ImageEffectsLensOpticAxis¶
Documentation¶
- Class name:
ImageEffectsLensOpticAxis
- Category:
image/effects/lens
- Output node:
False
This node applies a lens optic axis effect to images, manipulating their appearance based on parameters like lens shape, edge, curvy, zoom, brightness, and saturation. It creates a mask to selectively apply transformations, simulating the optical axis effect of a lens on the input images.
Input types¶
Required¶
images
- The input images to which the lens optic axis effect will be applied. This parameter is crucial as it serves as the base for the effect application.
- Comfy dtype:
IMAGE
- Python dtype:
torch.Tensor
lens_shape
- Defines the shape of the lens effect applied to the images, influencing the overall appearance of the optic axis effect.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
lens_edge
- Specifies the edge behavior of the lens effect, determining how the effect fades or transitions at the boundaries of the images.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
lens_curvy
- Adjusts the curvature of the lens effect, allowing for customization of the optic axis effect's intensity and shape.
- Comfy dtype:
FLOAT
- Python dtype:
float
lens_zoom
- Controls the zoom level of the lens effect, affecting the scale and intensity of the optic axis effect on the images.
- Comfy dtype:
FLOAT
- Python dtype:
float
lens_aperture
- Determines the aperture size of the lens effect, influencing the depth of field and focus area in the image.
- Comfy dtype:
FLOAT
- Python dtype:
float
blur_intensity
- Sets the intensity of the blur effect applied alongside the lens optic axis effect, contributing to the overall visual simulation of a camera lens.
- Comfy dtype:
INT
- Python dtype:
int
Output types¶
image
- Comfy dtype:
IMAGE
- The images after the application of the lens optic axis effect, showcasing the transformed appearance.
- Python dtype:
torch.Tensor
- Comfy dtype:
mask
- Comfy dtype:
MASK
- The mask used to apply the lens optic axis effect, indicating the areas of the images affected by the transformation.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes: unknown
Source code¶
class ImageEffectsLensOpticAxis:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"lens_shape": (["circle", "square", "rectangle", "corners"],),
"lens_edge": (["around", "symmetric"],),
"lens_curvy": ("FLOAT", {
"default": 4.0,
"max": 15.0,
"step": 0.1,
}),
"lens_zoom": ("FLOAT", {
"default": 2.0,
"step": 0.1,
}),
"lens_aperture": ("FLOAT", {
"default": 0.5,
"max": 10.0,
"step": 0.1,
}),
"blur_intensity": ("INT", {
"default": 30,
"min": 2,
"step": 2
}),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "node"
CATEGORY = "image/effects/lens"
def node(self, images, lens_shape, lens_edge, lens_curvy, lens_zoom, lens_aperture, blur_intensity):
blur_intensity -= 1
lens_zoom += 1
height, width = images[0, :, :, 0].shape
if lens_edge == "around":
mask = radialspace_2D((height, width), lens_curvy, lens_zoom, lens_shape, 0.0, 1.0 + lens_curvy).unsqueeze(0).unsqueeze(3)
elif lens_edge == "symmetric":
if height != width:
new_height = new_width = max(height, width)
crop_top_bottom = (new_height - height) // 2
crop_left_right = (new_width - width) // 2
mask = radialspace_2D((new_height, new_width), lens_curvy, lens_zoom, lens_shape, 0.0, 1.0 + lens_curvy)[
crop_top_bottom:-crop_top_bottom or None,
crop_left_right:-crop_left_right or None
].unsqueeze(0).unsqueeze(3)
else:
mask = radialspace_2D((height, width), lens_curvy, lens_zoom, lens_shape, 0.0, 1.0 + lens_curvy).unsqueeze(0).unsqueeze(3)
else:
raise ValueError("Not existing lens_edge parameter.")
center_x = width // 2
center_y = height // 2
y_v, x_v = torch.meshgrid(torch.arange(height), torch.arange(width), indexing='ij')
dx = x_v - center_x
dy = y_v - center_y
distance = torch.sqrt(dx ** 2 + dy ** 2)
map_x = x_v + mask[0, :, :, 0] * dx / distance * (-lens_aperture * 100)
map_y = y_v + mask[0, :, :, 0] * dy / distance * (-lens_aperture * 100)
map_x = map_x.to(torch.float32).numpy()
map_y = map_y.to(torch.float32).numpy()
shifted_images = cv2_layer(images, lambda x: cv2.remap(x, map_x, map_y, cv2.INTER_LINEAR))
shifted_mask = cv2_layer(mask, lambda x: cv2.remap(x, map_x, map_y, cv2.INTER_LINEAR))
edited_images = cv2_layer(shifted_images, lambda x: cv2.stackBlur(x, (blur_intensity, blur_intensity)))
mask = torch.clamp(mask, 0.0, 1.0)
shifted_mask = torch.clamp(shifted_mask, 0.0, 1.0)
result = shifted_images * (1 - mask) + edited_images * mask
return result, shifted_mask