import cv2
import matplotlib.pyplot as plt
from matplotlib.path import Path
plt.rcParams['figure.dpi'] = 100
from PIL import ImageColor
from pathlib import Path
import glob
import os
import json

def annotate_images_dataset(image_folder, label_folder, class_file_path, saving_folder, hex_class_colors=None, show=False):
    """
    Allows to visualize a set of images and corresponding YOLO labels.
    Args:
        image_folder (str): path of the folder containing the images for object detection
        label_folder (str): path of the folder containing the labels corresponding to the images for object detection
        class_file_path (str): path of the json file containing the labels of the object classes
        saving_folder (str): path of the folder 
        hex_class_colors (dict, optional): dictionary with HEX color for each label
        show (bool, optional): if True, a prompt with the labelled image opens
    """    
    class_dic = get_class_dic(class_file_path)
    Path(saving_folder).mkdir(parents=True, exist_ok=True)
    if not hex_class_colors:
        hex_class_colors = {class_name: (255, 0, 0) for class_name in class_dic.values()}
    color_map = {key: ImageColor.getcolor(hex_class_colors[class_dic[key]], 'RGB') for key in [*class_dic]}
    label_paths = sorted(glob.glob(os.path.join(label_folder, '*')))
    n_labels = len(label_paths)
    for i, label_path in enumerate(label_paths):
        i += 1
        if i % 100 == 0:
            progress = i / n_labels
            print(f'{progress: .0%} -> image {i} out of {n_labels}')
        file_name = str(label_path).split('/')[-1].split('.')[0]
        image_file = file_name + '.jpg'
        image_path = os.path.join(image_folder, image_file)
        if os.path.isfile(image_path):
            img = cv2.imread(image_path)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            dh, dw, _ = img.shape

            with open(label_path, 'r') as f:
                data = f.readlines()

            for yolo_box in data:
                yolo_box = yolo_box.strip()
                c, x, y, w, h = map(float, yolo_box.split(' '))
                l = int((x - w / 2) * dw)
                r = int((x + w / 2) * dw)
                t = int((y - h / 2) * dh)
                b = int((y + h / 2) * dh)

                if l < 0:
                    l = 0
                if r > dw - 1:
                    r = dw - 1
                if t < 0:
                    t = 0
                if b > dh - 1:
                    b = dh - 1
                cv2.rectangle(img, (l, t), (r, b), color_map[c], 3)
            if show:
                plt.imshow(img)
                plt.show()
            plt.imsave(os.path.join(saving_folder, f'annotated_{image_file}'), img)
        else:
            print(f'WARNING: {image_path} does not exists')


def get_class_dic(classe_file_path):
    """
    Turns a label list txt file into a dict with numerical class as key and corresponding label as value
    Args:
        classe_file_path (str): path to the json file listing the labels

    Returns:
        dict: dictionary of numerical class as key and corresponding label as value
    """    
    class_dic = {}
    with open(classe_file_path) as f:
        class_dic = json.load(f) 
    class_dic = {int(k):v for k,v in class_dic.items()}
    return class_dic


if __name__ == '__main__':
    dataset_folder_names = ['train', 'val']
    dataset_prefix_folder = '../data'
    saving_prefix_folder = '../_labelled_dataset_images'
    show = not True
    hex_class_colors = {'green_cherry': '#9CF09A',
                        'yellow_cherry': '#F3C63D',
                        'red_cherry': '#F44336',
                        'dark_brown_cherry': '#C36105',
                        'low_visibility_unsure': '#02D5FA'}
    for dataset_folder_name in dataset_folder_names:
        print(f'dataset: {dataset_folder_name}:\n')
        full_saving_folder = os.path.join(saving_prefix_folder, dataset_folder_name)
        full_dataset_folder = os.path.join(dataset_prefix_folder, dataset_folder_name)
        class_file_path = os.path.join('../', 'classes.json')
        image_folder = os.path.join(full_dataset_folder, 'images')
        label_folder = os.path.join(full_dataset_folder, 'labels')
        annotate_images_dataset(
            image_folder=image_folder,
            label_folder=label_folder,
            class_file_path=class_file_path,
            saving_folder=full_saving_folder,
            hex_class_colors=hex_class_colors,
            show=show,
        )