for (img1_file, img2_file) in tqdm(img_pairs): img1 = np.array(imread(img1_file)) img2 = np.array(imread(img2_file)) if args.arch == 'StrainNet_l' and img1.ndim == 3: img1 = img1[:,:,1] img2 = img2[:,:,1] img1 = img1/255 img2 = img2/255 if img1.ndim == 2: img1 = img1[np.newaxis, ...] img2 = img2[np.newaxis, ...] img1 = img1[np.newaxis, ...] img2 = img2[np.newaxis, ...] img1 = torch.from_numpy(img1).float() img2 = torch.from_numpy(img2).float() if args.arch == 'StrainNet_h' or args.arch == 'StrainNet_f': img1 = torch.cat([img1,img1,img1],1) img2 = torch.cat([img2,img2,img2],1) input_var = torch.cat([img1,img2],1) elif img1.ndim == 3: img1 = np.transpose(img1, (2, 0, 1)) img2 = np.transpose(img2, (2, 0, 1)) img1 = torch.from_numpy(img1).float() img2 = torch.from_numpy(img2).float() input_var = torch.cat([img1, img2]).unsqueeze(0) # compute output input_var = input_var.to(device) output = model(input_var) if args.arch == 'StrainNet_h' or args.arch == 'StrainNet_l': output = torch.nn.functional.interpolate(input=output, scale_factor=2, mode='bilinear') output_to_write = output.data.cpu() output_to_write = output_to_write.numpy() disp_x = output_to_write[0,0,:,:] disp_x = - disp_x * args.div_flow + 1 disp_y = output_to_write[0,1,:,:] disp_y = - disp_y * args.div_flow + 1 filenamex = save_path/'{}{}'.format(img1_file.stem[:-1], '_disp_x') filenamey = save_path/'{}{}'.format(img1_file.stem[:-1], '_disp_y') np.savetxt(filenamex + '.csv', disp_x,delimiter=',') np.savetxt(filenamey + '.csv', disp_y,delimiter=',')

VB.form.INI.file.values.programs.rar_VB ini_Will

INI文件是一种简单的文本格式，用于存储用户配置或程序设置，它由一系列节（Section）和键值对（Key-Value pairs）组成。在VB中，我们可以使用Microsoft.Win32命名空间下的IniFile类来读写INI文件，但这个类并...

vmci_datagram.rar_V2

3. **Queue Pairs (QP)**：VMCI 使用队列对（Queue Pairs）来管理发送和接收数据报。每个队列对由一个发送队列和一个接收队列组成，允许并发和异步的通信。 4. **Memory Handles**：VMCI 允许进程间共享内存，通过...

from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser('create image pairs') parser.add_argument('--fold_A', dest='fold_A', help='input directory for image A', type=str, default='../dataset/50kshoes_edges') parser.add_argument('--fold_B', dest='fold_B', help='input directory for image B', type=str, default='../dataset/50kshoes_jpg') parser.add_argument('--fold_AB', dest='fold_AB', help='output directory', type=str, default='../dataset/test_AB') parser.add_argument('--num_imgs', dest='num_imgs', help='number of images',type=int, default=1000000) parser.add_argument('--use_AB', dest='use_AB', help='if true: (0001_A, 0001_B) to (0001_AB)',action='store_true') args = parser.parse_args() for arg in vars(args): print('[%s] = ' % arg, getattr(args, arg)) splits = os.listdir(args.fold_A) for sp in splits: img_fold_A = os.path.join(args.fold_A, sp) img_fold_B = os.path.join(args.fold_B, sp) img_list = os.listdir(img_fold_A) if args.use_AB: img_list = [img_path for img_path in img_list if '_A.' in img_path] num_imgs = min(args.num_imgs, len(img_list)) print('split = %s, use %d/%d images' % (sp, num_imgs, len(img_list))) img_fold_AB = os.path.join(args.fold_AB, sp) if not os.path.isdir(img_fold_AB): os.makedirs(img_fold_AB) print('split = %s, number of images = %d' % (sp, num_imgs)) for n in range(num_imgs): name_A = img_list[n] path_A = os.path.join(img_fold_A, name_A) if args.use_AB: name_B = name_A.replace('_A.', '_B.') else: name_B = name_A path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) im_B = cv2.imread(path_B, cv2.IMREAD_COLOR) im_AB = np.concatenate([im_A, im_B], 1) cv2.imwrite(path_AB, im_AB)，解释上述代码，并告诉我怎么设置文件夹格式

1. 引入必要的库：pdb、os、numpy、cv2和argparse。 2. 使用argparse来解析命令行参数，包括输入目录和输出目录等。 3. 遍历输入目录中的所有文件夹，并获取每个文件夹中的图像列表。 4. 根据命令行参数，选择要...

img_pairs = [] for ext in args.img_exts: test_files = data_dir.files('*1.{}'.format(ext)) for file in test_files: img_pair = file.parent / (file.stem[:-1] + '2.{}'.format(ext)) if img_pair.isfile(): img_pairs.append([file, img_pair]) print('{} samples found'.format(len(img_pairs))) # create model network_data = torch.load(args.pretrained) print("=> using pre-trained model '{}'".format(args.arch)) model = models.dict[args.arch](network_data).to(device) model.eval() cudnn.benchmark = True

然后，代码构建了一个与 file 对应的图像对路径 img_pair，将其添加到 img_pairs 列表中。接下来，代码打印出找到的样本数量。然后，代码加载预训练模型。通过 torch.load(args.pretrained) 加载预训练...

x_train, t_train, x_test, t_test = load_data('F:\\2023\\archive\\train') network = DeepConvNet() network.load_params("deep_convnet_params.pkl") print("calculating test accuracy ... ") sampled = 1000 x_test = x_test[:sampled] t_test = t_test[:sampled] prediect_result = [] for i in x_test: i = np.expand_dims(i, 0) y = network.predict(i) _result = network.predict(i) _result = softmax(_result) result = np.argmax(_result) prediect_result.append(int(result)) acc_number = 0 err_number = 0 for i in range(len(prediect_result)): if prediect_result[i] == t_test[i]: acc_number += 1 else: err_number += 1 print("预测正确数：", acc_number) print("预测错误数：", err_number) print("预测总数：", x_test.shape[0]) print("预测正确率：", acc_number / x_test.shape[0]) classified_ids = [] acc = 0.0 batch_size = 100 for i in range(int(x_test.shape[0] / batch_size)): tx = x_test[i * batch_size:(i + 1) * batch_size] tt = t_test[i * batch_size:(i + 1) * batch_size] y = network.predict(tx, train_flg=False) y = np.argmax(y, axis=1) classified_ids.append(y) acc += np.sum(y == tt) acc = acc / x_test.shape[0] classified_ids = np.array(classified_ids) classified_ids = classified_ids.flatten() max_view = 20 current_view = 1 fig = plt.figure() fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.2, wspace=0.2) mis_pairs = {} for i, val in enumerate(classified_ids == t_test): if not val: ax = fig.add_subplot(4, 5, current_view, xticks=[], yticks=[]) ax.imshow(x_test[i].reshape(28, 28), cmap=plt.cm.gray_r, interpolation='nearest') mis_pairs[current_view] = (t_test[i], classified_ids[i]) current_view += 1 if current_view > max_view: break print("======= 错误预测结果展示 =======") print("{view index: (label, inference), ...}") print(mis_pairs) plt.show()

这段代码是一个深度卷积神经网络用于对手写数字图像进行分类的代码。首先，通过load_data函数加载训练数据和测试数据集，然后使用DeepConvNet()创建了一个深度卷积神经网络，并通过load_params函数加载了预训练的...

详细解释一下这段代码，每一句给出详细注解：results_df = pd.DataFrame(columns=['image_path', 'dataset', 'scene', 'rotation_matrix', 'translation_vector']) for dataset_scene in tqdm(datasets_scenes, desc='Running pipeline'): dataset, scene = dataset_scene.split('/') img_dir = f"{INPUT_ROOT}/{'train' if DEBUG else 'test'}/{dataset}/{scene}/images" if not os.path.exists(img_dir): continue feature_dir = f"{DATA_ROOT}/featureout/{dataset}/{scene}" os.system(f"rm -rf {feature_dir}") os.makedirs(feature_dir) fnames = sorted(glob(f"{img_dir}/*")) print('fnames',len(fnames)) # Similarity pipeline if sim_th: index_pairs, h_w_exif = get_image_pairs_filtered(similarity_model, fnames=fnames, sim_th=sim_th, min_pairs=20, all_if_less=20) else: index_pairs, h_w_exif = get_img_pairs_all(fnames=fnames) # Matching pipeline matching_pipeline(matching_model=matching_model, fnames=fnames, index_pairs=index_pairs, feature_dir=feature_dir) # Colmap pipeline maps = colmap_pipeline(img_dir, feature_dir, h_w_exif=h_w_exif) # Postprocessing results = postprocessing(maps, dataset, scene) # Create submission for fname in fnames: image_id = '/'.join(fname.split('/')[-4:]) if image_id in results: R = results[image_id]['R'].reshape(-1) T = results[image_id]['t'].reshape(-1) else: R = np.eye(3).reshape(-1) T = np.zeros((3)) new_row = pd.DataFrame({'image_path': image_id, 'dataset': dataset, 'scene': scene, 'rotation_matrix': arr_to_str(R), 'translation_vector': arr_to_str(T)}, index=[0]) results_df = pd.concat([results_df, new_row]).reset_index(drop=True)

for dataset_scene in tqdm(datasets_scenes, desc='Running pipeline'): dataset, scene = dataset_scene.split('/') img_dir = f"{INPUT_ROOT}/{'train' if DEBUG else 'test'}/{dataset}/{scene}/images" if ...

详细解释一下这段代码，每一句给出详细注解：def matching_pipeline(matching_model, fnames, index_pairs, feature_dir): cache = {} with h5py.File(f"{feature_dir}/matches_{matching_name}.h5", mode='w') as f_match: for pair_idx in tqdm(index_pairs, desc='Get matched keypoints using matching model'): idx1, idx2 = pair_idx fname1, fname2 = fnames[idx1], fnames[idx2] key1, key2 = fname1.split('/')[-1], fname2.split('/')[-1] mkpts1, mkpts2, num_sg_matches = matching_inference(matching_model, fname1, fname2, cache) group = f_match.require_group(key1) if num_sg_matches >= n_matches: data = np.concatenate([mkpts1, mkpts2], axis=1) # data = np.vstack(list({tuple(row) for row in np.concatenate([mkpts1, mkpts2], axis=1).astype(np.int32)})).astype(np.float32) group.create_dataset(key2, data=data) kpts = defaultdict(list) total_kpts = defaultdict(int) match_indexes = defaultdict(dict) with h5py.File(f"{feature_dir}/matches_{matching_name}.h5", mode='r') as f_match: for k1 in f_match.keys(): group = f_match[k1] for k2 in group.keys(): matches = group[k2][...] total_kpts[k1] kpts[k1].append(matches[:, :2]) kpts[k2].append(matches[:, 2:]) current_match = torch.arange(len(matches)).reshape(-1, 1).repeat(1, 2) current_match[:, 0] += total_kpts[k1] current_match[:, 1] += total_kpts[k2] total_kpts[k1] += len(matches) total_kpts[k2] += len(matches) match_indexes[k1][k2] = current_match

for pair_idx in tqdm(index_pairs, desc='Get matched keypoints using matching model'): # 遍历索引对列表 idx1, idx2 = pair_idx fname1, fname2 = fnames[idx1], fnames[idx2] # 获取文件名 key1, key2 = ...

% 读取红外图像和可见光图像 ir_img = imread('D:\桌面1\peizhun\invisual_T.jpg'); vis_img = imread('D:\桌面1\peizhun\visual_Z.jpg'); % 提取SIFT特征点 ir_pts = detectSURFFeatures(rgb2gray(ir_img)); vis_pts = detectSURFFeatures(rgb2gray(vis_img)); % 提取特征点描述符 [ir_features, ir_pts] = extractFeatures(rgb2gray(ir_img), ir_pts); [vis_features, vis_pts] = extractFeatures(rgb2gray(vis_img), vis_pts); % 匹配特征点 index_pairs = matchFeatures(ir_features, vis_features); matched_ir_pts = ir_pts(index_pairs(:,1)); matched_vis_pts = vis_pts(index_pairs(:,2)); % 用RANSAC算法计算单应性矩阵 [ir_inliers, vis_inliers] = estimateGeometricTransform(... matched_ir_pts, matched_vis_pts, 'projective'); % 对红外图像进行单应性变换 registered_ir = imwarp(ir_img, ir_inliers); registered_vis = vis_img; % 对齐两张图像 merged_image = imfuse(registered_vis, registered_ir, 'blend'); % 显示结果 imshow(merged_image);

1. 确认图像路径是否正确，是否成功读取了红外图像和可见光图像。 2. 检查是否成功提取到了SIFT特征点和特征点描述符。 3. 检查是否成功进行了特征点匹配，并且匹配点数是否足够。 4. 检查...

classified_ids = np.array(classified_ids) classified_ids = classified_ids.flatten() max_view = 20 current_view = 1 fig = plt.figure() fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.2, wspace=0.2) mis_pairs = {} for i, val in enumerate(classified_ids == t_test): if not val: ax = fig.add_subplot(4, 5, current_view, xticks=[], yticks=[]) ax.imshow(x_test[i].reshape(28, 28), cmap=plt.cm.gray_r, interpolation='nearest') mis_pairs[current_view] = (t_test[i], classified_ids[i]) current_view += 1 if current_view > max_view: break

这段代码的作用是将分类错误的样本可视化，...最后，将当前子图的编号和对应的真实标签和预测标签保存到 mis_pairs 字典中，并将 current_view 加 1。如果当前绘制的子图数量已经达到了 max_view 的限制，则跳出循环。

ir_img = imread('D:\桌面1\peizhun\invisual_T.jpg'); vis_img = imread('D:\桌面1\peizhun\visual_Z.jpg'); % 提取SIFT特征点 ir_pts = detectSURFFeatures(rgb2gray(ir_img)); vis_pts = detectSURFFeatures(rgb2gray(vis_img)); % 提取特征点描述符 [ir_features, ir_pts] = extractFeatures(rgb2gray(ir_img), ir_pts); [vis_features, vis_pts] = extractFeatures(rgb2gray(vis_img), vis_pts); % 匹配特征点 index_pairs = matchFeatures(ir_features, vis_features); matched_ir_pts = ir_pts(index_pairs(:,1)); matched_vis_pts = vis_pts(index_pairs(:,2)); % 用RANSAC算法计算单应性矩阵 [ir_inliers, vis_inliers] = estimateGeometricTransform(... matched_ir_pts, matched_vis_pts, 'projective'); % 对红外图像进行单应性变换 registered_ir = imwarp(ir_img, ir_inliers); registered_vis = vis_img; % 对齐两张图像 merged_image = imfuse(registered_vis, registered_ir, 'blend'); % 显示结果 imshow(merged_image);这段代码出现了错误错误使用 vision.internal.geotrans.algEstimateGeometricTransform>checkRuntimeStatus (第 99 行) matchedPoints1 and matchedPoints2 do not have enough points. The number of points in each set must be at least 4. 出错 vision.internal.geotrans.algEstimateGeometricTransform (第 70 行) checkRuntimeStatus(statusCode, status, sampleSize); 出错 estimateGeometricTransform (第 123 行) vision.internal.geotrans.algEstimateGeometricTransform(... 出错 peizhun (第 22 行) [ir_inliers, vis_inliers] = estimateGeometricTransform(...

这段代码中出现了一个错误，指出匹配的特征点数不足。这个错误可能是由于提取的SURF特征点数量不够所导致的。可以尝试增加特征点的数量，或者使用其他的特征点提取算法。另外，这段代码中读取图片的路径中包含了...

使用代码import numpy as np import pandas as pd from scipy.stats import pearsonr data = pd.read_csv('os2.csv') gene_pairs = pd.read_csv('os1.csv') gene_pair_names = gene_pairs['基因对名称'].values pearson_coeffs = [] for gene_pair in gene_pair_names: gene1, gene2 = gene_pair.split('_') expression1 = data[gene1].values expression2 = data[gene2].values coeff, _ = pearsonr(expression1, expression2) pearson_coeffs.append(coeff)出现了Traceback (most recent call last): File "/home/jialinlu/miniconda3[闪电]b/python3.9/site-packages/pandas/core/indexes/base.py", line 3621, in get_loc return self._engine.get_loc(casted_key) File "pandas/_libs/index.pyx", line 136, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/index.pyx", line 163, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/hashtable_class_helper.pxi", line 5198, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas/_libs/hashtable_class_helper.pxi", line 5206, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'Os01t0113150' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 4, in <module> File "/home/jialinlu/miniconda3[闪电]b/python3.9/site-packages/pandas/core/frame.py", line 3505, in getitem indexer = self.columns.get_loc(key) File "/home/jialinlu/miniconda3[闪电]b/python3.9/site-packages/pandas/core/indexes/base.py", line 3623, in get_loc raise KeyError(key) from err KeyError: 'Os01t0113150'的报错是什么原因

2. 列名不存在：检查一下 DataFrame data 中是否真的存在名为 'Os01t0113150' 的列。你可以使用 data.columns 属性查看 DataFrame 中的所有列名。解决这个错误的方法是检查你指定的列名是否正确，并确保该列名...

filtered_pairs = np.array([k for k,v in pairs.items() if v >= thresh_exhaustives]) matches_idxs = filtered_pairs if len(filtered_pairs) else np.empty((0,2), dtype=np.uint32) db.add_two_view_geometry_new(matches[idx][0], matches_idxs) 这段代码中的thresh_exhaustives取值对图像匹配精度有什么影响，怎么提高

这段代码中的 thresh_exhaustives 是一个阈值，影响着从所有可能的匹配中筛选出符合要求的匹配的数量。具体来说，如果一个匹配对的得分（即 v）大于等于 thresh_exhaustives，那么这个匹配对就会被保留下来，...

详细解释一下这段代码，每一句都要进行注解：for dataset in datasets: print(dataset) if dataset not in out_results: out_results[dataset] = {} for scene in data_dict[dataset]: print(scene) # Fail gently if the notebook has not been submitted and the test data is not populated. # You may want to run this on the training data in that case? img_dir = f'{src}/test/{dataset}/{scene}/images' if not os.path.exists(img_dir): continue # Wrap the meaty part in a try-except block. try: out_results[dataset][scene] = {} img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] print (f"Got {len(img_fnames)} images") feature_dir = f'featureout/{dataset}{scene}' if not os.path.isdir(feature_dir): os.makedirs(feature_dir, exist_ok=True) t=time() index_pairs = get_image_pairs_shortlist(img_fnames, sim_th = 0.5644583, # should be strict min_pairs = 33, # we select at least min_pairs PER IMAGE with biggest similarity exhaustive_if_less = 20, device=device) t=time() -t timings['shortlisting'].append(t) print (f'{len(index_pairs)}, pairs to match, {t:.4f} sec') gc.collect() t=time() if LOCAL_FEATURE != 'LoFTR': detect_features(img_fnames, 2048, feature_dir=feature_dir, upright=True, device=device, resize_small_edge_to=600 ) gc.collect() t=time() -t timings['feature_detection'].append(t) print(f'Features detected in {t:.4f} sec') t=time() match_features(img_fnames, index_pairs, feature_dir=feature_dir,device=device) else: match_loftr(img_fnames, index_pairs, feature_dir=feature_dir, device=device, resize_to=(600, 800)) t=time() -t timings['feature_matching'].append(t) print(f'Features matched in {t:.4f} sec') database_path = f'{feature_dir}/colmap.db' if os.path.isfile(database_path): os.remove(database_path) gc.collect() import_into_colmap(img_dir, feature_dir=feature_dir,database_path=database_path) output_path = f'{feature_dir}/colmap_rec_{LOCAL_FEATURE}' t=time() pycolmap.match_exhaustive(database_path) t=time() - t timings['RANSAC'].append(t) print(f'RANSAC in {t:.4f} sec')

img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] # 获取当前场景中的图像文件名列表 print(f"Got {len(img_fnames)} images") feature_dir = f'featureout/{dataset}{scene}' # 设置特征...

import pandas as pd data = pd.read_excel('C:\Users\home\Desktop\新建文件夹(1)\支撑材料\数据\111.xlsx','Sheet5',index_col=0) data.to_csv('data.csv',encoding='utf-8') import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt df = pd.read_csv(r"data.csv", encoding='utf-8', index_col=0).reset_index(drop=True) df from sklearn import preprocessing df = preprocessing.scale(df) df covX = np.around(np.corrcoef(df.T),decimals=3) covX featValue, featVec= np.linalg.eig(covX.T) featValue, featVec def meanX(dataX): return np.mean(dataX,axis=0) average = meanX(df) average m, n = np.shape(df) m,n data_adjust = [] avgs = np.tile(average, (m, 1)) avgs data_adjust = df - avgs data_adjust covX = np.cov(data_adjust.T) covX featValue, featVec= np.linalg.eig(covX) featValue, featVec tot = sum(featValue) var_exp = [(i / tot) for i in sorted(featValue, reverse=True)] cum_var_exp = np.cumsum(var_exp) plt.bar(range(1, 14), var_exp, alpha=0.5, align='center', label='individual explained variance') plt.step(range(1, 14), cum_var_exp, where='mid', label='cumulative explained variance') plt.ylabel('Explained variance ratio') plt.xlabel('Principal components') plt.legend(loc='best') plt.show() eigen_pairs = [(np.abs(featValue[i]), featVec[:, i]) for i in range(len(featValue))] eigen_pairs.sort(reverse=True) w = np.hstack((eigen_pairs[0][1][:, np.newaxis], eigen_pairs[1][1][:, np.newaxis])) X_train_pca = data_adjust.dot(w) colors = ['r', 'b', 'g'] markers = ['s', 'x', 'o'] for l, c, m in zip(np.unique(data_adjust), colors, markers): plt.scatter(data_adjust,data_adjust, c=c, label=l, marker=m) plt.xlabel('PC 1') plt.ylabel('PC 2') plt.legend(loc='lower left') plt.show()

这段代码是在进行主成分分析（PCA）的数据预处理和可视化操作。首先读取一个 Excel 文件并将其转换为 CSV 格式，然后使用 sklearn 库中的 preprocessing 模块对数据进行标准化处理，接着计算数据集的协方差矩阵并...

ir_img = imread('peizhun\invisual_T.jpg'); vis_img = imread('peizhun\visual_Z.jpg'); % 提取SIFT特征点 ir_pts = detectSURFFeatures(rgb2gray(ir_img)); vis_pts = detectSURFFeatures(rgb2gray(vis_img)); % 提取特征点描述符 [ir_features, ir_pts] = extractFeatures(rgb2gray(ir_img), ir_pts); [vis_features, vis_pts] = extractFeatures(rgb2gray(vis_img), vis_pts); % 匹配特征点 index_pairs = matchFeatures(ir_features, vis_features); matched_ir_pts = ir_pts(index_pairs(:,1)); matched_vis_pts = vis_pts(index_pairs(:,2)); % 用RANSAC算法计算单应性矩阵 [ir_inliers, vis_inliers] = estimateGeometricTransform(matched_ir_pts, matched_vis_pts, 'projective'); % 对红外图像进行单应性变换 registered_ir = imwarp(ir_img, ir_inliers); registered_vis = vis_img; % 对齐两张图像 merged_image = imfuse(registered_vis, registered_ir, 'blend'); % 显示结果 imshow(merged_image);出现错误错误使用 vision.internal.geotrans.algEstimateGeometricTransform>checkRuntimeStatus (第 99 行) matchedPoints1 and matchedPoints2 do not have enough points. The number of points in each set must be at least 4. 出错 vision.internal.geotrans.algEstimateGeometricTransform (第 70 行) checkRuntimeStatus(statusCode, status, sampleSize); 出错 estimateGeometricTransform (第 123 行) vision.internal.geotrans.algEstimateGeometricTransform(... 出错 peizhun (第 51 行) [ir_inliers, vis_inliers] = estimateGeometricTransform(matched_ir_pts, matched_vis_pts, 'projective');

这段代码的错误提示是 "matchedPoints1 and matchedPoints2 do not have enough points. The number of points in each set must be at least 4."，即匹配的特征点数量不足4个，无法进行单应性变换。这可能是由于...

这段代码无法运行，请为我修改一下并添加注释：import numpy as np import pandas as pd import matplotlib.pyplot as plt # 读入鸢尾花数据集 df = pd.read_csv('iris_pca.csv', header=None) # 将数据转换为NumPy数组 X = df.iloc[:, :-1].values y = df.iloc[:, -1].values # 对所有样本进行中心化 X_mean = np.mean(X, axis=0) X_centered = X - X_mean # 计算样本的协方差矩阵 cov_matrix = np.cov(X_centered, rowvar=False) # 对协方差矩阵做特征值分解 eigenvalues, eigenvectors = np.linalg.eigh(cov_matrix) # 将特征向量按照对应的特征值从大到小排序 eig_pairs = [(np.abs(eigenvalues[i]), eigenvectors[:, i]) for i in range(len(eigenvalues))] eig_pairs.sort(reverse=True) # 取最大的d个特征值所对应的特征向量 d = 2 w = np.hstack((eig_pairs[i][1].reshape(4, 1)) for i in range(d)) # 计算投影矩阵 X_new = X_centered.dot(w) # 将降维后的数据和标记合并 data_new = np.hstack((X_new, y.reshape(len(y), 1))) # 将降维后的数据可视化呈现 plt.scatter(X_new[:, 0], X_new[:, 1], c=y) plt.xlabel('PC1') plt.ylabel('PC2') plt.show()

w = np.hstack((eig_pairs[i][1].reshape(4, 1)) for i in range(d)) # 计算投影矩阵 X_new = X_centered.dot(w) # 将降维后的数据和标记合并 data_new = np.hstack((X_new, y.reshape(len(y), 1))) # 将降维后的...

def read_lfw_pairs(self, pairs_filename): pairs = [] with open(pairs_filename, 'r') as f: for line in f.readlines()[1:]: pair = line.strip().split() pairs.append(pair) return np.array(pairs) ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (6000,) + inhomogeneous part.

您可以尝试使用NumPy的np.array函数创建一个具有相同形状的数组，并将所有元素添加到该数组中。另外，还请确保您的数据格式正确，特别是在将字符串拆分为pair时。您可以使用print(pair)来调试并查看每个pair的...

解释这行代码matrix = np.zeros((24, 24)) for i in range(1, 25): for j in range(1, 25): matrix[i-1][j-1] = all_pairs_shortest_path_lengths[i][j] img=plt.imshow(matrix) plt.colorbar(img)

因为数组下标从 0 开始计数，而节点编号从 1 开始计数，所以需要将 i 和 j 都减 1。最后，使用 plt.imshow(matrix) 将二维数组 matrix 以彩色图像的形式显示出来，并使用 plt.colorbar 添加一个颜色条。其中，plt....

详细解释一下这段代码，每一句都要进行注解：def get_image_pairs_shortlist(fnames, sim_th = 0.6, # should be strict min_pairs = 20, exhaustive_if_less = 20, device=torch.device('cpu')): num_imgs = len(fnames) if num_imgs <= exhaustive_if_less: return get_img_pairs_exhaustive(fnames) model = timm.create_model('tf_efficientnet_b7', checkpoint_path='/kaggle/input/tf-efficientnet/pytorch/tf-efficientnet-b7/1/tf_efficientnet_b7_ra-6c08e654.pth') model.eval() descs = get_global_desc(fnames, model, device=device) #这段代码使用 PyTorch 中的 torch.cdist 函数计算两个矩阵之间的距离，其中参数 descs 是一个矩阵，表示一个数据集中的所有样本的特征向量。函数将计算两个矩阵的 p 范数距离，即对于矩阵 A 和 B，其 p 范数距离为： #dist_{i,j} = ||A_i - B_j||_p #其中 i 和 j 分别表示矩阵 A 和 B 中的第 i 和 j 行，||.||_p 表示 p 范数。函数的返回值是一个矩阵，表示所有样本之间的距离。 # detach() 和 cpu() 方法是为了将计算结果从 GPU 转移到 CPU 上，并将其转换为 NumPy 数组。最终的结果将会是一个 NumPy 数组。 dm = torch.cdist(descs, descs, p=2).detach().cpu().numpy() # removing half mask = dm <= sim_th total = 0 matching_list = [] ar = np.arange(num_imgs) already_there_set = [] for st_idx in range(num_imgs-1): mask_idx = mask[st_idx] to_match = ar[mask_idx] if len(to_match) < min_pairs: to_match = np.argsort(dm[st_idx])[:min_pairs] for idx in to_match: if st_idx == idx: continue if dm[st_idx, idx] < 1000: matching_list.append(tuple(sorted((st_idx, idx.item())))) total+=1 matching_list = sorted(list(set(matching_list))) return matching_list

首先，函数通过 len(fnames) 判断输入的图像数量，如果小于等于 exhaustive_if_less，就直接调用 get_img_pairs_exhaustive 函数执行穷举匹配操作。如果输入的图像数量大于 exhaustive_if_less，则使用 timm 库...

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解释这行代码matrix = np.zeros((24, 24)) for i in range(1, 25): for j in range(1, 25): matrix[i-1][j-1] = all_pairs_shortest_path_lengths[i][j] img=plt.imshow(matrix) plt.colorbar(img)

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