diff --git a/backend/gradio_webrtc/webrtc.py b/backend/gradio_webrtc/webrtc.py
index 25c2694..033eb29 100644
--- a/backend/gradio_webrtc/webrtc.py
+++ b/backend/gradio_webrtc/webrtc.py
@@ -2,6 +2,7 @@
from __future__ import annotations
+import asyncio
from collections.abc import Callable, Sequence
from pathlib import Path
from typing import TYPE_CHECKING, Any, Literal, Optional, cast
@@ -142,6 +143,7 @@ class WebRTC(Component):
min_length: int | None = None,
max_length: int | None = None,
rtc_configuration: dict[str, Any] | None = None,
+ time_limit: float | None = None,
):
"""
Parameters:
@@ -173,6 +175,7 @@ class WebRTC(Component):
streaming: 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.
watermark: an image file to be included as a watermark on the video. The image is not scaled and is displayed on the bottom right of the video. Valid formats for the image are: jpeg, png.
"""
+ self.time_limit = time_limit
self.height = height
self.width = width
self.mirror_webcam = mirror_webcam
@@ -227,37 +230,52 @@ class WebRTC(Component):
if webrtc_id in self.connections:
self.connections[webrtc_id].latest_args = ["__webrtc_value__"] + list(args)
- def webrtc_stream(
+ def stream(
self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
+ outputs: Block | Sequence[Block] | set[Block] | None = None,
js: str | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
- stream_every: float = 0.5,
time_limit: float | None = None):
- if inputs[0] != self:
- raise ValueError("In the webrtc_stream event, the first input component must be the WebRTC component.")
-
+ from gradio.blocks import Block
+
+ if isinstance(inputs, Block):
+ inputs = [inputs]
+ if isinstance(outputs, Block):
+ outputs = [outputs]
+
+ if cast(list[Block], inputs)[0] != self:
+ raise ValueError("In the webrtc stream event, the first input component must be the WebRTC component.")
+
+ if len(cast(list[Block], outputs)) != 1 and cast(list[Block], outputs)[0] != self:
+ raise ValueError("In the webrtc stream event, the only output component must be the WebRTC component.")
+
self.concurrency_limit = 1 if concurrency_limit in ["default", None] else concurrency_limit
self.event_handler = fn
+ self.time_limit = time_limit
return self.tick(self.set_output,
inputs=inputs,
outputs=None,
concurrency_id=concurrency_id,
concurrency_limit=None,
- stream_every=stream_every,
+ stream_every=0.5,
time_limit=None,
js=js
)
+ @staticmethod
+ async def wait_for_time_limit(pc: RTCPeerConnection, time_limit: float):
+ await asyncio.sleep(time_limit)
+ await pc.close()
+
@server
async def offer(self, body):
- if len(self.connections) >= self.concurrency_limit:
+ if len(self.connections) >= cast(int, self.concurrency_limit):
return {"status": "failed"}
-
offer = RTCSessionDescription(sdp=body['sdp'], type=body['type'])
@@ -278,6 +296,9 @@ class WebRTC(Component):
await pc.close()
self.connections.pop(body['webrtc_id'], None)
self.pcs.discard(pc)
+ if pc.connectionState == "connected":
+ if self.time_limit is not None:
+ asyncio.create_task(self.wait_for_time_limit(pc, self.time_limit))
@pc.on("track")
def on_track(track):
diff --git a/demo/app.py b/demo/app.py
index fadaf6c..d2870bb 100644
--- a/demo/app.py
+++ b/demo/app.py
@@ -1,88 +1,42 @@
import gradio as gr
import cv2
-import numpy as np
+from huggingface_hub import hf_hub_download
from gradio_webrtc import WebRTC
-from pathlib import Path
from twilio.rest import Client
import os
+from inference import YOLOv10
+
+model_file = hf_hub_download(
+ repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
+)
+
+model = YOLOv10(model_file)
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
-client = Client(account_sid, auth_token)
-token = client.tokens.create()
+if account_sid and auth_token:
+ client = Client(account_sid, auth_token)
-rtc_configuration = {
- "iceServers": token.ice_servers,
- "iceTransportPolicy": "relay",
-}
+ token = client.tokens.create()
-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))
+ rtc_configuration = {
+ "iceServers": token.ice_servers,
+ "iceTransportPolicy": "relay",
+ }
+else:
+ rtc_configuration = None
-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)
+rtc_configuration = None
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
+ image = cv2.resize(image, (model.input_width, model.input_height))
+ new_image = model.detect_objects(image, conf_threshold)
+ return cv2.resize(new_image, (500, 500))
-css=""".my-group {max-width: 600px !important; max-height: 600 !important;}
+css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
@@ -90,18 +44,20 @@ with gr.Blocks(css=css) as demo:
gr.HTML(
"""
- YOLOv10 Webcam Stream
+ YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
- """)
+ """
+ )
gr.HTML(
"""
- """)
+ """
+ )
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
- image = WebRTC(label="Strean", rtc_configuration=rtc_configuration)
+ image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
@@ -109,13 +65,10 @@ with gr.Blocks(css=css) as demo:
step=0.05,
value=0.30,
)
-
- image.webrtc_stream(
- fn=detection,
- inputs=[image],
- stream_every=0.05,
- time_limit=30
+
+ image.stream(
+ fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
)
-if __name__ == '__main__':
+if __name__ == "__main__":
demo.launch()
diff --git a/demo/draw_boxes.py b/demo/draw_boxes.py
index 9a0442e..a1552b0 100644
--- a/demo/draw_boxes.py
+++ b/demo/draw_boxes.py
@@ -25,7 +25,7 @@ def draw_bounding_boxes(image, results: dict, model, threshold=0.3):
color = get_color(label)
# Draw bounding box
- draw.rectangle(box, outline=color, width=3) # type: ignore
+ draw.rectangle(box, outline=color, width=3) # type: ignore
# Prepare text
text = f"{label}: {score:.2f}"
@@ -35,8 +35,8 @@ def draw_bounding_boxes(image, results: dict, model, threshold=0.3):
# Draw text background
draw.rectangle(
- [box[0], box[1] - text_height - 4, box[0] + text_width, box[1]], # type: ignore
- fill=color, # type: ignore
+ [box[0], box[1] - text_height - 4, box[0] + text_width, box[1]], # type: ignore
+ fill=color, # type: ignore
)
# Draw text
diff --git a/demo/inference.py b/demo/inference.py
new file mode 100644
index 0000000..ff54736
--- /dev/null
+++ b/demo/inference.py
@@ -0,0 +1,146 @@
+import time
+import cv2
+import numpy as np
+import onnxruntime
+
+from utils import draw_detections
+
+
+class YOLOv10:
+ def __init__(self, path):
+
+ # Initialize model
+ self.initialize_model(path)
+
+ def __call__(self, image):
+ return self.detect_objects(image)
+
+ def initialize_model(self, path):
+ self.session = onnxruntime.InferenceSession(
+ path, providers=onnxruntime.get_available_providers()
+ )
+ # Get model info
+ self.get_input_details()
+ self.get_output_details()
+
+ def detect_objects(self, image, conf_threshold=0.3):
+ input_tensor = self.prepare_input(image)
+
+ # Perform inference on the image
+ new_image = self.inference(image, input_tensor, conf_threshold)
+
+ return new_image
+
+ def prepare_input(self, image):
+ self.img_height, self.img_width = image.shape[:2]
+
+ input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+ # Resize input image
+ input_img = cv2.resize(input_img, (self.input_width, self.input_height))
+
+ # Scale input pixel values to 0 to 1
+ input_img = input_img / 255.0
+ input_img = input_img.transpose(2, 0, 1)
+ input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
+
+ return input_tensor
+
+ def inference(self, image, input_tensor, conf_threshold=0.3):
+ start = time.perf_counter()
+ outputs = self.session.run(
+ self.output_names, {self.input_names[0]: input_tensor}
+ )
+
+ print(f"Inference time: {(time.perf_counter() - start)*1000:.2f} ms")
+ boxes, scores, class_ids, = self.process_output(outputs, conf_threshold)
+ return self.draw_detections(image, boxes, scores, class_ids)
+
+ def process_output(self, output, conf_threshold=0.3):
+ predictions = np.squeeze(output[0])
+
+ # Filter out object confidence scores below threshold
+ scores = predictions[:, 4]
+ predictions = predictions[scores > conf_threshold, :]
+ scores = scores[scores > conf_threshold]
+
+ if len(scores) == 0:
+ return [], [], []
+
+ # Get the class with the highest confidence
+ class_ids = np.argmax(predictions[:, 4:], axis=1)
+
+ # Get bounding boxes for each object
+ boxes = self.extract_boxes(predictions)
+
+ return boxes, scores, class_ids
+
+ def extract_boxes(self, predictions):
+ # Extract boxes from predictions
+ boxes = predictions[:, :4]
+
+ # Scale boxes to original image dimensions
+ boxes = self.rescale_boxes(boxes)
+
+ # Convert boxes to xyxy format
+ #boxes = xywh2xyxy(boxes)
+
+ return boxes
+
+ def rescale_boxes(self, boxes):
+ # Rescale boxes to original image dimensions
+ input_shape = np.array(
+ [self.input_width, self.input_height, self.input_width, self.input_height]
+ )
+ boxes = np.divide(boxes, input_shape, dtype=np.float32)
+ boxes *= np.array(
+ [self.img_width, self.img_height, self.img_width, self.img_height]
+ )
+ return boxes
+
+ def draw_detections(self, image, boxes, scores, class_ids, draw_scores=True, mask_alpha=0.4):
+ return draw_detections(
+ image, boxes, scores, class_ids, mask_alpha
+ )
+
+ def get_input_details(self):
+ model_inputs = self.session.get_inputs()
+ self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
+
+ self.input_shape = model_inputs[0].shape
+ self.input_height = self.input_shape[2]
+ self.input_width = self.input_shape[3]
+
+ def get_output_details(self):
+ model_outputs = self.session.get_outputs()
+ self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]
+
+
+if __name__ == "__main__":
+ import requests
+ import tempfile
+ from huggingface_hub import hf_hub_download
+
+ model_file = hf_hub_download(
+ repo_id="onnx-community/yolov10s", filename="onnx/model.onnx"
+ )
+
+ yolov8_detector = YOLOv10(model_file)
+
+ with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f:
+ f.write(
+ requests.get(
+ "https://live.staticflickr.com/13/19041780_d6fd803de0_3k.jpg"
+ ).content
+ )
+ f.seek(0)
+ img = cv2.imread(f.name)
+
+ # # Detect Objects
+ combined_image = yolov8_detector.detect_objects(img)
+
+
+ # Draw detections
+ cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
+ cv2.imshow("Output", combined_image)
+ cv2.waitKey(0)
diff --git a/demo/requirements.txt b/demo/requirements.txt
index 1c604cb..b831040 100644
--- a/demo/requirements.txt
+++ b/demo/requirements.txt
@@ -2,4 +2,5 @@ safetensors==0.4.3
opencv-python
twilio
https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/gradio-5.0.0b3-py3-none-any.whl
-https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/gradio_webrtc-0.0.1-py3-none-any.whl
\ No newline at end of file
+https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/gradio_webrtc-0.0.1-py3-none-any.whl
+onnxruntime-gpu
\ No newline at end of file
diff --git a/demo/space.py b/demo/space.py
index 293214c..48a411c 100644
--- a/demo/space.py
+++ b/demo/space.py
@@ -1,9 +1,141 @@
-
import gradio as gr
from app import demo as app
import os
-_docs = {'WebRTC': {'description': 'Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output).\nFor the video to be playable in the browser it must have a compatible container and codec combination. Allowed\ncombinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with vp9 codec. If the component detects\nthat the output video would not be playable in the browser it will attempt to convert it to a playable mp4 video.\nIf the conversion fails, the original video is returned.\n', 'members': {'__init__': {'value': {'type': 'str\n | Path\n | tuple[str | Path, str | Path | None]\n | Callable\n | None', 'default': 'None', 'description': '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': {'type': 'int | str | None', 'default': 'None', 'description': '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': {'type': 'int | str | None', 'default': 'None', 'description': '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': {'type': 'str | None', 'default': 'None', 'description': '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': {'type': 'Timer | float | None', 'default': 'None', 'description': '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': {'type': 'Component | Sequence[Component] | set[Component] | None', 'default': 'None', 'description': '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': {'type': 'bool | None', 'default': 'None', 'description': 'if True, will display label.'}, 'container': {'type': 'bool', 'default': 'True', 'description': 'if True, will place the component in a container - providing some extra padding around the border.'}, 'scale': {'type': 'int | None', 'default': 'None', 'description': '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': {'type': 'int', 'default': '160', 'description': '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': {'type': 'bool | None', 'default': 'None', 'description': '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': {'type': 'bool', 'default': 'True', 'description': 'if False, component will be hidden.'}, 'elem_id': {'type': 'str | None', 'default': 'None', 'description': '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': {'type': 'list[str] | str | None', 'default': 'None', 'description': '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': {'type': 'bool', 'default': 'True', 'description': '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': {'type': 'int | str | None', 'default': 'None', 'description': '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': {'type': 'bool', 'default': 'True', 'description': 'if True webcam will be mirrored. Default is True.'}, 'show_share_button': {'type': 'bool | None', 'default': 'None', 'description': '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': {'type': 'bool | None', 'default': 'None', 'description': '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': {'type': 'int | None', 'default': 'None', 'description': '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': {'type': 'int | None', 'default': 'None', 'description': 'the maximum length of video (in seconds) that the user can pass into the prediction function. If None, there is no maximum length.'}, 'rtc_configuration': {'type': 'dict[str, Any] | None', 'default': 'None', 'description': None}}, 'postprocess': {'value': {'type': 'typing.Any', 'description': 'Expects a {str} or {pathlib.Path} filepath to a video which is displayed, or a {Tuple[str | pathlib.Path, str | pathlib.Path | None]} where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.'}}, 'preprocess': {'return': {'type': 'str', 'description': 'Passes the uploaded video as a `str` filepath or URL whose extension can be modified by `format`.'}, 'value': None}}, 'events': {'tick': {'type': None, 'default': None, 'description': ''}}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'WebRTC': []}}}
+_docs = {
+ "WebRTC": {
+ "description": "Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output).\nFor the video to be playable in the browser it must have a compatible container and codec combination. Allowed\ncombinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with vp9 codec. If the component detects\nthat the output video would not be playable in the browser it will attempt to convert it to a playable mp4 video.\nIf the conversion fails, the original video is returned.\n",
+ "members": {
+ "__init__": {
+ "value": {
+ "type": "str\n | Path\n | tuple[str | Path, str | Path | None]\n | Callable\n | None",
+ "default": "None",
+ "description": "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": {
+ "type": "int | str | None",
+ "default": "None",
+ "description": "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": {
+ "type": "int | str | None",
+ "default": "None",
+ "description": "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": {
+ "type": "str | None",
+ "default": "None",
+ "description": "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": {
+ "type": "Timer | float | None",
+ "default": "None",
+ "description": "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": {
+ "type": "Component | Sequence[Component] | set[Component] | None",
+ "default": "None",
+ "description": "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": {
+ "type": "bool | None",
+ "default": "None",
+ "description": "if True, will display label.",
+ },
+ "container": {
+ "type": "bool",
+ "default": "True",
+ "description": "if True, will place the component in a container - providing some extra padding around the border.",
+ },
+ "scale": {
+ "type": "int | None",
+ "default": "None",
+ "description": "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": {
+ "type": "int",
+ "default": "160",
+ "description": "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": {
+ "type": "bool | None",
+ "default": "None",
+ "description": "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": {
+ "type": "bool",
+ "default": "True",
+ "description": "if False, component will be hidden.",
+ },
+ "elem_id": {
+ "type": "str | None",
+ "default": "None",
+ "description": "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": {
+ "type": "list[str] | str | None",
+ "default": "None",
+ "description": "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": {
+ "type": "bool",
+ "default": "True",
+ "description": "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": {
+ "type": "int | str | None",
+ "default": "None",
+ "description": "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": {
+ "type": "bool",
+ "default": "True",
+ "description": "if True webcam will be mirrored. Default is True.",
+ },
+ "show_share_button": {
+ "type": "bool | None",
+ "default": "None",
+ "description": "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": {
+ "type": "bool | None",
+ "default": "None",
+ "description": "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": {
+ "type": "int | None",
+ "default": "None",
+ "description": "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": {
+ "type": "int | None",
+ "default": "None",
+ "description": "the maximum length of video (in seconds) that the user can pass into the prediction function. If None, there is no maximum length.",
+ },
+ "rtc_configuration": {
+ "type": "dict[str, Any] | None",
+ "default": "None",
+ "description": None,
+ },
+ },
+ "postprocess": {
+ "value": {
+ "type": "typing.Any",
+ "description": "Expects a {str} or {pathlib.Path} filepath to a video which is displayed, or a {Tuple[str | pathlib.Path, str | pathlib.Path | None]} where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.",
+ }
+ },
+ "preprocess": {
+ "return": {
+ "type": "str",
+ "description": "Passes the uploaded video as a `str` filepath or URL whose extension can be modified by `format`.",
+ },
+ "value": None,
+ },
+ },
+ "events": {"tick": {"type": None, "default": None, "description": ""}},
+ },
+ "__meta__": {"additional_interfaces": {}, "user_fn_refs": {"WebRTC": []}},
+}
abs_path = os.path.join(os.path.dirname(__file__), "css.css")
@@ -17,7 +149,7 @@ with gr.Blocks(
),
) as demo:
gr.Markdown(
-"""
+ """
# `gradio_webrtc`
@@ -25,10 +157,13 @@ with gr.Blocks(
Stream images in realtime with webrtc
-""", elem_classes=["md-custom"], header_links=True)
+""",
+ elem_classes=["md-custom"],
+ header_links=True,
+ )
app.render()
gr.Markdown(
-"""
+ """
## Installation
```bash
@@ -161,25 +296,28 @@ if __name__ == '__main__':
demo.launch()
```
-""", elem_classes=["md-custom"], header_links=True)
+""",
+ elem_classes=["md-custom"],
+ header_links=True,
+ )
-
- gr.Markdown("""
+ gr.Markdown(
+ """
## `WebRTC`
### Initialization
-""", elem_classes=["md-custom"], header_links=True)
+""",
+ elem_classes=["md-custom"],
+ header_links=True,
+ )
gr.ParamViewer(value=_docs["WebRTC"]["members"]["__init__"], linkify=[])
-
gr.Markdown("### Events")
- gr.ParamViewer(value=_docs["WebRTC"]["events"], linkify=['Event'])
+ gr.ParamViewer(value=_docs["WebRTC"]["events"], linkify=["Event"])
-
-
-
- gr.Markdown("""
+ gr.Markdown(
+ """
### User function
@@ -199,12 +337,14 @@ def predict(
) -> typing.Any:
return value
```
-""", elem_classes=["md-custom", "WebRTC-user-fn"], header_links=True)
+""",
+ elem_classes=["md-custom", "WebRTC-user-fn"],
+ header_links=True,
+ )
-
-
-
- demo.load(None, js=r"""function() {
+ demo.load(
+ None,
+ js=r"""function() {
const refs = {};
const user_fn_refs = {
WebRTC: [], };
@@ -238,6 +378,7 @@ def predict(
})
}
-""")
+""",
+ )
demo.launch()
diff --git a/demo/utils.py b/demo/utils.py
new file mode 100644
index 0000000..cdd791c
--- /dev/null
+++ b/demo/utils.py
@@ -0,0 +1,237 @@
+import numpy as np
+import cv2
+
+class_names = [
+ "person",
+ "bicycle",
+ "car",
+ "motorcycle",
+ "airplane",
+ "bus",
+ "train",
+ "truck",
+ "boat",
+ "traffic light",
+ "fire hydrant",
+ "stop sign",
+ "parking meter",
+ "bench",
+ "bird",
+ "cat",
+ "dog",
+ "horse",
+ "sheep",
+ "cow",
+ "elephant",
+ "bear",
+ "zebra",
+ "giraffe",
+ "backpack",
+ "umbrella",
+ "handbag",
+ "tie",
+ "suitcase",
+ "frisbee",
+ "skis",
+ "snowboard",
+ "sports ball",
+ "kite",
+ "baseball bat",
+ "baseball glove",
+ "skateboard",
+ "surfboard",
+ "tennis racket",
+ "bottle",
+ "wine glass",
+ "cup",
+ "fork",
+ "knife",
+ "spoon",
+ "bowl",
+ "banana",
+ "apple",
+ "sandwich",
+ "orange",
+ "broccoli",
+ "carrot",
+ "hot dog",
+ "pizza",
+ "donut",
+ "cake",
+ "chair",
+ "couch",
+ "potted plant",
+ "bed",
+ "dining table",
+ "toilet",
+ "tv",
+ "laptop",
+ "mouse",
+ "remote",
+ "keyboard",
+ "cell phone",
+ "microwave",
+ "oven",
+ "toaster",
+ "sink",
+ "refrigerator",
+ "book",
+ "clock",
+ "vase",
+ "scissors",
+ "teddy bear",
+ "hair drier",
+ "toothbrush",
+]
+
+# Create a list of colors for each class where each color is a tuple of 3 integer values
+rng = np.random.default_rng(3)
+colors = rng.uniform(0, 255, size=(len(class_names), 3))
+
+
+def nms(boxes, scores, iou_threshold):
+ # Sort by score
+ sorted_indices = np.argsort(scores)[::-1]
+
+ keep_boxes = []
+ while sorted_indices.size > 0:
+ # Pick the last box
+ box_id = sorted_indices[0]
+ keep_boxes.append(box_id)
+
+ # Compute IoU of the picked box with the rest
+ ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
+
+ # Remove boxes with IoU over the threshold
+ keep_indices = np.where(ious < iou_threshold)[0]
+
+ # print(keep_indices.shape, sorted_indices.shape)
+ sorted_indices = sorted_indices[keep_indices + 1]
+
+ return keep_boxes
+
+
+def multiclass_nms(boxes, scores, class_ids, iou_threshold):
+ unique_class_ids = np.unique(class_ids)
+
+ keep_boxes = []
+ for class_id in unique_class_ids:
+ class_indices = np.where(class_ids == class_id)[0]
+ class_boxes = boxes[class_indices, :]
+ class_scores = scores[class_indices]
+
+ class_keep_boxes = nms(class_boxes, class_scores, iou_threshold)
+ keep_boxes.extend(class_indices[class_keep_boxes])
+
+ return keep_boxes
+
+
+def compute_iou(box, boxes):
+ # Compute xmin, ymin, xmax, ymax for both boxes
+ xmin = np.maximum(box[0], boxes[:, 0])
+ ymin = np.maximum(box[1], boxes[:, 1])
+ xmax = np.minimum(box[2], boxes[:, 2])
+ ymax = np.minimum(box[3], boxes[:, 3])
+
+ # Compute intersection area
+ intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
+
+ # Compute union area
+ box_area = (box[2] - box[0]) * (box[3] - box[1])
+ boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+ union_area = box_area + boxes_area - intersection_area
+
+ # Compute IoU
+ iou = intersection_area / union_area
+
+ return iou
+
+
+def xywh2xyxy(x):
+ # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
+ y = np.copy(x)
+ y[..., 0] = x[..., 0] - x[..., 2] / 2
+ y[..., 1] = x[..., 1] - x[..., 3] / 2
+ y[..., 2] = x[..., 0] + x[..., 2] / 2
+ y[..., 3] = x[..., 1] + x[..., 3] / 2
+ return y
+
+
+def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3):
+ det_img = image.copy()
+
+ img_height, img_width = image.shape[:2]
+ font_size = min([img_height, img_width]) * 0.0006
+ text_thickness = int(min([img_height, img_width]) * 0.001)
+
+ #det_img = draw_masks(det_img, boxes, class_ids, mask_alpha)
+
+ # Draw bounding boxes and labels of detections
+ for class_id, box, score in zip(class_ids, boxes, scores):
+ color = colors[class_id]
+
+ draw_box(det_img, box, color)
+
+ label = class_names[class_id]
+ caption = f"{label} {int(score * 100)}%"
+ draw_text(det_img, caption, box, color, font_size, text_thickness)
+
+ return det_img
+
+
+def draw_box(
+ image: np.ndarray,
+ box: np.ndarray,
+ color: tuple[int, int, int] = (0, 0, 255),
+ thickness: int = 2,
+) -> np.ndarray:
+ x1, y1, x2, y2 = box.astype(int)
+ return cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)
+
+
+def draw_text(
+ image: np.ndarray,
+ text: str,
+ box: np.ndarray,
+ color: tuple[int, int, int] = (0, 0, 255),
+ font_size: float = 0.001,
+ text_thickness: int = 2,
+) -> np.ndarray:
+ x1, y1, x2, y2 = box.astype(int)
+ (tw, th), _ = cv2.getTextSize(
+ text=text,
+ fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+ fontScale=font_size,
+ thickness=text_thickness,
+ )
+ th = int(th * 1.2)
+
+ cv2.rectangle(image, (x1, y1), (x1 + tw, y1 - th), color, -1)
+
+ return cv2.putText(
+ image,
+ text,
+ (x1, y1),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ font_size,
+ (255, 255, 255),
+ text_thickness,
+ cv2.LINE_AA,
+ )
+
+
+def draw_masks(
+ image: np.ndarray, boxes: np.ndarray, classes: np.ndarray, mask_alpha: float = 0.3
+) -> np.ndarray:
+ mask_img = image.copy()
+
+ # Draw bounding boxes and labels of detections
+ for box, class_id in zip(boxes, classes):
+ color = colors[class_id]
+
+ x1, y1, x2, y2 = box.astype(int)
+
+ # Draw fill rectangle in mask image
+ cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)
+
+ return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)
diff --git a/frontend/Index.svelte b/frontend/Index.svelte
index ed66517..d6e9b89 100644
--- a/frontend/Index.svelte
+++ b/frontend/Index.svelte
@@ -1,7 +1,6 @@
-
+