import argparse
import os
import copy

import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import PIL
# OwlViT Detection
from transformers import OwlViTProcessor, OwlViTForObjectDetection

# segment anything
from segment_anything import build_sam, SamPredictor 
import cv2
import numpy as np
import matplotlib.pyplot as plt

import gc

def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], 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)
    ax.imshow(mask_image)


def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))  

def plot_boxes_to_image(image_pil, tgt):
    H, W = tgt["size"]
    boxes = tgt["boxes"]
    labels = tgt["labels"]
    assert len(boxes) == len(labels), "boxes and labels must have same length"

    draw = ImageDraw.Draw(image_pil)
    mask = Image.new("L", image_pil.size, 0)
    mask_draw = ImageDraw.Draw(mask)

    # draw boxes and masks
    for box, label in zip(boxes, labels):
        # random color
        color = tuple(np.random.randint(0, 255, size=3).tolist())
        # draw
        x0, y0, x1, y1 = box
        x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)

        draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
        draw.text((x0, y0), str(label), fill=color)

        font = ImageFont.load_default()
        if hasattr(font, "getbbox"):
            bbox = draw.textbbox((x0, y0), str(label), font)
        else:
            w, h = draw.textsize(str(label), font)
            bbox = (x0, y0, w + x0, y0 + h)
        # bbox = draw.textbbox((x0, y0), str(label))
        draw.rectangle(bbox, fill=color)
        draw.text((x0, y0), str(label), fill="white")

        mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)

    return image_pil, mask

# Use GPU if available
if torch.cuda.is_available():
    device = torch.device("cuda:4")
else:
    device = torch.device("cpu")

# load OWL-ViT model
owlvit_model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32").to(device)
owlvit_model.eval()
owlvit_processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")

# run segment anything (SAM)
sam_predictor = SamPredictor(build_sam(checkpoint="./sam_vit_h_4b8939.pth"))

def query_image(img, text_prompt):
    # load image
    if not isinstance(img, PIL.Image.Image):
        pil_img = Image.fromarray(np.uint8(img)).convert('RGB')

    text_prompt = text_prompt
    texts = text_prompt.split(",")

    box_threshold = 0.0

    # run object detection model
    with torch.no_grad():
        inputs = owlvit_processor(text=texts, images=pil_img, return_tensors="pt").to(device)
        outputs = owlvit_model(**inputs)
    
    # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
    target_sizes = torch.Tensor([pil_img.size[::-1]])
    # Convert outputs (bounding boxes and class logits) to COCO API
    results = owlvit_processor.post_process_object_detection(outputs=outputs, threshold=box_threshold, target_sizes=target_sizes.to(device))
    scores = torch.sigmoid(outputs.logits)
    topk_scores, topk_idxs = torch.topk(scores, k=1, dim=1)
    
    i = 0  # Retrieve predictions for the first image for the corresponding text queries
    text = texts[i]
     
    topk_idxs = topk_idxs.squeeze(1).tolist()
    topk_boxes = results[i]['boxes'][topk_idxs]
    topk_scores = topk_scores.view(len(text), -1)
    topk_labels = results[i]["labels"][topk_idxs]
    boxes, scores, labels = topk_boxes, topk_scores, topk_labels
    
    # boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
    

    # Print detected objects and rescaled box coordinates
    # for box, score, label in zip(boxes, scores, labels):
    #     box = [round(i, 2) for i in box.tolist()]
    #     print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")

    boxes = boxes.cpu().detach().numpy()
    normalized_boxes = copy.deepcopy(boxes)
    
    # # visualize pred
    size = pil_img.size
    pred_dict = {
        "boxes": normalized_boxes,
        "size": [size[1], size[0]], # H, W
        "labels": [text[idx] for idx in labels]
    }

    # release the OWL-ViT
    # owlvit_model.cpu()
    # del owlvit_model
    gc.collect()
    torch.cuda.empty_cache()

    # run segment anything (SAM)
    open_cv_image = np.array(pil_img) 
    image = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB)
    sam_predictor.set_image(image)
    
    H, W = size[1], size[0]

    for i in range(boxes.shape[0]):
        boxes[i] = torch.Tensor(boxes[i])

    boxes = torch.tensor(boxes, device=sam_predictor.device)

    transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2])
    
    masks, _, _ = sam_predictor.predict_torch(
        point_coords = None,
        point_labels = None,
        boxes = transformed_boxes,
        multimask_output = False,
    )
    plt.figure(figsize=(10, 10))
    plt.imshow(image)
    for mask in masks:
        show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
    for box in boxes:
        show_box(box.numpy(), plt.gca())
    plt.axis('off')
    
    import io
    buf = io.BytesIO()
    plt.savefig(buf)
    buf.seek(0)
    owlvit_segment_image = Image.open(buf).convert('RGB')

    # grounded results
    image_with_box = plot_boxes_to_image(pil_img, pred_dict)[0]
    
    # return owlvit_segment_image, image_with_box
    return owlvit_segment_image