import PIL.Image
import cv2
import gradio as gr
import huggingface_hub
import numpy as np
import onnxruntime as rt
from PIL import ImageOps
from carvekit.trimap.generator import TrimapGenerator
from pymatting import estimate_alpha_cf, estimate_foreground_ml, stack_images, load_image

providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
rmbg_model = rt.InferenceSession(model_path, providers=providers)

trimapGenerator = TrimapGenerator()


# def custom_background(background, foreground):
#     foreground = ImageOps.contain(foreground, background.size)
#     x = (background.size[0] - foreground.size[0]) // 2
#     y = (background.size[1] - foreground.size[1]) // 2
#     background.paste(foreground, (x, y), foreground)
#     return background

def custom_background(background: PIL.Image.Image, foreground: np.ndarray):
    final_foreground = PIL.Image.fromarray(foreground)
    x = (background.size[0] - final_foreground.size[0]) / 2
    y = (background.size[1] - final_foreground.size[1]) / 2
    box = (x, y, final_foreground.size[0] + x, final_foreground.size[1] + y)
    crop = background.crop(box)
    final_image = crop.copy()
    # put the foreground in the centre of the background
    paste_box = (0, final_image.size[1] - final_foreground.size[1], final_image.size[0], final_image.size[1])
    final_image.paste(final_foreground, paste_box, mask=final_foreground)
    return np.array(final_image)


def get_mask(img, s=1024):
    img = (img / 255).astype(np.float32)
    h, w = h0, w0 = img.shape[:-1]
    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
    ph, pw = s - h, s - w
    img_input = np.zeros([s, s, 3], dtype=np.float32)
    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
    img_input = np.transpose(img_input, (2, 0, 1))
    img_input = img_input[np.newaxis, :]
    mask = rmbg_model.run(None, {'img': img_input})[0][0]
    mask = np.transpose(mask, (1, 2, 0))
    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
    return mask


def change_background_color(image, color="blue"):
    mask = get_mask(image)
    image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
    mask = (mask * 255).astype(np.uint8)
    image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
    image = PIL.Image.fromarray(image)
    background = PIL.Image.new('RGB', image.size, color)
    background.paste(image, (0, 0), image)
    return background


def generate_trimap(probs, size=7, conf_threshold=0.95):
    """
    This function creates a trimap based on simple dilation algorithm
    Inputs [3]: an image with probabilities of each pixel being the foreground, size of dilation kernel,
    foreground confidence threshold
    Output    : a trimap
    """
    mask = (probs > 0.05).astype(np.uint8) * 255
    pixels = 2 * size + 1
    kernel = np.ones((pixels, pixels), np.uint8)
    dilation = cv2.dilate(mask, kernel, iterations=1)
    remake = np.zeros_like(mask)
    remake[dilation == 255] = 127  # Set every pixel within dilated region as probably foreground.
    remake[probs > conf_threshold] = 255  # Set every pixel with large enough probability as definitely foreground.
    return remake


def image2gray(image):
    image = PIL.Image.fromarray(image).convert("L")
    return np.array(image) / 255.0


def paste(img_orig, alpha):
    img_ = img_orig.astype(np.float32) / 255
    alpha_ = cv2.resize(alpha, (img_.shape[1], img_.shape[0]), cv2.INTER_LANCZOS4)
    fg_alpha = np.concatenate([img_, alpha_[:, :, np.newaxis]], axis=2)
    cv2.imwrite("new_back.png", (fg_alpha * 255).astype(np.uint8))


def predict(image, new_background):
    mask = get_mask(image)
    mask = (mask * 255).astype(np.uint8)
    mask = mask.repeat(3, axis=2)

    trimap = generate_trimap(mask)
    trimap = image2gray(trimap)
    # trimap = load_image("images/trimaps/lemur_trimap.png", "GRAY")

    original = PIL.Image.fromarray(image)
    # mask = image2gray(mask)
    mask = PIL.Image.fromarray(mask).convert("L")
    trimap = trimapGenerator(original_image=original, mask=mask)
    trimap = np.array(trimap) / 255.0

    foreground = image / 255
    alpha = estimate_alpha_cf(foreground, trimap)
    foreground = estimate_foreground_ml(foreground, alpha)
    cutout = stack_images(foreground, alpha)
    cutout = np.clip(cutout * 255, 0, 255).astype(np.uint8)

    if new_background is not None:
        return mask, trimap, custom_background(new_background, cutout)
    return alpha, trimap, cutout


# contours
def serendipity(image, new_background):
    mask = get_mask(image)
    mask = 255 - mask
    image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
    mask = (mask * 255).astype(np.uint8)
    image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
    return mask, image


def negative(image, new_background):
    mask = get_mask(image)
    image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
    image = 255 - image
    mask = (mask * 255).astype(np.uint8)
    image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
    return mask, image


def checkit(image, new_background):
    mask = get_mask(image)
    mask = 255 - mask
    image = (mask / image - 255 / (1 + mask)).astype(np.uint8)
    mask = (mask * 255).astype(np.uint8)
    mask = 255 - mask
    image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
    mask = mask.repeat(3, axis=2)
    # if new_background is not None:
    #     foreground = PIL.Image.fromarray(image)
    #     return mask, custom_background(new_background, foreground)
    return mask, image


footer = r"""
<center>
<b>
Demo based on <a href='https://github.com/SkyTNT/anime-segmentation'>SkyTNT Anime Segmentation</a>
</b>
</center>
"""

with gr.Blocks(title="Face Shine") as app:
    gr.HTML("<center><h1>Anime Remove Background</h1></center>")
    with gr.Row():
        with gr.Column():
            input_img = gr.Image(type="numpy", image_mode="RGB", label="Input image")
            new_img = gr.Image(type="pil", image_mode="RGBA", label="Custom background")
            run_btn = gr.Button(variant="primary")
        with gr.Column():
            with gr.Accordion(label="Image mask", open=False):
                output_mask = gr.Image(type="numpy", label="mask")
                output_trimap = gr.Image(type="numpy", label="trimap")
            output_img = gr.Image(type="numpy", label="result")

    run_btn.click(predict, [input_img, new_img], [output_mask, output_trimap, output_img])

    with gr.Row():
        examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
        examples = gr.Dataset(components=[input_img], samples=examples_data)
        examples.click(lambda x: x[0], [examples], [input_img])

    with gr.Row():
        gr.HTML(footer)

app.launch(share=False, debug=True, enable_queue=True, show_error=True)