Delete YOLOv5-Streamlit-Deployment

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/*
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-!data/*.yaml
-!data/hyps
-!data/scripts
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-!data/*.sh
-
-results*.csv
-
-# Datasets -------------------------------------------------------------------------------------------------------------
-coco/
-coco128/
-VOC/
-
-# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
-*.m~
-*.mat
-!targets*.mat
-
-# Neural Network weights -----------------------------------------------------------------------------------------------
-*.weights
-*.pt
-*.pb
-*.onnx
-*.engine
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-*.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__/
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-# C extensions
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-# Distribution / packaging
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-# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
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-# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
-# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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-# User-specific stuff:
-.idea/*
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-.idea/**/gradle.xml
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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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-!*