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Python语言挑战记录：nlp100_Python的深度解析

作者使用了多种Python内置函数和模块，如切片操作、print()函数、zip()、itertools.zip_longest()、*args参数、str.join()、functools.reduce()、len()、list.append()、str.split()、list.count()、enumerate()等。...

# 将图片移动到相应的文件夹 cluster_path = os.path.join(path, cluster_folder) if not os.path.exists(cluster_path): os.mkdir(cluster_path) for i in range(10): cluster_i_path = os.path.join(cluster_path, str(i)) if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join(path, f'{j}.png') new_path = os.path.join(cluster_i_path, f'{j}.png') os.rename(old_path, new_path) root_path = r'D:\jk' for i in range(10): cluster_i_path = os.path.join(root_path, f'cluster_{i}') if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join(path, f'{j}.png') new_path = os.path.join(cluster_i_path, f'{j}.png') os.rename(old_path, new_path)，这段代码有什么错误吗

cluster_i_path = os.path.join(root_path, f'cluster_{i}') if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join...

cluster_path = os.path.join(path, cluster_folder) if not os.path.exists(cluster_path): os.mkdir(cluster_path) for i in range(10): cluster_i_path = os.path.join(cluster_path, str(i)) if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join(path, f'{j}.png') new_path = os.path.join(cluster_i_path, f'{j}.png') os.rename(old_path, new_path)，这段代码是什么意思

具体来说，该代码首先通过 os.path.join() 函数将 path 和 cluster_folder 拼接成完整的聚类文件夹路径 cluster_path，然后使用 os.mkdir() 函数创建聚类文件夹，如果该文件夹已经存在则不进行任何操作。...

path = 'D:/wjd' # 设置图片所在文件夹路径 labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] # 设置聚类结果 for i in range(10): cluster_i_path = os.path.join(root_path, f'cluster_{i}') if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join(path, f'Al{j + 10000}.png') new_path = os.path.join(cluster_i_path, f'al{j + 10000}.png') os.rename(old_path, new_path)，这里j的取值是多少

对于每个 j，我们都会判断其对应的标签值 label 是否等于 i，如果等于，则将对应的文件移动到 cluster_i_path 文件夹中，并修改文件名。在修改文件名时，j 的值加上了 10000，以避免文件名重复。

for i_utt, (utt_id, start_samp_16k, n_end_samp_16k, speaker1_target_snr_db) in \ enumerate(mix_param_df.itertuples(index=False, name=None)): s1_path = os.path.join(wsj_path, S1_DIR, utt_id) s1_samples, _ = sf.read(s1_path) s2_path = os.path.join(wsj_path, S2_DIR, utt_id) s2_samples, _ = sf.read(s2_path)

其中，mix_param_df是一个包含混合参数的DataFrame，utt_id表示当前语音段的ID，start_samp_16k和n_end_samp_16k分别表示当前语音段的起始和终止采样点，speaker1_target_snr_db表示第一个说话人的目标...

def _construct_imdb(self, cfg): """Constructs the imdb.""" img_dir = self.get_imagedir() assert os.path.exists(img_dir), "{} dir not found".format(img_dir) anno = self.get_anno() # Map class ids to contiguous ids self._class_ids = sorted(list(set(anno.values()))) self._class_id_cont_id = {v: i for i, v in enumerate(self._class_ids)} # Construct the image db self._imdb = [] for img_name, cls_id in anno.items(): cont_id = self._class_id_cont_id[cls_id] im_path = os.path.join(img_dir, img_name) self._imdb.append({"im_path": im_path, "class": cont_id}) logger.info("Number of images: {}".format(len(self._imdb))) logger.info("Number of classes: {}".format(len(self._class_ids)))

接着，使用 _class_id_cont_id 字典将每个类别 id 映射到连续的 id（从 0 开始），以便后续处理。最后，遍历所有标注信息，将图像路径和对应的连续类别 id 存储在 _imdb 列表中。该方法最后输出日志信息，包括...

在这个路径下我的png图片都是Ag10000.png-Ag10299,Al10000.png-Al10299,Au10000.png-Au10299,Cu10000.png-Cu10299,W10000.png-W10299,V10000.png-V10299,Mo10000.png-Mo10299,Ta10000.png-Ta10299，这段代码应该怎么改啊for i in range(10): cluster_i_path = os.path.join(root_path, f'cluster_{i}') if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join(path, f'{Al}.png') new_path = os.path.join(cluster_i_path, f'{Al}.png') os.rename(old_path, new_path)

cluster_i_path = os.path.join(root_path, f'cluster_{i}') if not os.path.exists(cluster_i_path): os.mkdir(cluster_i_path) for j, label in enumerate(labels): if label == i: old_path = os.path.join...

def load_images_and_labels(dataset_dir, image_size): images = [] labels = [] class_labels = os.listdir(dataset_dir) for i, class_label in enumerate(class_labels): class_dir = os.path.join(dataset_dir, class_label) for image_file in os.listdir(class_dir): image_path = os.path.join(class_dir, image_file) image = Image.open(image_path).convert('RGB') image = image.resize(image_size) image = np.array(image) images.append(image) labels.append(i) images = np.array(images) labels = np.array(labels) return images, labels

具体实现过程为，首先通过os.listdir函数获取dataset_dir下的所有子目录，每个子目录表示一个类别。然后遍历每个子目录，读取该目录下的所有图像文件，并将其转换为RGB格式、调整大小为image_size，并转换为numpy...

解释代码：def main(args): obj_names = np.loadtxt(args.obj_file, dtype=str) N_map = np.load(args.N_map_file) mask = cv2.imread(args.mask_file, 0) N = N_map[mask > 0] L = np.loadtxt(args.L_file) if args.stokes_file is None: stokes = np.tile(np.array([[1, 0, 0, 0]]), (len(L), 1)) else: stokes = np.loadtxt(args.stokes_file) v = np.array([0., 0., 1.], dtype=float) H = (L + v) / np.linalg.norm(L + v, axis=1, keepdims=True) theta_d = np.arccos(np.sum(L * H, axis=1)) norm = np.linalg.norm(L - H, axis=1, keepdims=True) norm[norm == 0] = 1 Q = (L - H) / norm for i_obj, obj_name in enumerate(obj_names[args.obj_range[0]:args.obj_range[1]]): print('===== {} - {} start ====='.format(i_obj, obj_name)) obj_name = str(obj_name) pbrdf = PBRDF(os.path.join(args.pbrdf_dir, obj_name + 'matlab', obj_name + 'pbrdf.mat')) ret = Parallel(n_jobs=args.n_jobs, verbose=5, prefer='threads')([delayed(render)(i, pbrdf, n, L, stokes, H, theta_d, Q) for i, n in enumerate(N)]) ret.sort(key=lambda x: x[0]) M = np.array([x[1] for x in ret], dtype=float) if args.save_type != 'raw': M = M / M.max() pimgs = np.zeros((len(L), 4) + N_map.shape) pimgs[:, :, mask > 0] = M.transpose(2, 1, 0, 3) out_path = os.path.join(args.out_dir, obj_name) makedirs(out_path) print('Saving images...') fnames = [] for i, imgs in enumerate(tqdm(pimgs)): if args.save_type == 'npy' or args.save_type == 'raw': for img, pangle in zip(imgs, pangles): fname = '{:03d}{:03d}.npy'.format(i + 1, pangle) fnames.append(fname) np.save(os.path.join(out_path, fname), img) elif args.save_type == 'png': for img, pangle in zip(imgs, pangles): fname = '{:03d}{:03d}.png'.format(i + 1, pangle) fnames.append(fname) img = img * np.iinfo(np.uint16).max img = img[..., ::-1] cv2.imwrite(os.path.join(out_path, fname), img.astype(np.uint16)) np.save(os.path.join(out_path, 'normal_gt.npy'), N_map) shutil.copyfile(args.mask_file, os.path.join(out_path, 'mask.png')) shutil.copyfile(args.L_file, os.path.join(out_path, 'light_directions.txt')) print('===== {} - {} done ====='.format(i_obj, obj_name))

在保存渲染结果的时候，根据 save_type 参数的不同，可以选择将图像保存为 PNG 格式、原始二进制数据格式（npy），或者不进行格式转换直接保存。此外，函数还会将法线图、掩膜图、光源方向向量和保存的图像文件...

import os import random from PIL import Image import torchvision.transforms as transforms # 指定文件夹路径 folder_path = "your_folder_path" # 定义数据增强的transform transform = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.RandomRotation(degrees=[30, 60, 90]), transforms.RandomAffine(degrees=0, translate=(0.1, 0.1), scale=(0.5, 1.5)), ]) # 遍历文件夹中的所有图像文件 for file_name in os.listdir(folder_path): file_path = os.path.join(folder_path, file_name) # 读取图像 image = Image.open(file_path) # 应用数据增强 augmented_images = [] for _ in range(3): # 进行3次不同旋转角度的增强 augmented_image = transform(image) augmented_images.append(augmented_image) # 保存增强后的图像 for i, augmented_image in enumerate(augmented_images): augmented_file_path = os.path.join(folder_path, f"augmented_{i}_{file_name}") augmented_image.save(augmented_file_path) ValueError: degrees should be sequence of length 2.

for file_name in os.listdir(folder_path): file_path = os.path.join(folder_path, file_name) # 读取图像 image = Image.open(file_path) # 应用数据增强 augmented_images = [] for _ in range(3): # ...

for j, label in enumerate(labels): if label == i: old_path = os.path.join(path, f'Al{j + 10000}.png') new_path = os.path.join(cluster_i_path, f'al{j + 10000}.png') os.rename(old_path, new_path)

这段代码的作用是将 path 路径下以 'Al' 开头、以 .png 结尾的文件名中，标签值为 i 的文件移动到对应的 cluster_i_path 文件夹下，并将文件名修改为以 'al' 开头、以 .png 结尾，其中的 j 值加上了...

def get_all_worksheet_names(path): worksheet_names = [] for root, dirs, files in os.walk(path): for file in files: if file.endswith('.xlsx'): file_path = os.path.join(root, file) wb = load_workbook(filename=file_path, read_only=True) for sheet_name in wb.sheetnames: worksheet_names.append((file, sheet_name)) return worksheet_names def write_worksheet_names_to_excel(worksheet_names, output_file): wb = Workbook() ws = wb.active for i, (file, name) in enumerate(worksheet_names): ws.cell(row=i+1, column=1, value=file) ws.cell(row=i+1, column=2, value=name) wb.save(output_file)修改這段代碼中寫入文件名時不帶後綴

修改 write_worksheet_names_to_excel 函数中的 ws.cell(row=i+1, column=1, value=file) 为 ws.cell(row=i+1, column=1, value=os.path.splitext(file)[0])，这样就可以只写入文件名而不带后缀名了。

def evaluate(self, datloader_Test): Image_Feature_ALL = [] Image_Name = [] Sketch_Feature_ALL = [] Sketch_Name = [] start_time = time.time() self.eval() for i_batch, sanpled_batch in enumerate(datloader_Test): sketch_feature, positive_feature= self.test_forward(sanpled_batch) Sketch_Feature_ALL.extend(sketch_feature) Sketch_Name.extend(sanpled_batch['sketch_path']) for i_num, positive_name in enumerate(sanpled_batch['positive_path']): if positive_name not in Image_Name: Image_Name.append(sanpled_batch['positive_path'][i_num]) Image_Feature_ALL.append(positive_feature[i_num]) rank = torch.zeros(len(Sketch_Name)) Image_Feature_ALL = torch.stack(Image_Feature_ALL) for num, sketch_feature in enumerate(Sketch_Feature_ALL): s_name = Sketch_Name[num] sketch_query_name = '_'.join(s_name.split('/')[-1].split('_')[:-1]) position_query = Image_Name.index(sketch_query_name) distance = F.pairwise_distance(sketch_feature.unsqueeze(0), Image_Feature_ALL) target_distance = F.pairwise_distance(sketch_feature.unsqueeze(0), Image_Feature_ALL[position_query].unsqueeze(0)) rank[num] = distance.le(target_distance).sum() top1 = rank.le(1).sum().numpy() / rank.shape[0] top10 = rank.le(10).sum().numpy() / rank.shape[0] print('Time to EValuate:{}'.format(time.time() - start_time)) return top1, top10

这段代码是一个evaluate函数，用于评估模型在测试数据集上的性能。函数接受一个datloader_Test参数，该参数是一个数据加载器，用于加载测试数据集。函数首先初始化一些变量，包括用于存储图像特征、图像名称、素描...

def evaluate(self, datloader_Test): Image_Feature_ALL = [] Image_Name = [] Sketch_Feature_ALL = [] Sketch_Name = [] start_time = time.time() self.eval() for i_batch, sampled_batch in enumerate(datloader_Test): sketch_feature, positive_feature = self.test_forward(sampled_batch) Sketch_Feature_ALL.extend(sketch_feature) #草图特征模型的 Sketch_Name.extend(sampled_batch['sketch_path']) #草图名 for i_num, positive_name in enumerate(sampled_batch['positive_path']): #遍历正例图像 if positive_name not in Image_Name: Image_Name.append(positive_name) Image_Feature_ALL.append(positive_feature[i_num]) rank = torch.zeros(len(Sketch_Name)) Image_Feature_ALL = torch.stack(Image_Feature_ALL) Image_Feature_ALL = Image_Feature_ALL.view(Image_Feature_ALL.size(0), -1) for num, sketch_feature in enumerate(Sketch_Feature_ALL): s_name = Sketch_Name[num] sketch_query_name = os.path.basename(s_name) # 提取草图路径中的文件名作为查询名称 position_query = -1 for i, image_name in enumerate(Image_Name): if sketch_query_name in os.path.basename(image_name): # 提取图像路径中的文件名进行匹配 position_query = i break if position_query != -1: sketch_feature = sketch_feature.view(1, -1) distance = F.pairwise_distance(sketch_feature, Image_Feature_ALL) target_distance = F.pairwise_distance(sketch_feature, Image_Feature_ALL[position_query].view(1, -1)) rank[num] = distance.le(target_distance).sum() top1 = rank.le(1).sum().item() / rank.shape[0] top10 = rank.le(10).sum().item() / rank.shape[0] print('Time to Evaluate: {}'.format(time.time() - start_time)) return top1, top10

这是一个evaluate函数的更新版本。在这个版本中，函数接受一个datloader_Test参数，该参数是一个数据加载器，用于加载测试数据集。首先，函数初始化一些变量，包括用于存储图像特征、图像名称、草图特征和草图名称...

修改代码：import os from PIL import Image import glob import numpy as np # 遍历文件夹 folder_path = 'E:/机器学习/helefull/labels' folders = os.listdir(folder_path) # print(folders) for filename in glob.glob(r'E:/机器学习/helefull/labels/*.png'): img=Image.open(filename).convert("RGB") # images=np.asarray(img) # print(images) # 只处理其中的20个文件夹 folder for folder in folders[:20]: folder_full_path = os.path.join(folder_path, folder) # print(folder_full_path) if os.path.isdir(folder_full_path): images = os.listdir(folder_full_path) print(images) blank_img = Image.new('RGB', (417, 354), (0, 0, 0)) for i,image_name in images: # 打开当前图片 img_path = os.path.join(folder_full_path, image_name) img = Image.open(img_path) # 遍历每一个像素点 for x in range(img.width): for y in range(img.height): # 如果当前像素点值为255，则将该像素点在空白图片上标记为i+1 if img.getpixel((x, y)) == 255: blank_img.putpixel((x, y), i+100) blank_img.save(f'new_{folder}.png')

for i, image_name in enumerate(images): # 打开当前图片 img_path = os.path.join(folder_full_path, image_name) img = Image.open(img_path) # 遍历每一个像素点 for x in range(img.width): for y in ...

for i, sheet_name in enumerate(self.sheet_names): filtered_rows = [] # 优化第二段代码：检查文件是否存在 filepath = os.path.join(pinjie, self.la + '.xlsx') if os.path.exists(filepath): workbook = xl.load_workbook(filepath) sheet = workbook.active today = datetime.datetime.now().strftime('%Y/%m/%d') cell_value = sheet.cell(row=1, column=1).value if cell_value is not None and cell_value != '': for row in sheet.iter_rows(min_row=1): if row[2].value == today and row[8].value == sheet_name: datetime_obj = datetime.datetime.strptime(row[3].value, '%H:%M:%S') row_time = datetime_obj.time() if start_time_1 <= row_time <= end_time_1 and start_time_1 <= current_time <= end_time_1: filtered_rows.append(row) elif start_time_2 <= row_time or current_time <= end_time_2: filtered_rows.append(row) label = self.unique_listbox.grid_slaves(row=i // 3, column=i % 3)[0] if filtered_rows: label.config(text=f"{sheet_name} - 已點檢", fg="green") else: label.config(text=f"{sheet_name} - 未點檢", fg="red")是什麽意思

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