app.py

import streamlit as st
import sahi.utils.file
import sahi.utils.mmdet
from sahi import AutoDetectionModel
from PIL import Image
import random
from utils import sahi_mmdet_inference
from streamlit_image_comparison import image_comparison

MMDET_YOLOX_TINY_MODEL_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/resolve/main/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth"
MMDET_YOLOX_TINY_MODEL_PATH = "yolox.pt"
MMDET_YOLOX_TINY_CONFIG_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/raw/main/yolox_tiny_8x8_300e_coco.py"
MMDET_YOLOX_TINY_CONFIG_PATH = "config.py"

IMAGE_TO_URL = {
    "apple_tree.jpg": "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
    "highway.jpg": "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
    "highway2.jpg": "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
    "highway3.jpg": "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
    "highway2-yolox.jpg": "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
    "highway2-sahi.jpg": "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
}

slice_size=512
overlap_ratio=0.2
postprocess_match_metric = 'IOU'
postprocess_type = 'NMS'
postprocess_match_threshold = 0.5
postprocess_class_agnostic = True



@st.cache(allow_output_mutation=True, show_spinner=False)
def download_comparison_images():
    sahi.utils.file.download_from_url(
        "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
        "highway2-yolox.jpg",
    )
    sahi.utils.file.download_from_url(
        "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
        "highway2-sahi.jpg",
    )


@st.cache(allow_output_mutation=True, show_spinner=False)
def get_model():
    sahi.utils.file.download_from_url(
        MMDET_YOLOX_TINY_MODEL_URL,
        MMDET_YOLOX_TINY_MODEL_PATH,
    )
    sahi.utils.file.download_from_url(
        MMDET_YOLOX_TINY_CONFIG_URL,
        MMDET_YOLOX_TINY_CONFIG_PATH,
    )

    detection_model = AutoDetectionModel.from_pretrained(
        model_type='mmdet',
        model_path=MMDET_YOLOX_TINY_MODEL_PATH,
        config_path=MMDET_YOLOX_TINY_CONFIG_PATH,
        confidence_threshold=0.5,
        device="cpu",
    )
    return detection_model


class SpinnerTexts:
    def __init__(self):
        self.ind_history_list = []
        self.text_list = [
            "Loading...",
        ]

    def _store(self, ind):
        if len(self.ind_history_list) == 6:
            self.ind_history_list.pop(0)
        self.ind_history_list.append(ind)

    def get(self):
        ind = 0
        while ind in self.ind_history_list:
            ind = random.randint(0, len(self.text_list) - 1)
        self._store(ind)
        return self.text_list[ind]


st.set_page_config(
    page_title="A Demonstration of SARAI's Utility",
    page_icon="🐦",
    layout="wide",
    initial_sidebar_state="auto",
)

download_comparison_images()

if "last_spinner_texts" not in st.session_state:
    st.session_state["last_spinner_texts"] = SpinnerTexts()

if "output_1" not in st.session_state:
    st.session_state["output_1"] = Image.open("highway2-yolox.jpg")

if "output_2" not in st.session_state:
    st.session_state["output_2"] = Image.open("highway2-sahi.jpg")

st.markdown(
    """
    <h2 style='text-align: center'>
A Demonstration of SARAI's Utility
    </h2>
    """,
    unsafe_allow_html=True,
)

st.write("##")

with st.expander("Instructions for Use"):
    st.markdown(
        """
        <p>
        1. Upload or select the input image
        <br />
        2. Press "Perform Prediction" to start image processing"

        </p>
        """,
        unsafe_allow_html=True,
    )

st.write("##")

col1, col2, col3 = st.columns([4, 1, 6])
with col1:
    st.markdown(f"##### Set input image:")

    # set input image by upload
    image_file = st.file_uploader(
        "Upload an image:", type=["jpg", "jpeg", "png"]
    )

    # set input image from exapmles
    def slider_func(option):
        option_to_id = {
            "apple_tree.jpg": str(1),
            "highway.jpg": str(2),
            "highway2.jpg": str(3),
            "highway3.jpg": str(4),
        }
        return option_to_id[option]

    slider = st.select_slider(
        "Or select from our sample images:",
        options=["apple_tree.jpg", "highway.jpg", "highway2.jpg", "highway3.jpg"],
        format_func=slider_func,
        value="highway2.jpg",
    )

    # visualize input image
    if image_file is not None:
        image = Image.open(image_file)
    else:
        image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[slider])
    st.image(image, width=325)





with col3:
    st.markdown(f"##### YOLOX Standard vs SARAI Prediction:")
    static_component = image_comparison(
        img1=st.session_state["output_1"],
        img2=st.session_state["output_2"],
        label1="YOLOX",
        label2="SARAI",
        width=700,
        starting_position=50,
        show_labels=True,
        make_responsive=True,
        in_memory=True,
    )

    

col1, col2, col3, col4, col5= st.columns([1, 2, 4, 2, 2])
with col2:
    # submit button
    submit = st.button("Perform Prediction")

if submit:
    # perform prediction
    with st.spinner(
        text="Downloading model weight.. "
        + st.session_state["last_spinner_texts"].get()
    ):
        detection_model = get_model()

    image_size = 416

    with st.spinner(
        text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
    ):
        output_1, output_2 = sahi_mmdet_inference(
            image,
            detection_model,
            image_size=image_size,
            slice_height=slice_size,
            slice_width=slice_size,
            overlap_height_ratio=overlap_ratio,
            overlap_width_ratio=overlap_ratio,
            postprocess_type=postprocess_type,
            postprocess_match_metric=postprocess_match_metric,
            postprocess_match_threshold=postprocess_match_threshold,
            postprocess_class_agnostic=postprocess_class_agnostic,
        )

    st.session_state["output_1"] = output_1
    st.session_state["output_2"] = output_2

    
with col4:
    st.markdown(f"##### Slide to Compare")