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目录结构
/dataset/ xxxx.tif # 原始图像 image-3000 0.tif # 切割后的tif 1.tif ........ xxx_0.json # 生成的json文件也放在该文件夹下 xxx_1.json ....... xxx_0_json # 调用labelme_json_to_dataset 0.json 生成的0_json文件夹 img.png # 原始图片的png格式 info.yaml label.png # 标签图片 label_names.txt label_viz.png xxx_1_json ....... 0.png # 将json文件夹中的label.png 提取出来 1.png ....... label_0.tif # 将上边的png标签文件转换为tif格式
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收集数据，高分辨率图像
- 无人机数据，航空数据等
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图像切割，像素大小该为多少？
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本数据集平均像素大小（40000*50000） tif格式，LZW压缩方式压缩
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要考虑计算机显卡，目标建筑无尽量不被切割等问题，本利使用3000*3000
# data:2020-01-04 # user:dean # desc:图像切割脚本 import tifffile as tiff # 也可使用pillow或opencv 但若图片过大时可能会出问题 import os width = 1500*2 # 切割图像大小 height = 1500*2 # 切割图像大小 home = "/media/dean/Document/AI_dataset/DOM/" file_name = "裴庄村51-dom" image_dir = os.path.join(home,file_name) image = os.path.join(image_dir,file_name+".tif") target_dir = os.path.join(image_dir,"image-"+str(width)) # 切割后图片存储位置 if not os.path.exists(target_dir): os.mkdir(target_dir) img = tiff.imread(image) # 导入图片 print("导入图片完成",img.shape) # 原始图片大小 pic_width = img.shape[1] pic_height = img.shape[0] row_num = pic_width//width # 纵向切割数量 col_num = pic_height // height # 横向切割数量 print("开始进行切割，可切割总数为{}".format(col_num*row_num)) for j in range(col_num): for i in range(row_num): num = j * row_num + i print("正在进行第{}张切割".format(num + 1)) row = i * width row_end = row + width col = j * height col_end = col + height # print(col,col_end,row,row_end) cropped = img[col:col_end,row:row_end] name = "{}_{}.tif".format(file_name,num) image_path = os.path.join(target_dir,name) tiff.imsave(image_path, cropped)
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标注工具 labelme
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使用label标注每张图片
pip install labelme # 安装labelme -
每张图片标注后会生成对应name.json文件
labelme_json_to_dataset xxx.json
# data:2020-01-04 # user:dean # desc:批量将json文件转为 label import os dir = r"I:\人工智能数据\DOM\裴庄村51-dom\image-3000" files = [os.path.join(dir,file) for file in os.listdir(dir) if file.endswith(".json")] for file in files: cmd = "labelme_json_to_dataset {}".format(file) print(cmd) os.system(cmd) -
将所有的json/label.png 提取到统一文件夹
# data:2020-01-04 # user:dean # desc:将label文件夹中的laebl提取出来 import tifffile as tiff from PIL import Image import os target_dir = r"/media/dean/Document/AI_dataset/DOM/裴庄村51-dom/image-3000" # json_label 所在的文件夹 files = [os.path.join(target_dir,file) for file in os.listdir(target_dir)] for i in files: if os.path.isdir(i): lables = os.listdir(i) for file in lables: if file == "label.png": image_path = os.path.join(i, "label.png") imgae = Image.open(image_path) parent_dir_name = os.path.basename(os.path.dirname(image_path)) new_name = "{}.png".format(parent_dir_name.split("_")[1]) imgae.save(os.path.join(target_dir,new_name)) print("第{}个文件夹".format(i)) break; -
将所有的label.png转换为tif格式 并转换为单通道黑白照片
# coding:utf-8 # file: change_format.py # author: Dean # contact: 1028968939@qq.com # time: 2020/1/4 20:41 # desc: 将png 标签转化为单通道 黑白标签 并转化为tif import os from PIL import Image threshold = 0 table = [] for i in range(256): if i > threshold: table.append(255) else: table.append(0) target_dir = r"/media/dean/Document/AI_dataset/DOM/裴庄村51-dom/image-3000" files = [os.path.join(target_dir,file) for file in os.listdir(target_dir) if file.endswith(".png")] for file in files: image_file_name = os.path.basename(file) num = image_file_name.split(".")[0] image_file = Image.open(file) # open colour image # image_file = image_file.convert('L') # convert image to black and white image_file = image_file.point(table, '1') new_file = os.path.join(target_dir,"{}.tif".format(num)) image_file.save(new_file) print(new_file) -
结束（根据需要提取相应数据即可）
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