import numpy as npimport cv2from copy import deepcopyfrom PIL import Imagefrom matplotlib import pyplot as plt"""制作噪声图像"""def add_salt_noise(img, snr=0.5): SNR = snr # 指定信噪比(默认0.5) size = img.size # 获取总共像素个数 # print(type(size)) noiseSize = int(size * (1 - SNR)) for i in range(0, noiseSize): # 随机获取 某个点 xi = int(np.random.uniform(0, img.shape[1])) xj = int(np.random.uniform(0, img.shape[0])) if img.ndim == 2: # 判断是否为2维图像(即灰度图像) img[xj, xi] = 0 # 设置值为黑点 elif img.ndim == 3: img[xj, xi] = 0 # 设置值为黑点，也能够设置为红色255 return img"""黑白图中值滤波"""def median_Blur(img, filiter_size=3): # 当输出的图像为彩色图像 image_copy = np.array(img, copy=True).astype(np.float32) processed = np.zeros_like(image_copy) middle = int(filiter_size / 2) r = np.zeros(filiter_size * filiter_size) g = np.zeros(filiter_size * filiter_size) b = np.zeros(filiter_size * filiter_size) for i in range(middle, image_copy.shape[0] - middle): for j in range(middle, image_copy.shape[1] - middle): count = 0 # 顺次取出模板中对应的像素值 for m in range(i - middle, i + middle + 1): for n in range(j - middle, j + middle + 1): r[count] = image_copy[m][n][0] g[count] = image_copy[m][n][1] b[count] = image_copy[m][n][2] count += 1 r.sort() g.sort() b.sort() processed[i][j][0] = r[int(filiter_size * filiter_size / 2)] processed[i][j][1] = g[int(filiter_size * filiter_size / 2)] processed[i][j][2] = b[int(filiter_size * filiter_size / 2)] processed = np.clip(processed, 0, 255).astype(np.uint8) return processed"""灰度图中值滤波"""def median_Blur_gray(img, filiter_size=3): # 当输出的图像为灰度图像 image_copy = np.array(img, copy=True).astype(np.float32) processed = np.zeros_like(image_copy) middle = int(filiter_size / 2) for i in range(middle, image_copy.shape[0] - middle): for j in range(middle, image_copy.shape[1] - middle): temp = [] for m in range(i - middle, i + middle + 1): for n in range(j - middle, j + middle + 1): if m - middle < 0 or m + middle + 1 > image_copy.shape[0] or n - middle < 0 or n + middle + 1 > \ image_copy.shape[1]: temp.append(0) else: temp.append(image_copy[m][n]) # count += 1 temp.sort() processed[i][j] = temp[(int(filiter_size * filiter_size / 2) + 1)] processed = np.clip(processed, 0, 255).astype(np.uint8) return processedif __name__ == "__main__": img = input("Please input name of image:") # 默认是string类型 img = cv2.imread(img) print("img.shape:", img.shape) # plt.imshow显示cv2.imread读取的图像蓝与红有区别 b, g, r = cv2.split(img) src_img = cv2.merge([r, g, b]) img_demo = deepcopy(src_img) #使得两图像不烦扰 snr = float(input("Please input signal noise ratio:")) img_salt = add_salt_noise(img_demo, snr) filiter_size = int(input("Please input size of filiter:")) median_blur = median_Blur(img_salt, filiter_size) plt.figure("Image processing---MedianBlur") # 图像窗口名称 plt.subplot(1, 3, 1) plt.imshow(src_img) plt.axis('off') # 关掉坐标轴 plt.title("src_img") plt.subplot(1, 3, 2) plt.imshow(img_salt) plt.axis('off') # 关掉坐标轴 plt.title("img_salt") plt.subplot(1, 3, 3) plt.imshow(median_blur) plt.axis('off') # 关掉坐标轴 plt.title("median_blur") plt.tight_layout() # 调整整体空白 plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) # 调整子图间距 plt.savefig("reult.png", dpi=1000) plt.show()信噪比为0.5，卷积核为3，进行中值滤波失去了这样的比照图：
信噪比为0.9，卷积核为3，进行中值滤波失去了这样的比照图：