Merge pull request #3 from anras5/feature/webapp

Feature/webapp

.gitignore

@@ -1,2 +1,3 @@
 .idea/
+temp/
 unet_checkpoint.pth

main.py

@@ -1 +1,145 @@
-# LOOK INTO notebooks/main.ipynb
+import uuid
+import os
+from typing import Tuple
+
+import cv2
+import numpy as np
+
+from flask import Flask, render_template, redirect, url_for, flash
+from flask_wtf import FlaskForm
+from flask_wtf.file import FileField, FileRequired, FileAllowed
+from wtforms import SubmitField, BooleanField
+from werkzeug.utils import secure_filename
+
+from notebooks.classifiers.classic import ClassicClassifier
+from notebooks.classifiers.unet import UNetClassifier
+from notebooks.utils.confusion_matrix import ConfusionMatrix
+
+app = Flask(__name__)
+app.config.from_mapping(
+    SECRET_KEY='dev',
+)
+
+
+class InputForm(FlaskForm):
+    input_image = FileField("Prześlij plik",
+                            validators=[FileRequired(), FileAllowed(['jpg', 'png'])])
+    input_map = FileField("Prześlij mapę ekspercką (opcjonalne)",
+                          validators=[FileAllowed(['jpg', 'png'])])
+    submit = SubmitField()
+
+
+def predict_classic(image_path: str, user_path: str, true_path: str = '') -> Tuple[str, ConfusionMatrix]:
+    cc = ClassicClassifier([(5, 5), (12, 12), (20, 20)], 200)
+    image = cv2.imread(image_path)
+    pred_image = cc.predict(image)
+
+    back = '\\'
+    pred_path = f"{image_path.split(back)[-1].split('.')[0]}_pred_classic.png"
+    pred_path = os.path.join(user_path, pred_path)
+    cv2.imwrite(pred_path, pred_image)
+
+    if true_path:
+        true_image = cv2.imread(true_path, cv2.IMREAD_GRAYSCALE)
+        cm = ConfusionMatrix(true_image.flatten(), pred_image.flatten())
+        return pred_path, cm
+    else:
+        return pred_path, None
+
+
+def predict_unet(image_path: str, user_path: str, true_path: str = '') -> Tuple[str, ConfusionMatrix]:
+
+    def mask_parse(mask):
+        mask = np.expand_dims(mask, axis=-1)  # (512, 512, 1)
+        mask = np.concatenate([mask, mask, mask], axis=-1)  # (512, 512, 3)
+        return mask
+
+    uc = UNetClassifier('models/unet_checkpoint.pth')
+    image = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    y_pred = uc.predict(image)
+
+    pred_image = mask_parse(y_pred)
+    back = '\\'
+    pred_path = f"{image_path.split(back)[-1].split('.')[0]}_pred_unet.png"
+    pred_path = os.path.join(user_path, pred_path)
+    cv2.imwrite(pred_path, pred_image * 255)
+
+    if true_path:
+        true_image = cv2.imread(true_path, cv2.IMREAD_GRAYSCALE)
+        answer = cv2.resize(true_image, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)
+        ans = answer / 255.0
+        ans = ans[np.newaxis, ...]
+        y_true = ans > 0.5
+        y_true = y_true.astype(np.uint8)
+        y_true = y_true.reshape(-1)
+
+        cm = ConfusionMatrix(y_true, y_pred)
+
+        return pred_path, cm
+    else:
+        return pred_path, None
+
+
+@app.route("/", methods=['GET', 'POST'])
+def home():
+    form = InputForm()
+    if form.validate_on_submit():
+        # ------------------------------------------------------------------------------------------------------------ #
+        # HANDLE UPLOADED FILES
+
+        # get user's unique id
+        uid = str(uuid.uuid4())
+        user_path = os.path.join('static/temp', uid)
+        os.makedirs(user_path)
+
+        # get files from form
+        f = form.input_image.data
+        f_filename = secure_filename(f.filename)
+        image_path = os.path.join(user_path, f_filename)
+        f.save(image_path)
+
+        f_m = form.input_map.data
+        if f_m is not None:
+            f_m_filename = secure_filename(f_m.filename)
+            map_path = os.path.join(user_path, f_m_filename)
+            f_m.save(map_path)
+
+            classic_path, cm_classic = predict_classic(image_path, user_path, map_path)
+            unet_path, cm_unet = predict_unet(image_path, user_path, map_path)
+        else:
+            classic_path, cm_classic = predict_classic(image_path, user_path)
+            unet_path, cm_unet = predict_unet(image_path, user_path)
+
+        # ------------------------------------------------------------------------------------------------------------ #
+        # RESIZE UPLOADED FILES TO 512x512
+
+        img = cv2.imread(image_path, cv2.IMREAD_COLOR)
+        img = cv2.resize(img, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)
+        cv2.imwrite(image_path, img)
+
+        if f_m is not None:
+            ans = cv2.imread(map_path, cv2.IMREAD_GRAYSCALE)
+            ans = cv2.resize(ans, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)
+            cv2.imwrite(map_path, ans)
+
+        pred = cv2.imread(classic_path, cv2.IMREAD_GRAYSCALE)
+        pred = cv2.resize(pred, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)
+        cv2.imwrite(classic_path, pred)
+
+        if f_m is not None:
+            return render_template('result.html',
+                                   image_path=image_path, map_path=map_path,
+                                   classic_path=classic_path, unet_path=unet_path,
+                                   cm_classic=cm_classic, cm_unet=cm_unet)
+        else:
+            return render_template('result.html',
+                                   image_path=image_path,
+                                   classic_path=classic_path, unet_path=unet_path,
+                                   cm_classic=cm_classic, cm_unet=cm_unet)
+
+    print(form.errors)
+    return render_template('index.html', form=form)
+
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port='5000')

notebooks/classifiers/unet.py

@@ -0,0 +1,35 @@
+import torch
+import cv2
+import numpy as np
+from notebooks.unet.unet import UNet
+
+
+def mask_parse(mask):
+    mask = np.expand_dims(mask, axis=-1)  # (512, 512, 1)
+    mask = np.concatenate([mask, mask, mask], axis=-1)  # (512, 512, 3)
+    return mask
+
+
+class UNetClassifier:
+
+    def __init__(self, model_path):
+        self.model = UNet(3)
+        self.model.load_state_dict(torch.load(model_path, map_location='cuda'))
+        self.model.eval()
+
+    def predict(self, image: np.ndarray) -> np.ndarray:
+        image = cv2.resize(image, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)
+        img = image / 255.0
+        img = np.transpose(img, (2, 0, 1))
+        img = img[np.newaxis, ...]
+        img = torch.from_numpy(img.astype(np.float32))
+
+        with torch.no_grad():
+            pred_y = self.model(img)
+
+            pred_y = pred_y[0].cpu().numpy()
+            pred_y = np.squeeze(pred_y, axis=0)
+            pred_y = pred_y > 0.5
+            pred_y = np.array(pred_y, dtype=np.uint8)
+
+        return pred_y

notebooks/utils/confusion_matrix.py

@@ -21,10 +21,10 @@ def __init__(self, true, pred):
                                       columns=['Predicted Negative', 'Predicted Positive'])
 
     def __str__(self):
-        return f"{'Accuracy:'.ljust(13, ' ')}{self.accuracy}\n" \
-               f"{'Recall:'.ljust(13, ' ')}{self.recall}\n" \
-               f"{'Precision'.ljust(13, ' ')}{self.precision}\n" \
-               f"{'Specificity:'.ljust(13, ' ')}{self.specificity}\n"
+        return f"{'Accuracy:'.ljust(13, ' ')}{self.accuracy:.3f}\n" \
+               f"{'Recall:'.ljust(13, ' ')}{self.recall:.3f}\n" \
+               f"{'Precision'.ljust(13, ' ')}{self.precision:.3f}\n" \
+               f"{'Specificity:'.ljust(13, ' ')}{self.specificity:.3f}\n"
 
     def heatmap(self, annot=True, fmt=".0f", norm=LogNorm(), cmap='crest', cbar_kws=None):
         if cbar_kws is None:

static/css/style.css

@@ -0,0 +1,41 @@
+h1 {
+    padding: 2%;
+}
+
+html, body {
+    height: 100%;
+}
+
+body {
+    display: flex;
+    flex-direction: column;
+    align-content: center;
+    text-align: center;
+    font-family: 'Montserrat', sans-serif;
+}
+
+.content {
+    flex: 1 0 auto;
+}
+
+.footer {
+    flex-shrink: 0;
+}
+
+.img-box img {
+    object-fit: contain;
+}
+
+
+/*SCROLL BAR*/
+::-webkit-scrollbar {
+    display: none;
+}
+
+::-webkit-scrollbar-track {
+    display: none;
+}
+
+::-webkit-scrollbar-thumb {
+    display: none;
+}

templates/index.html

@@ -0,0 +1,46 @@
+{% extends "layout.html" %}
+
+{% block content %}
+
+    <h1 class="mt-2">WYKRYWANIE NACZYŃ KRWIONOŚNYCH DNA OKA</h1>
+
+    <p>Prześlij swój obraz korzystając poniższego formularza:</p>
+    <div class="container form">
+        <form method="post" class="row g-3" enctype="multipart/form-data" id="photoForm">
+            {{ form.hidden_tag() }}
+            <div class="row my-3">
+                <div class="col-12 mx-auto">
+                    {{ form.input_image.label(class_='form-label') }}
+                    {{ form.input_image(class_='form-control') }}
+                </div>
+            </div>
+            <div class="row my-3">
+                <div class="col-12 mx-auto">
+                    {{ form.input_map.label(class_='form-label') }} <br>
+                    {{ form.input_map(class_='form-control') }}
+                </div>
+            </div>
+            <div class="row my-2">
+                <div class="col-12 mx-auto" id="submit-btn">
+                    {{ form.submit(class_='btn btn-success') }}
+                </div>
+            </div>
+        </form>
+        <div class="col-12 d-none mt-3" id="spinner">
+            <div class="spinner-border text-warning" role="status"></div>
+            <p>Twoje żądanie jest przetwarzane. Nie zamykaj okna przeglądarki.</p>
+        </div>
+    </div>
+
+{% endblock %}
+
+{% block js %}
+
+    <script>
+        document.getElementById("photoForm").addEventListener("submit", (event) => {
+            document.getElementById("spinner").classList.remove("d-none");
+            document.getElementById("submit-btn").classList.add("d-none");
+        });
+
+    </script>
+{% endblock %}

templates/layout.html

@@ -0,0 +1,83 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Blood Vessels</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1"/>
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet"
+          integrity="sha384-aFq/bzH65dt+w6FI2ooMVUpc+21e0SRygnTpmBvdBgSdnuTN7QbdgL+OapgHtvPp" crossorigin="anonymous">
+
+    {#Google Fonts#}
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link
+            href="https://fonts.googleapis.com/css2?family=Montserrat:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900&display=swap"
+            rel="stylesheet">
+
+    {#custom css#}
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+
+    {# Notie #}
+    <link rel="stylesheet" type="text/css" href="https://unpkg.com/notie/dist/notie.min.css">
+
+    {#Font awesome#}
+    <script src="https://kit.fontawesome.com/bd9dc0e408.js" crossorigin="anonymous"></script>
+</head>
+<body>
+<div class="content mb-3">
+    <nav class="navbar navbar-expand-lg bg-body-tertiary">
+        <div class="container-fluid">
+            <a class="navbar-brand" href={{ url_for('home') }}><i class="fa-solid fa-eye"></i></a>
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav"
+                    aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+            <div class="collapse navbar-collapse" id="navbarNav">
+                <ul class="navbar-nav">
+                    <li class="nav-item">
+                        <a class="nav-link active" aria-current="page" href="{{ url_for('home') }}">Strona główna</a>
+                    </li>
+                </ul>
+            </div>
+        </div>
+    </nav>
+
+    {% block content %} {% endblock %}
+</div>
+
+<footer class="footer mt-auto py-3 bg-body-tertiary">
+    <div class="container">
+        <span class="text-body-secondary">Filip Marciniak, Szymon Pasternak</span><br>
+        <span class="text-body-secondary">2023</span>
+    </div>
+</footer>
+
+<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"
+        integrity="sha384-qKXV1j0HvMUeCBQ+QVp7JcfGl760yU08IQ+GpUo5hlbpg51QRiuqHAJz8+BrxE/N"
+        crossorigin="anonymous"></script>
+
+{# Notie #}
+<script src="https://unpkg.com/notie"></script>
+
+<script>
+    {# Notie notifications #}
+    {# flash messaging #}
+    {% with messages = get_flashed_messages(with_categories=true) %}
+        {% if messages %}
+            {% for category, message in messages %}
+                notie.alert({
+                    type: '{{ category }}', // optional, default = 4, enum: [1, 2, 3, 4, 5, 'success', 'warning', 'error', 'info', 'neutral']
+                    text: '{{ message }}',
+                    stay: false, // optional, default = false
+                    time: 4, // optional, default = 3, minimum = 1,
+                    position: 'bottom' // optional, default = 'top', enum: ['top', 'bottom']
+                })
+            {% endfor %}
+        {% endif %}
+    {% endwith %}
+
+</script>
+
+{% block js %} {% endblock %}
+</body>
+</html>