PERF 清理代码，完善注释
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__init__.py
87
__init__.py
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@ -23,59 +23,11 @@ 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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人脸遮挡检测
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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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@ -91,9 +43,7 @@ class FaceDetect:
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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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CATEGORY = "不忘科技-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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@ -103,26 +53,13 @@ class FaceDetect:
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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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"""人脸提取 By YOLO"""
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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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@ -136,7 +73,7 @@ class FaceExtract():
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FUNCTION = "crop"
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CATEGORY = "自定义节点"
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CATEGORY = "不忘科技-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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@ -182,6 +119,8 @@ class FaceExtract():
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class COSDownload:
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"""腾讯云COS下载"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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@ -193,7 +132,7 @@ class COSDownload:
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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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CATEGORY = "不忘科技-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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@ -220,6 +159,8 @@ class COSDownload:
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class COSUpload:
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"""腾讯云COS上传"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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@ -232,7 +173,7 @@ class COSUpload:
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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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CATEGORY = "不忘科技-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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@ -254,10 +195,8 @@ class COSUpload:
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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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"""FFMPEG视频剪辑 -- ！有卡顿问题 暂废弃"""
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@classmethod
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def INPUT_TYPES(s):
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@ -278,7 +217,7 @@ class VideoCut:
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# OUTPUT_NODE = False
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CATEGORY = "自定义节点"
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CATEGORY = "不忘科技-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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