v1.0.7 support auto crop and tag image in foreach

easyapi/BboxNode.py

@@ -2,6 +2,7 @@
 
 import torch
 
+import nodes
 from .util import any_type
 
 
@@ -221,12 +222,12 @@ def INPUT_TYPES(cls):
             "required": {
                 "image": ("IMAGE",),
                 "bbox": ("BBOX",),
-                "margin": ("INT", {"default": 16}),
+                "margin": ("INT", {"default": 16, "tooltip": "bbox矩形区域向外扩张的像素距离"}),
             }
         }
 
-    RETURN_TYPES = ("IMAGE", "MASK", "BBOX")
-    RETURN_NAMES = ("crop_image", "mask", "crop_bbox")
+    RETURN_TYPES = ("IMAGE", "MASK", "BBOX", "INT", "INT")
+    RETURN_NAMES = ("crop_image", "mask", "crop_bbox", "w", "h")
     FUNCTION = "crop"
     CATEGORY = "EasyApi/Bbox"
     DESCRIPTION = "根据bbox区域裁剪图片。 bbox的格式是左上角和右下角坐标： [x,y,x1,y1]"
@@ -257,7 +258,99 @@ def crop(self, image: torch.Tensor, bbox, margin):
         mask[new_bbox[1]:new_bbox[3], new_bbox[0]:new_bbox[2]] = 1
         # 如果需要转换为浮点数，并且增加一个通道维度, 形状变为 (1, height, width)
         mask_tensor = mask.unsqueeze(0)
-        return crop_img, mask_tensor, new_bbox,
+        return crop_img, mask_tensor, new_bbox, to_x - x, to_y - y,
+
+
+class CropTargetSizeImageByBbox:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "bbox": ("BBOX",{"forceInput": True, "tooltip": "参考区域坐标"}),
+                "width": ("INT", {"default": 512, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 1,  "tooltip": "目标宽度"}),
+                "height": ("INT", {"default": 512, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 1, "tooltip": "目标高度"}),
+                "contain": ("BOOLEAN", {"default": False, "tooltip": "是否始终包含bbox完整区域"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "BBOX", "INT", "INT")
+    RETURN_NAMES = ("crop_image", "mask", "crop_bbox", "w", "h")
+    FUNCTION = "crop"
+    CATEGORY = "EasyApi/Bbox"
+    DESCRIPTION = "根据bbox区域中心裁剪指定大小图片。 bbox的格式是左上角和右下角坐标： [x,y,x1,y1]"
+
+    def calc_area(self, image_width, image_height, rect_top_left, rect_bottom_right, w, h):
+        """
+        以给定的矩形中心点为中心计算指定宽高的矩形框坐标
+        Args:
+            image_width: 图片高度
+            image_height: 图片宽度
+            rect_top_left: 矩形框左上角坐标
+            rect_bottom_right: 矩形框右下角坐标
+            w: 目标宽度
+            h: 目标高度
+
+        Returns:
+
+        """
+        # 计算矩形的宽和高
+        x, y = rect_top_left
+        x1, y1 = rect_bottom_right
+
+        # 否则，计算矩形的中心(取整)
+        center_x = (x + x1) // 2
+        center_y = (y + y1) // 2
+        left_w = w // 2
+        right_w = w - left_w
+        top_h = h // 2
+        bottom_h = h - top_h
+
+        # 计算新的坐标
+        new_top_left_x = max(0, center_x - left_w)
+        new_top_left_y = max(0, center_y - top_h)
+        new_bottom_right_x = min(image_width, center_x + right_w)
+        new_bottom_right_y = min(image_height, center_y + bottom_h)
+
+        # 如果坐标越界，调整坐标
+        if new_top_left_x == 0:
+            # 左边可能超过边界了，尝试把左边超出部分加到右边
+            new_bottom_right_x = min(image_width, new_bottom_right_x + (left_w - center_x))
+        elif new_bottom_right_x == image_width:
+            # 右边可能超过边界了，尝试把右边超出部分加到左边
+            new_top_left_x = max(0, new_top_left_x - (center_x + left_w - image_width))
+
+        if new_top_left_y == 0:
+            # 上边可能超过边界了，尝试把上边超出部分加到下边
+            new_bottom_right_y = min(image_height, new_bottom_right_y + (top_h - center_y))
+        elif new_bottom_right_y == image_height:
+            # 下边可能超过边界了，尝试把下边超出部分加到上边
+            new_top_left_y = max(0, new_top_left_y - (center_y + top_h - image_height))
+
+        return new_top_left_x, new_top_left_y, new_bottom_right_x, new_bottom_right_y
+
+    def crop(self, image: torch.Tensor, bbox, width, height, contain):
+        x, y, x1, y1 = bbox
+        image_height = image.shape[1]
+        image_width = image.shape[2]
+
+        new_x, new_y, to_x, to_y = self.calc_area(image_width, image_height, (x, y), (x1, y1), width, height)
+
+        if contain:
+            new_x = min(new_x, x)
+            new_y = min(new_y, y)
+            to_x = max(to_x, x1)
+            to_y = max(to_y, y1)
+        # 按区域截取图片
+        crop_img = image[:, new_y:to_y, new_x:to_x, :]
+        new_bbox = (new_x, new_y, to_x, to_y)
+        # 创建与image相同大小的全零张量作为遮罩
+        mask = torch.zeros((image_height, image_width), dtype=torch.uint8)  # 使用uint8类型
+        # 在mask上设置new_bbox区域为1
+        mask[new_bbox[1]:new_bbox[3], new_bbox[0]:new_bbox[2]] = 1
+        # 如果需要转换为浮点数，并且增加一个通道维度, 形状变为 (1, height, width)
+        mask_tensor = mask.unsqueeze(0)
+        return crop_img, mask_tensor, new_bbox, to_x - new_x, to_y - new_y,
 
 
 class MaskByBboxes:
@@ -299,6 +392,7 @@ def crop(self, image: torch.Tensor, bboxes):
     "SelectBboxes": SelectBboxes,
     "CropImageByBbox": CropImageByBbox,
     "MaskByBboxes": MaskByBboxes,
+    "CropTargetSizeImageByBbox": CropTargetSizeImageByBbox,
 }
 
 
@@ -310,4 +404,5 @@ def crop(self, image: torch.Tensor, bboxes):
     "SelectBboxes": "SelectBboxes",
     "CropImageByBbox": "CropImageByBbox",
     "MaskByBboxes": "MaskByBboxes",
+    "CropTargetSizeImageByBbox": "CropTargetSizeImageByBbox",
 }

easyapi/ImageNode.py

@@ -7,6 +7,7 @@
 import torch
 from PIL import ImageOps, Image, ImageSequence
 
+import folder_paths
 import node_helpers
 from nodes import LoadImage
 from comfy.cli_args import args
@@ -446,6 +447,158 @@ def load_mask(self, image_path, channel):
         return (mask.unsqueeze(0),)
 
 
+class SaveImagesWithoutOutput:
+    """
+    保存图片，非输出节点
+    """
+
+    def __init__(self):
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "filename_prefix": ("STRING", {"default": "ComfyUI",
+                                               "tooltip": "要保存的文件的前缀。可以使用格式化信息，如%date:yyyy-MM-dd%或%Empty Latent Image.width%"}),
+                "output_dir": ("STRING", {"default": "", "tooltip": "若为空，存放到output目录"}),
+            },
+            "optional": {
+                "addMetadata": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ("STRING", )
+    RETURN_NAMES = ("file_paths",)
+    OUTPUT_TOOLTIPS = ("保存的图片路径列表",)
+
+    FUNCTION = "save_images"
+
+    CATEGORY = "EasyApi/Image"
+
+    DESCRIPTION = "保存图像到指定目录，可根据返回的文件路径进行后续操作，此节点为非输出节点，适合批量处理和用于惰性求值的前置节点"
+    OUTPUT_NODE = False
+
+    def save_images(self, images, output_dir, filename_prefix="ComfyUI", addMetadata=False, prompt=None, extra_pnginfo=None):
+        imageList = list()
+        if not isinstance(images, list):
+            imageList.append(images)
+        else:
+            imageList = images
+
+        if output_dir is None or len(output_dir.strip()) == 0:
+            output_dir = folder_paths.get_output_directory()
+
+        results = list()
+        for (index, images) in enumerate(imageList):
+            for (batch_number, image) in enumerate(images):
+                full_output_folder, filename, counter, subfolder, curr_filename_prefix = folder_paths.get_save_image_path(
+                    filename_prefix, output_dir, image.shape[1], image.shape[0])
+                img = tensor_to_pil(image)
+                metadata = None
+                if not args.disable_metadata and addMetadata:
+                    metadata = PngInfo()
+                    if prompt is not None:
+                        metadata.add_text("prompt", json.dumps(prompt))
+                    if extra_pnginfo is not None:
+                        for x in extra_pnginfo:
+                            metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+                filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+                file = f"{filename_with_batch_num}_{counter:05}_.png"
+                image_save_path = os.path.join(full_output_folder, file)
+                img.save(image_save_path, pnginfo=metadata, compress_level=self.compress_level)
+                results.append(image_save_path)
+                counter += 1
+
+        return (results,)
+
+
+class SaveSingleImageWithoutOutput:
+    """
+    保存图片，非输出节点
+    """
+
+    def __init__(self):
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "要保存的文件的前缀。可以使用格式化信息，如%date:yyyy-MM-dd%或%Empty Latent Image.width%"}),
+                "full_file_name": ("STRING", {"default": "", "tooltip": "完整的相对路径文件名，包括扩展名。若为空，则使用filename_prefix生成带序号的文件名"}),
+                "output_dir": ("STRING", {"default": "", "tooltip": "目标目录(绝对路径)，不会自动创建。若为空，存放到output目录"}),
+            },
+            "optional": {
+                "addMetadata": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ("STRING", )
+    RETURN_NAMES = ("file_path",)
+
+    FUNCTION = "save_image"
+
+    CATEGORY = "EasyApi/Image"
+
+    DESCRIPTION = "保存图像到指定目录，可根据返回的文件路径进行后续操作，此节点为非输出节点，适合循环批处理和用于惰性求值的前置节点。只会处理一个"
+    OUTPUT_NODE = False
+
+    def save_image(self, image, full_file_name, output_dir, filename_prefix="ComfyUI", addMetadata=False, prompt=None, extra_pnginfo=None):
+        imageList = list()
+        if not isinstance(image, list):
+            imageList.append(image)
+        else:
+            imageList = image
+
+        if output_dir is None or len(output_dir.strip()) == 0:
+            output_dir = folder_paths.get_output_directory()
+
+        if not os.path.isdir(output_dir) or not os.path.isabs(output_dir):
+            raise RuntimeError(f"目录 {output_dir} 不存在")
+
+        if len(imageList) > 0:
+            image = imageList[0]
+            for (batch_number, image) in enumerate(image):
+                img = tensor_to_pil(image)
+                metadata = None
+                if not args.disable_metadata and addMetadata:
+                    metadata = PngInfo()
+                    if prompt is not None:
+                        metadata.add_text("prompt", json.dumps(prompt))
+                    if extra_pnginfo is not None:
+                        for x in extra_pnginfo:
+                            metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+                if full_file_name is not None and len(full_file_name.strip()) > 0:
+                    # full_file_name是相对路径，添加校验，并自动创建子目录
+                    full_path = os.path.join(output_dir, full_file_name)
+                    full_normpath_name = os.path.normpath(full_path)
+                    file_dir = os.path.dirname(full_normpath_name)
+                    # 确保路径是out_dir 的子目录
+                    if not os.path.isabs(file_dir) or not file_dir.startswith(output_dir):
+                        raise RuntimeError(f"文件 {full_file_name} 不在 {output_dir} 目录下")
+                    if not os.path.isdir(file_dir):
+                        os.makedirs(file_dir, exist_ok=True)
+                    image_save_path = full_normpath_name
+                else:
+                    full_output_folder, filename, counter, subfolder, curr_filename_prefix = folder_paths.get_save_image_path(
+                        filename_prefix, output_dir, image.shape[1], image.shape[0])
+                    filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+                    file = f"{filename_with_batch_num}_{counter:05}_.png"
+                    image_save_path = os.path.join(full_output_folder, file)
+
+                img.save(image_save_path, pnginfo=metadata, compress_level=self.compress_level)
+                return image_save_path,
+
+        return (None,)
+
+
 NODE_CLASS_MAPPINGS = {
     "Base64ToImage": Base64ToImage,
     "LoadImageFromURL": LoadImageFromURL,
@@ -459,6 +612,8 @@ def load_mask(self, image_path, channel):
     "LoadImageToBase64": LoadImageToBase64,
     "LoadImageFromLocalPath": LoadImageFromLocalPath,
     "LoadMaskFromLocalPath": LoadMaskFromLocalPath,
+    "SaveImagesWithoutOutput": SaveImagesWithoutOutput,
+    "SaveSingleImageWithoutOutput": SaveSingleImageWithoutOutput,
 }
 
 # A dictionary that contains the friendly/humanly readable titles for the nodes
@@ -475,4 +630,6 @@ def load_mask(self, image_path, channel):
     "LoadImageToBase64": "Load Image To Base64",
     "LoadImageFromLocalPath": "Load Image From Local Path",
     "LoadMaskFromLocalPath": "Load Mask From Local Path",
+    "SaveImagesWithoutOutput": "Save Images Without Output",
+    "SaveSingleImageWithoutOutput": "Save Single Image Without Output",
 }