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README.md

@@ -1,10 +1,10 @@
 ---
-title: Lung Nodule-Segmentation
-emoji: 📉
-colorFrom: green
-colorTo: indigo
+title: Lung Nodules Segmentation
+emoji: ⚡
+colorFrom: indigo
+colorTo: purple
 sdk: gradio
-sdk_version: 4.38.1
+sdk_version: 4.37.2
 app_file: app.py
 pinned: false
 license: mit

YOLOv5-Streamlit-Deployment/.gitignore

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+# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tif
+*.tiff
+*.heic
+*.JPG
+*.JPEG
+*.PNG
+*.BMP
+*.TIF
+*.TIFF
+*.HEIC
+*.mp4
+*.mov
+*.MOV
+*.avi
+*.data
+*.json
+*.cfg
+!setup.cfg
+!cfg/yolov3*.cfg
+
+storage.googleapis.com
+runs/*
+
+
+!data/*.yaml
+!data/hyps
+!data/scripts
+
+!data/*.sh
+
+results*.csv
+
+# Datasets -------------------------------------------------------------------------------------------------------------
+coco/
+coco128/
+VOC/
+
+# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
+*.m~
+*.mat
+!targets*.mat
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+darknet53.conv.74
+yolov3-tiny.conv.15
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+/wandb/
+.installed.cfg
+*.egg
+
+
+# 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/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+*/.DS_Store
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

YOLOv5-Streamlit-Deployment/.pre-commit-config.yaml

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+# Define hooks for code formations
+# Will be applied on any updated commit files if a user has installed and linked commit hook
+
+default_language_version:
+  python: python3.8
+
+# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: monthly
+  # submodules: true
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.2.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-case-conflict
+      - id: check-yaml
+      - id: check-toml
+      - id: pretty-format-json
+      - id: check-docstring-first
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v2.32.1
+    hooks:
+      - id: pyupgrade
+        name: Upgrade code
+        args: [ --py37-plus ]
+
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+        name: Sort imports
+
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+        name: YAPF formatting
+
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.14
+    hooks:
+      - id: mdformat
+        name: MD formatting
+        additional_dependencies:
+          - mdformat-gfm
+          - mdformat-black
+        exclude: |
+          (?x)^(
+              README.md
+          )$
+
+  - repo: https://github.com/asottile/yesqa
+    rev: v1.3.0
+    hooks:
+      - id: yesqa
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 4.0.1
+    hooks:
+      - id: flake8
+        name: PEP8

YOLOv5-Streamlit-Deployment/LICENSE

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+MIT License
+
+Copyright (c) 2022 Thepbordin Jaiinsom
+
+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.

YOLOv5-Streamlit-Deployment/README.md

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+
+# 🚀 YOLOv5 Streamlit Deployment
+[![HitCount](https://hits.dwyl.com/thepbordin/YOLOv5-Streamlit-Deployment.svg?style=flat&show=unique)](http://hits.dwyl.com/thepbordin/YOLOv5-Streamlit-Deployment)
+
+
+A Easy way to deploy [YOLOv5](https://github.com/ultralytics/yolov5) object detection model with [Streamlit](https://streamlit.io/). 
+
+**Please feel free to use/edit.** 
+
+
+code modified by GitHub/thepbordin from GitHub/zhoroh
+
+## ✨ Features
+
+- YOLO Weights Source
+	-  Load from Local
+	- Download Weights from URL
+- Example Dataset 
+  - Videos
+  -	 Images
+ - Upload Data 
+   - Video
+   - Image
+- Select computing device (cuda/cpu)
+
+
+
+## ⚙️ Installation
+
+
+
+### Local Use
+1. Install Requirements 
+	`pip install -r requirements.txt`
+2. Install ffmpeg (for video inferencing)
+	- For Windows [read here](https://www.geeksforgeeks.org/how-to-install-ffmpeg-on-windows/)
+	- For Mac (brew)
+		`brew install ffmpeg`
+3. Strart Stremlit
+	```
+	cd YOLOv5-Streamlit-Deployment
+    streamlit run app.py
+    ```
+### Streamlit Cloud
+1. Edit a configuration in app.py (read ⚙️ Config Instruction)
+2. (Optional) Upload example datas in
+	- `example_images`
+	- `example_videos`
+4. Deploy on [Streamlit](https://share.streamlit.io/deploy)
+
+
+## ⚙️ Config Instruction
+### Download model from URL
+
+1. Upload model to [Internet Archive](https://archive.org/)
+2. Go to your uploaded file page.
+3. From `DOWNLOAD OPTIONS` select `SHOW ALL`
+4. Right click at <yourmodelname>.pt and Copy link address.
+5. Edit config in [app.py](https://github.com/thepbordin/YOLOv5-Streamlit-Deployment/blob/main/app.py)
+
+	```python
+	cfg_enable_url_download = True
+	url = "your_model_url"
+	```
+
+### Use local .pt file:
+Edit config in [app.py](https://github.com/thepbordin/YOLOv5-Streamlit-Deployment/blob/main/app.py)
+```python
+## CFG
+cfg_model_path = "models/your_model_name.pt" 
+```
+
+## Reference
+[Yolov5 Real-time Inference using Streamlit](https://github.com/moaaztaha/Yolo-Interface-using-Streamlit)

YOLOv5-Streamlit-Deployment/app.py

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+import streamlit as st
+import torch
+from PIL import Image
+from io import *
+import glob
+from datetime import datetime
+import os
+import wget
+from video_predict import runVideo
+
+
+# Configurations
+CFG_MODEL_PATH = "models/yourModel.pt"
+CFG_ENABLE_URL_DOWNLOAD = True
+CFG_ENABLE_VIDEO_PREDICTION = True
+if CFG_ENABLE_URL_DOWNLOAD:
+    # Configure this if you set cfg_enable_url_download to True
+    url = "https://archive.org/download/yoloTrained/yoloTrained.pt"
+# End of Configurations
+
+
+def imageInput(model, src):
+
+    if src == 'Upload your own data.':
+        image_file = st.file_uploader(
+            "Upload An Image", type=['png', 'jpeg', 'jpg'])
+        col1, col2 = st.columns(2)
+        if image_file is not None:
+            img = Image.open(image_file)
+            with col1:
+                st.image(img, caption='Uploaded Image',
+                         use_column_width='always')
+            ts = datetime.timestamp(datetime.now())
+            imgpath = os.path.join('data/uploads', str(ts)+image_file.name)
+            outputpath = os.path.join(
+                'data/outputs', os.path.basename(imgpath))
+            with open(imgpath, mode="wb") as f:
+                f.write(image_file.getbuffer())
+
+            with st.spinner(text="Predicting..."):
+                # Load model
+                pred = model(imgpath)
+                pred.render()
+                # save output to file
+                for im in pred.ims:
+                    im_base64 = Image.fromarray(im)
+                    im_base64.save(outputpath)
+
+            # Predictions
+            img_ = Image.open(outputpath)
+            with col2:
+                st.image(img_, caption='Model Prediction(s)',
+                         use_column_width='always')
+
+    elif src == 'From example data.':
+        # Image selector slider
+        imgpaths = glob.glob('data/example_images/*')
+        if len(imgpaths) == 0:
+            st.write(".")
+            st.error(
+                'No images found, Please upload example images in data/example_images', icon="")
+            return
+        imgsel = st.slider('Select random images from example data.',
+                           min_value=1, max_value=len(imgpaths), step=1)
+        image_file = imgpaths[imgsel-1]
+        submit = st.button("Predict!")
+        col1, col2 = st.columns(2)
+        with col1:
+            img = Image.open(image_file)
+            st.image(img, caption='Selected Image', use_column_width='always')
+        with col2:
+            if image_file is not None and submit:
+                with st.spinner(text="Predicting..."):
+                    # Load model
+
+                    pred = model(image_file)
+                    pred.render()
+                    # save output to file
+                    for im in pred.ims:
+                        im_base64 = Image.fromarray(im)
+                        im_base64.save(os.path.join(
+                            'data/outputs', os.path.basename(image_file)))
+                # Display predicton
+                img_ = Image.open(os.path.join(
+                    'data/outputs', os.path.basename(image_file)))
+                st.image(img_, caption='Model Prediction(s)')
+
+
+def videoInput(model, src):
+    if src == 'Upload your own data.':
+        uploaded_video = st.file_uploader(
+            "Upload A Video", type=['mp4', 'mpeg', 'mov'])
+        pred_view = st.empty()
+        warning = st.empty()
+        if uploaded_video != None:
+
+            # Save video to disk
+            ts = datetime.timestamp(datetime.now())  # timestamp a upload
+            uploaded_video_path = os.path.join(
+                'data/uploads', str(ts)+uploaded_video.name)
+            with open(uploaded_video_path, mode='wb') as f:
+                f.write(uploaded_video.read())
+
+            # Display uploaded video
+            with open(uploaded_video_path, 'rb') as f:
+                video_bytes = f.read()
+            st.video(video_bytes)
+            st.write("Uploaded Video")
+            submit = st.button("Run Prediction")
+            if submit:
+                runVideo(model, uploaded_video_path, pred_view, warning)
+
+    elif src == 'From example data.':
+        # Image selector slider
+        videopaths = glob.glob('data/example_videos/*')
+        if len(videopaths) == 0:
+            st.error(
+                'No videos found, Please upload example videos in data/example_videos', icon="⚠️")
+            return
+        imgsel = st.slider('Select random video from example data.',
+                           min_value=1, max_value=len(videopaths), step=1)
+        pred_view = st.empty()
+        video = videopaths[imgsel-1]
+        submit = st.button("Predict!")
+        if submit:
+            runVideo(model, video, pred_view, warning)
+
+
+def main():
+    if CFG_ENABLE_URL_DOWNLOAD:
+        downloadModel()
+        
+    else:
+        if not os.path.exists(CFG_MODEL_PATH):
+            st.error(
+                'Model not found, please config if you wish to download model from url set `cfg_enable_url_download = True`  ', icon="⚠️")
+
+    # -- Sidebar
+    st.sidebar.title('⚙️ Options')
+    datasrc = st.sidebar.radio("Select input source.", [
+                               'From example data.', 'Upload your own data.'])
+
+    if CFG_ENABLE_VIDEO_PREDICTION:
+        option = st.sidebar.radio("Select input type.", ['Image', 'Video'])
+    else:
+        option = st.sidebar.radio("Select input type.", ['Image'])
+    if torch.cuda.is_available():
+        deviceoption = st.sidebar.radio("Select compute Device.", [
+                                        'cpu', 'cuda'], disabled=False, index=1)
+    else:
+        deviceoption = st.sidebar.radio("Select compute Device.", [
+                                        'cpu', 'cuda'], disabled=True, index=0)
+    # -- End of Sidebar
+
+    st.header('📦 YOLOv5 Streamlit Deployment Example')
+    st.sidebar.markdown(
+        "https://github.com/thepbordin/Obstacle-Detection-for-Blind-people-Deployment")
+
+    if option == "Image":
+        imageInput(loadmodel(deviceoption), datasrc)
+    elif option == "Video":
+        videoInput(loadmodel(deviceoption), datasrc)
+
+
+# Downlaod Model from url.
+@st.cache_resource
+def downloadModel():
+    if not os.path.exists(CFG_MODEL_PATH):
+        wget.download(url, out="models/")
+        
+
+@st.cache_resource
+def loadmodel(device):
+    if CFG_ENABLE_URL_DOWNLOAD:
+        CFG_MODEL_PATH = f"models/{url.split('/')[-1:][0]}"
+    model = torch.hub.load('ultralytics/yolov5', 'custom',
+                           path=CFG_MODEL_PATH, force_reload=True, device=device)
+    return model
+
+
+if __name__ == '__main__':
+    main()

YOLOv5-Streamlit-Deployment/data/example_images/.gitignore

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+!*

YOLOv5-Streamlit-Deployment/data/example_videos/.gitignore

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+!*