Building a Deep Face Detection Model with Python and TensorFlow (Part 1)

#ai #machinelearning #python #deeplearning

In this tutorial, we'll walk through the process of building a deep learning model for face detection using Python and TensorFlow. Face detection is a crucial component of many computer vision applications, including facial recognition, surveillance, and image understanding. We'll leverage the power of convolutional neural networks (CNNs) and the VGG16 architecture for this task.

1. Setup and Data Collection

1.1 Install Dependencies and Setup

!pip install labelme tensorflow tensorflow-gpu opencv-python matplotlib albumentations
# Import necessary libraries
import os
import time
import uuid
import cv2

1.2 Collect Images Using OpenCV

IMAGES_PATH = os.path.join('data', 'images')
number_images = 30
cap = cv2.VideoCapture(1)

for imgnum in range(number_images):
    print('Collecting image {}'.format(imgnum))
    ret, frame = cap.read()
    imgname = os.path.join(IMAGES_PATH, f'{str(uuid.uuid1())}.jpg')
    cv2.imwrite(imgname, frame)
    cv2.imshow('frame', frame)
    time.sleep(0.5)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

1.3 Annotate Images with LabelMe

!labelme

2. Review Dataset and Build Image Loading Function

2.1 Import TF and Dependencies

import tensorflow as tf
import json
import numpy as np
from matplotlib import pyplot as plt

2.2 Limit GPU Memory Growth

# Avoid OOM errors by setting GPU Memory Consumption Growth
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus: 
    tf.config.experimental.set_memory_growth(gpu, True)
tf.config.list_physical_devices('GPU')

2.3 Load Image into TF Data Pipeline

images = tf.data.Dataset.list_files('data\\images\\*.jpg')

def load_image(x): 
    byte_img = tf.io.read_file(x)
    img = tf.io.decode_jpeg(byte_img)
    return img

images = images.map(load_image)

2.4 View Raw Images with Matplotlib

image_generator = images.batch(4).as_numpy_iterator()
plot_images = image_generator.next()

fig, ax = plt.subplots(ncols=4, figsize=(20,20))
for idx, image in enumerate(plot_images):
    ax[idx].imshow(image) 
plt.show()

3. Partition Unaugmented Data

3.1 MANUALLY SPLT DATA INTO TRAIN TEST AND VAL

90 * 0.7 # 63 to train
90 * 0.15 # 14 and 13 to test and val

3.2 Move the Matching Labels

for folder in ['train', 'test', 'val']:
    for file in os.listdir(os.path.join('data', folder, 'images')):
        filename = file.split('.')[0] + '.json'
        existing_filepath = os.path.join('data', 'labels', filename)
        if os.path.exists(existing_filepath): 
            new_filepath = os.path.join('data', folder, 'labels', filename)
            os.replace(existing_filepath, new_filepath)