从文件夹批量导出，高级版
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AbyssYuan0
+128
-12
@@ -99,7 +99,6 @@ class LoadImageAdvanced:
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"round": 1,
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"display": "number"}),
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},
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}
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CATEGORY = "image"
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@@ -186,21 +185,137 @@ class LoadImageAdvanced:
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return "Invalid image file: {}".format(image)
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return True
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class LoadImagesFromDirListAdvanced:
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upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
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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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"directory": ("STRING", {"default": ""}),
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},
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"optional": {
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"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
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"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
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"load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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"color": ("STRING", {"default": "#FFFFFF"}),
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"upscale_method": (s.upscale_methods, {"default": "lanczos"}),
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"target_width": ("INT", {
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"default": None,
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"min": 0,
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"max": 4096,
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"step": 1,
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"round": 1,
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"display": "number"}),
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"target_height": ("INT", {
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"default": None,
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"min": 0,
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"max": 4096,
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"step": 1,
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"round": 1,
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"display": "number"}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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OUTPUT_IS_LIST = (True, True)
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FUNCTION = "load_images"
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CATEGORY = "image"
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@classmethod
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def IS_CHANGED(s, image):
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image_path = folder_paths.get_annotated_filepath(image)
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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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def IS_CHANGED(cls, **kwargs):
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if 'load_always' in kwargs and kwargs['load_always']:
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return float("NaN")
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else:
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return hash(frozenset(kwargs))
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@classmethod
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def VALIDATE_INPUTS(s, image):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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def load_images(self, directory: str,color,upscale_method,target_width,target_height, image_load_cap: int = 0, start_index: int = 0, load_always=False):
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if not os.path.isdir(directory):
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raise FileNotFoundError(f"Directory '{directory}' cannot be found.")
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dir_files = os.listdir(directory)
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if len(dir_files) == 0:
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raise FileNotFoundError(f"No files in directory '{directory}'.")
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return True
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# Filter files by extension
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valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
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dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
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dir_files = sorted(dir_files)
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dir_files = [os.path.join(directory, x) for x in dir_files]
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# start at start_index
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dir_files = dir_files[start_index:]
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images = []
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masks = []
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limit_images = False
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if image_load_cap > 0:
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limit_images = True
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image_count = 0
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for image_path in dir_files:
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if os.path.isdir(image_path) and os.path.ex:
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continue
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if limit_images and image_count >= image_load_cap:
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break
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img = Image.open(image_path)
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width = img.size[0]
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height = img.size[1]
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nw = width
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nh = height
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top = 0
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left = 0
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bottom = 0
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right = 0
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if target_width > 0 and target_height > 0 and target_width != width and target_height != height:
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o_ratio = width / height
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ratio = target_width / target_height
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# 原图比期望尺寸更扁，对齐宽，计算高，补上下
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if (o_ratio >= ratio):
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upratio = target_width / width
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nw = target_width
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nh = round(height * upratio)
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hdiff = target_height - nh
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top = math.floor(hdiff / 2)
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bottom = math.ceil(hdiff / 2)
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else:
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upratio = target_height / height
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nw = round(width * upratio)
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nh = target_height
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wdiff = target_width - nw
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left = math.floor(wdiff / 2)
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right = math.ceil(wdiff / 2)
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image = imgToTensor(img)
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samples = image.movedim(-1,1)
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s = comfy.utils.common_upscale(samples, nw, nh, upscale_method, crop="disabled")
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s = s.movedim(1,-1)
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img = tensorToImg(s)
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if color:
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rgba_color = hex_to_rgba(color)
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new_img = Image.new("RGBA",(nw+left+right,nh+top+bottom), rgba_color)
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new_img.paste(img, (left, top),img.convert("RGBA"))
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img = new_img
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for i in ImageSequence.Iterator(img):
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i = ImageOps.exif_transpose(i)
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if i.mode == 'I':
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i = i.point(lambda i: i * (1 / 255))
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image = i.convert("RGB" if color else "RGBA")
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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. - 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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images.append(image)
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masks.append(mask)
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image_count += 1
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return images, masks
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class ImageOverlap:
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@@ -1515,6 +1630,7 @@ NODE_CLASS_MAPPINGS = {
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"FloatToInt-badger": FloatToInt,
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"IntToString-badger": IntToString,
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"LoadImageAdvanced-badger": LoadImageAdvanced,
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"LoadImagesFromDirListAdvanced-badger":LoadImagesFromDirListAdvanced,
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"IntToStringAdvanced-badger":IntToStringAdvanced,
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"FloatToString-badger": FloatToString,
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"ImageNormalization-badger": ImageNormalization,
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