PERF 重构项目，优化命名和结构

FIX 补齐requirements
FIX 剔除硬编码

nodes/image_face_nodes.py

@@ -0,0 +1,152 @@
+import json
+import os
+
+import cv2
+import numpy as np
+import torch
+from comfy import model_management
+from ultralytics import YOLO
+
+from ..utils.face_occu_detect import face_occu_detect
+
+
+class FaceDetect:
+    """
+    人脸遮挡检测
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "main_seed": (
+                    "INT:seed",
+                    {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
+                ),
+                "model": (["convnext_tiny", "convnext_base"],),
+                "length": ("INT", {"default": 10, "min": 3, "max": 60, "step": 1}),
+                "threshold": (
+                    "FLOAT",
+                    {"default": 94, "min": 55, "max": 99, "step": 0.1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = (
+        "IMAGE",
+        "IMAGE",
+        "STRING",
+        "STRING",
+        "STRING",
+        "STRING",
+        "STRING",
+        "INT",
+        "INT",
+    )
+    RETURN_NAMES = (
+        "图像",
+        "选中人脸",
+        "分类",
+        "概率",
+        "采用帧序号",
+        "全部帧序列",
+        "剪辑配置",
+        "起始帧序号",
+        "帧数量",
+    )
+
+    FUNCTION = "predict"
+
+    CATEGORY = "不忘科技-自定义节点🚩/图片/人脸"
+
+    def predict(self, image, main_seed, model, length, threshold):
+        image, image_selected, cls, prob, nums, period = face_occu_detect(
+            image, length=length, thres=threshold, model_name=model
+        )
+        print("全部帧序列", period)
+        if len(period) > 0:
+            start, end = period[main_seed % len(period)]
+            config = {"start": start, "end": end}
+        else:
+            raise RuntimeError("未找到符合要求的视频片段")
+        return (
+            image,
+            image_selected,
+            cls,
+            prob,
+            nums,
+            str(period),
+            json.dumps(config),
+            start,
+            end - start + 1,
+        )
+
+
+class FaceExtract:
+    """人脸提取 By YOLO"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("图片",)
+
+    FUNCTION = "crop"
+
+    CATEGORY = "不忘科技-自定义节点🚩/图片/人脸"
+
+    def crop(self, image):
+        device = model_management.get_torch_device()
+        image_np = 255.0 * image.cpu().numpy()
+        model = YOLO(
+            model=os.path.join(
+                os.path.dirname(os.path.abspath(__file__)),
+                "model",
+                "yolov8n-face-lindevs.pt",
+            )
+        )
+        total_images = image_np.shape[0]
+        out_images = np.ndarray(shape=(total_images, 512, 512, 3))
+        print("shape", image_np.shape)
+        idx = 0
+        for image_item in image_np:
+            results = model.predict(
+                image_item, imgsz=640, conf=0.75, iou=0.7, device=device, verbose=False
+            )
+            n = 512
+            r = results[0]
+            if len(r.boxes.data.cpu().numpy()) == 1:
+                y1, x1, y2, x2, p, cls = r.boxes.data.cpu().numpy()[0]
+                face_size = int(max(y2 - y1, x2 - x1))
+                center = (x1 + x2) // 2, (y1 + y2) // 2
+                x1, x2, y1, y2 = (
+                    center[0] - face_size // 2,
+                    center[0] + face_size // 2,
+                    center[1] - face_size // 2,
+                    center[1] + face_size // 2,
+                )
+                template = np.ndarray(shape=(face_size, face_size, 3))
+                template.fill(20)
+                for a, a1 in zip(list(range(int(x1), int(x2))), list(range(face_size))):
+                    for b, b1 in zip(
+                            list(range(int(y1), int(y2))), list(range(face_size))
+                    ):
+                        if (a >= 0 and a < r.orig_img.shape[0]) and (
+                                b >= 0 and b < r.orig_img.shape[1]
+                        ):
+                            template[a1][b1] = r.orig_img[a][b]
+                print(int(x1), int(x2), int(y1), int(y2))
+                img = cv2.resize(template, (n, n))
+                out_images[idx] = img
+                idx += 1
+            else:
+                idx += 1
+        cropped_face = np.array(out_images).astype(np.float32) / 255.0
+        cropped_face = torch.from_numpy(cropped_face)
+        return (cropped_face,)