mindspore.dataset.PhotoTourDataset

class mindspore.dataset.PhotoTourDataset(dataset_dir, name, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, num_shards=None, shard_id=None, cache=None)[source]

PhotoTour dataset.

According to the given usage configuration, the generated dataset has different output columns: - usage = ‘train’, output columns: [image, dtype=uint8] . - usage ≠ ‘train’, output columns: [image1, dtype=uint8] , [image2, dtype=uint8] , [matches, dtype=uint32] .

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
name (str) – Name of the dataset to load, should be one of 'notredame', 'yosemite', 'liberty', 'notredame_harris', 'yosemite_harris' or 'liberty_harris'.
usage (str, optional) – Usage of the dataset, can be 'train' or 'test'. Default: None , will be set to ‘train’. When usage is ‘train’, number of samples for each name is {‘notredame’: 468159, ‘yosemite’: 633587, ‘liberty’: 450092, ‘liberty_harris’: 379587, ‘yosemite_harris’: 450912, ‘notredame_harris’: 325295}. When usage is ‘test’, will read 100,000 samples for testing.
num_samples (int, optional) – The number of images to be included in the dataset. Default: None , will read all images.
num_parallel_workers (int, optional) – Number of worker threads to read the data. Default: None , will use global default workers(8), it can be set by mindspore.dataset.config.set_num_parallel_workers() .
shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Default: None , expected order behavior shown in the table below.
sampler (Sampler, optional) – Object used to choose samples from the dataset. Default: None , expected order behavior shown in the table below.
num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the max sample number of per shard.
shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.
cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default: None , which means no cache is used.

Raises

RuntimeError – If dataset_dir does not contain data files.
RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and num_shards/shard_id are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If dataset_dir is not exist.
ValueError – If usage is not "train" or "test".
ValueError – If name is not "notredame", "yosemite", "liberty", "notredame_harris", "yosemite_harris" or "liberty_harris".
ValueError – If num_parallel_workers exceeds the max thread numbers.
ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

Load & Process Data With Dataset Pipeline

Note

This dataset can take in a sampler . sampler and shuffle are mutually exclusive. The table below shows what input arguments are allowed and their expected behavior.

Expected Order Behavior of Using sampler and shuffle
Parameter sampler	Parameter shuffle	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Examples

>>> import mindspore.dataset as ds
>>> # Read 3 samples from PhotoTour dataset.
>>> dataset = ds.PhotoTourDataset(dataset_dir="/path/to/photo_tour_dataset_directory",
...                               name='liberty', usage='train', num_samples=3)

About PhotoTour dataset:

The data is taken from Photo Tourism reconstructions from Trevi Fountain (Rome), Notre Dame (Paris) and Half Dome (Yosemite). Each dataset consists of a series of corresponding patches, which are obtained by projecting 3D points from Photo Tourism reconstructions back into the original images.

The dataset consists of 1024 x 1024 bitmap (.bmp) images, each containing a 16 x 16 array of image patches. Each patch is sampled as 64 x 64 grayscale, with a canonical scale and orientation. For details of how the scale and orientation is established, please see the paper. An associated metadata file info.txt contains the match information. Each row of info.txt corresponds to a separate patch, with the patches ordered from left to right and top to bottom in each bitmap image. The first number on each row of info.txt is the 3D point ID from which that patch was sampled – patches with the same 3D point ID are projected from the same 3D point (into different images). The second number in info.txt corresponds to the image from which the patch was sampled, and is not used at present.

You can unzip the original PhotoTour dataset files into this directory structure and read by MindSpore’s API.

.
└── photo_tour_dataset_directory
    ├── liberty/
    │    ├── info.txt                 // two columns: 3D_point_ID, unused
    │    ├── m50_100000_100000_0.txt  // seven columns: patch_ID1, 3D_point_ID1, unused1,
    │    │                            // patch_ID2, 3D_point_ID2, unused2, unused3
    │    ├── patches0000.bmp          // 1024*1024 pixels, with 16 * 16 patches.
    │    ├── patches0001.bmp
    │    ├── ...
    ├── yosemite/
    │    ├── ...
    ├── notredame/
    │    ├── ...
    ├── liberty_harris/
    │    ├── ...
    ├── yosemite_harris/
    │    ├── ...
    ├── notredame_harris/
    │    ├── ...

Citation:

@INPROCEEDINGS{4269996,
    author={Winder, Simon A. J. and Brown, Matthew},
    booktitle={2007 IEEE Conference on Computer Vision and Pattern Recognition},
    title={Learning Local Image Descriptors},
    year={2007},
    volume={},
    number={},
    pages={1-8},
    doi={10.1109/CVPR.2007.382971}
}

Pre-processing Operation

`mindspore.dataset.Dataset.apply`	Apply a function in this dataset.
`mindspore.dataset.Dataset.concat`	Concatenate the dataset objects in the input list.
`mindspore.dataset.Dataset.filter`	Filter dataset by prediction.
`mindspore.dataset.Dataset.flat_map`	Map func to each row in dataset and flatten the result.
`mindspore.dataset.Dataset.map`	Apply each operation in operations to this dataset.
`mindspore.dataset.Dataset.project`	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
`mindspore.dataset.Dataset.rename`	Rename the columns in input datasets.
`mindspore.dataset.Dataset.repeat`	Repeat this dataset count times.
`mindspore.dataset.Dataset.reset`	Reset the dataset for next epoch.
`mindspore.dataset.Dataset.save`	Save the dynamic data processed by the dataset pipeline in common dataset format.
`mindspore.dataset.Dataset.shuffle`	Shuffle the dataset by creating a cache with the size of buffer_size .
`mindspore.dataset.Dataset.skip`	Skip the first N elements of this dataset.
`mindspore.dataset.Dataset.split`	Split the dataset into smaller, non-overlapping datasets.
`mindspore.dataset.Dataset.take`	Takes at most given numbers of elements from the dataset.
`mindspore.dataset.Dataset.zip`	Zip the datasets in the sense of input tuple of datasets.

Batch

`mindspore.dataset.Dataset.batch`	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
`mindspore.dataset.Dataset.bucket_batch_by_length`	Bucket elements according to their lengths.
`mindspore.dataset.Dataset.padded_batch`	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

`mindspore.dataset.Dataset.create_dict_iterator`	Create an iterator over the dataset.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Return the class index.
`mindspore.dataset.Dataset.get_col_names`	Return the names of the columns in dataset.
`mindspore.dataset.Dataset.get_dataset_size`	Return the number of batches in an epoch.
`mindspore.dataset.Dataset.get_repeat_count`	Get the replication times in RepeatDataset.
`mindspore.dataset.Dataset.input_indexs`	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
`mindspore.dataset.Dataset.num_classes`	Get the number of classes in a dataset.
`mindspore.dataset.Dataset.output_shapes`	Get the shapes of output data.
`mindspore.dataset.Dataset.output_types`	Get the types of output data.

Apply Sampler

`mindspore.dataset.MappableDataset.add_sampler`	Add a child sampler for the current dataset.
`mindspore.dataset.MappableDataset.use_sampler`	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

`mindspore.dataset.Dataset.device_que`	Return a transferred Dataset that transfers data through a device.
`mindspore.dataset.Dataset.sync_update`	Release a blocking condition and trigger callback with given data.
`mindspore.dataset.Dataset.sync_wait`	Add a blocking condition to the input Dataset and a synchronize action will be applied.
`mindspore.dataset.Dataset.to_json`	Serialize a pipeline into JSON string and dump into file if filename is provided.