import gradio as gr
import numpy as np
import cv2  # type: ignore

def transform_cv2(frame, transform):
    if transform == "cartoon":
        # prepare color
        img_color = cv2.pyrDown(cv2.pyrDown(frame))
        for _ in range(6):
            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
        img_color = cv2.pyrUp(cv2.pyrUp(img_color))

        # prepare edges
        img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        img_edges = cv2.adaptiveThreshold(
            cv2.medianBlur(img_edges, 7),
            255,
            cv2.ADAPTIVE_THRESH_MEAN_C,
            cv2.THRESH_BINARY,
            9,
            2,
        )
        img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
        # combine color and edges
        img = cv2.bitwise_and(img_color, img_edges)
        return img
    elif transform == "edges":
        # perform edge detection
        img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
        return img
    else:
        return np.flipud(frame)


css=""".my-group {max-width: 500px !important; max-height: 500px !important;}
            .my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""

with gr.Blocks() as demo:
    with gr.Column(elem_classes=["my-column"]):
        with gr.Group(elem_classes=["my-group"]):
            transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
                                    value="flip", label="Transformation")
            input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
    input_img.stream(transform_cv2, [input_img, transform], [input_img], time_limit=30, stream_every=0.1)


if __name__ == "__main__":
    demo.launch(css=css)
