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import cv2
import numpy as np
from registry import registry
@registry.register("Original")
def original(image):
return image
@registry.register("Dot Effect", defaults={
"dot_size": 10,
"dot_spacing": 2,
"invert": False,
}, min_vals={
"dot_size": 1,
"dot_spacing": 1,
}, max_vals={
"dot_size": 20,
"dot_spacing": 10,
}, step_vals={
"dot_size": 1,
"dot_spacing": 1,
})
def dot_effect(image, dot_size: int = 10, dot_spacing: int = 2, invert: bool = False):
"""
## Convert your image into a dotted pattern.
**Args:**
* `image` (numpy.ndarray): Input image (BGR or grayscale)
* `dot_size` (int): Size of each dot
* `dot_spacing` (int): Spacing between dots
* `invert` (bool): Invert the dots
**Returns:**
* `numpy.ndarray`: Dotted image
"""
# Convert to grayscale if image is color
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image
# Apply adaptive thresholding to improve contrast
gray = cv2.adaptiveThreshold(
gray,
255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
25, # Block size
5 # Constant subtracted from mean
)
height, width = gray.shape
canvas = np.zeros_like(gray) if not invert else np.full_like(gray, 255)
y_dots = range(0, height, dot_size + dot_spacing)
x_dots = range(0, width, dot_size + dot_spacing)
dot_color = 255 if not invert else 0
for y in y_dots:
for x in x_dots:
region = gray[y:min(y+dot_size, height), x:min(x+dot_size, width)]
if region.size > 0:
brightness = np.mean(region)
# Dynamic dot sizing based on brightness
relative_brightness = brightness / 255.0
if invert:
relative_brightness = 1 - relative_brightness
# Draw circle with size proportional to brightness
radius = int((dot_size/2) * relative_brightness)
if radius > 0:
cv2.circle(canvas,
(x + dot_size//2, y + dot_size//2),
radius,
(dot_color),
-1)
return canvas
@registry.register("Pixelize", defaults={
"pixel_size": 10,
}, min_vals={
"pixel_size": 1,
}, max_vals={
"pixel_size": 50,
}, step_vals={
"pixel_size": 1,
})
def pixelize(image, pixel_size: int = 10):
"""
## Apply a pixelization effect to the image.
**Args:**
* `image` (numpy.ndarray): Input image (BGR or grayscale)
* `pixel_size` (int): Size of each pixel block
**Returns:**
* `numpy.ndarray`: Pixelized image
"""
height, width = image.shape[:2]
# Resize the image to a smaller size
small_height = height // pixel_size
small_width = width // pixel_size
small_image = cv2.resize(
image, (small_width, small_height), interpolation=cv2.INTER_LINEAR)
# Resize back to the original size with nearest neighbor interpolation
pixelized_image = cv2.resize(
small_image, (width, height), interpolation=cv2.INTER_NEAREST)
return pixelized_image
@registry.register("Sketch Effect")
def sketch_effect(image):
"""
## Apply a sketch effect to the image.
**Args:**
* `image` (numpy.ndarray): Input image (BGR or grayscale)
**Returns:**
* `numpy.ndarray`: Sketch effect applied image
"""
# Convert the image to grayscale
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image
# Invert the grayscale image
inverted_gray = cv2.bitwise_not(gray)
# Apply Gaussian blur to the inverted image
blurred = cv2.GaussianBlur(inverted_gray, (21, 21), 0) # Fixed kernel size
# Blend the grayscale image with the blurred inverted image
sketch = cv2.divide(gray, 255 - blurred, scale=256)
return sketch
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