Duplicate from fcakyon/sahi-yolox

Co-authored-by: Fatih <fcakyon@users.noreply.huggingface.co>

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

LICENSE

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+MIT License
+
+Copyright (c) 2021 fatih
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: Small Object Detection with YOLOX
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.10.0
+app_file: app.py
+pinned: false
+duplicated_from: fcakyon/sahi-yolox
+---
+
+# Configuration
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+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",
+}
+
+
+@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 = [
+            "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="🚀",
+    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 🖼️
+        <br />
+        2. (Optional) Set SAHI parameters ✔️
+        <br />
+        3. Press to "🚀 Perform Prediction"
+        <br />
+        4. Enjoy sliding image comparison 🔥
+        </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("🚀 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,
+)

packages.txt

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+libgl1

requirements.txt

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+-f https://download.pytorch.org/whl/torch_stable.html
+-f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
+torch==1.12.1+cpu
+torchvision==0.13.1+cpu
+sahi==0.11.6
+mmdet==2.25.2
+mmcv-full==1.6.1
+streamlit-image-comparison==0.0.4
+streamlit==1.22.0

utils.py

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+import numpy
+import sahi.predict
+import sahi.utils
+from PIL import Image
+
+TEMP_DIR = "temp"
+
+
+def sahi_mmdet_inference(
+    image,
+    detection_model,
+    slice_height=512,
+    slice_width=512,
+    overlap_height_ratio=0.2,
+    overlap_width_ratio=0.2,
+    image_size=640,
+    postprocess_type="GREEDYNMM",
+    postprocess_match_metric="IOS",
+    postprocess_match_threshold=0.5,
+    postprocess_class_agnostic=False,
+):
+
+    # standard inference
+    detection_model.image_size = image_size
+    prediction_result_1 = sahi.predict.get_prediction(
+        image=image, detection_model=detection_model
+    )
+    visual_result_1 = sahi.utils.cv.visualize_object_predictions(
+        image=numpy.array(image),
+        object_prediction_list=prediction_result_1.object_prediction_list,
+    )
+    output_1 = Image.fromarray(visual_result_1["image"])
+
+    # sliced inference
+    prediction_result_2 = sahi.predict.get_sliced_prediction(
+        image=image,
+        detection_model=detection_model,
+        slice_height=slice_height,
+        slice_width=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,
+    )
+
+    output_2 = Image.fromarray(visual_result_2["image"])
+
+    return output_1, output_2