import argparse
from functools import partial
import cv2
import requests
import os
from io import BytesIO
from PIL import Image
import numpy as np
from pathlib import Path
import gradio as gr
import warnings
import torch
import Equirec2Perspec as E2P
import cv2
import numpy as np

os.system("python setup.py build develop --user")
os.system("pip install packaging==21.3")
warnings.filterwarnings("ignore")

from groundingdino.models import build_model
from groundingdino.util.slconfig import SLConfig
from groundingdino.util.utils import clean_state_dict
from groundingdino.util.inference import annotate, load_image, predict
import groundingdino.datasets.transforms as T

from huggingface_hub import hf_hub_download

picture_height = 360
picture_width = 540
picture_fov = 45

# Use this command for evaluate the GLIP-T model
config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
ckpt_repo_id = "ShilongLiu/GroundingDINO"
ckpt_filenmae = "groundingdino_swint_ogc.pth"


def detection(image):
    sub_images = processPanorama(image)
    processed_images = [np.array(sub_image) for sub_image in sub_images]

    return processed_images


def processPanorama(image):
    equ = E2P.Equirectangular(image)
    FOV = picture_fov
    y_axis = 0

    sub_images = []
    while y_axis <= 0:
        z_axis = -150
        while z_axis <= 90:
            img = equ.GetPerspective(FOV, z_axis, y_axis, picture_height, picture_width)
            # cv2.imwrite(f'{directory_name}_{z_axis}z.jpg', img)
            sub_images.append(img)
            z_axis += FOV
        y_axis += FOV
    return sub_images


def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
    args = SLConfig.fromfile(model_config_path)
    model = build_model(args)
    args.device = device

    cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
    checkpoint = torch.load(cache_file, map_location='cpu')
    log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)
    print("Model loaded from {} \n => {}".format(cache_file, log))
    _ = model.eval()
    return model


def image_transform_grounding(init_image):
    transform = T.Compose([
        T.RandomResize([800], max_size=1333),
        T.ToTensor(),
        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    image, _ = transform(init_image, None)  # 3, h, w
    return init_image, image


def image_transform_grounding_for_vis(init_image):
    transform = T.Compose([
        T.RandomResize([800], max_size=1333),
    ])
    image, _ = transform(init_image, None)  # 3, h, w
    return image


model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)


def run_grounding(input_image, grounding_caption, box_threshold, text_threshold):
    init_image = input_image.convert("RGB")
    original_size = init_image.size

    _, image_tensor = image_transform_grounding(init_image)
    image_pil: Image = image_transform_grounding_for_vis(init_image)

    # run grounidng
    boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold,
                                     device='cpu')
    annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases)
    image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))

    return image_with_box


if __name__ == "__main__":
    detect_app = gr.Blocks()
    with detect_app:
        gr.Markdown("# Panorama Traffic Sign Detection Demo")
        gr.Markdown("Note the model runs on CPU for demo, so it may take a while to run the model.")

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy", label="Please upload a panorama picture.")
                run_button = gr.Button(label="Process & Detect")

            with gr.Column():
                gallery = gr.Gallery(label="Detection Results").style(
                    columns=[3], preview=False, object_fit="none")

        run_button.click(fn=detection, inputs=[
            input_image], outputs=[gallery])

    detect_app.launch(share=False, show_api=False, show_error=True)