app.py

import cv2
from matplotlib import pyplot as plt
import gradio as gr


def my_app(img):
    # Opening image
    # img = cv2.imread("image.jpg")

# OpenCV opens images as BRG
# but we want it as RGB We'll
# also need a grayscale version
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)


# Use minSize because for not
# bothering with extra-small
# dots that would look like STOP signs
    stop_data = cv2.CascadeClassifier('stop_data.xml')

    found = stop_data.detectMultiScale(img_gray,
                                       minSize=(20, 20))

# Don't do anything if there's
# no sign
    amount_found = len(found)

    if amount_found != 0:

        # There may be more than one
        # sign in the image
        for (x, y, width, height) in found:

            # We draw a green rectangle around
            # every recognized sign
            cv2.rectangle(img_rgb, (x, y),
                          (x + height, y + width),
                          (0, 255, 0), 5)

# Creates the environment of
# the picture and shows it
    plt.subplot(1, 1, 1)
    plt.imshow(img_rgb)
    plt.show()


gr.interface.Interface(fn=my_app, live=True, inputs=gr.Image(
    source='webcam', streaming=True), outputs="text").launch()