import os
import torch
import numpy as np
from PIL import Image, ImageChops, ImageEnhance
import cv2
from simple_lama_inpainting import SimpleLama
from transformers import pipeline
from huggingface_hub import hf_hub_download

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def load_groundingdino_model(device='cpu'):
    model = pipeline(model="IDEA-Research/grounding-dino-base", task="zero-shot-object-detection", device=device)
    return model

groundingdino_model = load_groundingdino_model(device=device)
sam_predictor = None
simple_lama = SimpleLama()

def detect(image, model, text_prompt='insect . flower . cloud', box_threshold=0.15, text_threshold=0.15):
    labels = [label if label.endswith('.') else label + '.' for label in text_prompt.split('.')]
    results = model(image, candidate_labels=labels, threshold=box_threshold)
    return results

def segment(image, sam_model, boxes):
    sam_model.set_image(image)
    H, W, _ = image.shape
    boxes_xyxy = torch.Tensor(boxes) * torch.Tensor([W, H, W, H])

    transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_xyxy.to(device), image.shape[:2])
    masks, _, _ = sam_model.predict_torch(
        point_coords=None,
        point_labels=None,
        boxes=transformed_boxes,
        multimask_output=True,
    )
    return masks.cpu()

def draw_mask(mask, image, random_color=True):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.8])], axis=0)
    else:
        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    annotated_frame_pil = Image.fromarray(image).convert("RGBA")
    mask_image_pil = Image.fromarray((mask_image.numpy() * 255).astype(np.uint8)).convert("RGBA")
    return np.array(Image.alpha_composite(annotated_frame_pil, mask_image_pil))

def dilate_mask(mask, dilate_factor=15):
    mask = mask.astype(np.uint8)
    mask = cv2.dilate(
        mask,
        np.ones((dilate_factor, dilate_factor), np.uint8),
        iterations=1
    )
    return mask

def gsl_process_image(image):
    image_source = Image.fromarray(image)
    detected_boxes = detect(image_source, groundingdino_model)
    boxes = [[d['box']['xmin'], d['box']['ymin'], d['box']['xmax'], d['box']['ymax']] for d in detected_boxes]
    segmented_frame_masks = segment(image, sam_predictor, boxes)

    final_mask = None
    for i in range(len(segmented_frame_masks) - 1):
        if final_mask is None:
            final_mask = np.bitwise_or(segmented_frame_masks[i][0].cpu(), segmented_frame_masks[i + 1][0].cpu())
        else:
            final_mask = np.bitwise_or(final_mask, segmented_frame_masks[i + 1][0].cpu())

    annotated_frame_with_mask = draw_mask(final_mask, image)

    mask = final_mask.numpy()
    mask = mask.astype(np.uint8) * 255
    mask = dilate_mask(mask)
    dilated_image_mask_pil = Image.fromarray(mask)

    result = simple_lama(image, dilated_image_mask_pil)

    diff = ImageChops.difference(result, Image.fromarray(image))
    threshold = 7
    diff2 = diff.convert('L').point(lambda p: 255 if p > threshold else 0).convert('1')
    img3 = Image.new('RGB', Image.fromarray(image).size, (255, 236, 10))
    diff3 = Image.composite(Image.fromarray(image), img3, diff2)

    return diff3