face_mask_detection/data_preprocessing.ipynb

import glob
import pandas as pd
import cv2
import os
from xml.etree import ElementTree

ANNOTATIONS_DIR = './data/annotations'
IMAGES_DIR = './data/images'
CROPPED_DIR = './data/cropped_images'

metadata = {'xmin': [], 'ymin': [], 'xmax': [], 'ymax': [], 'label': [], 'file': []}

for file in glob.glob(ANNOTATIONS_DIR + '/*.xml'):
    tree = ElementTree.parse(file)
    file_name = os.path.splitext(os.path.basename(file))[0]
    
    for annotation in tree.iter():

        if 'object' in annotation.tag:
            for attr in list(annotation):
                
                if 'name' in attr.tag:
                    name = attr.text                 
                    metadata['label'].append(name)
                    metadata['file'].append(file_name)
                            
                if 'bndbox' in attr.tag:
                    for dimensions in list(attr):
                        if 'xmin' in dimensions.tag:
                            xmin = int(round(float(dimensions.text)))
                            metadata['xmin'].append(xmin)
                        if 'ymin' in dimensions.tag:
                            ymin = int(round(float(dimensions.text)))
                            metadata['ymin'].append(ymin)                               
                        if 'xmax' in dimensions.tag:
                            xmax = int(round(float(dimensions.text)))
                            metadata['xmax'].append(xmax)                                
                        if 'ymax' in dimensions.tag:
                            ymax = int(round(float(dimensions.text)))
                            metadata['ymax'].append(ymax)

metadata_df = pd.DataFrame(metadata)
metadata_df

	xmin	ymin	xmax	ymax	label	file
0	79	105	109	142	without_mask	maksssksksss0
1	185	100	226	144	with_mask	maksssksksss0
2	325	90	360	141	without_mask	maksssksksss0
3	321	34	354	69	with_mask	maksssksksss1
4	224	38	261	73	with_mask	maksssksksss1
...	...	...	...	...	...	...
4067	263	62	287	85	with_mask	maksssksksss98
4068	344	80	377	106	with_mask	maksssksksss98
4069	181	54	273	162	mask_weared_incorrect	maksssksksss99
4070	99	87	176	165	with_mask	maksssksksss99
4071	289	99	355	233	with_mask	maksssksksss99

4072 rows × 6 columns

for i in range(len(metadata_df)):
    
    path = IMAGES_DIR + '/' + metadata_df['file'].iloc[i] + '.png'

    image = cv2.imread(path)
    
    cropped_name = str(i) + '.png'
    
    xmin = metadata_df['xmin'].iloc[i]
    ymin = metadata_df['ymin'].iloc[i]
    xmax = metadata_df['xmax'].iloc[i]
    ymax = metadata_df['ymax'].iloc[i]

    cropped_image = image[ymin:ymax, xmin:xmax]
    
    cropped_path = CROPPED_DIR + '/' + cropped_name
    
    cv2.imwrite(cropped_path, cropped_image)

metadata_df['label'].to_csv(r'./data/labels.txt', index=False, header=False)