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Update nodes.py

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kijai 2023-11-09 01:03:03 +02:00
parent 9680b343d7
commit 9504b46674
1 changed files with 31 additions and 27 deletions
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58
nodes.py
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@ -1281,7 +1281,7 @@ class BatchCropFromMask:
"required": {
"original_images": ("IMAGE",),
"masks": ("MASK",),
"bbox_size": ("INT", {"default": 256, "min": 64, "max": 1024, "step": 8}),
"crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
},
}
@ -1302,14 +1302,31 @@ class BatchCropFromMask:
FUNCTION = "crop"
CATEGORY = "KJNodes/masking"
def crop(self, masks, original_images, bbox_size):
def crop(self, masks, original_images, crop_size_mult):
bounding_boxes = []
cropped_images = []
# First, calculate the maximum bounding box size across all masks
max_bbox_size = 0
for mask in masks:
_mask = tensor2pil(mask)[0]
non_zero_indices = np.nonzero(np.array(_mask))
min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
width = max_x - min_x
height = max_y - min_y
bbox_size = max(width, height)
max_bbox_size = max(max_bbox_size, bbox_size)
# Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is
max_bbox_size = math.ceil(max_bbox_size / 32) * 32
# Apply the crop size multiplier
max_bbox_size = int(max_bbox_size * crop_size_mult)
# Then, for each mask and corresponding image...
for mask, img in zip(masks, original_images):
_mask = tensor2pil(mask)[0]
# Calculate bounding box coordinates
non_zero_indices = np.nonzero(np.array(_mask))
min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
@ -1318,36 +1335,23 @@ class BatchCropFromMask:
center_x = (max_x + min_x) // 2
center_y = (max_y + min_y) // 2
# Create fixed-size bounding box around center
half_box_size = bbox_size // 2
min_x = center_x - half_box_size
max_x = center_x + half_box_size
min_y = center_y - half_box_size
max_y = center_y + half_box_size
# Check if the bounding box dimensions go outside the image dimensions
if min_x < 0:
max_x -= min_x
min_x = 0
if max_x > img.shape[1]:
min_x -= max_x - img.shape[1]
max_x = img.shape[1]
if min_y < 0:
max_y -= min_y
min_y = 0
if max_y > img.shape[0]:
min_y -= max_y - img.shape[0]
max_y = img.shape[0]
# Create bounding box using max_bbox_size
half_box_size = max_bbox_size // 2
min_x = max(0, center_x - half_box_size)
max_x = min(img.shape[1], center_x + half_box_size)
min_y = max(0, center_y - half_box_size)
max_y = min(img.shape[0], center_y + half_box_size)
# Append bounding box coordinates
bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
# Crop the image from the bounding box
cropped_img = img[min_y:max_y, min_x:max_x, :]
cropped_images.append(cropped_img)
cropped_out = torch.stack(cropped_images, dim=0)
return (original_images, cropped_out, bounding_boxes, bbox_size, bbox_size,)
return (original_images, cropped_out, bounding_boxes, max_bbox_size, max_bbox_size, )
def bbox_to_region(bbox, target_size=None):