ChameleonMask¶
Documentation¶
- Class name:
ChameleonMask - Category:
Bmad/CV/C.Photography - Output node:
False
The ChameleonMask node is designed to adaptively blend or modify images or masks based on certain criteria or conditions. It aims to provide a flexible approach to image manipulation, allowing for dynamic adjustments that cater to the specific needs of the input data.
Input types¶
Required¶
dst- Specifies the destination image over which the source image or mask will be applied, serving as the backdrop for the operation.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
src- Defines the source image or mask to be blended or modified onto the destination image.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
thresh_blur- Sets the threshold for blurring, affecting the smoothness of the mask edges.
- Comfy dtype:
INT - Python dtype:
int
close_dist- Determines the distance for the closing operation, impacting the closure of small holes in the mask.
- Comfy dtype:
INT - Python dtype:
int
open_dist- Specifies the distance for the opening operation, influencing the removal of small objects from the foreground of the mask.
- Comfy dtype:
INT - Python dtype:
int
size_dist- Controls the size distribution for filtering, affecting the scale of features retained in the mask.
- Comfy dtype:
INT - Python dtype:
int
mask_blur- Defines the blur level for the mask, impacting the overall softness and blending of edges.
- Comfy dtype:
INT - Python dtype:
int
contrast_adjust- Adjusts the contrast of the image, enhancing or reducing the visual differences between elements.
- Comfy dtype:
FLOAT - Python dtype:
float
mode- Selects the operational mode, determining the specific blending or modification technique used.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
output_format- Chooses the output format of the image, affecting the visual style and compatibility.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Optional¶
optional_roi_mask- An optional mask defining regions of interest, focusing the operation on specific areas of the image.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
Output types¶
image- Comfy dtype:
IMAGE - The resulting image after adaptive blending or modification, showcasing the applied effects.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class ChameleonMask: # wtf would I name this node as?
mode_func_map = {
"GRAY": lambda i: cv.cvtColor(i, cv.COLOR_BGR2GRAY),
"VALUE": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HSV)[:, :, 2],
"LIGHTNESS": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HLS)[:, :, 1],
# not sure if these would be useful, but costs nothing to leave them here
"HUE": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HSV)[:, :, 0],
"SATURATION (HSV)": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HSV)[:, :, 1],
"SATURATION (HSL)": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HLS)[:, :, 2],
}
modes = list(mode_func_map.keys())
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"dst": ("IMAGE",),
"src": ("IMAGE",),
"thresh_blur": ("INT", {"default": 30, "min": 2, "step": 2}),
"close_dist": ("INT", {"default": 32, "min": 0, "step": 1}),
"open_dist": ("INT", {"default": 32, "min": 0, "step": 1}),
"size_dist": ("INT", {"default": 8, "min": -99999, "step": 1}),
"mask_blur": ("INT", {"default": 64, "min": 0, "step": 2}),
"contrast_adjust": ("FLOAT", {"default": 2.4, "min": 0, "max": 20, "step": .5}),
"mode": (cls.modes, {"default": cls.modes[0]}),
"output_format": (image_output_formats_options, {
"default": image_output_formats_options[0]
}),
},
"optional": {
"optional_roi_mask": ("IMAGE",)
}
}
RETURN_TYPES = ("IMAGE", "IMAGE",)
FUNCTION = "create_mask"
CATEGORY = f"{cv_category_path}/C.Photography"
def create_mask(self, src, dst, thresh_blur, close_dist, open_dist, size_dist, mask_blur,
contrast_adjust, mode: str, output_format, optional_roi_mask=None):
src = tensor2opencv(src)
dst = tensor2opencv(dst)
thresh_blur += 1
if mask_blur > 0:
mask_blur += 1
# compute the difference between images based on mode
src = self.mode_func_map[mode](src) # type:ignore
dst = self.mode_func_map[mode](dst) # type:ignore
diff = cv.absdiff(src, dst)
if mode == "HUE":
diff = np.minimum(diff, 180 - diff)
# binary thresholding
# _, mask = cv.threshold(diff, threshold, 255, cv.THRESH_BINARY)
diff = cv.GaussianBlur(diff, (thresh_blur, thresh_blur), 0)
_, mask = cv.threshold(diff, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
if optional_roi_mask is not None:
optional_roi_mask = tensor2opencv(optional_roi_mask, 1)
mask[optional_roi_mask < 127] = 0
# morphological closing > closing > dilate/erode
if close_dist > 0:
close_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (close_dist, close_dist))
mask = cv.morphologyEx(mask, cv.MORPH_CLOSE, close_kernel)
if open_dist > 0:
open_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (open_dist, open_dist))
mask = cv.morphologyEx(mask, cv.MORPH_OPEN, open_kernel)
if size_dist > 0:
size_op = cv.MORPH_DILATE
size = size_dist
else:
size_op = cv.MORPH_ERODE
size = abs(size_dist)
if size_dist != 0:
size_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (size, size))
mask = cv.morphologyEx(mask, size_op, size_kernel)
# gaussian blur + contrast adjust
if mask_blur > 0:
mask = cv.GaussianBlur(mask, (mask_blur, mask_blur), 0)
mask = cv.convertScaleAbs(mask, alpha=1 + contrast_adjust, beta=0) # / 100, beta=0)
# convert to target format and output as tensor
# note: diff is only meant to be used for debug purposes
mask = maybe_convert_img(mask, 1, image_output_formats_options_map[output_format])
diff = opencv2tensor(diff)
mask = opencv2tensor(mask)
return (mask, diff,)