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
# Import necessary libraries
import os
import time
import uuid
import cv2
1.2 Collect Images Using OpenCV
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
2. Review Dataset and Build Image Loading Function
2.1 Import TF and Dependencies
import json
import numpy as np
from matplotlib import pyplot as plt
2.2 Limit GPU Memory 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
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
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 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)
Stay tuned for the next steps in the upcoming sections of this tutorial!
