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import cv2 |
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import matplotlib.pyplot as plt |
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from matplotlib.path import Path |
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plt.rcParams['figure.dpi'] = 100 |
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from PIL import ImageColor |
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from pathlib import Path |
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import glob |
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import os |
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import json |
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def annotate_images_dataset(image_folder, label_folder, class_file_path, saving_folder, hex_class_colors=None, show=False): |
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""" |
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Allows to visualize a set of images and corresponding YOLO labels. |
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Args: |
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image_folder (str): path of the folder containing the images for object detection |
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label_folder (str): path of the folder containing the labels corresponding to the images for object detection |
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class_file_path (str): path of the json file containing the labels of the object classes |
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saving_folder (str): path of the folder |
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hex_class_colors (dict, optional): dictionary with HEX color for each label |
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show (bool, optional): if True, a prompt with the labelled image opens |
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""" |
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class_dic = get_class_dic(class_file_path) |
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Path(saving_folder).mkdir(parents=True, exist_ok=True) |
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if not hex_class_colors: |
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hex_class_colors = {class_name: (255, 0, 0) for class_name in class_dic.values()} |
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color_map = {key: ImageColor.getcolor(hex_class_colors[class_dic[key]], 'RGB') for key in [*class_dic]} |
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label_paths = sorted(glob.glob(os.path.join(label_folder, '*'))) |
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n_labels = len(label_paths) |
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for i, label_path in enumerate(label_paths): |
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i += 1 |
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if i % 100 == 0: |
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progress = i / n_labels |
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print(f'{progress: .0%} -> image {i} out of {n_labels}') |
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file_name = str(label_path).split('/')[-1].split('.')[0] |
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image_file = file_name + '.jpg' |
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image_path = os.path.join(image_folder, image_file) |
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if os.path.isfile(image_path): |
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img = cv2.imread(image_path) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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dh, dw, _ = img.shape |
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with open(label_path, 'r') as f: |
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data = f.readlines() |
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for yolo_box in data: |
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yolo_box = yolo_box.strip() |
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c, x, y, w, h = map(float, yolo_box.split(' ')) |
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l = int((x - w / 2) * dw) |
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r = int((x + w / 2) * dw) |
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t = int((y - h / 2) * dh) |
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b = int((y + h / 2) * dh) |
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if l < 0: |
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l = 0 |
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if r > dw - 1: |
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r = dw - 1 |
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if t < 0: |
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t = 0 |
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if b > dh - 1: |
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b = dh - 1 |
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cv2.rectangle(img, (l, t), (r, b), color_map[c], 3) |
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if show: |
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plt.imshow(img) |
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plt.show() |
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plt.imsave(os.path.join(saving_folder, f'annotated_{image_file}'), img) |
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else: |
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print(f'WARNING: {image_path} does not exists') |
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def get_class_dic(classe_file_path): |
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""" |
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Turns a label list txt file into a dict with numerical class as key and corresponding label as value |
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Args: |
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classe_file_path (str): path to the json file listing the labels |
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Returns: |
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dict: dictionary of numerical class as key and corresponding label as value |
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""" |
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class_dic = {} |
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with open(classe_file_path) as f: |
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class_dic = json.load(f) |
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class_dic = {int(k):v for k,v in class_dic.items()} |
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return class_dic |
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if __name__ == '__main__': |
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dataset_folder_names = ['train', 'val'] |
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dataset_prefix_folder = '../data' |
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saving_prefix_folder = '../_labelled_dataset_images' |
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show = not True |
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hex_class_colors = {'green_cherry': '#9CF09A', |
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'yellow_cherry': '#F3C63D', |
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'red_cherry': '#F44336', |
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'dark_brown_cherry': '#C36105', |
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'low_visibility_unsure': '#02D5FA'} |
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for dataset_folder_name in dataset_folder_names: |
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print(f'dataset: {dataset_folder_name}:\n') |
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full_saving_folder = os.path.join(saving_prefix_folder, dataset_folder_name) |
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full_dataset_folder = os.path.join(dataset_prefix_folder, dataset_folder_name) |
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class_file_path = os.path.join('../', 'classes.json') |
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image_folder = os.path.join(full_dataset_folder, 'images') |
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label_folder = os.path.join(full_dataset_folder, 'labels') |
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annotate_images_dataset( |
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image_folder=image_folder, |
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label_folder=label_folder, |
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class_file_path=class_file_path, |
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saving_folder=full_saving_folder, |
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hex_class_colors=hex_class_colors, |
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show=show, |
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) |
