|
|
import cv2
|
|
|
import matplotlib.pyplot as plt
|
|
|
import ImageFilter as filter
|
|
|
import numpy as np
|
|
|
#浮雕
|
|
|
def relief(img, Degree): # 参数为原图像和浮雕图像程度
|
|
|
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
|
|
h, w = gray.shape[0:2]
|
|
|
# 定义空白图像，存放图像浮雕处理之后的图片
|
|
|
img1 = np.zeros((h, w), dtype=gray.dtype)
|
|
|
# 通过对原始图像进行遍历，通过浮雕公式修改像素值，然后进行浮雕处理
|
|
|
for i in range(h):
|
|
|
for j in range(w - 1):
|
|
|
# 前一个像素值
|
|
|
a = gray[i, j]
|
|
|
# 后一个像素值
|
|
|
b = gray[i, j + 1]
|
|
|
# 新的像素值,防止像素溢出
|
|
|
img1[i, j] = min(max((int(a) - int(b) + Degree), 0), 255)
|
|
|
return img1
|
|
|
|
|
|
#素描
|
|
|
def Nostalgia(img):
|
|
|
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
|
|
#通过高斯滤波过滤噪声
|
|
|
gaussian = cv2.GaussianBlur(gray, (3,3), 0)
|
|
|
#通过canny算法提取图像轮轮廓
|
|
|
canny = cv2.Canny(gaussian, 50, 140)
|
|
|
#对轮廓图像进行反二进制阈值化处理
|
|
|
ret, result = cv2.threshold(canny, 90, 255, cv2.THRESH_BINARY_INV)
|
|
|
return result
|
|
|
|
|
|
#水墨画
|
|
|
def stylization(img):
|
|
|
result = cv2.stylization(img, sigma_s=60, sigma_r=0.6)
|
|
|
return result
|
|
|
|
|
|
#连环画
|
|
|
|
|
|
#美颜磨皮
|
|
|
def beauty_filter(image):
|
|
|
# 转换为灰度图
|
|
|
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
|
# 使用Bilateral Filter双边滤波平滑皮肤，同时保留边缘细节
|
|
|
bilateral = cv2.bilateralFilter(image, 9, 75, 75)
|
|
|
# 使用Gaussian Blur进一步模糊，达到柔化效果
|
|
|
blur = cv2.GaussianBlur(bilateral, (23, 23), 30)
|
|
|
# 计算原图与模糊后的图像的差异
|
|
|
diff = image - blur
|
|
|
# 将差异图与原图融合，控制美颜程度
|
|
|
result = image + diff * 0.5
|
|
|
# 确保像素值不超过255或低于0
|
|
|
result = np.clip(result, 0, 255).astype(np.uint8)
|
|
|
return result
|
|
|
|
|
|
#button3：图像转换为灰度图像
|
|
|
def grayscale(image):
|
|
|
global display_img,current_img
|
|
|
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
|
|
|
|
|
|
|
|
|
|
|
