SAHI-YOLOv8 / app.py
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import streamlit as st
from PIL import Image
import random
#from sahi.utils.yolov8
from sahi import AutoDetectionModel
from utils import sahi_yolov8m_inference
import sahi.utils.file
from streamlit_image_comparison import image_comparison
#import sahi.utils.mmdet
#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"
#YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt"
#YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8m.pt"
#YOLOV8M_MODEL_PATH = 'models/yolov8m.pt'
# 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-yolov8m.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",
# }
# @st.cache_data(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-yolov8m.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_data(show_spinner=False)
# def get_model():
# sahi.utils.file.download_from_url(
# YOLOV8M_MODEL_URL,
# YOLOV8M_MODEL_PATH,
# )
# #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,
# #)
# #sahi.utils.yolov8.download_yolov8m_model(destination_path = YOLOV8M_MODEL_PATH)
# detection_model = AutoDetectionModel.from_pretrained(
# model_type='yolov8',
# model_path=YOLOV8M_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 = [
# "Meanwhile check out [MMDetection Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb)!",
# "Meanwhile check out [YOLOv5 Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb)!",
# "Meanwhile check out [aerial object detection with SAHI](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98?gi=b434299595d4)!",
# "Meanwhile check out [COCO Utilities of SAHI](https://github.com/obss/sahi/blob/main/docs/COCO.md)!",
# "Meanwhile check out [FiftyOne utilities of SAHI](https://github.com/obss/sahi#fiftyone-utilities)!",
# "Meanwhile [give a Github star to SAHI](https://github.com/obss/sahi/stargazers)!",
# "Meanwhile see [how easy is to install SAHI](https://github.com/obss/sahi#getting-started)!",
# "Meanwhile check out [Medium blogpost of SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)!",
# "Meanwhile try out [YOLOv5 HF Spaces demo of SAHI](https://huggingface.co/spaces/fcakyon/sahi-yolov5)!",
# ]
# 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="small object detection with sahi + yolov8",
page_icon="πŸš€",
layout="centered",
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-yolov8m.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'>
Small Object Detection <br />
with SAHI + YOLOv8
</h2>
""",
unsafe_allow_html=True,
)
# # st.markdown(
# # """
# # <p style='text-align: center'>
# # <a href='https://github.com/obss/sahi' target='_blank'>SAHI Github</a> | <a href='https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox' target='_blank'>YOLOX Github</a> | <a href='https://huggingface.co/spaces/fcakyon/sahi-yolov5' target='_blank'>SAHI+YOLOv5 Demo</a>
# # <br />
# # Follow me for more! <a href='https://twitter.com/fcakyon' target='_blank'> <img src="https://img.icons8.com/color/48/000000/twitter--v1.png" height="30"></a><a href='https://github.com/fcakyon' target='_blank'><img src="https://img.icons8.com/fluency/48/000000/github.png" height="27"></a><a href='https://www.linkedin.com/in/fcakyon/' target='_blank'><img src="https://img.icons8.com/fluency/48/000000/linkedin.png" height="30"></a> <a href='https://fcakyon.medium.com/' target='_blank'><img src="https://img.icons8.com/ios-filled/48/000000/medium-monogram.png" height="26"></a>
# # </p>
# # """,
# # unsafe_allow_html=True,
# # )
# st.write("##")
# with st.expander("Usage"):
# st.markdown(
# """
# <p>
# 1. Upload or select the input image πŸ–ΌοΈ
# <br />
# 2. (Optional) Set SAHI parameters βœ”οΈ
# <br />
# 3. Press to "πŸš€ Perform Prediction"
# <br />
# 4. Enjoy sliding image comparison πŸ”₯
# </p>
# """,
# unsafe_allow_html=True,
# )
# st.write("##")
# col1, col2, col3 = st.columns([6, 1, 6])
# with col1:
# st.markdown(f"##### Set input image:")
# # set input image by upload
# image_file = st.file_uploader(
# "Upload an image to test:", 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 example 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=300)
# with col3:
# st.markdown(f"##### Set SAHI parameters:")
# slice_size = st.number_input("slice_size", min_value=256, value=512, step=256)
# overlap_ratio = st.number_input(
# "overlap_ratio", min_value=0.0, max_value=0.6, value=0.2, step=0.2
# )
# #postprocess_type = st.selectbox(
# # "postprocess_type", options=["NMS", "GREEDYNMM"], index=0
# #)
# #postprocess_match_metric = st.selectbox(
# # "postprocess_match_metric", options=["IOU", "IOS"], index=0
# #)
# postprocess_match_threshold = st.number_input(
# "postprocess_match_threshold", value=0.5, step=0.1
# )
# #postprocess_class_agnostic = st.checkbox("postprocess_class_agnostic", value=True)
# col1, col2, col3 = st.columns([4, 3, 4])
# with col2:
# submit = st.button("πŸš€ 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 = 1280
# with st.spinner(
# text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
# ):
# output_1, output_2 = sahi_yolov8m_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
# st.markdown(f"##### YOLOv8 Standard vs SAHI Prediction:")
# static_component = image_comparison(
# img1=st.session_state["output_1"],
# img2=st.session_state["output_2"],
# label1="YOLOX",
# label2="SAHI+YOLOX",
# width=700,
# starting_position=50,
# show_labels=True,
# make_responsive=True,
# in_memory=True,
# )
# # st.markdown(
# # """
# # <p style='text-align: center'>
# # prepared with <a href='https://github.com/fcakyon/streamlit-image-comparison' target='_blank'>streamlit-image-comparison</a>
# # </p>
# # """,
# # unsafe_allow_html=True,
# # )