Patch Masking Nodes and Add Some
This commit is contained in:
Jordan Thompson
+217
-39
@@ -241,18 +241,15 @@ def pil2tensor(image):
|
||||
def pil2hex(image):
|
||||
return hashlib.sha256(np.array(tensor2pil(image)).astype(np.uint16).tobytes()).hexdigest()
|
||||
|
||||
# Mask to PIL
|
||||
def mask2pil(mask):
|
||||
mask = mask.numpy()
|
||||
mask = (mask * 255).astype(np.uint8)
|
||||
return Image.fromarray(mask)
|
||||
|
||||
# PIL to Mask
|
||||
def pil2mask(image):
|
||||
mask = np.array(image).astype(np.float32) / 255.0
|
||||
mask_tensor = 1.0 - torch.from_numpy(mask)
|
||||
return mask_tensor
|
||||
image_np = np.array(image.convert("L")).astype(np.float32) / 255.0
|
||||
mask = torch.from_numpy(image_np)
|
||||
return 1.0 - mask if image.mode == "L" else mask
|
||||
|
||||
def mask2pil(mask):
|
||||
mask_np = (mask.numpy() * 255).astype(np.uint8).squeeze()
|
||||
return Image.fromarray(mask_np, mode="L")
|
||||
|
||||
# Tensor to SAM-compatible NumPy
|
||||
def tensor2sam(image):
|
||||
# Convert tensor to numpy array in HWC uint8 format with pixel values in [0, 255]
|
||||
@@ -1126,25 +1123,25 @@ class WAS_Tools_Class():
|
||||
@staticmethod
|
||||
def dominant_region(image, threshold=128):
|
||||
from scipy.ndimage import label
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
|
||||
l, n = label(np.array(binary_image))
|
||||
sizes = np.bincount(l.flatten())
|
||||
dominant = np.argmax(sizes[1:]) + 1
|
||||
dominant_region_mask = (l == dominant).astype(np.uint8) * 255
|
||||
result = Image.fromarray(dominant_region_mask, mode="L")
|
||||
return ImageOps.invert(result.convert("RGB"))
|
||||
return result.convert("RGB")
|
||||
|
||||
@staticmethod
|
||||
def minority_region(image, threshold=128):
|
||||
from scipy.ndimage import label
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
|
||||
labeled_array, num_features = label(np.array(binary_image))
|
||||
sizes = np.bincount(labeled_array.flatten())
|
||||
smallest_region = np.argmin(sizes[1:]) + 1
|
||||
smallest_region_mask = (labeled_array == smallest_region).astype(np.uint8) * 255
|
||||
inverted_mask = ImageOps.invert(Image.fromarray(smallest_region_mask, mode="L"))
|
||||
inverted_mask = Image.fromarray(smallest_region_mask, mode="L")
|
||||
rgb_image = Image.merge("RGB", [inverted_mask, inverted_mask, inverted_mask])
|
||||
|
||||
return rgb_image
|
||||
@@ -1152,7 +1149,7 @@ class WAS_Tools_Class():
|
||||
@staticmethod
|
||||
def arbitrary_region(image, size, threshold=128):
|
||||
from skimage.measure import label, regionprops
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
binary_image = image.point(lambda x: 255 if x > threshold else 0, mode="1")
|
||||
labeled_image = label(np.array(binary_image))
|
||||
regions = regionprops(labeled_image)
|
||||
@@ -1166,48 +1163,48 @@ class WAS_Tools_Class():
|
||||
smallest_region = filtered_regions[0]
|
||||
region_mask = (labeled_image == smallest_region.label).astype(np.uint8) * 255
|
||||
result = Image.fromarray(region_mask, mode="L")
|
||||
return ImageOps.invert(result)
|
||||
return result
|
||||
|
||||
return ImageOps.invert(image)
|
||||
return image
|
||||
|
||||
@staticmethod
|
||||
def smooth_region(image, tolerance):
|
||||
from scipy.ndimage import gaussian_filter
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
mask_array = np.array(image)
|
||||
smoothed_array = gaussian_filter(mask_array, sigma=tolerance)
|
||||
threshold = np.max(smoothed_array) / 2
|
||||
smoothed_mask = np.where(smoothed_array >= threshold, 255, 0).astype(np.uint8)
|
||||
smoothed_image = Image.fromarray(smoothed_mask, mode="L")
|
||||
return ImageOps.invert(smoothed_image.convert("RGB"))
|
||||
return smoothed_image.convert("RGB")
|
||||
|
||||
@staticmethod
|
||||
def erode_region(image, iterations=1):
|
||||
from scipy.ndimage import binary_erosion
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
binary_mask = np.array(image) > 0
|
||||
eroded_mask = binary_erosion(binary_mask, iterations=iterations)
|
||||
eroded_image = Image.fromarray(eroded_mask.astype(np.uint8) * 255, mode="L")
|
||||
return ImageOps.invert(eroded_image.convert("RGB"))
|
||||
return eroded_image.convert("RGB")
|
||||
|
||||
@staticmethod
|
||||
def dilate_region(image, iterations=1):
|
||||
from scipy.ndimage import binary_dilation
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
binary_mask = np.array(image) > 0
|
||||
dilated_mask = binary_dilation(binary_mask, iterations=iterations)
|
||||
dilated_image = Image.fromarray(dilated_mask.astype(np.uint8) * 255, mode="L")
|
||||
return ImageOps.invert(dilated_image.convert("RGB"))
|
||||
return dilated_image.convert("RGB")
|
||||
|
||||
@staticmethod
|
||||
def fill_region(image):
|
||||
from scipy.ndimage import binary_fill_holes
|
||||
image = ImageOps.invert(image.convert("L"))
|
||||
image = image.convert("L")
|
||||
binary_mask = np.array(image) > 0
|
||||
filled_mask = binary_fill_holes(binary_mask)
|
||||
filled_image = Image.fromarray(filled_mask.astype(np.uint8) * 255, mode="L")
|
||||
|
||||
return ImageOps.invert(filled_image.convert("RGB"))
|
||||
return filled_image.convert("RGB")
|
||||
|
||||
@staticmethod
|
||||
def combine_masks(*masks):
|
||||
@@ -1218,12 +1215,54 @@ class WAS_Tools_Class():
|
||||
if mask.size != dimensions:
|
||||
raise ValueError("\033[34mWAS NS\033[0m Error: All masks must have the same dimensions.")
|
||||
|
||||
inverted_masks = [ImageOps.invert(mask.convert("L")) for mask in masks]
|
||||
inverted_masks = [mask.convert("L") for mask in masks]
|
||||
combined_mask = Image.new("L", dimensions, 255)
|
||||
for mask in inverted_masks:
|
||||
combined_mask = Image.fromarray(np.minimum(np.array(combined_mask), np.array(mask)), mode="L")
|
||||
|
||||
return combined_mask
|
||||
return combined_mask
|
||||
|
||||
@staticmethod
|
||||
def threshold_region(image, black_threshold=0, white_threshold=255):
|
||||
gray_image = image.convert("L")
|
||||
mask_array = np.array(gray_image)
|
||||
mask_array[mask_array < black_threshold] = 0
|
||||
mask_array[mask_array > white_threshold] = 255
|
||||
thresholded_image = Image.fromarray(mask_array, mode="L")
|
||||
return ImageOps.invert(thresholded_image)
|
||||
|
||||
@staticmethod
|
||||
def floor_region(image):
|
||||
gray_image = image.convert("L")
|
||||
mask_array = np.array(gray_image)
|
||||
non_black_pixels = mask_array[mask_array > 0]
|
||||
|
||||
if non_black_pixels.size > 0:
|
||||
threshold_value = non_black_pixels.min()
|
||||
mask_array[mask_array > threshold_value] = 255 # Set whites to 255
|
||||
mask_array[mask_array <= threshold_value] = 0 # Set blacks to 0
|
||||
|
||||
thresholded_image = Image.fromarray(mask_array, mode="L")
|
||||
return ImageOps.invert(thresholded_image)
|
||||
|
||||
@staticmethod
|
||||
def ceiling_region(image, offset=30):
|
||||
if offset < 0:
|
||||
offset = 0
|
||||
elif offset > 255:
|
||||
offset = 255
|
||||
grayscale_image = image.convert("L")
|
||||
mask_array = np.array(grayscale_image)
|
||||
mask_array[mask_array < 255 - offset] = 0
|
||||
mask_array[mask_array >= 250] = 255
|
||||
filtered_image = Image.fromarray(mask_array, mode="L")
|
||||
return ImageOps.invert(filtered_image)
|
||||
|
||||
@staticmethod
|
||||
def gaussian_region(image, radius=5.0):
|
||||
image.convert("L")
|
||||
image = image.filter(ImageFilter.GaussianBlur(radius=int(radius)))
|
||||
return image.convert("RGB")
|
||||
|
||||
# SHADOWS AND HIGHLIGHTS ADJUSTMENTS
|
||||
|
||||
@@ -5205,6 +5244,105 @@ class WAS_Mask_Fill_Region:
|
||||
def fill_region(self, mask, sigma=128):
|
||||
return (pil2mask(self.WT.Masking.fill_region(mask2pil(mask))),)
|
||||
|
||||
# MASK THRESHOLD
|
||||
|
||||
class WAS_Mask_Threshold_Region:
|
||||
|
||||
def __init__(self):
|
||||
self.WT = WAS_Tools_Class()
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"mask": ("MASK",),
|
||||
"black_threshold": ("INT",{"default":75, "min":0, "max": 255, "step": 1}),
|
||||
"white_threshold": ("INT",{"default":175, "min":0, "max": 255, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
CATEGORY = "WAS Suite/Image/Masking"
|
||||
|
||||
RETURN_TYPES = ("MASK",)
|
||||
|
||||
FUNCTION = "threshold_region"
|
||||
|
||||
def threshold_region(self, mask, black_threshold=75, white_threshold=255):
|
||||
return (pil2mask(self.WT.Masking.threshold_region(mask2pil(mask), black_threshold, white_threshold)),)
|
||||
|
||||
# MASK FLOOR REGION
|
||||
|
||||
class WAS_Mask_Floor_Region:
|
||||
|
||||
def __init__(self):
|
||||
self.WT = WAS_Tools_Class()
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"mask": ("MASK",),
|
||||
}
|
||||
}
|
||||
|
||||
CATEGORY = "WAS Suite/Image/Masking"
|
||||
|
||||
RETURN_TYPES = ("MASK",)
|
||||
|
||||
FUNCTION = "floor_region"
|
||||
|
||||
def floor_region(self, mask):
|
||||
return (pil2mask(self.WT.Masking.floor_region(mask2pil(mask))),)
|
||||
|
||||
# MASK CEILING REGION
|
||||
|
||||
class WAS_Mask_Ceiling_Region:
|
||||
|
||||
def __init__(self):
|
||||
self.WT = WAS_Tools_Class()
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"mask": ("MASK",),
|
||||
}
|
||||
}
|
||||
|
||||
CATEGORY = "WAS Suite/Image/Masking"
|
||||
|
||||
RETURN_TYPES = ("MASK",)
|
||||
|
||||
FUNCTION = "ceiling_region"
|
||||
|
||||
def ceiling_region(self, mask):
|
||||
return (pil2mask(self.WT.Masking.ceiling_region(mask2pil(mask))),)
|
||||
|
||||
# MASK GAUSSIAN REGION
|
||||
|
||||
class WAS_Mask_Gaussian_Region:
|
||||
|
||||
def __init__(self):
|
||||
self.WT = WAS_Tools_Class()
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"mask": ("MASK",),
|
||||
"blur": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1024, "step": 0.1}),
|
||||
}
|
||||
}
|
||||
|
||||
CATEGORY = "WAS Suite/Image/Masking"
|
||||
|
||||
RETURN_TYPES = ("MASK",)
|
||||
|
||||
FUNCTION = "gaussian_region"
|
||||
|
||||
def gaussian_region(self, mask, radius=5.0):
|
||||
return (pil2mask(self.WT.Masking.gaussian_region(mask2pil(mask), radius)),)
|
||||
|
||||
# MASK COMBINE
|
||||
|
||||
class WAS_Mask_Combine:
|
||||
@@ -5245,11 +5383,8 @@ class WAS_Mask_Combine:
|
||||
masks.append(mask2pil(mask_f))
|
||||
return (pil2mask(self.WT.Masking.combine_masks(*masks)),)
|
||||
|
||||
|
||||
# LATENT UPSCALE NODE
|
||||
|
||||
|
||||
|
||||
class WAS_Latent_Upscale:
|
||||
def __init__(self):
|
||||
pass
|
||||
@@ -6614,9 +6749,6 @@ class WAS_String_To_Text:
|
||||
return (string, )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# BLIP CAPTION IMAGE
|
||||
|
||||
class WAS_BLIP_Analyze_Image:
|
||||
@@ -6755,7 +6887,48 @@ class WAS_BLIP_Analyze_Image:
|
||||
else:
|
||||
print(f"\033[34mWAS NS\033[0m Error: The selected mode `{mode}` is not a valid selection!")
|
||||
return ('Invalid BLIP mode!', )
|
||||
|
||||
# CLIPSeg Node
|
||||
|
||||
class WAS_CLIPSeg:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"text": ("STRING", {"default":"", "multiline": True}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MASK", "IMAGE")
|
||||
RETURN_NAMES = ("MASK", "MASK_IMAGE")
|
||||
FUNCTION = "CLIPSeg_image"
|
||||
|
||||
CATEGORY = "WAS Suite/Image/Masking"
|
||||
|
||||
def CLIPSeg_image(self, image, text=None):
|
||||
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
|
||||
import os
|
||||
|
||||
image = tensor2pil(image)
|
||||
|
||||
inputs = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=os.path.join(MODELS_DIR, 'clipseg'))
|
||||
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined", cache_dir=os.path.join(MODELS_DIR, 'clipseg'))
|
||||
|
||||
with torch.no_grad():
|
||||
result = model(**inputs(text=text, images=image, padding="max_length", return_tensors="pt"))
|
||||
|
||||
tensor = torch.sigmoid(result[0])
|
||||
mask = (tensor - tensor.min()) / tensor.max()
|
||||
mask = tensor2pil(mask).convert("L")
|
||||
mask = mask.resize(image.size)
|
||||
|
||||
return (pil2mask(ImageOps.invert(mask)), pil2tensor(ImageOps.invert(mask.convert("RGB"))))
|
||||
|
||||
|
||||
|
||||
# SAM MODEL LOADER
|
||||
class WAS_SAM_Model_Loader:
|
||||
@@ -8349,6 +8522,7 @@ NODE_CLASS_MAPPINGS = {
|
||||
"Create Morph Image": WAS_Image_Morph_GIF,
|
||||
"Create Morph Image from Path": WAS_Image_Morph_GIF_By_Path,
|
||||
"Create Video from Path": WAS_Create_Video_From_Path,
|
||||
"CLIPSeg Masking": WAS_CLIPSeg,
|
||||
"Convert Mask to Image": WAS_Mask_To_Image,
|
||||
"Debug Number to Console": WAS_Debug_Number_to_Console,
|
||||
"Dictionary to Console": WAS_Dictionary_To_Console,
|
||||
@@ -8412,14 +8586,18 @@ NODE_CLASS_MAPPINGS = {
|
||||
"Latent Upscale by Factor (WAS)": WAS_Latent_Upscale,
|
||||
"Load Image Batch": WAS_Load_Image_Batch,
|
||||
"Load Text File": WAS_Text_Load_From_File,
|
||||
"Mask Dominant Region": WAS_Mask_Dominant_Region,
|
||||
"Mask Minority Region": WAS_Mask_Minority_Region,
|
||||
"Mask Arbitrary Region": WAS_Mask_Arbitrary_Region,
|
||||
"Mask Smooth Boundaries": WAS_Mask_Smooth_Region,
|
||||
"Mask Erode Boundaries": WAS_Mask_Erode_Region,
|
||||
"Mask Dilate Boundaries": WAS_Mask_Dilate_Region,
|
||||
"Mask Ceiling Region": WAS_Mask_Ceiling_Region,
|
||||
"Mask Dilate Region": WAS_Mask_Dilate_Region,
|
||||
"Mask Dominant Region": WAS_Mask_Dominant_Region,
|
||||
"Mask Erode Region": WAS_Mask_Erode_Region,
|
||||
"Mask Fill Holes": WAS_Mask_Fill_Region,
|
||||
"Masks Combine": WAS_Mask_Combine,
|
||||
"Mask Floor Region": WAS_Mask_Floor_Region,
|
||||
"Mask Gaussian Region": WAS_Mask_Gaussian_Region,
|
||||
"Mask Minority Region": WAS_Mask_Minority_Region,
|
||||
"Mask Smooth Region": WAS_Mask_Smooth_Region,
|
||||
"Mask Threshold Region": WAS_Mask_Threshold_Region,
|
||||
"Masks Combine Regions": WAS_Mask_Combine,
|
||||
"MiDaS Depth Approximation": MiDaS_Depth_Approx,
|
||||
"MiDaS Mask Image": MiDaS_Background_Foreground_Removal,
|
||||
"Number Operation": WAS_Number_Operation,
|
||||
|
||||
Reference in New Issue
Block a user