value |
```python
str
| Path
| tuple[str | Path, str | Path | None]
| Callable
| None
```
|
None |
path or URL for the default value that WebRTC component is going to take. Can also be a tuple consisting of (video filepath, subtitle filepath). If a subtitle file is provided, it should be of type .srt or .vtt. Or can be callable, in which case the function will be called whenever the app loads to set the initial value of the component. |
height |
```python
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 video file, but will affect the displayed video. |
width |
```python
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 video file, but will affect the displayed video. |
label |
```python
str | 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 |
```python
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 |
```python
Component | Sequence[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 |
```python
bool | None
```
|
None |
if True, will display label. |
container |
```python
bool
```
|
True |
if True, will place the component in a container - providing some extra padding around the border. |
scale |
```python
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 |
```python
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 |
```python
bool | None
```
|
None |
if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output. |
visible |
```python
bool
```
|
True |
if False, component will be hidden. |
elem_id |
```python
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 |
```python
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 |
```python
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 |
```python
int | str | None
```
|
None |
if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved. |
mirror_webcam |
```python
bool
```
|
True |
if True webcam will be mirrored. Default is True. |
show_share_button |
```python
bool | None
```
|
None |
if True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise. |
show_download_button |
```python
bool | None
```
|
None |
if True, will show a download icon in the corner of the component that allows user to download the output. If False, icon does not appear. By default, it will be True for output components and False for input components. |
min_length |
```python
int | None
```
|
None |
the minimum length of video (in seconds) that the user can pass into the prediction function. If None, there is no minimum length. |
max_length |
```python
int | None
```
|
None |
the maximum length of video (in seconds) that the user can pass into the prediction function. If None, there is no maximum length. |
streaming |
```python
bool
```
|
False |
when used set as an output, takes video chunks yielded from the backend and combines them into one streaming video output. Each chunk should be a video file with a .ts extension using an h.264 encoding. Mp4 files are also accepted but they will be converted to h.264 encoding. |
Stream images in realtime with webrtc
## Installation
```bash
pip install gradio_webrtc
```
## Usage
```python
import gradio as gr
import cv2
import numpy as np
from gradio_webrtc import WebRTC
from pathlib import Path
CLASSES = [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
directory = Path(__file__).parent
MODEL = str((directory / "MobileNetSSD_deploy.caffemodel").resolve())
PROTOTXT = str((directory / "MobileNetSSD_deploy.prototxt.txt").resolve())
net = cv2.dnn.readNetFromCaffe(PROTOTXT, MODEL)
def detection(image, conf_threshold=0.3):
blob = cv2.dnn.blobFromImage(
cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5
)
net.setInput(blob)
detections = net.forward()
image = cv2.resize(image, (500, 500))
(h, w) = image.shape[:2]
labels = []
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
# extract the index of the class label from the `detections`,
# then compute the (x, y)-coordinates of the bounding box for
# the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# display the prediction
label = f"{CLASSES[idx]}: {round(confidence * 100, 2)}%"
labels.append(label)
cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(
image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2
)
return image
css=""".my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""