最近一直在學unet，所以分享一個如何做一個自己想要的訓練集，而不是去從網上找的博客，
首先打開cmd
輸入你自己的虛擬環境，我是在base直接把所有環境都安好了，就不想在創建虛擬環境在安裝了，
然后
pip install labelme==3.16.7
下載好了之后直接輸入labelme
這就是打開后的樣子
然后對所選擇的圖形進行描圖，就像這樣
最后他會在你的指定檔案下生成一個josn檔案，你現在還不能直接打開這個josn檔案，需要運行代碼
import argparse
import base64
import json
import os
import os.path as osp
import warnings
import numpy as np
import PIL.Image
import yaml
from labelme import utils
if __name__ == '__main__':
jpgs_path = "datasets/JPEGImages"
pngs_path = "datasets/SegmentationClass"
classes = ["_background_", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
"diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train",
"tvmonitor"]
# classes = ["_background_","cat","dog"]
count = os.listdir("./datasets/before/")
for i in range(0, len(count)):
path = os.path.join("./datasets/before", count[i])
if os.path.isfile(path) and path.endswith('json'):
data = json.load(open(path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
PIL.Image.fromarray(img).save(osp.join(jpgs_path, count[i].split(".")[0] + '.jpg'))
new = np.zeros([np.shape(img)[0], np.shape(img)[1]])
for name in label_names:
index_json = label_names.index(name)
index_all = classes.index(name)
new = new + index_all * (np.array(lbl) == index_json)
utils.lblsave(osp.join(pngs_path, count[i].split(".")[0] + '.png'), new)
print('Saved ' + count[i].split(".")[0] + '.jpg and ' + count[i].split(".")[0] + '.png')
在這里說一下classes這一行代碼，這個是需要你根據你所處理影像里面的內容而定的，這里需要稍做修改，
最后，你所需要的unet訓練集就出來了，
轉載請註明出處，本文鏈接：https://www.uj5u.com/houduan/279801.html
標籤：python
上一篇：創建了一個Django專案，但是運行時報錯 Error: [WinError 10013] 以一種訪問權限不允許的方式做了一個訪問套接字的嘗試。