nodes/MarkoCa1-ComfyUI_Segment_Mask
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Update node.py

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Marko
2024-04-20 15:28:45 +08:00
committed by GitHub
parent 07d9c453da
commit 2ace95b407
1 changed files with 44 additions and 17 deletions
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node.py
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@@ -3,6 +3,7 @@ import os
import numpy as np
from PIL import Image
import torch
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
sys.path.append(
@@ -20,7 +21,6 @@ def show_anns(anns, image_shape):
m = ann['segmentation']
color_mask = np.concatenate([np.random.random(3), [0.35]])
img[m] = color_mask
# 将带有标注的numpy图像转化为torch张量
annotated_img_tensor = torch.from_numpy(img)
return annotated_img_tensor
@@ -35,6 +35,7 @@ class AutomaticMask:
"required": {
"sam_model": ('SAM_MODEL', ),
"image": ("IMAGE", ),
"mask": ('MASK', ),
"points_per_side": ("INT", {
"default": 32,
"min": 0,
@@ -74,12 +75,12 @@ class AutomaticMask:
},
}
# RETURN_NAMES = ("IMAGE",)
FUNCTION = "main"
CATEGORY = "MK/segment_anything"
RETURN_TYPES = ("IMAGE",)
RETURN_TYPES = ("IMAGE","MASK","IMAGE")
RETURN_NAMES = ("Image","Mask","Segment Image")
def main(self, sam_model, image, points_per_side, pred_iou_thresh, stability_score_thresh, crop_n_layers, crop_n_points_downscale_factor, min_mask_region_area):
def main(self, sam_model, image, mask, points_per_side, pred_iou_thresh, stability_score_thresh, crop_n_layers, crop_n_points_downscale_factor, min_mask_region_area):
mask_generator = SamAutomaticMaskGenerator(
model=sam_model,
points_per_side=points_per_side,
@@ -90,20 +91,46 @@ class AutomaticMask:
min_mask_region_area=min_mask_region_area, # Requires open-cv to run post-processing
)
image_res = []
for item in image:
image_shape = (item.shape[0], item.shape[1])
print(image_shape)
item = Image.fromarray(
np.clip(255. * item.cpu().numpy(), 0, 255).astype(np.uint8)).convert('RGBA')
image_np = np.array(item)
image_np_rgb = image_np[..., :3]
original_image = image[0].clone()
image = image[0]
source_mask = mask[0].to(torch.uint8)
image_shape = (image.shape[0], image.shape[1])
image = Image.fromarray(np.clip(255. * image.clone().cpu().numpy(), 0, 255).astype(np.uint8)).convert('RGBA')
image_np = np.array(image)
image_np_rgb = image_np[..., :3]
# 生成蒙版
masks = mask_generator.generate(image_np_rgb)
annotated_image_tensor = show_anns(masks, image_shape)
masks = mask_generator.generate(image_np_rgb)
source_mask_np_array = np.array(source_mask)
for mask_item in masks:
segmentation = torch.from_numpy(mask_item["segmentation"])
segmentation = segmentation.clone().to(torch.uint8)
segmentation_np_array = np.array(segmentation)
image_res.append(annotated_image_tensor)
if source_mask_np_array.shape != segmentation_np_array.shape:
print("The size of the mask is different and cannot be compared")
else:
overlap = np.sum(source_mask_np_array * segmentation_np_array) > 0
if overlap :
source_mask = (source_mask | segmentation).type(torch.uint8)
annotated_image_tensor = show_anns(masks, image_shape)
transparent_image_tensor = mask_to_transparent(original_image, source_mask)
return ([annotated_image_tensor],source_mask, [transparent_image_tensor])
def mask_to_transparent(original_image, source_mask):
original_image_np = original_image.numpy()
source_mask_np = source_mask.numpy().astype(np.uint8)
source_mask_np = source_mask_np * 255
height, width = original_image_np.shape[0], original_image_np.shape[1]
transparent_image = np.zeros((height, width, 4), dtype=np.float64)
transparent_image[..., :3] = original_image_np
transparent_image[..., 3] = source_mask_np
transparent_image_tensor = torch.from_numpy(transparent_image)
return transparent_image_tensor
return (image_res,)