import os
import sys
# sys.path.append(os.path.abspath(os.path.dirname(os.getcwd())))
# os.chdir("../")
import gradio as gr
import numpy as np
from pathlib import Path
from matplotlib import pyplot as plt
import torch
import tempfile
from lama_inpaint import inpaint_img_with_lama, build_lama_model, inpaint_img_with_builded_lama
from utils import load_img_to_array, save_array_to_img, dilate_mask, \
    show_mask, show_points
from PIL import Image
sys.path.insert(0, str(Path(__file__).resolve().parent / "third_party" / "segment-anything"))
from segment_anything import SamPredictor, sam_model_registry
import argparse

def setup_args(parser):
    parser.add_argument(
        "--lama_config", type=str,
        default="./third_party/lama/configs/prediction/default.yaml",
        help="The path to the config file of lama model. "
             "Default: the config of big-lama",
    )
    parser.add_argument(
        "--lama_ckpt", type=str,
        default="pretrained_models/big-lama",
        help="The path to the lama checkpoint.",
    )
    parser.add_argument(
        "--sam_ckpt", type=str,
        default="./pretrained_models/sam_vit_h_4b8939.pth",
        help="The path to the SAM checkpoint to use for mask generation.",
    )
def mkstemp(suffix, dir=None):
    fd, path = tempfile.mkstemp(suffix=f"{suffix}", dir=dir)
    os.close(fd)
    return Path(path)


def get_sam_feat(img):
    model['sam'].set_image(img)
    features = model['sam'].features 
    orig_h = model['sam'].orig_h 
    orig_w = model['sam'].orig_w 
    input_h = model['sam'].input_h 
    input_w = model['sam'].input_w 
    model['sam'].reset_image()
    return features, orig_h, orig_w, input_h, input_w

 
def get_masked_img(img, w, h, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size):
    point_coords = [w, h]
    point_labels = [1]

    model['sam'].is_image_set = True
    model['sam'].features = features
    model['sam'].orig_h = orig_h
    model['sam'].orig_w = orig_w
    model['sam'].input_h = input_h
    model['sam'].input_w = input_w

    # model['sam'].set_image(img) # todo : update here for accelerating
    masks, _, _ = model['sam'].predict(
        point_coords=np.array([point_coords]),
        point_labels=np.array(point_labels),
        multimask_output=True,
    )

    masks = masks.astype(np.uint8) * 255

    dilate_kernel_size = 20
    # dilate mask to avoid unmasked edge effect
    if dilate_kernel_size is not None:
        masks = [dilate_mask(mask, dilate_kernel_size) for mask in masks]
    else:
        masks = [mask for mask in masks]

    figs = []
    for idx, mask in enumerate(masks):
        # save the pointed and masked image
        tmp_p = mkstemp(".png")
        dpi = plt.rcParams['figure.dpi']
        height, width = img.shape[:2]
        fig = plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
        plt.imshow(img)
        plt.axis('off')
        show_points(plt.gca(), [point_coords], point_labels,
                    size=(width*0.04)**2)
        show_mask(plt.gca(), mask, random_color=False)
        plt.tight_layout()
        plt.savefig(tmp_p, bbox_inches='tight', pad_inches=0)
        figs.append(fig)
        plt.close()
    return *figs, *masks


def get_inpainted_img(img,mask):
    lama_config = args.lama_config
    device = "cuda" if torch.cuda.is_available() else "cpu"
    if len(mask.shape)==3:
        mask = mask[:,:,0]
    img_inpainted = inpaint_img_with_builded_lama(
        model['lama'], img, mask, lama_config, device=device)
    return img_inpainted


# get args 
parser = argparse.ArgumentParser()
setup_args(parser)
args = parser.parse_args(sys.argv[1:])
# build models
model = {}
# build the sam model
model_type="vit_h"
ckpt_p=args.sam_ckpt
model_sam = sam_model_registry[model_type](checkpoint=ckpt_p)
device = "cuda" if torch.cuda.is_available() else "cpu"
model_sam.to(device=device)
model['sam'] = SamPredictor(model_sam)

# build the lama model
lama_config = args.lama_config
lama_ckpt = args.lama_ckpt
device = "cuda" if torch.cuda.is_available() else "cpu"
model['lama'] = build_lama_model(lama_config, lama_ckpt, device=device)

image_input = gr.Image(label="Input Image")
mask_input = gr.Image(label="Mask Image")
demo = gr.Interface(
    fn=get_inpainted_img,
    inputs=[image_input, mask_input],
    outputs=gr.Image(type="numpy", label="Output Image"),
    title="Image and Mask Processor",
    description="Upload an image and a mask to process the image. The mask highlights the areas to be processed.",
)

if __name__ == "__main__":
    demo.queue(api_open=True)
    demo.launch(show_api=True)