# Preprocessing function adapted for Gradio
# This script preprocesses original pictures and turns them into 2D-projections.

import numpy as np
import cv2
from matplotlib import pyplot as plt

from rescale import *
from slid import detect_lines
from laps import LAPS
from llr import LLR, llr_pad


def preprocess_image_from_array(img_array):
    ''' 
    Preprocesses an image from a numpy array (RGB format).
    
    Args:
        img_array: numpy array of the image in RGB format
        
    Returns:
        Preprocessed image as numpy array
    '''
    # Convert RGB to BGR for OpenCV (if needed)
    # Gradio images are usually in RGB format, but cv2.imread expects BGR
    # Since we're getting an array, we need to convert RGB to BGR
    if len(img_array.shape) == 3 and img_array.shape[2] == 3:
        res = img_array[..., ::-1]  # RGB to BGR
    else:
        res = img_array.copy()
    
    # Crop twice, just like Czyzewski et al. did
    for _ in range(2):
        img, shape, scale = image_resize(res)
        lines = detect_lines(img)
        lattice_points = LAPS(img, lines)
        # Sometimes LLR() or llr_pad() will produce an error. In this case,
        # the picture needs to be retaken
        inner_points = LLR(img, lattice_points, lines)
        four_points = llr_pad(inner_points, img)  # padcrop

        try:
            res = crop(res, four_points, scale)
        except:
            print("WARNING: couldn't crop around outer points")
            res = crop(res, inner_points, scale)
    
    # Convert BGR back to RGB for display
    return res[..., ::-1]