import gradio as gr
import cv2
import json
import numpy as np
import glob2 as glob
from PIL import Image

from pyAAMED import pyAAMED

title = """
<h1>Arc Adjacency Matrix based Fast Ellipse Detection</h1>
<a href="https://github.com/Li-Zhaoxi/AAMED">Gitub</a>
"""

def detect_ellipses(img_path):
    imgC = cv2.imread(img_path)
    imgG = cv2.cvtColor(imgC, cv2.COLOR_BGR2GRAY)

    ammed_size = 600

    iheight, iwidth = imgG.shape
    imax = max(iheight, iwidth)
    iscale = ammed_size / imax
    is_landscape = iwidth >= iheight
    if is_landscape:
        iw = imax * iscale
        ih = iheight * iscale
    else:
        iw = iwidth * iscale
        ih = imax * iscale

    imgG = cv2.resize(imgG, (int(iw), int(ih)))

    if is_landscape:
        ipad = int(ammed_size - ih)
        imgG = cv2.copyMakeBorder(imgG, ipad, ipad, 0, 0, cv2.BORDER_REPLICATE)
    else:
        ipad = int(ammed_size - iw)
        imgG = cv2.copyMakeBorder(imgG, 0, 0, ipad, ipad, cv2.BORDER_REPLICATE)

    aamed = pyAAMED(ammed_size, ammed_size)
    aamed.setParameters(3.1415926/3, 3.4, 0.77)

    print(ammed_size, iw, ih, imgG.shape)
    
    result = aamed.run_AAMED(imgG)
    print(result)

    """
    if result != "":
        result = ",".join(filter(lambda s: s != "", result.split(" ")))
        print(result)
    result = json.loads(result)
    print(result)
    """

    return [Image.fromarray(imgG), result]

examples = [
    ["./AAMED/python/002_0038.jpg"]
]

test_files = glob.glob('./examples/*.jpg') + glob.glob('./examples/*.png')
for f in test_files:
    examples = examples + [[f]]

gr.Interface(
    fn=detect_ellipses,
    inputs=gr.Image(label="Upload image with ellipses", type="filepath"),
    outputs=[
        gr.Image(type="pil", label="Detected ellipses"),
        gr.Textbox(label="Detected ellipses")
    ],
    title=title,
    examples=examples,
    allow_flagging='never'
).launch(
    debug=True,
    server_name="0.0.0.0", 
    server_port=7860
)