img_dir = QtWidgets.QFileDialog.getExistingDirectory(self,"图像文件夹",".") files = os.listdir(img_dir) self.image_files.clear self.current_index = -1 forfinfiles: iff.endswith(".png")orf.endswith(".jpg")orf.endsw
print(os.extsep) #linesep 获取操作系统的换行符号 window -> \r\n linux/unix -> \n print(repr(os.linesep)) #导入os模块 import os #以下内容都是os.path子模块中的内容 #abspath() 将相对路径转化为绝对路径 path = './boys'#相对 result = os.path.abspath(path) print(result) #dirname() ...
# 示例:基于地理位置信息自动整理照片import osfrom exif import Imagedef organizePhotos(photoDir): # 创建一个字典用来存储不同位置的照片 locationDict = {} # 遍历指定文件夹下的所有照片 for filename in os.listdir(photoDir): with open(os.path.join(photoDir, filename), 'rb') as photoFile: im...
classImageBrowserPanel(QtWidgets.QWidget):def__init__(self, parent=None):super.__init__(parent)self.image_files = []self.current_index = -1# 文本标签self.pathLabel = QtWidgets.QLabelself.pathLabel.setText("文件名称: test.png")self.pathLabel.setStyleSheet("background-color:deeppink; color...
for filename in os.listdir(directory_path): if filename.endswith('.jsonl'): file_path = os.path.join(directory_path, filename) with open(file_path, 'r', encoding='utf-8') as file: for line in file: # 解析每一行的JSON对象并添加到列表中 data = json.loads(line) text = data.ge...
"folder_path = '/root/autodl-tmp/alpaca-chinese-dataset/data/'\n", "\n", "# 获取文件夹中所有的 JSON 文件名\n", "json_files = [f for f in os.listdir(folder_path) if f.endswith('.json')]\n", "\n", "# 创建一个空的 DataFrame 用于存放所有数据\n", "combined_df = pd.Dat...
self.visualise(batch_offset+batch_index, imgs[batch_index], dataset.classes[target['classes'][batch_index]][0], bin_pred_mask, plot_dir) num_batches_in_file += 1 if use_multiple_files and num_batches_in_file == max_num_batches_per_file: cur_segmentations_file_path = os.path...
if not os.path.isdir(file) and file[len(file)-3:len(file)] == 'jpg': try: key = int(file[0]) image_array, label_array = process_img(path+"/"+file, key) train_imgs = np.vstack((train_imgs, image_array)) train_labels = np.vstack((train_labels, label_array)) ...
( path_imgrec = 'valid.rec', data_name = 'data', label_name = 'softmax_label', batch_size = batch_size, data_shape = data_shape, rand_crop = False, rand_mirror = False) return (train, val) batch_size = 16 train_iter, val_iter = get_itera...
self.img.append('/home/aistudio/data/data188843/total_images/'+img) self.label.append(paddle.to_tensor(int(la), dtype='int64')) defload_img(self, image_path): # 实际使用时使用Pillow相关库进行图片读取即可,这里我们对数据先做个模拟