added flash

yolov5-flask-master/.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: robmarkcole

yolov5-flask-master/.gitignore

@@ -0,0 +1,6 @@
+venv/
+.vscode
+*.pt
+#*.jpg
+static/tmp.jpg
+*.pyc

yolov5-flask-master/Dockerfile

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+FROM python:3.8-slim-buster
+
+RUN apt-get update
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+WORKDIR /app
+ADD . /app
+RUN pip install -r requirements.txt
+
+EXPOSE 5000
+
+CMD ["python", "restapi.py", "--port=5000"]

yolov5-flask-master/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 jzhang533
+
+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-flask-master/README.md

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+# Yolov5 object detection model deployment using flask
+This repo contains example apps for exposing the [yolo5](https://github.com/ultralytics/yolov5) object detection model from [pytorch hub](https://pytorch.org/hub/ultralytics_yolov5/) via a [flask](https://flask.palletsprojects.com/en/1.1.x/) api/app.
+
+## Web app
+Simple app consisting of a form where you can upload an image, and see the inference result of the model in the browser. Run:
+
+`$ python3 webapp.py --port 5000`
+
+then visit http://localhost:5000/ in your browser:
+
+<p align="center">
+<img src="https://github.com/robmarkcole/yolov5-flask/blob/master/docs/app_form.jpg" width="450">
+</p>
+
+<p align="center">
+<img src="https://github.com/robmarkcole/yolov5-flask/blob/master/docs/app_result.jpg" width="450">
+</p>
+
+## Rest API
+Simple rest API exposing the model for consumption by another service. Run:
+
+`$ python3 restapi.py --port 5000`
+
+Then use [curl](https://curl.se/) to perform a request:
+
+`$ curl -X POST -F image=@tests/zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'`
+
+The model inference results are returned:
+
+```
+[{'class': 0,
+  'confidence': 0.8197850585,
+  'name': 'person',
+  'xmax': 1159.1403808594,
+  'xmin': 750.912902832,
+  'ymax': 711.2583007812,
+  'ymin': 44.0350036621},
+ {'class': 0,
+  'confidence': 0.5667674541,
+  'name': 'person',
+  'xmax': 1065.5523681641,
+  'xmin': 116.0448303223,
+  'ymax': 713.8904418945,
+  'ymin': 198.4603881836},
+ {'class': 27,
+  'confidence': 0.5661227107,
+  'name': 'tie',
+  'xmax': 516.7975463867,
+  'xmin': 416.6880187988,
+  'ymax': 717.0524902344,
+  'ymin': 429.2020568848}]
+```
+
+An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `tests/test_request.py`
+
+## Run & Develop locally
+Run locally and dev:
+* `python3 -m venv venv`
+* `source venv/bin/activate`
+* `(venv) $ pip install -r requirements.txt`
+* `(venv) $ python3 webapp.py --port 5000`
+
+## Docker
+The example dockerfile shows how to expose the rest API:
+```
+# Build
+docker build -t yolov5-flask .
+# Run
+docker run -p 5000:5000 yolov5-flask:latest
+```
+
+## reference
+- https://github.com/ultralytics/yolov5
+- https://github.com/jzhang533/yolov5-flask (this repo was forked from here)
+- https://github.com/avinassh/pytorch-flask-api-heroku

yolov5-flask-master/requirements.txt

@@ -0,0 +1,20 @@
+flask
+requests
+black
+
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.2
+Pillow
+PyYAML>=5.3.1
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+tensorboard>=2.4.1
+
+seaborn>=0.11.0
+pandas
+
+thop  # FLOPs computation

yolov5-flask-master/restapi.py

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+"""
+Run a rest API exposing the yolov5s object detection model
+"""
+import argparse
+import io
+from PIL import Image
+
+import torch
+from flask import Flask, request
+
+app = Flask(__name__)
+
+DETECTION_URL = "/v1/object-detection/yolov5s"
+
+
+@app.route(DETECTION_URL, methods=["POST"])
+def predict():
+    if not request.method == "POST":
+        return
+
+    if request.files.get("image"):
+        image_file = request.files["image"]
+        image_bytes = image_file.read()
+
+        img = Image.open(io.BytesIO(image_bytes))
+
+        results = model(img, size=640)
+        data = results.pandas().xyxy[0].to_json(orient="records")
+        return data
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Flask api exposing yolov5 model")
+    parser.add_argument("--port", default=5000, type=int, help="port number")
+    args = parser.parse_args()
+
+    model = torch.hub.load(
+        "ultralytics/yolov5", "yolov5s", pretrained=True, force_reload=True
+    ).autoshape()  # force_reload = recache latest code
+    model.eval()
+    app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat

yolov5-flask-master/static/style.css

@@ -0,0 +1,29 @@
+html,
+body {
+  height: 100%;
+}
+
+body {
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-align: center;
+  align-items: center;
+  padding-top: 40px;
+  padding-bottom: 40px;
+  background-color: #f5f5f5;
+}
+
+.form-signin {
+  width: 100%;
+  max-width: 330px;
+  padding: 15px;
+  margin: auto;
+}
+
+.form-signin .form-control {
+  position: relative;
+  box-sizing: border-box;
+  height: auto;
+  padding: 10px;
+  font-size: 16px;
+}

yolov5-flask-master/templates/index.html

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+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+    <link rel="stylesheet" href="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
+    <style>
+      .bd-placeholder-img {
+        font-size: 1.125rem;
+        text-anchor: middle;
+      }
+
+      @media (min-width: 768px) {
+        .bd-placeholder-img-lg {
+          font-size: 3.5rem;
+        }
+      }
+    </style>
+    <link rel="stylesheet" href="/static/style.css">
+
+    <title>yolov5 object detection</title>
+  </head>
+  <body class="text-center">
+    <form class="form-signin" method=post enctype=multipart/form-data>
+        <img class="mb-4" src="/static/pytorch.png" alt="" width="72">
+        <h1 class="h3 mb-3 font-weight-normal">Upload any image</h1>
+        <input type="file" name="file" class="form-control-file" id="inputfile">
+        <br/>
+        <button class="btn btn-lg btn-primary btn-block" type="submit">Upload</button>
+        <p class="mt-5 mb-3 text-muted">Built using Pytorch & Flask</p>
+    </form>
+    <script src="//code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
+    <script src="//cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.6/umd/popper.min.js" integrity="sha384-wHAiFfRlMFy6i5SRaxvfOCifBUQy1xHdJ/yoi7FRNXMRBu5WHdZYu1hA6ZOblgut" crossorigin="anonymous"></script>
+    <script src="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/js/bootstrap.min.js" integrity="sha384-B0UglyR+jN6CkvvICOB2joaf5I4l3gm9GU6Hc1og6Ls7i6U/mkkaduKaBhlAXv9k" crossorigin="anonymous"></script>
+    <script type="text/javascript">
+      $('#inputfile').bind('change', function() {
+          let fileSize = this.files[0].size/1024/1024; // this gives in MB
+          if (fileSize > 1) {
+            $("#inputfile").val(null);
+            alert('file is too big. images more than 1MB are not allowed')
+            return
+          }
+
+          let ext = $('#inputfile').val().split('.').pop().toLowerCase();
+          if($.inArray(ext, ['jpg','jpeg']) == -1) {
+            $("#inputfile").val(null);
+            alert('only jpeg/jpg files are allowed!');
+          }
+      });
+    </script>
+  </body>
+</html>

yolov5-flask-master/tests/test_inference.py

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+import io
+import torch
+from PIL import Image
+
+# Model
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", pretrained=True, force_reload=True)
+
+# img = Image.open("zidane.jpg")  # PIL image direct open
+
+# Read from bytes as we do in app
+with open("zidane.jpg", "rb") as file:
+    img_bytes = file.read()
+img = Image.open(io.BytesIO(img_bytes))
+
+results = model(img, size=640)  # includes NMS
+
+print(results.pandas().xyxy[0])

yolov5-flask-master/tests/test_request.py

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+"""Perform test request"""
+import pprint
+import requests
+
+DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
+TEST_IMAGE = "zidane.jpg"
+
+image_data = open(TEST_IMAGE, "rb").read()
+
+response = requests.post(DETECTION_URL, files={"image": image_data}).json()
+
+pprint.pprint(response)

yolov5-flask-master/webapp.py

@@ -0,0 +1,56 @@
+"""
+Simple app to upload an image via a web form 
+and view the inference results on the image in the browser.
+"""
+import argparse
+import io
+import os
+from PIL import Image
+
+import torch
+from flask import Flask, render_template, request, redirect
+
+app = Flask(__name__)
+
+
+@app.route("/", methods=["GET", "POST"])
+def predict():
+    if request.method == "POST":
+        if "file" not in request.files:
+            return redirect(request.url)
+        file = request.files["file"]
+        if not file:
+            return
+
+        img_bytes = file.read()
+        img = Image.open(io.BytesIO(img_bytes))
+        results = model(img, size=640)
+
+        # for debugging
+        # data = results.pandas().xyxy[0].to_json(orient="records")
+        # return data
+
+        results.render()  # updates results.imgs with boxes and labels
+        for img in results.imgs:
+            img_base64 = Image.fromarray(img)
+            img_base64.save("static/image0.jpg", format="JPEG")
+        return redirect("static/image0.jpg")
+
+    return render_template("index.html")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Flask app exposing yolov5 models")
+    parser.add_argument("--port", default=5000, type=int, help="port number")
+    args = parser.parse_args()
+
+    model = torch.hub.load('../yolov5', 'custom', path='../saved_model/s1000_best.pt', source='local')  # force_reload = recache latest code
+    model.eval()
+    app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat
+
+
+#python3 webapp.py --port 5000
+
+
+#   python webapp.py --port 5000
+#     http://localhost:5000/