def testFlip():
base_dir = Path.db_root_dir('rssrai')
image_dir = os.path.join(base_dir, 'test')
save_dir = save_path(os.path.join(base_dir, 'test_output'))
_img_path_list = glob(os.path.join(image_dir, '*.tif'))
img_name_list = [name.split('/')[-1] for name in _img_path_list]
pprint(img_name_list)
pprint(image_dir)
rssraiImage = RssraiTestOneImage(img_name_list[0], image_dir, save_dir, 10,
4)
type = "origin"
# type = "vertical"
# type = "horizontal"
print(rssraiImage.images[type].shape)
for dateSet in rssraiImage.get_slide_dataSet(rssraiImage.images[type]):
print(dateSet)
for i in dateSet:
shape = i['image'].shape
print(shape)
# i['image'] = torch.zeros(shape[0], rssraiImage.num_classes, shape[2], shape[3]).cuda()
# rssraiImage.fill_image(type, i)
rssraiImage.saveResultRGBImage()
def test_one_merge_image():
base_path = Path.db_root_dir("rssrai")
# 图片
image_path = os.path.join(base_path, "split_test_520")
save_image_path = os.path.join(base_path, "merge_test", "img")
if not os.path.exists(save_image_path):
os.makedirs(save_image_path)
merge_image(image_path, "GF2_PMS1__20150902_L1A0001015646-MSS1.tif",
save_image_path, "CMYK")
def testOneImage():
base_path = Path.db_root_dir("rssrai")
path = '/home/arron/Documents/grey/Project_Rssrai/rssrai/train/img'
name = 'GF2_PMS1__20150212_L1A0000647768-MSS1.tif'
file_image = Image.open(os.path.join(path, name))
np_image = np.array(file_image)[:, :, 1:]
image = Image.fromarray(np_image.astype('uint8')).convert("RGB")
image.save(os.path.join(base_path, name))
def test_spilt_test_image():
base_path = Path.db_root_dir("rssrai")
# 图片
image_path = os.path.join(base_path, "test")
image_list = glob(os.path.join(image_path, "*.tif"))
save_image_path = os.path.join(base_path, "split_test_520", "img")
spilt_all_images(image_list,
save_image_path,
mode="CMYK",
output_image_h_w=(520, 520))
def test_merge_images():
import pandas as pd
base_path = Path.db_root_dir("rssrai")
df = pd.read_csv("test_name_list.csv")
name_list = df['name'].tolist()
# 图片
image_path = os.path.join(base_path, "temp_test", "img")
save_image_path = os.path.join(base_path, "merge_test", "img")
if not os.path.exists(save_image_path):
os.makedirs(save_image_path)
for name in tqdm(name_list):
merge_image(image_path, name, save_image_path, "CMYK")
def spilt_image(split, output_image_h_w):
base_path = Path.db_root_dir("rssrai")
import pandas as pd
df = pd.read_csv(os.path.join(base_path, f"{split}_set.csv"))
label_name_list = df["文件名"].values.tolist()
print(label_name_list)
base_path = Path.db_root_dir("rssrai")
save_label_path = os.path.join(base_path,
f"split_{split}_{output_image_h_w[0]}",
"label")
make_sure_path_exists(save_label_path)
label_path = os.path.join(base_path, "split_train", "label")
save_image_path = os.path.join(base_path,
f"split_{split}_{output_image_h_w[0]}",
"img")
make_sure_path_exists(save_image_path)
image_path = os.path.join(base_path, "split_train", "img")
# label_name_list = [path_name.split("/")[-1] for path_name in glob(os.path.join(label_path, "*"))]
print(len(label_name_list))
for label_name in tqdm(label_name_list):
image_name = label_name.replace("_label", "")
# print(image_name)
# print(label_name)
split_image(label_path,
label_name,
save_label_path,
mode="RGB",
output_image_h_w=output_image_h_w)
split_image(image_path,
image_name,
save_image_path,
mode="CMYK",
output_image_h_w=output_image_h_w)
def test_one_spilt_test_image():
base_path = Path.db_root_dir("rssrai")
image_path = os.path.join(base_path, "test")
name = 'GF2_PMS1__20150902_L1A0001015646-MSS1.tif'
save_image_path = os.path.join(base_path, "split_test_520")
if not os.path.exists(save_image_path):
os.makedirs(save_image_path)
# 图片
split_image(image_path,
name,
save_image_path,
mode="CMYK",
output_image_h_w=(520, 520))
def test_spilt_train_image():
base_path = Path.db_root_dir("rssrai")
# 图片
image_path = os.path.join(base_path, "train", "img")
image_list = glob(os.path.join(image_path, "*.tif"))
save_image_path = os.path.join(base_path, "split_train", "img")
spilt_all_images(image_list,
save_image_path,
mode="CMYK",
output_image_h_w=(680, 720))
# 标签
label_path = os.path.join(base_path, "train", "label")
label_list = glob(os.path.join(label_path, "*.tif"))
save_label_path = os.path.join(base_path, "split_train", "label")
spilt_all_images(label_list,
save_label_path,
mode="RGB",
output_image_h_w=(680, 720))
def testGetValid():
base_path = Path.db_root_dir("rssrai")
label_path = os.path.join(base_path, "split_train", "label")
img_path = os.path.join(base_path, "split_train", "img")
valid_label_path = os.path.join(base_path, "split_valid", "label")
valid_img_path = os.path.join(base_path, "split_valid", "img")
shutil.rmtree(valid_label_path)
os.makedirs(valid_label_path)
shutil.rmtree(valid_img_path)
os.makedirs(valid_img_path)
label_name_list = [
path_name.split("/")[-1]
for path_name in glob(os.path.join(label_path, "*"))
]
random.shuffle(label_name_list)
print(len(label_name_list))
valid_label_name_list = label_name_list[850:]
pprint(len(valid_label_name_list))
for label_name in valid_label_name_list:
img_name = label_name.replace("_label", "")
print(valid_label_path, label_name)
print(valid_img_path, img_name)
shutil.move(os.path.join(label_path, label_name),
os.path.join(valid_label_path, label_name))
shutil.move(os.path.join(img_path, img_name),
os.path.join(valid_img_path, img_name))
def testSplitLabel():
print(color_name_map)
name_list = ["文件名"]
for _, v in color_name_map.items():
name_list.append(v)
print(name_list)
all_statistic_list = []
base_path = Path.db_root_dir("rssrai")
image_path = os.path.join(base_path, "split", "label")
from glob import glob
image_list = glob(os.path.join(image_path, "*.tif"))
# 多进程
pool = Pool(16)
for image in tqdm(image_list):
list = image.split("/")
path = "/".join(list[:-1])
name = list[-1]
result = pool.apply_async(statistic_label, args=(path, name))
all_statistic_list.append(result.get())
df = pd.DataFrame(all_statistic_list, columns=name_list)
print(df)
df.to_csv(os.path.join(base_path, "split_label.csv"))
import os
from glob import glob
import numpy as np
import torch
from experiments.datasets.path import Path
crop_size = 256
base_dir = Path.db_root_dir('rssrai')
def load_numpy(name_list, index):
i = index
d = None
while d is None:
try:
sample = np.load(name_list[i])
d = {
'image': torch.from_numpy(sample['image']),
"label": torch.from_numpy(sample['label']).long()
}
except:
print(f"{name_list[i]} is bad! auto remove it.")
# os.remove(path_list[i])
# path_list[i] = path_list[0]
return d
path_list = glob(
os.path.join(base_dir, f'test_train_numpy_{crop_size}', '*.npz'))