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import gradio as gr
import cv2
import numpy as np
from gradio_webrtc import WebRTC
from pathlib import Path
from twilio.rest import Client
import os
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()
rtc_configuration = {
"iceServers": token.ice_servers,
"iceTransportPolicy": "relay",
}
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(
"""
<h1 style='text-align: center'>
Image Detection from Webcam Stream (powered by WebRTC ⚡️)
</h1>
""")
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
image = WebRTC(label="Strean", rtc_configuration=rtc_configuration)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.webrtc_stream(
fn=detection,
inputs=[image, conf_threshold],
stream_every=0.05,
time_limit=30,
concurrency_limit=10
)
if __name__ == '__main__':
demo.launch()
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