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