# Documentation - Class name: WAS_Load_Image_Batch - Category: WAS Suite/IO - Output node: False - Repo Ref: https://github.com/WASasquatch/was-node-suite-comfyui WAS_Load_Image_Batch 节点旨在高效地加载和管理图像文件批次。它提供以不同模式加载图像的功能，如单张图像、增量图像或随机选择。该节点确保图像正确定向，并且在需要时可以处理 RGBA 到 RGB 的转换。它还能够根据指定去除文件名中的文件扩展名。 # Input types ## Required - path - 'path' 参数指定了图像文件所在的目录路径。这对于节点定位和访问所需处理的图像文件至关重要。 - Comfy dtype: STRING - Python dtype: str ## Optional - pattern - 'pattern' 参数用于根据特定模式过滤图像文件。它对于在指定目录内选择符合给定标准的图像子集很重要。 - Comfy dtype: STRING - Python dtype: str - index - 'index' 参数是一个整数，表示要加载的批次中图像的具体位置。当模式设置为 'single_image' 时，它在确定要检索的确切图像时起着重要作用。 - Comfy dtype: INT - Python dtype: int - mode - 'mode' 参数确定加载图像的方法。它可以是 'single_image' 用于加载特定图像，'incremental_image' 用于顺序访问，或 'random' 用于随机选择。 - Comfy dtype: COMBO['single_image', 'incremental_image', 'random'] - Python dtype: str - label - 'label' 参数是一个字符串，用于标识和分类图像批次。它有助于在节点操作中组织和跟踪不同的图像集。 - Comfy dtype: STRING - Python dtype: str - allow_RGBA_output - 'allow_RGBA_output' 参数决定是否允许输出图像具有 RGBA 通道。如果设置为 'false'，则节点将图像转换为 RGB。 - Comfy dtype: COMBO['false', 'true'] - Python dtype: str - filename_text_extension - 'filename_text_extension' 参数指示是否在文件名文本输出中包含文件扩展名。在不需要文件扩展名的文件名中，它很有用。 - Comfy dtype: COMBO['true', 'false'] - Python dtype: str # Output types - image - 'image' 输出提供了指定批次中加载的图像。它是节点功能内图像处理任务的主要输出。 - Comfy dtype: IMAGE - Python dtype: PIL.Image.Image - filename_text - 'filename_text' 输出返回加载的图像文件的名称，可以用于参考或在节点之外进行进一步处理。 - Comfy dtype: STRING - Python dtype: str # Usage tips - Infra type: CPU # Source code ``` class WAS_Load_Image_Batch: def __init__(self): self.HDB = WASDatabase(WAS_HISTORY_DATABASE) @classmethod def INPUT_TYPES(cls): return {'required': {'mode': (['single_image', 'incremental_image', 'random'],), 'index': ('INT', {'default': 0, 'min': 0, 'max': 150000, 'step': 1}), 'label': ('STRING', {'default': 'Batch 001', 'multiline': False}), 'path': ('STRING', {'default': '', 'multiline': False}), 'pattern': ('STRING', {'default': '*', 'multiline': False}), 'allow_RGBA_output': (['false', 'true'],)}, 'optional': {'filename_text_extension': (['true', 'false'],)}} RETURN_TYPES = ('IMAGE', TEXT_TYPE) RETURN_NAMES = ('image', 'filename_text') FUNCTION = 'load_batch_images' CATEGORY = 'WAS Suite/IO' def load_batch_images(self, path, pattern='*', index=0, mode='single_image', label='Batch 001', allow_RGBA_output='false', filename_text_extension='true'): allow_RGBA_output = allow_RGBA_output == 'true' if not os.path.exists(path): return (None,) fl = self.BatchImageLoader(path, label, pattern) new_paths = fl.image_paths if mode == 'single_image': (image, filename) = fl.get_image_by_id(index) if image == None: cstr(f'No valid image was found for the inded `{index}`').error.print() return (None, None) elif mode == 'incremental_image': (image, filename) = fl.get_next_image() if image == None: cstr(f'No valid image was found for the next ID. Did you remove images from the source directory?').error.print() return (None, None) else: newindex = int(random.random() * len(fl.image_paths)) (image, filename) = fl.get_image_by_id(newindex) if image == None: cstr(f'No valid image was found for the next ID. Did you remove images from the source directory?').error.print() return (None, None) update_history_images(new_paths) if not allow_RGBA_output: image = image.convert('RGB') if filename_text_extension == 'false': filename = os.path.splitext(filename)[0] return (pil2tensor(image), filename) class BatchImageLoader: def __init__(self, directory_path, label, pattern): self.WDB = WDB self.image_paths = [] self.load_images(directory_path, pattern) self.image_paths.sort() stored_directory_path = self.WDB.get('Batch Paths', label) stored_pattern = self.WDB.get('Batch Patterns', label) if stored_directory_path != directory_path or stored_pattern != pattern: self.index = 0 self.WDB.insert('Batch Counters', label, 0) self.WDB.insert('Batch Paths', label, directory_path) self.WDB.insert('Batch Patterns', label, pattern) else: self.index = self.WDB.get('Batch Counters', label) self.label = label def load_images(self, directory_path, pattern): for file_name in glob.glob(os.path.join(glob.escape(directory_path), pattern), recursive=True): if file_name.lower().endswith(ALLOWED_EXT): abs_file_path = os.path.abspath(file_name) self.image_paths.append(abs_file_path) def get_image_by_id(self, image_id): if image_id < 0 or image_id >= len(self.image_paths): cstr(f'Invalid image index `{image_id}`').error.print() return i = Image.open(self.image_paths[image_id]) i = ImageOps.exif_transpose(i) return (i, os.path.basename(self.image_paths[image_id])) def get_next_image(self): if self.index >= len(self.image_paths): self.index = 0 image_path = self.image_paths[self.index] self.index += 1 if self.index == len(self.image_paths): self.index = 0 cstr(f'{cstr.color.YELLOW}{self.label}{cstr.color.END} Index: {self.index}').msg.print() self.WDB.insert('Batch Counters', self.label, self.index) i = Image.open(image_path) i = ImageOps.exif_transpose(i) return (i, os.path.basename(image_path)) def get_current_image(self): if self.index >= len(self.image_paths): self.index = 0 image_path = self.image_paths[self.index] return os.path.basename(image_path) @classmethod def IS_CHANGED(cls, **kwargs): if kwargs['mode'] != 'single_image': return float('NaN') else: fl = WAS_Load_Image_Batch.BatchImageLoader(kwargs['path'], kwargs['label'], kwargs['pattern']) filename = fl.get_current_image() image = os.path.join(kwargs['path'], filename) sha = get_sha256(image) return sha ```