sahi-yolox / app.py
import streamlit as st
import sahi.utils.file
import sahi.utils.mmdet
import sahi.model
from PIL import Image
import random
from utils import sahi_mmdet_inference
from streamlit_image_comparison import image_comparison
MMDET_YOLOX_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth"
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",
}
@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():
model_path = "yolox.pt"
sahi.utils.file.download_from_url(
MMDET_YOLOX_MODEL_URL,
model_path,
)
config_path = sahi.utils.mmdet.download_mmdet_config(
model_name="yolox", config_file_name="yolox_tiny_8x8_300e_coco.py"
)
detection_model = sahi.model.MmdetDetectionModel(
model_path=model_path,
config_path=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 + YOLOX",
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-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'>
Small Object Detection <br />
with SAHI + YOLOX
</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=1
)
postprocess_match_metric = st.selectbox(
"postprocess_match_metric", options=["IOU", "IOS"], index=1
)
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 = 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
st.markdown(f"##### YOLOX 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,
)