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filter_methods.py

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+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)
+
+
+
+