# Function Differences with tf.io.decode_image

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## tf.io.decode_image

```python
tf.io.decode_image(
    contents,
    channels=None,
    dtype=tf.dtypes.uint8,
    name=None,
    expand_animations=True
)
```

For more information, see [tf.io.decode_image](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/io/decode_image).

## mindspore.dataset.vision.Decode

```python
class mindspore.dataset.vision.Decode(
    to_pil=False
)
```

For more information, see [mindspore.dataset.vision.Decode](https://mindspore.cn/docs/en/r1.8/api_python/dataset_vision/mindspore.dataset.vision.Decode.html#mindspore.dataset.vision.Decode).

## Differences

TensorFlow: Decode the raw image bytes into an image with the specified number of channels and data type. It supports decoding GIF images.

MindSpore: Decode the raw image bytes into a RGB image, `to_pil` decides that whether output in PIL format or NumPy Format.

## Code Example

```python
# The following implements Decode with MindSpore.
import numpy as np
import mindspore.dataset as ds

image = np.fromfile("/tmp/file.jpg", dtype=np.uint8)
result = ds.vision.Decode()(image)

# The following implements decode_image with TensorFlow.
import tensorflow as tf

raw = tf.io.read_file("/tmp/file.jpg")
result = tf.io.decode_image(raw, channels=3)
```