from typing import List

import cv2
import torch
import gradio as gr
import numpy as np
import supervision as sv
from inference.models import YOLOWorld

from utils.efficient_sam import load, inference_with_box

MARKDOWN = """
# YOLO-World 🔥 [with Efficient-SAM]

This is a demo of zero-shot instance segmentation using [YOLO-World](https://github.com/AILab-CVC/YOLO-World) and [Efficient-SAM](https://github.com/yformer/EfficientSAM).

Powered by Roboflow [Inference](https://github.com/roboflow/inference) and [Supervision](https://github.com/roboflow/supervision).
"""

EXAMPLES = [
    ['https://media.roboflow.com/dog.jpeg', 'dog, eye, nose, tongue, car', 0.005, 0.1, True, False, False],
]

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EFFICIENT_SAM_MODEL = load(device=DEVICE)
YOLO_WORLD_MODEL = YOLOWorld(model_id="yolo_world/l")

BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
MASK_ANNOTATOR = sv.MaskAnnotator()
LABEL_ANNOTATOR = sv.LabelAnnotator()


def process_categories(categories: str) -> List[str]:
    return [category.strip() for category in categories.split(',')]


def process_image(
        input_image: np.ndarray,
        categories: str,
        confidence_threshold: float = 0.005,
        iou_threshold: float = 0.5,
        with_segmentation: bool = True,
        with_confidence: bool = False,
        with_class_agnostic_nms: bool = False,
) -> np.ndarray:
    categories = process_categories(categories)
    YOLO_WORLD_MODEL.set_classes(categories)
    results = YOLO_WORLD_MODEL.infer(input_image, confidence=confidence_threshold)
    detections = sv.Detections.from_inference(results)
    detections = detections.with_nms(class_agnostic=with_class_agnostic_nms, threshold=iou_threshold)
    if with_segmentation:
        masks = []
        for [x_min, y_min, x_max, y_max] in detections.xyxy:
            box = np.array([[x_min, y_min], [x_max, y_max]])
            mask = inference_with_box(input_image, box, EFFICIENT_SAM_MODEL, DEVICE)
            masks.append(mask)
        detections.mask = np.array(masks)

    labels = [
        f"{categories[class_id]}: {confidence:.2f}" if with_confidence else f"{categories[class_id]}"
        for class_id, confidence in
        zip(detections.class_id, detections.confidence)
    ]
    output_image = input_image.copy()
    output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
    output_image = MASK_ANNOTATOR.annotate(output_image, detections)
    output_image = BOUNDING_BOX_ANNOTATOR.annotate(output_image, detections)
    output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
    output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
    return output_image


with gr.Blocks() as demo:
    gr.Markdown(MARKDOWN)
    with gr.Row():
        input_image_component = gr.Image(
            type='numpy',
            label='Input Image'
        )
        output_image_component = gr.Image(
            type='numpy',
            label='Output Image'
        )
    with gr.Row():
        categories_text_component = gr.Textbox(
            label='Categories',
            placeholder='comma separated list of categories',
            scale=5
        )
        submit_button_component = gr.Button('Submit', scale=1)
    gr.Examples(
        fn=process_image,
        examples=EXAMPLES,
        inputs=[input_image_component, categories_text_component],
        outputs=output_image_component
    )

    submit_button_component.click(
        fn=process_image,
        inputs=[input_image_component, categories_text_component],
        outputs=output_image_component
    )

demo.launch(debug=False, show_error=True)