import numpy as np
from mmdet.apis import init_detector, inference_detector
import easyocr
import matplotlib.patches as patches
import os
from pathlib import Path

def load_detection_model():

    model = init_detector('configs/rotated_rtmdet/custom.py', 'Fine_tuned_Roberta_model.pth', device='cpu')
    return model
def load_ocr_model():


    model_storage_directory = '/code/.EasyOCR/model'  # Ensure this path matches the one in Dockerfile
    reader = easyocr.Reader(['en'], model_storage_directory=model_storage_directory)
    return reader
    
def plot_rotated_box(ax, center, width, height, angle):
    angle_rad = angle
    rotation_matrix = np.array([
        [np.cos(angle_rad), -np.sin(angle_rad)],
        [np.sin(angle_rad), np.cos(angle_rad)]
    ])
    half_width = width / 2
    half_height = height / 2
    corners = np.array([
        [-half_width, -half_height],
        [half_width, -half_height],
        [half_width, half_height],
        [-half_width, half_height]
    ])
    rotated_corners = np.dot(corners, rotation_matrix.T)
    rotated_corners[:, 0] += center[0]
    rotated_corners[:, 1] += center[1]
    rotated_box = patches.Polygon(rotated_corners, closed=True, edgecolor='r', facecolor='none', linewidth=2)
    ax.add_patch(rotated_box)
    return rotated_corners