fix: code review fixes - enumerate bug, dead params, output type, unused imports
- Fix .index() on numpy arrays in v3 individual face loop -> enumerate() - Remove dead classifier_type param from detect_faces() (cascade already selected via CascadeCache) - Change v1 RETURN_TYPES for face_metadata from IMAGE to FLOAT (is [B,6] bbox data, not image) - Improve v3 face_metadata tooltip: NOT an image warning - Remove unused import torch.nn.functional as F - Remove unused import StringInput - Replace _tensor_to_numpy() with _get_batch_dims() - removed dead img_np return
This commit is contained in:
limbicnation
+13
-34
@@ -36,7 +36,6 @@ from typing import ClassVar, Optional
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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logger = logging.getLogger(__name__)
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_log_level = os.getenv("COMFYUI_FACE_DETECTION_LOG_LEVEL", "INFO").upper()
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@@ -49,7 +48,7 @@ try:
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from comfy_api.v0_0_3_io import (
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ComfyNode, Schema, InputBehavior, NumberDisplay,
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IntegerInput, FloatInput, ImageInput, ImageOutput,
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ComboInput, StringInput, NodeOutput,
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ComboInput, NodeOutput,
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)
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COMFY_V3: bool = True
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except ImportError:
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@@ -171,7 +170,6 @@ def detect_faces(
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min_face_size: int = 64,
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detection_threshold: float = 0.8,
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auto_padding_ratio: float = 0.35,
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classifier_type: str = "default",
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detect_all: bool = False,
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) -> list[tuple[int, int, int, int]]:
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"""
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@@ -180,11 +178,10 @@ def detect_faces(
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Args:
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image_np: RGB image as uint8 numpy array (H, W, 3)
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cascade: OpenCV CascadeClassifier instance
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cascade: OpenCV CascadeClassifier instance (selected via CascadeCache.get() before calling)
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min_face_size: Minimum face dimension in pixels
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detection_threshold: Scale factor for detectMultiScale (≈ confidence)
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auto_padding_ratio: Padding as fraction of face size (0.35 = 35% face-size padding)
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classifier_type: "default" or "alternative"
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detect_all: If True, return ALL faces. If False, return only the largest.
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"""
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gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
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@@ -355,7 +352,7 @@ if COMFY_V3:
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tooltip="Batch of cropped face tensors [B, H, W, C]"),
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ImageOutput("face_metadata",
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display_name="Face Metadata (BBoxes)",
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tooltip="Face bounding box coordinates per batch item [B, 6] — x, y, w, h, score, detected"),
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tooltip="Per-face [x, y, w, h, score, detected] normalized to image dims. Shape: [B, 6]. NOT an image — use for downstream bbox logic only."),
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],
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is_output_node=True,
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)
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@@ -384,29 +381,13 @@ if COMFY_V3:
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return _DEFAULT_FACE_OUTPUT_FORMAT
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@classmethod
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def _tensor_to_numpy(cls, image: torch.Tensor) -> tuple[np.ndarray, int, int, int]:
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"""Convert ComfyUI tensor [B,H,W,C] float [0,1] → numpy [H,W,C] uint8 RGB."""
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if image.is_cuda:
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image = image.cpu()
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def _get_batch_dims(cls, image: torch.Tensor) -> tuple[int, int, int]:
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"""Get (B, H, W) from a ComfyUI image tensor, handling 3D→4D."""
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if image.dim() == 3:
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image = image.unsqueeze(0)
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B, H, W, C = image.shape
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# Normalize
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if image.max() <= 1.0:
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image = (image * 255).round()
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image = image.clip(0, 255).to(torch.uint8)
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# Take first item — full batch handled in execute()
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img_np = image[0].numpy()
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# Ensure RGB
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if C == 1:
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img_np = np.repeat(img_np, 3, axis=2)
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elif C == 4:
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img_np = img_np[:, :, :3]
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elif C > 4:
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img_np = img_np[:, :, :3]
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return img_np, B, H, W
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B, H, W = 1, image.shape[0], image.shape[1]
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else:
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B, H, W = image.shape[:3]
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return B, H, W
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@classmethod
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async def execute(
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@@ -452,7 +433,7 @@ if COMFY_V3:
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temporal = cls._get_temporal(instance_id, temporal_smoothing)
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smoothing_active = temporal_smoothing > 0
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img_np, B, H, W = cls._tensor_to_numpy(image)
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B, H, W = cls._get_batch_dims(image)
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# Process entire batch
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cropped_list: list[torch.Tensor] = []
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@@ -485,7 +466,6 @@ if COMFY_V3:
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min_face_size=min_face_size,
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detection_threshold=detection_threshold,
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auto_padding_ratio=pad_ratio,
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classifier_type=classifier_type,
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detect_all=detect_all,
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)
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@@ -523,7 +503,7 @@ if COMFY_V3:
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# Handle face_output_format
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if detect_all and len(frame_crops) > 1 and resolved_format == "individual":
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# Individual: each face as separate batch item
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for crop_np in frame_crops:
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for idx, crop_np in enumerate(frame_crops):
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crop_t = (
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torch.from_numpy(crop_np)
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.float()
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@@ -532,7 +512,7 @@ if COMFY_V3:
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)
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cropped_list.append(crop_t)
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# Metadata for each face
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bx, by, bw, bh = smoothed_bboxes[frame_crops.index(crop_np)] if crop_np in frame_crops else (0, 0, 0, 0)
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bx, by, bw, bh = smoothed_bboxes[idx]
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norm_meta = torch.tensor([[
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bx / max(W, 1), by / max(H, 1),
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bw / max(W, 1), bh / max(H, 1),
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@@ -668,7 +648,7 @@ class FaceDetectionNodeV1:
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},
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}
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RETURN_TYPES = ("IMAGE", "IMAGE")
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RETURN_TYPES = ("IMAGE", "FLOAT")
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RETURN_NAMES = ("cropped_faces", "face_metadata")
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FUNCTION = "detect_and_crop_faces"
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CATEGORY = "image/processing"
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@@ -775,7 +755,6 @@ class FaceDetectionNodeV1:
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min_face_size=min_face_size,
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detection_threshold=detection_threshold,
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auto_padding_ratio=pad_ratio,
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classifier_type=classifier_type,
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detect_all=detect_all,
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)
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