Build a Web App With Python and OpenCv : Image Editing App

Define the main function and guard with if __name__ == '__main__', then set the application's title and subtitle to describe an image editing app built with streamlines and python.

Build a left sidebar with a select box to choose between detection and about, then use a file uploader to upload an image and display it for face detection.

Create the about section to display developer or app information using markdown links with the Tremlett library, linking to personal sites and social profiles, and testing with run-and-stop workflow.

Build a web app with Python and OpenCV that edits uploaded images using sidebar radio buttons for grayscale, contrast, brightness, and blur, with sliders to adjust each effect in detection.

Add a contrast slider in the sidebar to control image intensity, with a 0.5 to 6.0 range, applying the contrast enhancement to the uploaded image.

Demonstrate brightness control in a web app using a brightness slider (0.0 to 8.0) that applies brightness method from the image enhanced module to the uploaded image and displays results.

Apply gaussian blur to a selected image with OpenCV's GaussianBlur, adjust the blur via a slider, using a 15x15 odd kernel, then display and save the result.

Add a sharpness option to the image editor and use a slider linked to the image enhanced sharpness method on the uploaded image to adjust sharpening.

Display the uploaded image as-is for the original option, without applying any image processing. Ensure a medium size by resizing to a width of 300 pixels when needed.

Set up a select box in the sidebar labeled find features and list tasks like detect faces, detect eyes, cartoon version, and edge detection for image editing.

Build a web app that detects faces in uploaded images via a selectable feature and a process button. Implement a from-scratch detect faces function that returns results with marked faces.

Set up haar cascade files for face and eye detection with OpenCV's CascadeClassifier in a Python image editing app, configuring paths to enable detection, edge detection, and cartoon effects.

Define a detect faces function that converts the image to rgb format, detects faces with a cascade using detect multiscale, and draws rectangles around each face.

Connect the detect multiscale function to the eye cascade to detect eyes, tweak scale factor and neighbors, and draw green rectangles around each eye in the uploaded image.

Convert the image to grayscale, apply median blur, extract edges with adaptive gaussian threshold, then use bilateral filter to colorize for a cartoon look.

Learn to combine edge detection and color smoothing to cartoonize an uploaded image using OpenCV, bilateral filtering, and bitwise operations to produce a cartoon version.

Implement edge detection in a Python and OpenCV web app by applying a Gaussian blur with a 13 by 13 kernel and a Canny detector with thresholds 100 and 150.