IMAGE_SMOOTHING
Apply an image smoothing operation on an input image. Note: For "gaussian" and "median" type, you are only allowed an odd number for the kernel value.
See https://docs.opencv.org/4.x/d4/d13/tutorial_py_filtering.html for smoothing function information.
Inputs
------
default : Image
The input image to apply smoothing to. Params: kernel : int The strength of the smoothing (larger = stronger smoothing). smoothing_type : select The type of smoothing function to use. Returns: out : Image The smoothed image.
Python Code
from typing import Literal
import cv2
import numpy as np
from flojoy import Image, flojoy
@flojoy(deps={"opencv-python-headless": "4.8.1.78"})
def IMAGE_SMOOTHING(
default: Image,
kernel: int = 5,
smoothing_type: Literal["average", "gaussian", "median", "bilateral"] = "average",
) -> Image:
"""Apply an image smoothing operation on an input image.
Note: For "gaussian" and "median" type, you are only allowed an odd number for the kernel value.
See https://docs.opencv.org/4.x/d4/d13/tutorial_py_filtering.html for smoothing function information.
Inputs
------
default : Image
The input image to apply smoothing to.
Parameters
----------
kernel : int
The strength of the smoothing (larger = stronger smoothing).
smoothing_type : select
The type of smoothing function to use.
Returns
-------
Image
The smoothed image.
"""
r = default.r
g = default.g
b = default.b
a = default.a
if a is not None:
rgba_image = np.stack((r, g, b, a), axis=2)
else:
rgba_image = np.stack((r, g, b), axis=2)
try:
match smoothing_type:
case "average":
image = cv2.blur(rgba_image, (kernel, kernel))
case "gaussian":
assert kernel & 1, "Kernel must be odd for 'gaussian' smoothing."
image = cv2.GaussianBlur(rgba_image, (kernel, kernel), 0)
case "median":
assert kernel & 1, "Kernel must be odd for 'median' smoothing."
image = cv2.medianBlur(rgba_image, kernel)
case "bilateral":
rgba_image = cv2.cvtColor(rgba_image, cv2.COLOR_BGRA2BGR)
image = cv2.bilateralFilter(rgba_image, kernel, kernel * 5, kernel * 5)
try:
r, g, b, a = cv2.split(image)
except Exception:
r, g, b = cv2.split(image)
if a is None:
a = None
return Image(
r=r,
g=g,
b=b,
a=a,
)
except Exception as e:
raise e
Example
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In this example, it takes an image from the local file and process image smoothing.
The result is available through the IMAGE node.