368 lines
14 KiB
Python
368 lines
14 KiB
Python
import glob
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import json
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import os
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import shutil
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import traceback
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import uuid
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from datetime import datetime
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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 yaml
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from ultralytics import YOLO
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from comfy import model_management
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from qcloud_cos import CosConfig, CosClientError, CosServiceError
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from qcloud_cos import CosS3Client
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from .test_single_image import test_node
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import ffmpy
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video_extensions = ['webm', 'mp4', 'mkv', 'gif', 'mov']
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class FaceDetect:
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"""
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A example node
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Class methods
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-------------
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INPUT_TYPES (dict):
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Tell the main program input parameters of nodes.
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IS_CHANGED:
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optional method to control when the node is re executed.
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Attributes
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----------
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RETURN_TYPES (`tuple`):
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The type of each element in the output tuple.
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RETURN_NAMES (`tuple`):
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Optional: The name of each output in the output tuple.
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FUNCTION (`str`):
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The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
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OUTPUT_NODE ([`bool`]):
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If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
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The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
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Assumed to be False if not present.
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CATEGORY (`str`):
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The category the node should appear in the UI.
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DEPRECATED (`bool`):
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Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain
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functional in existing workflows that use them.
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EXPERIMENTAL (`bool`):
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Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to
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significant changes or removal in future versions. Use with caution in production workflows.
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execute(s) -> tuple || None:
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The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
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For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
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"""
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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"""
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Return a dictionary which contains config for all input fields.
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Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
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The type can be a list for selection.
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Returns: `dict`:
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- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
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- Value input_fields (`dict`): Contains input fields config:
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* Key field_name (`string`): Name of a entry-point method's argument
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* Value field_config (`tuple`):
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+ First value is a string indicate the type of field or a list for selection.
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+ Second value is a config for type "INT", "STRING" or "FLOAT".
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"""
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return {
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"required": {
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"image": ("IMAGE",),
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"main_seed": ("INT:seed", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"model": (["convnext_tiny", "convnext_base"],),
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"length": ("INT", {"default": 10, "min": 3, "max": 60, "step": 1}),
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"threshold": ("FLOAT", {"default": 94, "min": 55, "max": 99, "step": 0.1})
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},
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}
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RETURN_TYPES = ("IMAGE", "IMAGE", "STRING", "STRING", "STRING", "STRING", "STRING", "INT", "INT")
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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")
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FUNCTION = "predict"
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# OUTPUT_NODE = False
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CATEGORY = "自定义节点"
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def predict(self, image, main_seed, model, length, threshold):
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image, image_selected, cls, prob, nums, period = test_node(image, length=length, thres=threshold,
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model_name=model)
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print("全部帧序列", period)
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if len(period) > 0:
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start, end = period[main_seed % len(period)]
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config = {"start": start, "end": end}
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else:
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config = {}
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start = 0
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end = 0
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raise RuntimeError("未找到符合要求的视频片段")
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return (image, image_selected, cls, prob, nums, str(period), json.dumps(config), start, end - start)
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"""
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The node will always be re executed if any of the inputs change but
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this method can be used to force the node to execute again even when the inputs don't change.
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You can make this node return a number or a string. This value will be compared to the one returned the last time the node was
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executed, if it is different the node will be executed again.
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This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash
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changes between executions the LoadImage node is executed again.
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"""
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# @classmethod
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# def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
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# return ""
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class FaceExtract():
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("图片",)
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FUNCTION = "crop"
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CATEGORY = "自定义节点"
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def crop(self, image):
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device = model_management.get_torch_device()
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image_np = 255. * image.cpu().numpy()
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model = YOLO(model=os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", "yolov8n-face-lindevs.pt"))
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total_images = image_np.shape[0]
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out_images = np.ndarray(shape=(total_images, 512, 512, 3))
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print("shape", image_np.shape)
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print("aaaaa")
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idx = 0
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for image_item in image_np:
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results = model.predict(
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image_item,
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imgsz=640,
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conf=0.75,
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iou=0.7,
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device=device,
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verbose=False
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)
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n = 512
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r = results[0]
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if len(r.boxes.data.cpu().numpy()) == 1:
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y1, x1, y2, x2, p, cls = r.boxes.data.cpu().numpy()[0]
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face_size = int(max(y2 - y1, x2 - x1))
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center = (x1 + x2) // 2, (y1 + y2) // 2
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x1, x2, y1, y2 = center[0] - face_size // 2, center[0] + face_size // 2, center[1] - face_size // 2, \
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center[1] + face_size // 2
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template = np.ndarray(shape=(face_size, face_size, 3))
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template.fill(20)
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for a, a1 in zip(list(range(int(x1), int(x2))), list(range(face_size))):
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for b, b1 in zip(list(range(int(y1), int(y2))), list(range(face_size))):
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if (a >= 0 and a <= r.orig_img.shape[1]) and (b >= 0 and b <= r.orig_img.shape[0]):
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template[a1][b1] = r.orig_img[a][b]
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print(int(x1), int(x2), int(y1), int(y2))
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img = cv2.resize(template, (n, n))
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out_images[idx] = img
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idx += 1
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else:
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idx += 1
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cropped_face = np.array(out_images).astype(np.float32) / 255.0
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cropped_face = torch.from_numpy(cropped_face)
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return (cropped_face,)
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class COSDownload:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"cos_key": ("STRING", {"multiline": True}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("视频存储路径",)
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FUNCTION = "download"
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CATEGORY = "自定义节点"
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def download(self, cos_key):
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if os.sep in cos_key or "/" in cos_key or "\\" in cos_key:
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os.makedirs(os.path.join(os.path.dirname(os.path.abspath(__file__)), "download", os.path.dirname(cos_key)),
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exist_ok=True)
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for i in range(0, 10):
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try:
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with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "config.yaml"), encoding="utf-8",
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mode="r+") as f:
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yaml_config = yaml.load(f, Loader=yaml.FullLoader)
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config = CosConfig(Region=yaml_config["region"], SecretId=yaml_config["secret_id"],
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SecretKey=yaml_config["secret_key"])
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client = CosS3Client(config)
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response = client.download_file(
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Bucket=yaml_config["bucket"],
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Key=cos_key,
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DestFilePath=os.path.join(os.path.dirname(os.path.abspath(__file__)), "download",
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os.path.dirname(cos_key), os.path.basename(cos_key)))
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break
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except CosClientError or CosServiceError as e:
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print(f"下载失败 {e}")
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return (os.path.join(os.path.dirname(os.path.abspath(__file__)), "download", os.path.dirname(cos_key),
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os.path.basename(cos_key)),)
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class COSUpload:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"path": ("STRING", {"multiline": True}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("COS文件Key",)
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FUNCTION = "upload"
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CATEGORY = "自定义节点"
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def upload(self, path):
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for i in range(0, 10):
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try:
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with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "config.yaml"), encoding="utf-8",
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mode="r+") as f:
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yaml_config = yaml.load(f, Loader=yaml.FullLoader)
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config = CosConfig(Region=yaml_config["region"], SecretId=yaml_config["secret_id"],
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SecretKey=yaml_config["secret_key"])
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client = CosS3Client(config)
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response = client.upload_file(
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Bucket=yaml_config["bucket"],
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Key="/".join(
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[yaml_config["subfolder"], path.split("/")[-1] if "/" in path else path.split("\\")[-1]]),
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LocalFilePath=path)
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break
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except CosClientError or CosServiceError as e:
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print(e)
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return ("/".join([yaml_config["subfolder"], path.split("/")[-1] if "/" in path else path.split("\\")[-1]]),)
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# 有问题
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class VideoCut:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"config": ("STRING",),
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"video_path": ("STRING",),
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"mod": ("INT",),
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"fps": ("FLOAT",),
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"period_length": ("INT", {"default": 10, "min": 4, "max": 100, "step": 1, "forceInput": True})
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("视频路径",)
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FUNCTION = "cut"
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# OUTPUT_NODE = False
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CATEGORY = "自定义节点"
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def cut(self, config, video_path, mod, fps, period_length):
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# 原文件名
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origin_fname = ".".join(video_path.split(os.sep)[-1].split(".")[:-1])
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# 配置获取
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mul = mod / fps
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print("fps", fps)
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config = json.loads(config)
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if len(config.keys()) == 0:
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return ("无法生成符合要求的片段",)
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start, end = config["start"], config["end"]
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# 新文件名 复制改名适配ffmpeg
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uid = uuid.uuid1()
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temp_fname = os.sep.join([*video_path.split(os.sep)[:-1], "%s.%s" % (str(uid), video_path.split(".")[-1])])
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try:
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shutil.copy(video_path, temp_fname)
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except:
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return ("请检查输入文件权限",)
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video_path = temp_fname
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# 组装输出文件名
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output_name = (".".join([*video_path.split(os.sep)[-1].split(".")[:-2],
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video_path.split(os.sep)[-1].split(".")[-2] +
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"_output_%%03d_%s" % datetime.now().strftime('%Y%m%d_%H%M%S'),
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video_path.split(os.sep)[-1].split(".")[-1]]))
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output = (os.sep.join([*video_path.split(os.sep)[:-1], output_name])
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.replace(os.sep.join(["ComfyUI", "input"]), os.sep.join(["ComfyUI", "output"])).replace(" ", ""))
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#调用ffmpeg
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ff = ffmpy.FFmpeg(
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inputs={video_path: ['-accurate_seek']},
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outputs={output: [
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'-f', 'segment',
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'-ss', str(round(start * mul, 3)),
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'-to', str(round(end * mul, 3)),
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'-segment_times', str(period_length),
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'-c', 'copy',
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'-map', '0',
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'-avoid_negative_ts', '1'
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]}
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)
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print(ff.cmd)
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ff.run()
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# uuid填充改回原文件名
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try:
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os.remove(temp_fname)
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except:
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pass
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try:
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files = glob.glob(output.replace("%03d", "*"))
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for file in files:
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shutil.move(file, file.replace(str(uid), origin_fname))
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files = glob.glob(output.replace(str(uid), origin_fname).replace("%03d", "*"))
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return (str(files),)
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except:
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files = glob.glob(output.replace("%03d", "*"))
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traceback.print_exc()
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return (str(files),)
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# Add custom API routes, using router
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from aiohttp import web
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from server import PromptServer
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@PromptServer.instance.routes.get("/hello")
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async def get_hello(request):
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return web.json_response("hello")
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"FaceOccDetect": FaceDetect,
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"FaceExtract": FaceExtract,
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"COSUpload": COSUpload,
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"COSDownload": COSDownload,
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"VideoCutCustom": VideoCut
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FaceOccDetect": "面部遮挡检测",
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"FaceExtract": "面部提取",
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"COSUpload": "COS上传",
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"COSDownload": "COS下载",
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"VideoCutCustom": "视频剪裁"
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}
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