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# import gradio as gr
# import cv2
# import numpy as np
# import onnxruntime as ort
# # Load the ONNX model using onnxruntime
# onnx_model_path = "Model_IV.onnx" # Update with your ONNX model path
# session = ort.InferenceSession(onnx_model_path)
# # Function to perform object detection with the ONNX model
# def detect_objects(frame, confidence_threshold=0.5):
# # Convert the frame from BGR (OpenCV) to RGB
# image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# # Preprocessing: Resize and normalize the image
# # Assuming YOLO model input is 640x640, update according to your model's input size
# input_size = (640, 640)
# image_resized = cv2.resize(image, input_size)
# image_normalized = image_resized / 255.0 # Normalize to [0, 1]
# image_input = np.transpose(image_normalized, (2, 0, 1)) # Change to CHW format
# image_input = np.expand_dims(image_input, axis=0).astype(np.float32) # Add batch dimension
# # Perform inference
# inputs = {session.get_inputs()[0].name: image_input}
# outputs = session.run(None, inputs)
# # # Assuming YOLO model outputs are in the form of [boxes, confidences, class_probs]
# # boxes, confidences, class_probs = outputs
# # # Post-processing: Filter boxes by confidence threshold
# # detections = []
# # for i, confidence in enumerate(confidences[0]):
# # if confidence >= confidence_threshold:
# # x1, y1, x2, y2 = boxes[0][i]
# # class_id = np.argmax(class_probs[0][i]) # Get class with highest probability
# # detections.append((x1, y1, x2, y2, confidence, class_id))
# # # Draw bounding boxes and labels on the image
# # for (x1, y1, x2, y2, confidence, class_id) in detections:
# # color = (0, 255, 0) # Green color for bounding boxes
# # cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
# # label = f"Class {class_id}: {confidence:.2f}"
# # cv2.putText(image, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# # # Convert the image back to BGR for displaying in Gradio
# # image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# return outputs
# # Gradio interface to use the webcam for real-time object detection
# # Added a slider for the confidence threshold
# iface = gr.Interface(fn=detect_objects,
# #inputs=[
# # gr.Video(sources="webcam", type="numpy"), # Webcam input
# inputs = gr.Image(sources=["webcam"], type="numpy"),
# # gr.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Confidence Threshold") # Confidence slider
# # ],
# outputs="image") # Show output image with bounding boxes
# iface.launch()
import gradio as gr
import cv2
from huggingface_hub import hf_hub_download
from gradio_webrtc import WebRTC
from twilio.rest import Client
import os
from inference import YOLOv8
model_file = hf_hub_download(
repo_id="aje6/ASL-Fingerspelling-Detection", filename="onnx/Model_IV.onnx"
)
model = YOLOv8(model_file)
account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
if account_sid and auth_token:
client = Client(account_sid, auth_token)
token = client.tokens.create()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
else:
rtc_configuration = None
def detection(image, conf_threshold=0.3):
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;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
"""
)
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()
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