mmsegmentation使用.csv文件读取数据
自定义数据读取方式
即将原本的mmcv.imfrombytes读取固定目录格式的图片直接改成mmcv.imread读取csv文件的格式
定义数据读取方式
在/mmsegmentation-master/mmseg/datasets路径下新建一个mydata.py文件
@DATASETS.register_module()
class MyDataset(Dataset):
CLASSES = ('gt',)
PALETTE = [[255, 255, 255],]
def __init__(self,
pipeline,
test_mode=False,
csv_file = 'tmp.csv'):
self.pipeline = Compose(pipeline)
self.test_mode = test_mode
self.csv_file = csv_file
self.img_infos = self.load_img_annotations_fromcsv(csv_file=self.csv_file)
def __len__(self):
"""Total number of samples of data."""
return len(self.img_infos)
def load_img_annotations_fromcsv(self, csv_file): # 通过csv文件读取img,depth,gt,seg_thresh路径
frames = pd.read_csv(csv_file, sep=";", header=None)
img_infos = []
for idx in range(len(frames)):
img_info = dict(filename=frames.iloc[idx, 0])
img_info['ann'] = frames.iloc[idx, 1]
img_infos.append(img_info)
return img_infos
def get_ann_info(self, idx): # 获取gt路径
return self.img_infos[idx]['ann']
def __getitem__(self, idx):
if self.test_mode:
return self.prepare_test_img(idx)
else:
return self.prepare_train_img(idx)
def prepare_train_img(self, idx):
img_info = self.img_infos[idx]
ann_info = self.get_ann_info(idx)
results = dict(img_info=img_info, ann_info=ann_info)
results['seg_fields'] = []
return self.pipeline(results)
def prepare_test_img(self, idx):
img_info = self.img_infos[idx]
ann_info = self.get_ann_info(idx)
results = dict(img_info=img_info, ann_info=ann_info)
results['seg_fields'] = []
return self.pipeline(results)
def get_gt_seg_map_by_idx(self, index):
"""Get one ground truth segmentation map for evaluation."""
ann_info = self.get_ann_info(index)
results = dict(ann_info=ann_info)
results['gt_semantic_seg'] = mmcv.imread(results['ann_info'], -1)
return results['gt_semantic_seg']
def get_gt_seg_maps(self, efficient_test=None):
"""Get ground truth segmentation maps for evaluation."""
for idx in range(len(self)):
ann_info = self.get_ann_info(idx)
results = dict(ann_info=ann_info)
results['gt_semantic_seg'] = mmcv.imread(results['ann_info'], -1)
yield results['gt_semantic_seg']
在/mmsegmentation-master/mmseg/datasets/pipelines/loading.py新增相应的函数
@PIPELINES.register_module()
class LoadImageFromCSV(object):
"""Load an image from csv file.
Required keys are "img_prefix" and "img_info" (a dict that must contain the
key "filename"). Added or updated keys are "filename", "img", "img_shape",
"ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
"scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
Args:
to_float32 (bool): Whether to convert the loaded image to a float32
numpy array. If set to False, the loaded image is an uint8 array.
Defaults to False.
"""
def __init__(self,
to_float32=False,
):
self.to_float32 = to_float32
def __call__(self, results):
"""Call functions to load image and get image meta information."""
img = mmcv.imread(results['img_info']['filename'])
if self.to_float32:
img = img.astype(np.float32)
results['filename'] = results['img_info']['filename']
results['ori_filename'] = results['img_info']['filename']
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
num_channels = 1 if len(img.shape) < 3 else img.shape[2]
results['img_norm_cfg'] = dict(
mean=np.zeros(num_channels, dtype=np.float32),
std=np.ones(num_channels, dtype=np.float32),
to_rgb=False)
return results
@PIPELINES.register_module()
class LoadAnnotationsFromCSV(object):
"""Load annotations for semantic segmentation."""
def __call__(self, results):
"""Call function to load multiple types annotations."""
gt_semantic_seg = mmcv.imread(results['ann_info'], -1)
results['gt_semantic_seg'] = gt_semantic_seg
results['seg_fields'].append('gt_semantic_seg')
return results
在init文件中增加MyDataset
同时在/mmsegmentation-master/mmseg/datasets中的____init____.py添加刚刚新建的数据集
...
from .mydata import MyDataset
__all__ = [
..., 'MyDataset ',
]
在config文件中修改数据读取路径
dict(type='LoadImageFromCSV'),
dict(type='LoadAnnotationsFromCSV'),
至此大功告成！！！