111 lines
4.4 KiB
Markdown
111 lines
4.4 KiB
Markdown
# Documentation
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- Class name: WAS_Load_Image
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- Category: WAS Suite/IO
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- Output node: False
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- Repo Ref: https://github.com/WASasquatch/was-node-suite-comfyui
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WAS_Load_Image节点旨在高效地加载和处理来自指定路径或URL的图像。它处理图像转换为所需格式，并提供创建来自alpha通道的掩码和提取文件名等附加功能。
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# Input types
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## Required
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- image_path
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- image_path参数至关重要，因为它决定了要加载的图像的来源。它可以是本地文件路径或在线图像的URL。此参数直接影响节点访问和处理图像数据的能力。
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- Comfy dtype: STRING
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- Python dtype: str
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## Optional
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- RGBA
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- RGBA参数指定是否应加载带有alpha通道的图像。这会影响图像的透明度，对于某些图像处理任务可能非常重要。
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- Comfy dtype: ['false', 'true']
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- Python dtype: str
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- filename_text_extension
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- filename_text_extension参数决定是否在文件名文本中包含文件扩展名。这对于需要特定命名约定的下游流程可能很重要。
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- Comfy dtype: ['true', 'false']
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- Python dtype: str
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# Output types
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- image
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- 图像输出提供已加载和处理的图像数据。它是任何后续图像分析或操作任务的基础输出。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 掩码输出来源于图像的alpha通道（如果存在），用于区分图像中的透明区域。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- filename_text
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- filename_text输出是图像文件名的文本表示。它可以用于记录、显示或其他基于文本的操作。
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- Comfy dtype: TEXT_TYPE
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- Python dtype: str
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class WAS_Load_Image:
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def __init__(self):
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self.input_dir = comfy_paths.input_directory
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self.HDB = WASDatabase(WAS_HISTORY_DATABASE)
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@classmethod
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def INPUT_TYPES(cls):
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return {'required': {'image_path': ('STRING', {'default': './ComfyUI/input/example.png', 'multiline': False}), 'RGBA': (['false', 'true'],)}, 'optional': {'filename_text_extension': (['true', 'false'],)}}
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RETURN_TYPES = ('IMAGE', 'MASK', TEXT_TYPE)
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RETURN_NAMES = ('image', 'mask', 'filename_text')
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FUNCTION = 'load_image'
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CATEGORY = 'WAS Suite/IO'
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def load_image(self, image_path, RGBA='false', filename_text_extension='true'):
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RGBA = RGBA == 'true'
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if image_path.startswith('http'):
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from io import BytesIO
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i = self.download_image(image_path)
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else:
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try:
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i = Image.open(image_path)
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except OSError:
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cstr(f"The image `{image_path.strip()}` specified doesn't exist!").error.print()
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i = Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0))
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if not i:
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return
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update_history_images(image_path)
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image = i
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if not RGBA:
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image = image.convert('RGB')
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1.0 - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device='cpu')
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if filename_text_extension == 'true':
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filename = os.path.basename(image_path)
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else:
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filename = os.path.splitext(os.path.basename(image_path))[0]
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return (image, mask, filename)
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def download_image(self, url):
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try:
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response = requests.get(url)
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response.raise_for_status()
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img = Image.open(BytesIO(response.content))
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return img
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except requests.exceptions.HTTPError as errh:
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cstr(f'HTTP Error: ({url}): {errh}').error.print()
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except requests.exceptions.ConnectionError as errc:
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cstr(f'Connection Error: ({url}): {errc}').error.print()
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except requests.exceptions.Timeout as errt:
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cstr(f'Timeout Error: ({url}): {errt}').error.print()
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except requests.exceptions.RequestException as err:
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cstr(f'Request Exception: ({url}): {err}').error.print()
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@classmethod
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def IS_CHANGED(cls, image_path):
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if image_path.startswith('http'):
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return float('NaN')
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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``` |