def capture(self):
toolsConfig = ToolsConfig(self.captureName)
camera = toolsConfig.getCamera()
for x in range(self.captureAmount):
try:
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = self.face.detect_single(image)
if result is None:
print('Could not detect single face!' +
' Check the image in capture.pgm' +
' to see what was captured and try' +
' again with only one face visible.')
continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = self.face.crop(image, x, y, w, h,
int(ToolsConfig.getFaceFactor() * w))
# Save image to file.
filename, count = toolsConfig.getNewCaptureFile()
cv2.imwrite(filename, crop)
sleep(0.3) #sleep for half a second
print('Found face and wrote training image', filename)
except KeyboardInterrupt:
camera.stop()
break
def train(self):
print("Reading training images...")
print('-' * 20)
faces = []
labels = []
imageDirsWithLabel = [[0, "negative"]]
imageDirs = os.listdir(ToolsConfig.TRAINING_DIR)
imageDirs = [
x for x in imageDirs
if not x.startswith('.') and not x.startswith('negative')
]
pos_count = 0
for i in range(len(imageDirs)):
print("Assign label " + str(i + 1) + " to " + imageDirs[i])
imageDirsWithLabel.append([i + 1, imageDirs[i]])
print('-' * 20)
print('')
# Für jedes Label/Namen Paar:
# for every label/name pair:
for j in range(0, len(imageDirsWithLabel)):
# Label zu den Labels hinzufügen / Bilder zu den Gesichtern
for filename in ToolsConfig.walkFiles(
ToolsConfig.TRAINING_DIR + str(imageDirsWithLabel[j][1]),
'*.pgm'):
faces.append(self.prepareImage(filename))
labels.append(imageDirsWithLabel[j][0])
if imageDirsWithLabel[j][0] != 0:
pos_count += 1
# Print statistic on how many pictures per person we have collected
print('Read ' + str(pos_count) + ' positive images and ' +
str(labels.count(0)) + ' negative images.')
print('')
for j in range(1, max(labels) + 1):
print(
str(labels.count(j)) + " images from subject " +
imageDirs[j - 1])
# Train model
print('-' * 20)
print('')
print('Training model with threshold {0}'.format(
ToolsConfig.POSITIVE_THRESHOLD))
model = ToolsConfig.model()
model.train(np.asarray(faces), np.asarray(labels))
# Save model results
model.write(ToolsConfig.TRAINING_FILE)
print('Training data saved to', ToolsConfig.TRAINING_FILE)
print('')
print(
"Please add or update (if you added new people not just new images) "
+ str(imageDirs) +
" inside config.js (mirror module) or config.py (model tester). You can change the names to whatever you want, just keep the same order and you'll be fine."
)
import cv2 # OpenCV Library
from lib.common.face import FaceDetection
from lib.tools.config import ToolsConfig
import time
import os
import signal
import sys
model = ToolsConfig.model()
face = ToolsConfig.getFaceAndEyesDetection()
camera = ToolsConfig.getCamera()
print('Loading training data...')
model.load("training.xml")
print('Training data loaded!')
def clean_shutdown(signum, frame):
"""Release camera and close windows
"""
camera.stop()
cv2.destroyAllWindows()
sys.exit(0)
signal.signal(signal.SIGINT, clean_shutdown)
signal.signal(signal.SIGTERM, clean_shutdown)
signal.signal(signal.SIGSEGV, clean_shutdown)
# Loadking the training data should give enough time to
# warm up the picamera. If not, uncomment the following
def __init__(self, capName=None, captureAmount=20):
self.face = ToolsConfig.getFaceDetection()
self.captureName = capName
self.captureAmount = captureAmount
def __init__(self):
self.face = ToolsConfig.getFaceDetection()