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import cv2
import numpy as np
def LinearMotionBlur(image, size, angle):
k = np.zeros((size, size), dtype=np.float32)
k[ (size-1)// 2 , :] = np.ones(size, dtype=np.float32)
k = cv2.warpAffine(k, cv2.getRotationMatrix2D( (size / 2 -0.5 , size / 2 -0.5 ) , angle, 1.0), (size, size) )
k = k * ( 1.0 / np.sum(k) )
return cv2.filter2D(image, -1, k)
def blursharpen (img, sharpen_mode=0, kernel_size=3, amount=100):
if kernel_size % 2 == 0:
kernel_size += 1
if amount > 0:
if sharpen_mode == 1: #box
kernel = np.zeros( (kernel_size, kernel_size), dtype=np.float32)
kernel[ kernel_size//2, kernel_size//2] = 1.0
box_filter = np.ones( (kernel_size, kernel_size), dtype=np.float32) / (kernel_size**2)
kernel = kernel + (kernel - box_filter) * amount
return cv2.filter2D(img, -1, kernel)
elif sharpen_mode == 2: #gaussian
blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
img = cv2.addWeighted(img, 1.0 + (0.5 * amount), blur, -(0.5 * amount), 0)
return img
elif amount < 0:
n = -amount
while n > 0:
img_blur = cv2.medianBlur(img, 5)
if int(n / 10) != 0:
img = img_blur
else:
pass_power = (n % 10) / 10.0
img = img*(1.0-pass_power)+img_blur*pass_power
n = max(n-10,0)
return img
return img |