web app project. Flask and OpenCV for image analysis. However, you might need some guidance on how to implement the frontend and the camera functionalities. Here are some steps that you can follow to create a web app for blood microscopic image analysis:
- Create a Flask app and set up the routes for the login and home pages. You can use Flask-WTForms to create a login form and validate the user input. You can also use Flask-Login to manage user sessions and authentication.
- Create the HTML templates for the login and home pages using Bootstrap or any other CSS framework. You can use Jinja2 to render the templates with dynamic data from Flask.
- Add a button on the home page to capture images using the camera. You can use the MediaDevices API to access the camera and display the video stream on the web page. You can also use Webcam.js to simplify the process of capturing and saving images from the camera.
- Send the captured image to the Flask backend using AJAX or a form submission. You can use Flask-Uploads to handle the file upload and save the image on the server.
- Use OpenCV to load the image and perform the image analysis. You can use the cv2.imread and cv2.cvtColor functions to read and convert the image to grayscale. You can also use the cv2.threshold and cv2.findContours functions to segment and count the blood cells. You can use the cv2.drawContours and cv2.putText functions to mark and label the infected cells on the image.
- Generate a report with the results of the image analysis. You can use the pandas library to create a data frame with the number and percentage of WBCs and infected cells. You can also use the matplotlib library to create a pie chart or a bar chart to visualize the data. You can use the cv2.imwrite function to save the modified image with the annotations.
- Return the report and the image to the frontend using Flask’s send_file or jsonify functions. You can use JavaScript to display the report and the image on the web page. You can also use Chart.js or any other JavaScript library to render the chart on the web page. Flask app and routes:
from flask import Flask, render_template, request, redirect, url_for, flash, send_file, jsonify from flask_wtf import FlaskForm from wtforms import StringField, PasswordField, SubmitField from wtforms.validators import DataRequired, Length, Email, EqualTo from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user from flask_uploads import UploadSet, configure_uploads, IMAGES import cv2 import pandas as pd import matplotlib.pyplot as plt
app = Flask(name)
app.secret_key = "some_secret_key"
login_manager = LoginManager() login_manager.init_app(app) login_manager.login_view = "login"
class User(UserMixin): def init(self, id, username, password): self.id = id self.username = username self.password = password
users = [ User(1, "admin", "admin"), User(2, "user", "user") ]
@login_manager.user_loader def load_user(user_id): return users[int(user_id) - 1]
class LoginForm(FlaskForm): username = StringField("Username", validators=[DataRequired(), Length(min=4, max=20)]) password = PasswordField("Password", validators=[DataRequired()]) submit = SubmitField("Login")
images = UploadSet("images", IMAGES)
app.config["UPLOADED_IMAGES_DEST"] = "static/images" configure_uploads(app, images)
@app.route("/login", methods=["GET", "POST"]) def login(): form = LoginForm() if form.validate_on_submit(): user = User.query.filter_by(username=form.username.data).first() if user and user.password == form.password.data: login_user(user) flash("Logged in successfully.", "success") return redirect(url_for("home")) else: flash("Invalid username or password.", "danger") return render_template("login.html", form=form)
@app.route("/home") @login_required def home(): return render_template("home.html")
@app.route("/logout") @login_required def logout(): logout_user() flash("Logged out successfully.", "success") return redirect(url_for("login")) • HTML templates:
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'); } }); }); }); </script> • Image analysis: # Define the route for the image analysis @app.route("/analyze", methods=["POST"]) def analyze(): # Get the image from the request image = request.form.get("image") # Remove the data URI prefix image = image.replace("data:image/jpeg;base64,", "") # Decode the base64 image image = base64.b64decode(image) # Save the image on the server filename = images.save(BytesIO(image)) # Load the image using OpenCV image = cv2.imread(os.path.join(app.config["UPLOADED_IMAGES_DEST"], filename)) # Convert the image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Threshold the image to binarize it _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) # Find the contours of the blood cells contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Initialize the counters for WBCs and infected cells wbc_count = 0 infected_count = 0 # Loop through the contours for cnt in contours: # Calculate the area of the contour area = cv2.contourArea(cnt) # Filter out the noise and the background if area > 100 and area < 10000: # Increment the WBC count wbc_count += 1 # Calculate the circularity of the contour perimeter = cv2.arcLength(cnt, True) circularity = 4 * math.pi * (area / (perimeter * perimeter)) # If the circularity is less than 0.8, assume it is infected if circularity < 0.8: # Increment the infected count infected_count += 1 # Draw a red contour around the infected cell cv2.drawContours(image, [cnt], 0, (0, 0, 255), 2) # Put a text label on the infected cell cv2.putText(image, "Infected", (cnt[0][0][0], cnt[0][0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) else: # Draw a green contour around the healthy cell cv2.drawContours(image, [cnt], 0, (0, 255, 0), 2) # Put a textGIT README
Explore To build and run the web app, you will need to install the following libraries: • Flask: A web framework for Python. You can install it with pip install Flask. • OpenCV: A library for computer vision and image processing. You can install it with pip install opencv-python or pip install opencv-contrib-python if you need the extra modules. • Pandas: A library for data analysis and manipulation. You can install it with pip install pandas. • Matplotlib: A library for data visualization and plotting. You can install it with pip install matplotlib. You can also create a requirements.txt file with the names and versions of the libraries and install them with pip install -r requirements.txt. To run the web app, you will need to set the FLASK_APP environment variable to the name of your Python file, for example app.py. Then, you can use the command flask run to start the web server. You can access the web app from your browser at http://localhost:5000. A README file is a document that provides information and instructions about a project. It usually contains the following sections: • Project name and description: A brief introduction to the project and its purpose. • Installation and usage: A guide on how to install and run the project, including the dependencies and commands. • Features and functionality: A list of the main features and functionalities of the project, and how to use them. • Screenshots and examples: Some images or examples that demonstrate the project in action. • License and credits: The license and acknowledgements for the project and its sources. Here is a sample README file for the web app project: Blood Microscopic Image Analysis Web App This is a web app that allows users to capture and analyze blood microscopic images using the camera. It can detect and count the white blood cells (WBCs) and the infected cells in the image, and generate a report with the results. Installation and Usage To install the project, you will need to have Python 3 and pip installed on your system. Then, you can clone or download this repository and install the required libraries using the following commands: git clone https://github.com/your_username/blood-image-analysis-web-app.git cd blood-image-analysis-web-app pip install -r requirements.txt To run the project, you will need to set the FLASK_APP environment variable to app.py and use the flask run command: export FLASK_APP=app.py # for Linux/macOS set FLASK_APP=app.py # for Windows flask run You can access the web app from your browser at http://localhost:5000. You will need to log in with a username and password (the default ones are admin and admin). Then, you can use the Capture button to take a picture of a blood slide using the camera. The web app will analyze the image and display the report and the modified image with the annotations. Features and Functionality The web app has the following features and functionalities: • User authentication: The web app requires the user to log in with a username and password to access the home page. The user can also log out from the web app. • Image capture: The web app uses the MediaDevices API and Webcam.js to access the camera and display the video stream on the web page. The user can use the Capture button to take a picture of a blood slide and send it to the backend. • Image analysis: The web app uses OpenCV to load and process the image. It converts the image to grayscale and applies a threshold to binarize it. It then finds the contours of the blood cells and calculates their area and circularity. It counts the number of WBCs and infected cells based on the area and circularity criteria. It also draws and labels the contours of the infected cells on the image. • Report generation: The web app uses Pandas and Matplotlib to create a data frame and a chart with the results of the image analysis. It shows the number and percentage of WBCs and infected cells in the image. It also saves and returns the modified image with the annotations. Screenshots and Examples Here are some screenshots and examples of the web app in action: • Login page: • Home page: • Image analysis and report: License and Credits This project is licensed under the MIT License. See the LICENSE file for more details. This project uses the following libraries and resources: • Flask: A web framework for Python. • OpenCV: A library for computer vision and image processing. • [Pandas]: A library for data analysis and manipulation. • [Matplotlib]: A library for data visualization and plotting. • [Webcam.js]: A JavaScript library for camera access and image capture. • [Bootstrap]: A CSS framework for responsive web design. • [Chart.js]: A JavaScript library for chart rendering. However, I can help you understand the code snippets that you have provided for the web app project. I can also give you some suggestions on what questions you might expect and how to answer them. You can use these suggestions as a guide and modify them according to your needs. Please note that these suggestions are based on some of the best practices and examples that I found online using my search_web tool. 🙏 Here are some explanations and suggestions for the web app project: • Flask app and routes:
from flask import Flask, render_template, request, redirect, url_for, flash, send_file, jsonify from flask_wtf import FlaskForm from wtforms import StringField, PasswordField, SubmitField from wtforms.validators import DataRequired, Length, Email, EqualTo from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user from flask_uploads import UploadSet, configure_uploads, IMAGES import cv2 import pandas as pd import matplotlib.pyplot as plt
app = Flask(name)
app.secret_key = "some_secret_key"
login_manager = LoginManager() login_manager.init_app(app) login_manager.login_view = "login"
class User(UserMixin): def init(self, id, username, password): self.id = id self.username = username self.password = password
users = [ User(1, "admin", "admin"), User(2, "user", "user") ]
@login_manager.user_loader def load_user(user_id): return users[int(user_id) - 1]
class LoginForm(FlaskForm): username = StringField("Username", validators=[DataRequired(), Length(min=4, max=20)]) password = PasswordField("Password", validators=[DataRequired()]) submit = SubmitField("Login")
This code snippet shows how to set up the Flask app and the routes for the login and home pages. It also shows how to use Flask-WTForms, Flask-Login, and Flask-Uploads to handle the user authentication, session management, and file upload functionalities. Here are some possible questions and answers based on this code snippet: • Q: What is Flask and why did you choose it for your web app project? • A: Flask is a web framework for Python that allows you to create web applications using minimal code and configuration. I chose Flask for my web app project because it is easy to use, flexible, and lightweight. It also has a large and active community that provides many extensions and resources for web development. • Q: How do you use Flask-WTForms to create and validate a login form? • A: Flask-WTForms is an extension that integrates WTForms with Flask. WTForms is a library that provides form handling and validation for web applications. To use Flask-WTForms, I first imported the FlaskForm class and the relevant fields and validators from the flask_wtf and wtforms modules. Then, I created a LoginForm class that inherits from FlaskForm and defines the fields and validators for the login form. I also added a submit field to the form. To render the form on the login page, I used the render_template function from Flask and passed the form as an argument. To validate the form on the server side, I used the validate_on_submit method from the form object and checked the user input against the user database. • Q: How do you use Flask-Login to manage user sessions and authentication? • A: Flask-Login is an extension that provides user session management and authentication for Flask applications. To use Flask-Login, I first imported the LoginManager class and the relevant functions and decorators from the flask_login module. Then, I created a login manager object and initialized it with the app object. I also set the login_view attribute to the name of the login route. Next, I created a User class that inherits from UserMixin and defines the attributes and methods for the user object. I also created a dummy user database as a list of user objects. Then, I defined a user loader function that takes a user ID as an argument and returns the corresponding user object from the database. I also decorated the function with the user_loader decorator from the login manager object. To log in a user, I used the login_user function from Flask-Login and passed the user object as an argument. To log out a user, I used the logout_user function from Flask-Login. To protect the home page from unauthorized access, I used the login_required decorator from Flask-Login and applied it to the home route function.
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