[logodetection data set processing] (2) draw the label box of the training set picture

Data set introduction reference ：【LogoDetection Dataset processing 】（1） The data set is divided into training set and verification set

To view the training set annotation , We need to draw the callout box of the training set picture . The code is as follows ：

"""  Draw the callout box of the training set picture  """""

import os
import json
import cv2
import numpy as np
from tqdm import tqdm


# _COLORS Yes 70 Elements , If the number of categories is greater than 70, be _COLORS Need to increase the 
_COLORS = np.array(
    [
        0.000, 0.447, 0.741,
        0.850, 0.325, 0.098,
        0.929, 0.694, 0.125,
        0.494, 0.184, 0.556,
        0.466, 0.674, 0.188,
        0.301, 0.745, 0.933,
        0.635, 0.078, 0.184,
        0.300, 0.300, 0.300,
        0.600, 0.600, 0.600,
        1.000, 0.000, 0.000,
        1.000, 0.500, 0.000,
        0.749, 0.749, 0.000,
        0.000, 1.000, 0.000,
        0.000, 0.000, 1.000,
        0.667, 0.000, 1.000,
        0.333, 0.333, 0.000,
        0.333, 0.667, 0.000,
        0.333, 1.000, 0.000,
        0.667, 0.333, 0.000,
        0.667, 0.667, 0.000,
        0.667, 1.000, 0.000,
        1.000, 0.333, 0.000,
        1.000, 0.667, 0.000,
        1.000, 1.000, 0.000,
        0.000, 0.333, 0.500,
        0.000, 0.667, 0.500,
        0.000, 1.000, 0.500,
        0.333, 0.000, 0.500,
        0.333, 0.333, 0.500,
        0.333, 0.667, 0.500,
        0.333, 1.000, 0.500,
        0.667, 0.000, 0.500,
        0.667, 0.333, 0.500,
        0.667, 0.667, 0.500,
        0.667, 1.000, 0.500,
        1.000, 0.000, 0.500,
        1.000, 0.333, 0.500,
        1.000, 0.667, 0.500,
        1.000, 1.000, 0.500,
        0.000, 0.333, 1.000,
        0.000, 0.667, 1.000,
        0.000, 1.000, 1.000,
        0.333, 0.000, 1.000,
        0.333, 0.333, 1.000,
        0.333, 0.667, 1.000,
        0.333, 1.000, 1.000,
        0.667, 0.000, 1.000,
        0.667, 0.333, 1.000,
        0.667, 0.667, 1.000,
        0.667, 1.000, 1.000,
        1.000, 0.000, 1.000,
        1.000, 0.333, 1.000,
        1.000, 0.667, 1.000,
        0.333, 0.000, 0.000,
        0.500, 0.000, 0.000,
        0.667, 0.000, 0.000,
        0.833, 0.000, 0.000,
        1.000, 0.000, 0.000,
        0.000, 0.167, 0.000,
        0.000, 0.333, 0.000,
        0.000, 0.500, 0.000,
        0.000, 0.667, 0.000,
        0.000, 0.833, 0.000,
        0.000, 1.000, 0.000,
        0.000, 0.000, 0.167,
        0.000, 0.000, 0.333,
        0.000, 0.000, 0.500,
        0.000, 0.000, 0.667,
        0.000, 0.000, 0.833,
        0.000, 0.000, 1.000,
        0.000, 0.000, 0.000,
        0.143, 0.143, 0.143,
        0.286, 0.286, 0.286,
        0.429, 0.429, 0.429,
        0.571, 0.571, 0.571,
        0.714, 0.714, 0.714,
        0.857, 0.857, 0.857,
        0.000, 0.447, 0.741,
        0.314, 0.717, 0.741,
        0.50, 0.5, 0
    ]
).astype(np.float32).reshape(-1, 3)


#  Mark... On the training set picture bbox
def visual(img_path, bbox, cls_id, class_names):
    img = cv2.imread(img_path)
    color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist()
    text = '{}'.format(class_names[cls_id - 1])
    txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255, 255)
    font = cv2.FONT_HERSHEY_SIMPLEX
    txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
    txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist()

    x_min = bbox[0]
    y_min = bbox[1]
    width = bbox[2]
    height = bbox[3]
    cv2.rectangle(img, (x_min, y_min), (x_min + width, y_min + height), color, thickness=2)
    cv2.rectangle(img, (x_min, y_min - int(1.5 * txt_size[1])), (x_min + txt_size[0] + 1, y_min), txt_bk_color, -1)
    cv2.putText(img, text, (x_min, y_min - 3), font, 0.4, txt_color, thickness=1)

    cv2.imwrite(img_path, img)


if __name__=='__main__':
    #  Raw data path 
    data_path = "dataset/fewshotlogodetection_round1_train_202204/train"
    annoations_path = os.path.join(data_path, "annotations/instances_train2017.json")
    images_path = os.path.join(data_path, "images")

    #  Read annoations Of json file 
    with open(annoations_path, 'r', encoding='utf-8') as f:
        annoations_dict = json.load(f)
    images_list = annoations_dict["images"]
    annotations_list = annoations_dict["annotations"]
    categories_list = annoations_dict["categories"]

    #  visualization 
    images_name = os.listdir(images_path)
    for image_name in tqdm(images_name,desc="visual process"):
        image_path=os.path.join(images_path,image_name)
        for i in range(len(images_list)):
            pic_file_name = os.path.basename(image_path)
            if pic_file_name == images_list[i]["file_name"]:
                image_id = images_list[i]["id"]
                for j in range(len(annotations_list)):
                    if image_id == annotations_list[j]["image_id"]:
                        bbox = annotations_list[j]["bbox"]
                        cls_id = annotations_list[j]["category_id"]
                        visual(image_path, bbox, cls_id, categories_list)

Take a look at the visualization results ：