# date : 2020.3.30import osimport cv2import randomimport numpy as npfrom matplotlib import pyplot as pltimport torchimport torch.nn.functional as Ffrom train_mnist import Net ##重要，尽管显示灰色(即在次代码中没用到)，但若没有引入这个模型代码，加载模型时会找不到模型from torch.autograd import Variableimport pandas as pdres = {}# 读取图像def FourierAddGauss_Blur(img, mu, sigma, k): # 归一化解决 img = np.array(img / 255, dtype=float) # 傅里叶变换 f = np.fft.fft2(img) # print('Fourier Done') fshift = np.fft.fftshift(f) print(fshift) [rows, cols] = fshift.shape for i in range(0, k): for j in range(0, k): # print('before') # print(fshift[i][j]) f_real = fshift[i][j].real f_imag = fshift[i][j].imag f_real += random.gauss(mu, sigma) fshift[i][j] = f_real + (1j * f_imag) # print(fshift[i][j]) # print('Add GaussNoise to (0,10) real Done') res = np.log(np.abs(fshift)) # 傅里叶逆变换 ishift = np.fft.ifftshift(fshift) iimg = np.fft.ifft2(ishift) iimg = np.abs(iimg) # 自适应高斯滤波 out = cv2.GaussianBlur(iimg, (3, 3), 1) # print('Gaussian Filter Done') # # 滤波后频率图 # f_out = np.fft.fft2(out) # fshift_out = np.fft.fftshift(f_out) # res_out = np.log(np.abs(fshift_out)) # 展现后果 plt.subplot(221), plt.imshow(img, 'gray'), plt.title('Original Image') plt.subplot(222), plt.imshow(res, 'gray'), plt.title('Fourier Image') plt.subplot(223), plt.imshow(iimg, 'gray'), plt.title('Inverse Fourier Image') plt.subplot(224), plt.imshow(out, 'gray'), plt.title('out Image') # plt.subplot(325), plt.imshow(res_out, 'gray'), plt.title('Out Fourier Image') plt.show() return outdef FourierAddGauss_NoBlur(img, mu, var, k): # 归一化解决 img = np.array(img / 255, dtype=float) # 傅里叶变换 f = np.fft.fft2(img) # print('Fourier Done') fshift = np.fft.fftshift(f) [rows, cols] = img.shape f_real = np.zeros(img.shape) f_imag = np.zeros(img.shape) for i in range(0, rows): for j in range(0, cols): f_real[i][j] = fshift[i][j].real f_imag[i][j] = fshift[i][j].imag # 生成高频高斯噪声 noise = np.random.normal(mu, var ** 0.5, img.shape) for i in range(0, rows): for j in range(0, cols): if i > k or j > k: noise[i][j] = 0 print("noise") print(noise) print("f_real") print(f_real) # 退出噪声 f_real = f_real + noise for i in range(0, rows): for j in range(0, cols): fshift[i][j] = f_real[i][j] + (1j * f_imag[i][j]) res = np.log(np.abs(fshift)) # 傅里叶逆变换 print("fshift") print(fshift) ishift = np.fft.ifftshift(fshift) iimg = np.fft.ifft2(ishift) iimg = np.abs(iimg) iimg = iimg * 255 # 自适应高斯滤波 out = cv2.GaussianBlur(iimg, (3, 3), 1) # print('Gaussian Filter Done') # # 滤波后频率图 # f_out = np.fft.fft2(out) # fshift_out = np.fft.fftshift(f_out) # res_out = np.log(np.abs(fshift_out)) # 展现后果 plt.subplot(221), plt.imshow(img, 'gray'), plt.title('Original Image') plt.subplot(222), plt.imshow(res, 'gray'), plt.title('Fourier Image') plt.subplot(223), plt.imshow(iimg, 'gray'), plt.title('Inverse Fourier Image') plt.subplot(224), plt.imshow(out, 'gray'), plt.title('out Image') # plt.subplot(325), plt.imshow(res_out, 'gray'), plt.title('Out Fourier Image') plt.show() return outdef train_pngAddGauss(root): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = Net() model.load_state_dict(torch.load('mnist_cnn.pt')) # 加载模型 model = model.to(device) model.eval() # 把模型转为test模式 # root = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test' # root = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test_AddGaussion2000_NoFiltering' # root = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test_AddGaussion_NoFiltering' files_path = os.listdir(root) print("start testing:...") for png_name in files_path: # print(png_name) img = cv2.imread(root + "/" + png_name, 0) # print(img.shape) # img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 图片转为灰度图，因为mnist数据集都是灰度图 img = np.array(img).astype(np.float32) img = np.expand_dims(img, 0) img = np.expand_dims(img, 0) # 扩大后，为[1，1，28，28] img = torch.from_numpy(img) img = img.to(device) output = model(Variable(img)) prob = F.softmax(output, dim=1) prob = Variable(prob) prob = prob.cpu().numpy() # 用GPU的数据训练的模型保留的参数都是gpu模式的，要显示则先要转回cpu，再转回numpy模式 # print(prob) # prob是10个分类的概率 pred = np.argmax(prob) # 选出概率最大的一个 res[png_name] = pred.item() print("train over") data = pd.read_csv(r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test.csv') right = 0 for key, val in res.items(): # print(key,val) if int(data[data['pic_name'] == key]['label']) == val: right = right + 1 # else: # print("Actually:" + key, val) # Wrong_pred = int(data[data['pic_name'] == key]['label']) # print("Wrong predicted:" + str(Wrong_pred)) acc = right / len(res) print(acc) return accdef gasuss_noise(image, mean=0, var=0.001): # ''' # 增加高斯噪声 # image:原始图像 # mean : 均值 # var : 方差,越大，噪声越大 # ''' image = cv2.imread(image) image = np.array(image / 255, dtype=float) # 将原始图像的像素值进行归一化，除以255使得像素值在0-1之间 noise = np.random.normal(mean, var ** 0.5, image.shape) # 创立一个均值为mean，方差为var呈高斯分布的图像矩阵 out = image + noise # 将噪声和原始图像进行相加失去加噪后的图像 if out.min() < 0: low_clip = -1. else: low_clip = 0. out = np.clip(out, low_clip, 1.0) # clip函数将元素的大小限度在了low_clip和1之间了，小于的用low_clip代替，大于1的用1代替 out = np.uint8(out * 255) # 解除归一化，乘以255将加噪后的图像的像素值复原 # cv.imshow("gasuss", out) noise = noise * 255 return [noise, out]def dctAddLowFre(img,mu,var,k): # 归一化解决 img = np.array(img / 255, dtype=float) img_dct = cv2.dct(np.float32(img)) # img_dct_log = np.log(abs(img_dct)) # print('img_dct:') # print(img_dct[0:k, 0:k]) # 生成低频噪声 # 学生成高斯噪声 noise = np.random.normal(mu, var ** 0.5, img.shape) # 创立一个均值为mu，方差为var呈高斯分布的图像矩阵 # 对 GaussionNoise 进行剪裁 for i in range(noise.shape[0]): for j in range(noise.shape[1]): if i > k or j > k: noise[i][j] = 0 # print('noise:') # print(noise) # 对 GaussionNoise 进行 IDCT 失去低频噪声 lowFre_noise = np.zeros(img.shape) lowFre_noise[0:k, 0:k] = cv2.idct(noise[0:k, 0:k]) # print('lowFre_noise:') # print(lowFre_noise[0:k, 0:k]) # 将 低频噪声 退出 img_dct img_dct_AddLowFreNoise = img_dct + lowFre_noise # print('img_dct_AddLowFreNoise') # print(img_dct_AddLowFreNoise[0:k, 0:k]) # 解除归一化 img_dct_AddLowFreNoise = img_dct_AddLowFreNoise * 255 # 逆离散余弦变换，变换图像回实数域 img_idct = cv2.idct(img_dct_AddLowFreNoise) img_idct_Blur = cv2.GaussianBlur(img_idct, (3, 3), 1) # show result plt.subplot(241), plt.imshow(img, 'gray'), plt.title('Img') plt.subplot(242), plt.imshow(noise, 'gray'), plt.title('Gaussion Noise') plt.subplot(243), plt.imshow(lowFre_noise, 'gray'), plt.title('Low Frequency Noise') plt.subplot(244), plt.imshow(img_dct, 'gray'), plt.title('Img_dct') plt.subplot(245), plt.imshow(img_dct_AddLowFreNoise, 'gray'), plt.title('Img_dct_AddLowFreNoise') plt.subplot(246), plt.imshow(img_idct, 'gray'), plt.title('IDCT Image') plt.subplot(247), plt.imshow(img_idct_Blur, 'gray'), plt.title('IDCT-Blur Image') plt.show() return img_idctdef train_pngAddLowFre(root,root_out,mu,var,k): files_path = os.listdir(root) for png_name in files_path: img = cv2.imread(root + "/" + png_name, 0) out = dctAddLowFre(img, mu, var, k) cv2.imwrite(root_out + "/" + png_name, out) train_pngAddGauss(root_out)def main(): # # test # root = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test' # root_out = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test_AddGaussion_NoFiltering' # # # # train # # root = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\training\train' # # root_out = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\training\train_AddGuassion4000_Filtering' # # files_path = os.listdir(root) # # array = [100,200,500,1000,1200,1500,2000,2500,3000,3500,4000,4500,5000] # array2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # k = 28 # for sigma in array2: # for png_name in files_path: # img = cv2.imread(root + "/" + png_name, 0) # # out = FourierAddGauss_Blur(img, 0, sigma, k) # out = FourierAddGauss_NoBlur(img, 0, sigma, k) # cv2.imwrite(root_out + "/" + png_name, out) # # print(sigma) # train_pngAddGauss(root_out) #单张图片测试 img = cv2.imread(r'D:\PythonProjects\Fourier\0.png',0) #第一个参数是文件门路，可本人批改 out = FourierAddGauss_NoBlur(img, 0, 50, 10) # # 单张图片测试 # # 离散余弦变换 并获取其幅频谱 # img = cv2.imread(r'D:\PythonProjects\Fourier\0.png', 0) # img_addLowFre = dctAddLowFre(img, 0, 0.1, 10) # # 数据集测试 # root = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test' # root_out = r'D:\PythonProjects\Fourier\mnist_png_all_files_with_traincsv\testing\test_AddLowFre' # mu = 0 # array_var = [0, 0.01, 0.02, 0.05, 0.1] # array_var2 = [0.6, 0.7, 0.8, 0.9, 1] # k = 10 # # for var in array_var2: # print(var) # train_pngAddLowFre(root, root_out, mu, var, k)if __name__ == '__main__': main()