import os import numpy as np import pandas as pd from PIL import Image from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA import cv2 def read_and_process_images(folder_path, target_shape): image_data = [] image_labels = [] image_names = [] image_paths = [] for root, dirs, files in os.walk(folder_path): for file in files: fileLower = file.lower() if fileLower.endswith(".jpg") or fileLower.endswith(".png") or fileLower.endswith(".jpeg"): # 仅处理 jpg 和 png 格式的图像文件 image_path = os.path.join(root, file) image = Image.open(image_path).convert('L') # 转换为灰度图像 resized_image = image.resize(target_shape) # 调整图像大小 image_array = np.array(resized_image).reshape(-1) # 将图像转换为一维数组 image_data.append(image_array) image_labels.append(os.path.basename(root)) # 文件夹名称作为标签 image_names.append(file) image_paths.append(image_path) image_data = np.array(image_data) image_labels = np.array(image_labels) image_names = np.array(image_names) image_paths = np.array(image_paths) return image_data, image_labels, image_names, image_paths def save_to_csv(data, labels, names, paths, pca_projection, lda_projection, output_file): num_pca_components = pca_projection.shape[1] num_lda_components = lda_projection.shape[1] columns = ['类别', '图片名', '图片路径'] + [f'pca_{i}' for i in range(num_pca_components)] + [f'lda_{i}' for i in range(num_lda_components)] df = pd.DataFrame(columns=columns) for i in range(len(data)): row_data = np.concatenate(([labels[i]], [names[i]], [paths[i]], pca_projection[i], lda_projection[i])) df.loc[i] = row_data df.to_csv(output_file, index=False) print(f"Data saved to {output_file}") '''将keypoints转换为可以使用的向量''' def keypoints_to_vector(keypoints): # 创建一个空的向量列表 vectors = [] # 遍历所有关键点 for kp in keypoints: # 提取关键点的属性 x = kp.pt[0] # x 坐标 y = kp.pt[1] # y 坐标 scale = kp.size # 尺度 orientation = kp.angle # 方向 # 将关键点属性组合成一个向量 vector = np.array([x, y, scale, orientation]) # 添加到向量列表中 vectors.append(vector) # 将列表转换为 numpy 数组 vectors = np.array(vectors) return vectors from skimage.feature import graycomatrix, graycoprops from skimage import io, color, img_as_ubyte import matplotlib.pyplot as plt def feature_glcm(input_img_path,blockPlt=True): gray_image = io.imread(input_img_path) # gray_image = io.imread('data/TRAIN/BTR70/HB04039.004.jpeg') gray_image = img_as_ubyte(gray_image) # 定义GLCM参数 distances = [1, 2, 3, 4] # 邻距离 angles = [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4] # 邻角度 # 计算GLCM glcm = graycomatrix(gray_image, distances=distances, angles=angles, symmetric=True, normed=True) # 计算GLCM特征 contrast = graycoprops(glcm, prop='contrast') dissimilarity = graycoprops(glcm, prop='dissimilarity') homogeneity = graycoprops(glcm, prop='homogeneity') energy = graycoprops(glcm, prop='energy') correlation = graycoprops(glcm, prop='correlation') # 打印GLCM特征 # print("Contrast:", contrast) # print("Dissimilarity:", dissimilarity) # print("Homogeneity:", homogeneity) # print("Energy:", energy) # print("Correlation:", correlation) # 保存GLCM特征图,保持目录结构 glcm_dir = 'glcm' glcm_img_path = os.path.join(glcm_dir, input_img_path) glcm_img_dir = os.path.dirname(glcm_img_path) os.makedirs(glcm_img_dir, exist_ok=True) # 保存GLCM Energy特征图 cv2.imwrite(glcm_img_path, energy) fea = np.concatenate([contrast,dissimilarity,homogeneity,energy,correlation]).flatten() return fea from skimage.feature import hog from skimage import exposure, io, color from skimage import io import matplotlib.pyplot as plt def feature_hog(input_img_path,blockPlt=True): image = io.imread(input_img_path) # 提取HOG特征 features, hog_image = hog(image, pixels_per_cell=(8, 8), cells_per_block=(2, 2), visualize=True) # # 可视化HOG图像 # fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) # print(len(features)) # print(hog_image.shape) # 对HOG图像进行对比度增强 hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10)) # plt.imshow(hog_image_rescaled,cmap='gray') # 保存HOG图像,保持目录结构 hog_dir = 'hog' hog_img_path = os.path.join(hog_dir, input_img_path) hog_img_dir = os.path.dirname(hog_img_path) os.makedirs(hog_img_dir, exist_ok=True) cv2.imwrite(hog_img_path, hog_image_rescaled) return features import cv2 import numpy as np import matplotlib.pyplot as plt from skimage import feature from skimage import io def feature_lbp(input_img_path,blockPlt=True): # 定义LBP算子的计算函数 def calculate_lbp_pixel(center, pixels): lbp_code = 0 for i, pixel in enumerate(pixels): lbp_code |= (pixel >= center) << i return lbp_code # 定义LBP特征提取函数 def extract_lbp_features(image, radius, num_points): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) height, width = gray.shape lbp_image = np.zeros((height, width), dtype=np.uint8) for y in range(radius, height - radius): for x in range(radius, width - radius): center = gray[y, x] pixels = [ gray[y - radius, x - radius], gray[y - radius, x], gray[y - radius, x + radius], gray[y, x + radius], gray[y + radius, x + radius], gray[y + radius, x], gray[y + radius, x - radius], gray[y, x - radius] ] lbp_code = calculate_lbp_pixel(center, pixels) lbp_image[y, x] = lbp_code hist = np.histogram(lbp_image, bins=np.arange(0, num_points + 2), range=(0, num_points + 1))[0] hist = hist.astype("float") hist /= (hist.sum() + 1e-6) # 归一化 return hist # 加载图像 image = cv2.imread(input_img_path) # 读取图像并转换为灰度图 # image = cv2.imread('./data/TRAIN/2S1/HB19404.jpg', cv2.IMREAD_GRAYSCALE) # image = io.imread('./data/TRAIN/2S1/HB19404.jpg')#color.rgb2gray(image) # print(image.shape) # 设置LBP算子的半径和邻域点数 radius = 1 num_points = 8 * radius # 提取LBP特征 lbp_features = extract_lbp_features(image, radius, num_points) # 打印特征向量 # print("LBP特征向量:") # print(lbp_features) # 计算LBP特征 image = io.imread(input_img_path) radius = 1 n_points = 8 * radius lbp_image = feature.local_binary_pattern(image, n_points, radius, method='uniform') # 保存LBP特征图,保持目录结构 lbp_dir = 'lbp' lbp_img_path = os.path.join(lbp_dir, input_img_path) lbp_img_dir = os.path.dirname(lbp_img_path) os.makedirs(lbp_img_dir, exist_ok=True) cv2.imwrite(lbp_img_path, lbp_image) return lbp_features def feature_orb(input_img_path,blockPlt=True): image = io.imread(input_img_path) # 创建ORB对象 orb = cv2.ORB_create() # 检测ORB特征点 keypoints, descriptors = orb.detectAndCompute(image, None) # 绘制特征点 image_with_keypoints = cv2.drawKeypoints(image, keypoints, None) # 保存带有特征点的图像,保持目录结构 orb_dir = 'orb' orb_img_path = os.path.join(orb_dir, input_img_path) orb_img_dir = os.path.dirname(orb_img_path) os.makedirs(orb_img_dir, exist_ok=True) cv2.imwrite(orb_img_path, image_with_keypoints) fea_keypoints = keypoints_to_vector(keypoints) fea_keypoints = fea_keypoints.flatten() return fea_keypoints def feature_shift(input_img_path,blockPlt=True): image = io.imread(input_img_path) # 创建SIFT特征检测器 sift = cv2.SIFT_create() # 检测关键点和提取描述符 keypoints, descriptors = sift.detectAndCompute(image, None) # print(keypoints) # print(descriptors) # 显示关键点 output_image = cv2.drawKeypoints(image, keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # 保存带有特征点的图像,保持目录结构 sift_dir = 'sift' sift_img_path = os.path.join(sift_dir, input_img_path) sift_img_dir = os.path.dirname(sift_img_path) os.makedirs(sift_img_dir, exist_ok=True) cv2.imwrite(sift_img_path, output_image) fea_keypoints = keypoints_to_vector(keypoints) fea_keypoints = fea_keypoints.flatten() return fea_keypoints def batch_pca_lda(): # 文件夹路径 # folder_path = 'data/TEST/' folder_path = 'data/TRAIN/' # 图像调整大小 target_shape = (100, 100) # PCA 和 LDA 的主成分数量 pca_components = 128 lda_components = 7 # lda降维数目不能超过类别数目 # 输出 CSV 文件名 # output_file = 'sar_test_pca_lda.csv' output_file = 'sar_train_pca_lda.csv' # 读取并处理图像数据 image_data, image_labels, image_names, image_paths = read_and_process_images(folder_path, target_shape) # 使用 PCA 进行降维 pca = PCA(n_components=pca_components) pca_projection = pca.fit_transform(image_data) # 使用 LDA 进行降维 lda = LDA(n_components=lda_components) lda_projection = lda.fit_transform(image_data, image_labels) # 保存数据到 CSV 文件 save_to_csv(image_data, image_labels, image_names, image_paths, pca_projection, lda_projection, output_file) # 传入输入图像路径以及需要展示的特征，展示特征图像 def display_feature(input_img_path, fea_type): fea_dir = fea_type fea_img_path = os.path.join(fea_dir, input_img_path) if os.path.exists(fea_img_path): fea_img = io.imread(fea_img_path) plt.imshow(fea_img, cmap='gray') plt.show() else: print(f"Feature image not found at {fea_img_path}") if __name__ == '__main__': # batch_pca_lda() # img_path = 'data/TRAIN/BTR70/HB04039.004.jpeg' # img_path = 'data/TEST/2S1/HB14931.JPG' # img_path = 'data/TEST/2S1/HB14938.JPG' # img_path = 'test_data\\TEST\\2S1\\HB14934.JPG' img_path = 'D:\\hiddz\\SAR\\test_data\\TEST\\2S1\\HB14934.JPG' fea = feature_glcm(img_path,False) fea = feature_hog(img_path,False) fea = feature_lbp(img_path,False) fea = feature_orb(img_path,False) fea = feature_shift(img_path,False) print(fea) display_feature(img_path,'glcm') display_feature(img_path,'hog') display_feature(img_path,'lbp') display_feature(img_path,'orb') display_feature(img_path,'sift')