app.py

from typing import Union

import gradio as gr
import numpy as np
import supervision as sv
from PIL import Image
from rfdetr import RFDETRBase, RFDETRLarge
from rfdetr.detr import RFDETR
from rfdetr.util.coco_classes import COCO_CLASSES

from utils.image import calculate_resolution_wh
from utils.video import create_directory

MARKDOWN = """
# RF-DETR 🔥

<div>
  <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="colab" style="display:inline-block;">
  </a>
  <a href="https://blog.roboflow.com/rf-detr">
    <img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="roboflow" style="display:inline-block;">
  </a>
  <a href="https://github.com/roboflow/rf-detr">
    <img src="https://badges.aleen42.com/src/github.svg" alt="roboflow" style="display:inline-block;">
  </a>
</div>

RF-DETR is a real-time, transformer-based object detection model architecture developed 
by [Roboflow](https://roboflow.com/) and released under the Apache 2.0 license.
"""

IMAGE_EXAMPLES = [
    ['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 728, "large"],
    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 728, "large"],
    ['https://media.roboflow.com/notebooks/examples/dog-2.jpeg', 0.5, 560, "base"],
]

COLOR = sv.ColorPalette.from_hex([
    "#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff",
    "#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00"
])

VIDEO_SCALE_FACTOR = 0.5
VIDEO_TARGET_DIRECTORY = "tmp"
create_directory(directory_path=VIDEO_TARGET_DIRECTORY)


def detect_and_annotate(model: RFDETR, image: Union[Image.Image, np.ndarray], confidence: float):
    detections = model.predict(image, threshold=confidence)

    resolution_wh = calculate_resolution_wh(image)
    text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh) - 0.2
    thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)

    bbox_annotator = sv.BoxAnnotator(color=COLOR, thickness=thickness)
    label_annotator = sv.LabelAnnotator(
        color=COLOR,
        text_color=sv.Color.BLACK,
        text_scale=text_scale,
        smart_position=True
    )

    labels = [
        f"{COCO_CLASSES[class_id]} {confidence:.2f}"
        for class_id, confidence
        in zip(detections.class_id, detections.confidence)
    ]

    annotated_image = image.copy()
    annotated_image = bbox_annotator.annotate(annotated_image, detections)
    annotated_image = label_annotator.annotate(annotated_image, detections, labels)
    return annotated_image


def image_processing_inference(input_image: Image.Image, confidence: float, resolution: int, checkpoint: str):
    model_class = RFDETRBase if checkpoint == "base" else RFDETRLarge
    model = model_class(resolution=resolution)
    return detect_and_annotate(model=model, image=input_image, confidence=confidence)


def video_processing_inference(input_video: str, confidence: float, resolution: int, checkpoint: str):
    model_class = RFDETRBase if checkpoint == "base" else RFDETRLarge
    model = model_class(resolution=resolution)
    return input_video

with gr.Blocks() as demo:
    gr.Markdown(MARKDOWN)
    with gr.Tab("Image"):
        with gr.Row():
            image_processing_input_image = gr.Image(
                label="Upload image",
                image_mode='RGB',
                type='pil',
                height=600
            )
            image_processing_output_image = gr.Image(
                label="Output image",
                image_mode='RGB',
                type='pil',
                height=600
            )
        with gr.Row():
            with gr.Column():
                image_processing_confidence_slider = gr.Slider(
                    label="Confidence",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.5,
                )
                image_processing_resolution_slider = gr.Slider(
                    label="Inference resolution",
                    minimum=560,
                    maximum=1120,
                    step=56,
                    value=728,
                )
                image_processing_checkpoint_dropdown = gr.Dropdown(
                    label="Checkpoint",
                    choices=["base", "large"],
                    value="base"
                )
            with gr.Column():
                image_processing_submit_button = gr.Button("Submit", value="primary")

        gr.Examples(
            fn=image_processing_inference,
            examples=IMAGE_EXAMPLES,
            inputs=[
                image_processing_input_image,
                image_processing_confidence_slider,
                image_processing_resolution_slider,
                image_processing_checkpoint_dropdown
            ],
            outputs=image_processing_output_image,
            cache_examples=True
        )

        image_processing_submit_button.click(
            image_processing_inference,
            inputs=[
                image_processing_input_image,
                image_processing_confidence_slider,
                image_processing_resolution_slider,
                image_processing_checkpoint_dropdown
            ],
            outputs=image_processing_output_image
        )
    with gr.Tab("Video"):
        with gr.Row():
            video_processing_input_video = gr.Video(
                label='Upload video',
                height=600
            )
            video_processing_output_video = gr.Video(
                label='Output video',
                height=600
            )
        with gr.Row():
            with gr.Column():
                video_processing_confidence_slider = gr.Slider(
                    label="Confidence",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.5,
                )
                video_processing_resolution_slider = gr.Slider(
                    label="Inference resolution",
                    minimum=560,
                    maximum=1120,
                    step=56,
                    value=728,
                )
                video_processing_checkpoint_dropdown = gr.Dropdown(
                    label="Checkpoint",
                    choices=["base", "large"],
                    value="base"
                )
            with gr.Column():
                video_processing_submit_button = gr.Button("Submit", value="primary")

        video_processing_submit_button.click(
            video_processing_inference,
            inputs=[
                video_processing_input_video,
                video_processing_confidence_slider,
                video_processing_resolution_slider,
                video_processing_checkpoint_dropdown
            ],
            outputs=video_processing_output_video
        )

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