app.py

import gradio as gr
import sahi.utils
from sahi import AutoDetectionModel
import sahi.predict
import sahi.slicing
from PIL import Image
import numpy
from huggingface_hub import hf_hub_download
import torch


IMAGE_SIZE = 640

model_path = hf_hub_download(
    "deprem-ml/Binafarktespit-yolo5x-v1-xview", filename="last.pt", revision="main"
)


current_device = "cuda" if torch.cuda.is_available() else "cpu"
model_types = ["YOLOv5", "YOLOv5 + SAHI"]
# Model
model = AutoDetectionModel.from_pretrained(
    model_type="yolov5",
    model_path=model_path,
    device=current_device,
    confidence_threshold=0.5,
    image_size=IMAGE_SIZE,
)


def sahi_yolo_inference(
    model_type,
    image,
    slice_height=512,
    slice_width=512,
    overlap_height_ratio=0.1,
    overlap_width_ratio=0.1,
    postprocess_type="NMS",
    postprocess_match_metric="IOU",
    postprocess_match_threshold=0.25,
    postprocess_class_agnostic=False,
):

    # image_width, image_height = image.size
    # sliced_bboxes = sahi.slicing.get_slice_bboxes(
    #     image_height,
    #     image_width,
    #     slice_height,
    #     slice_width,
    #     False,
    #     overlap_height_ratio,
    #     overlap_width_ratio,
    # )
    # if len(sliced_bboxes) > 60:
    #     raise ValueError(
    #         f"{len(sliced_bboxes)} slices are too much for huggingface spaces, try smaller slice size."
    #     )

    rect_th = None or max(round(sum(image.size) / 2 * 0.0001), 1)
    text_th = None or max(rect_th - 2, 1)

    if "SAHI" in model_type:
        prediction_result_2 = sahi.predict.get_sliced_prediction(
            image=image,
            detection_model=model,
            slice_height=int(slice_height),
            slice_width=int(slice_width),
            overlap_height_ratio=overlap_height_ratio,
            overlap_width_ratio=overlap_width_ratio,
            postprocess_type=postprocess_type,
            postprocess_match_metric=postprocess_match_metric,
            postprocess_match_threshold=postprocess_match_threshold,
            postprocess_class_agnostic=postprocess_class_agnostic,
        )
        visual_result_2 = sahi.utils.cv.visualize_object_predictions(
            image=numpy.array(image),
            object_prediction_list=prediction_result_2.object_prediction_list,
            rect_th=rect_th,
            text_th=text_th,
        )
        output = Image.fromarray(visual_result_2["image"])
        return output

    else:
        # standard inference
        prediction_result_1 = sahi.predict.get_prediction(
            image=image, detection_model=model
        )
        print(image)
        visual_result_1 = sahi.utils.cv.visualize_object_predictions(
            image=numpy.array(image),
            object_prediction_list=prediction_result_1.object_prediction_list,
            rect_th=rect_th,
            text_th=text_th,
        )
        output = Image.fromarray(visual_result_1["image"])
        return output

    # sliced inference


inputs = [
    gr.Dropdown(
        choices=model_types,
        label="Choose Model Type",
        type="value",
        value=model_types[1],
    ),
    gr.Image(type="pil", label="Original Image"),
    gr.Number(default=512, label="slice_height"),
    gr.Number(default=512, label="slice_width"),
    gr.Number(default=0.1, label="overlap_height_ratio"),
    gr.Number(default=0.1, label="overlap_width_ratio"),
    gr.Dropdown(
        ["NMS", "GREEDYNMM"],
        type="value",
        value="NMS",
        label="postprocess_type",
    ),
    gr.Dropdown(["IOU", "IOS"], type="value", value="IOU", label="postprocess_type"),
    gr.Number(default=0.5, label="postprocess_match_threshold"),
    gr.Checkbox(default=True, label="postprocess_class_agnostic"),
]

outputs = [gr.outputs.Image(type="pil", label="Output")]

title = "Small Object Detection with SAHI + YOLOv5"
description = "SAHI + YOLOv5 demo for small object detection. Upload an image or click an example image to use."
article = "<p style='text-align: center'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://github.com/fcakyon/yolov5-pip'>YOLOv5 Github</a> </p>"
examples = [
    [model_types[1], "26.jpg", 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False],
    [model_types[1], "27.jpg", 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False],
    [model_types[1], "28.jpg", 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False],
    [model_types[1], "31.jpg", 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False],
]
gr.Interface(
    sahi_yolo_inference,
    inputs,
    outputs,
    title=title,
    description=description,
    article=article,
    examples=examples,
    theme="huggingface",
).launch(debug=True, enable_queue=True)