SmartConversion AI - 2D to 3D Images Conversion

This code creates a graphical user interface (GUI) application for detecting faces in images. Here's a breakdown of its functionalities. Lets the user select an image file using a file dialog. It then reads the image with OpenCV, converts it to grayscale, and displays a thumbnail of the loaded image in the GUI window. Takes an image and resizes it to a smaller thumbnail size (200x200 pixels). It then converts the image from BGR (OpenCV format) to RGB (used by Python's Image library) and displays it on a designated label in the GUI. Checks if an image has been loaded first. If not, it displays a warning message. Otherwise, it uses the pre-trained face cascade classifier (face_cascade) to detect faces in the grayscale version of the image. It allows for some adjustments in detection parameters like scaling factor (scaleFactor) and minimum number of neighboring detections (minNeighbors) to improve accuracy. If no faces are found, it displays an info message. If faces are found, it creates a directory named assets/output to store the detected faces. It iterates through each detected face and saves it as a separate JPG image. Finally, it displays a success message with the number of detected faces and the output directory path. The code creates a main window (root) with the title "Detector de Rostros" (Face Detector) and sets its size. It defines a label (panel) in the window to display the image thumbnail.