Exception:
              return source # Return original source if target invalid
 

modules/run.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
-# Import the tkinter fix to patch the ScreenChanged error
-import tkinter_fix
+# Import the tkinter fix to patch the ScreenChanged error (module patches Tk on import)
+import tkinter_fix  # noqa: F401
 
 import core
 

modules/video_capture.py

@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-import sys
 import time
 from typing import Optional, Tuple, Callable
 import platform

pyproject.toml

@@ -0,0 +1,9 @@
+[tool.ruff]
+target-version = "py310"
+
+[tool.ruff.lint]
+# Deterministic, low-risk rules enforced in CI. Other rules (F841, E402, F821)
+# surface real findings but require human judgement to fix safely, so they are
+# left out of the gate for now. Intentional side-effect imports should be
+# annotated with `# noqa: F401`.
+select = ["E701", "E711", "E712", "F401", "F541"]