# Image

```python
gradio.Image(···)
```

### Description

Creates an image component that can be used to upload images (as an input) or display images (as an output).

### Behavior

### Initialization

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `value` | `str \| PIL.Image.Image \| np.ndarray \| Callable \| None` | `None` | A `PIL.Image`, `numpy.array`, `pathlib.Path`, or `str` filepath or URL for the default value that Image component is going to take. If a function is provided, the function will be called each time the app loads to set the initial value of this component. |
| `format` | `str` | `"webp"` | File format (e.g. "png" or "gif"). Used to save image if it does not already have a valid format (e.g. if the image is being returned to the frontend as a numpy array or PIL Image). The format should be supported by the PIL library. Applies both when this component is used as an input or output. This parameter has no effect on SVG files. |
| `height` | `int \| str \| None` | `None` | The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed image file or numpy array, but will affect the displayed image. |
| `width` | `int \| str \| None` | `None` | The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed image file or numpy array, but will affect the displayed image. |
| `image_mode` | `Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] \| None` | `"RGB"` | The pixel format and color depth that the image should be loaded and preprocessed as. "RGB" will load the image as a color image, or "L" as black-and-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning. This parameter has no effect on SVG or GIF files. If set to None, the image_mode will be inferred from the image file type (e.g. "RGBA" for a .png image, "RGB" in most other cases). |
| `sources` | `list[Literal['upload', 'webcam', 'clipboard']] \| Literal['upload', 'webcam', 'clipboard'] \| None` | `None` | List of sources for the image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "clipboard" allows users to paste an image from the clipboard. If None, defaults to ["upload", "webcam", "clipboard"] if streaming is False, otherwise defaults to ["webcam"]. |
| `type` | `Literal['numpy', 'pil', 'filepath']` | `"numpy"` | The format the image is converted before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. To support animated GIFs in input, the `type` should be set to "filepath" or "pil". To support SVGs, the `type` should be set to "filepath". |
| `label` | `str \| I18nData \| None` | `None` | the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to. |
| `every` | `Timer \| float \| None` | `None` | Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. |
| `inputs` | `Component \| list[Component] \| set[Component] \| None` | `None` | Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change. |
| `show_label` | `bool \| None` | `None` | if True, will display label. |
| `buttons` | `list[Literal['download', 'share', 'fullscreen'] \| Button] \| None` | `None` | A list of buttons to show in the corner of the component. Valid options are "download", "share", "fullscreen", or a gr.Button() instance. The "download" button allows the user to download the image. The "share" button allows the user to share to Hugging Face Spaces Discussions. The "fullscreen" button allows the user to view in fullscreen mode. Custom gr.Button() instances will appear in the toolbar with their configured icon and/or label, and clicking them will trigger any .click() events registered on the button. by default, all of the built-in buttons are shown. |
| `container` | `bool` | `True` | If True, will place the component in a container - providing some extra padding around the border. |
| `scale` | `int \| None` | `None` | relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True. |
| `min_width` | `int` | `160` | minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. |
| `interactive` | `bool \| None` | `None` | if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output. |
| `visible` | `bool \| Literal['hidden']` | `True` | If False, component will be hidden. If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM |
| `streaming` | `bool` | `False` | If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'. If the component is an output component, will automatically convert images to base64. |
| `elem_id` | `str \| None` | `None` | An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. |
| `elem_classes` | `list[str] \| str \| None` | `None` | An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. |
| `render` | `bool` | `True` | If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later. |
| `key` | `int \| str \| tuple[int \| str, ...] \| None` | `None` | in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render. |
| `preserved_by_key` | `list[str] \| str \| None` | `"value"` | A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor. |
| `webcam_options` | `WebcamOptions \| None` | `None` |  |
| `placeholder` | `str \| None` | `None` | Custom text for the upload area. Overrides default upload messages when provided. Accepts new lines and `#` to designate a heading. |
| `watermark` | `WatermarkOptions \| None` | `None` | If provided and this component is used to display a `value` image, the `watermark` image will be displayed on the bottom right of the `value` image, 10 pixels from the bottom and 10 pixels from the right. The watermark image will not be resized. Supports `PIL.Image`, `numpy.array`, `pathlib.Path`, and `str` filepaths. SVGs and GIFs are not supported as `watermark` images nor can they be watermarked. |
### Shortcuts

| Class | Interface String Shortcut | Initialization |
|-------|--------------------------|----------------|
| `gradio.Image` | `"image"` | Uses default values |
### Understanding Image Types

The `type` parameter controls the format of the data passed to your Python function. Choosing the right type avoids unnecessary conversions in your code:

```python
import gradio as gr

# For a model that expects a numpy array:
def predict(img):
    # img is a numpy array with shape (H, W, 3)
    return model(img)

demo = gr.Interface(fn=predict, inputs=gr.Image(type="numpy"), outputs="label")

# For a pipeline that works with file paths:
def process(path):
    # path is a string like "/tmp/gradio/abcdef.webp"
    return my_pipeline(path)

demo = gr.Interface(fn=process, inputs=gr.Image(type="filepath"), outputs="image")
```

If you need grayscale input, set `image_mode="L"` — the array shape becomes `(height, width)` instead of `(height, width, 3)`.

### `GIF` and `SVG` Image Formats

The `gr.Image` component can process or display any image format that is , including animated GIFs. In addition, it also supports the SVG image format. 

When the `gr.Image` component is used as an input component, the image is converted into a `str` filepath, a `PIL.Image` object, or a `numpy.array`, depending on the `type` parameter. However, animated GIF and SVG images are treated differently:

```py
import gradio as gr

demo = gr.Interface(
    fn=lambda x:x, 
    inputs=gr.Image(type="filepath"), 
    outputs=gr.Image()
)
    
demo.launch()
```

### Demos

**sepia_filter**

[See demo on Hugging Face Spaces](https://huggingface.co/spaces/gradio/sepia_filter)

```python
import numpy as np
import gradio as gr

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189],
        [0.349, 0.686, 0.168],
        [0.272, 0.534, 0.131]
    ])
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(), "image", api_name="predict")
if __name__ == "__main__":
    demo.launch()
```

**fake_diffusion**

[See demo on Hugging Face Spaces](https://huggingface.co/spaces/gradio/fake_diffusion)

```python
import gradio as gr
import numpy as np
import time

def fake_diffusion(steps):
    rng = np.random.default_rng()
    for i in range(steps):
        time.sleep(1)
        image = rng.random(size=(600, 600, 3))
        yield image
    image = np.ones((1000,1000,3), np.uint8)
    image[:] = [255, 124, 0]
    yield image

demo = gr.Interface(fake_diffusion,
                    inputs=gr.Slider(1, 10, 3, step=1),
                    outputs="image",
                    api_name="predict")

if __name__ == "__main__":
    demo.launch()
```

### Event Listeners

#### Description

Event listeners allow you to respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.

#### Supported Event Listeners

The `Image` component supports the following event listeners:

- `Image.clear(fn, ...)`: This listener is triggered when the user clears the Image using the clear button for the component.
- `Image.change(fn, ...)`: Triggered when the value of the Image changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
- `Image.stream(fn, ...)`: This listener is triggered when the user streams the Image.
- `Image.select(fn, ...)`: Event listener for when the user selects or deselects the Image. Uses event data gradio.SelectData to carry `value` referring to the label of the Image, and `selected` to refer to state of the Image. See https://www.gradio.app/main/docs/gradio/eventdata for more details.
- `Image.upload(fn, ...)`: This listener is triggered when the user uploads a file into the Image.
- `Image.input(fn, ...)`: This listener is triggered when the user changes the value of the Image.

#### Event Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `fn` | `Callable \| None \| Literal['decorator']` | `"decorator"` | the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. |
| `inputs` | `Component \| BlockContext \| list[Component \| BlockContext] \| Set[Component \| BlockContext] \| None` | `None` | List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. |
| `outputs` | `Component \| BlockContext \| list[Component \| BlockContext] \| Set[Component \| BlockContext] \| None` | `None` | List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. |
| `api_name` | `str \| None` | `None` | defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. |
| `api_description` | `str \| None \| Literal[False]` | `None` | Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. |
| `scroll_to_output` | `bool` | `False` | If True, will scroll to output component on completion |
| `show_progress` | `Literal['full', 'minimal', 'hidden']` | `"full"` | how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all |
| `show_progress_on` | `Component \| list[Component] \| None` | `None` | Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. |
| `queue` | `bool` | `True` | If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. |
| `batch` | `bool` | `False` | If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. |
| `max_batch_size` | `int` | `4` | Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) |
| `preprocess` | `bool` | `True` | If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). |
| `postprocess` | `bool` | `True` | If False, will not run postprocessing of component data before returning 'fn' output to the browser. |
| `cancels` | `dict[str, Any] \| list[dict[str, Any]] \| None` | `None` | A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. |
| `trigger_mode` | `Literal['once', 'multiple', 'always_last'] \| None` | `None` | If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. |
| `js` | `str \| Literal[True] \| None` | `None` | Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. |
| `concurrency_limit` | `int \| None \| Literal['default']` | `"default"` | If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). |
| `concurrency_id` | `str \| None` | `None` | If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. |
| `api_visibility` | `Literal['public', 'private', 'undocumented']` | `"public"` | controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by clients), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". |
| `time_limit` | `int \| None` | `None` |  |
| `stream_every` | `float` | `0.5` |  |
| `key` | `int \| str \| tuple[int \| str, ...] \| None` | `None` | A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. |
| `validator` | `Callable \| None` | `None` | Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function and should return a `gr.validate()` for each input value. |
### Helper Classes

### Webcam Options

```python
gradio.WebcamOptions(···)
```

#### Description

A dataclass for specifying options for the webcam tool in the ImageEditor component. An instance of this class can be passed to the `webcam_options` parameter of `gr.ImageEditor`.

#### Initialization

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `mirror` | `bool` | `True` | If True, the webcam will be mirrored. |
| `constraints` | `dict[str, Any] \| None` | `None` | A dictionary of constraints for the webcam. |
- [Streaming Inputs](https://www.gradio.app/guides/streaming-inputs/)
- [Streaming Outputs](https://www.gradio.app/guides/streaming-outputs/)