#!/usr/bin/env python

"""A demo of the DAB-DETR model."""

import pathlib
import tempfile

import cv2
import gradio as gr
import numpy as np
import PIL.Image
import spaces
import supervision as sv
import torch
import tqdm
from transformers import AutoProcessor, AutoModelForObjectDetection

DESCRIPTION = """
# DAB-DETR
##### [ArXiv](https://arxiv.org/abs/2201.12329) | [Docs](https://huggingface.co/docs/transformers/main/en/model_doc/dab-detr)
"""

MAX_NUM_FRAMES = 300

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

checkpoint = "IDEA-Research/dab-detr-resnet-50-dc5-pat3"
image_processor = AutoProcessor.from_pretrained(checkpoint)
model = AutoModelForObjectDetection.from_pretrained(checkpoint, device_map=device)


@spaces.GPU(duration=5)
@torch.inference_mode()
def process_image(image: PIL.Image.Image) -> tuple[PIL.Image.Image, list[dict]]:
    inputs = image_processor(images=image, return_tensors="pt").to(device)
    outputs = model(**inputs)
    results = image_processor.post_process_object_detection(
        outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
    )
    result = results[0]  # take first image results
    boxes_xyxy = result["boxes"].cpu().numpy()
    indexes = result["labels"].cpu().numpy()
    scores = result["scores"].cpu().numpy()
    text_labels = [
        f"{model.config.id2label[index]} [{score.item():.2f}]" for index, score in zip(indexes, scores)
    ]

    detections = sv.Detections(xyxy=boxes_xyxy, class_id=indexes, confidence=scores)
    bounding_box_annotator = sv.BoxAnnotator(color=sv.Color.WHITE, color_lookup=sv.ColorLookup.INDEX, thickness=1)
    label_annotator = sv.LabelAnnotator()

    # annotate bounding boxes
    annotated_frame = bounding_box_annotator.annotate(scene=image.copy(), detections=detections)
    annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=text_labels)

    return annotated_frame


@spaces.GPU(duration=90)
def process_video(
    video_path: str,
    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
) -> str:
    cap = cv2.VideoCapture(video_path)

    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    fps = cap.get(cv2.CAP_PROP_FPS)
    num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as out_file:
        writer = cv2.VideoWriter(out_file.name, fourcc, fps, (width, height))
        for _ in tqdm.auto.tqdm(range(min(MAX_NUM_FRAMES, num_frames))):
            ok, frame = cap.read()
            if not ok:
                break
            rgb_frame = frame[:, :, ::-1]
            annotated_frame = process_image(PIL.Image.fromarray(rgb_frame))
            writer.write(np.asarray(annotated_frame)[:, :, ::-1])
        writer.release()
    cap.release()
    return out_file.name


with gr.Blocks(css_paths="style.css") as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Tabs():
        with gr.Tab("Image"):
            with gr.Row():
                with gr.Column():
                    input_image = gr.Image(label="Input Image", type="pil")
                    run_button_image = gr.Button()
                with gr.Column():
                    output_image = gr.Image(label="Output Image")
            gr.Examples(
                examples=sorted(pathlib.Path("images").glob("*.jpg")),
                inputs=input_image,
                outputs=[output_image],
                fn=process_image,
            )

            run_button_image.click(
                fn=process_image,
                inputs=input_image,
                outputs=[output_image],
            )

        with gr.Tab("Video"):
            gr.Markdown(f"The input video will be truncated to {MAX_NUM_FRAMES} frames.")

            with gr.Row():
                with gr.Column():
                    input_video = gr.Video(label="Input Video")
                    run_button_video = gr.Button()
                with gr.Column():
                    output_video = gr.Video(label="Output Video")

            gr.Examples(
                examples=sorted(pathlib.Path("videos").glob("*.mp4")),
                inputs=input_video,
                outputs=output_video,
                fn=process_video,
                cache_examples=False,
            )
            run_button_video.click(
                fn=process_video,
                inputs=input_video,
                outputs=output_video,
            )


if __name__ == "__main__":
    demo.queue(max_size=20).launch()