def get_images_and_labels(path):
# put training images in 'path' directory
image_dirs = [d for d in os.listdir(path)]
print image_dirs
# array of face images
images = []
# label assigned to image
labels = []
for dir in image_dirs:
if not dir.startswith('.'):
image_paths = [os.path.join(path + "/" + dir, f) for f in filter( lambda f: not f.startswith('.'), os.listdir(path + "/" + dir))]
for image_path in image_paths:
image_pil = Image.open(image_path).convert('L')
image = numpy.array(image_pil, 'uint8')
# get label of image from directory name
label = ':' + dir + ':'
faces = frontal_face_cascade.detectMultiScale(image, minSize=(100,100))
faces = classification_utils.group_faces(faces)
for (x, y, w, h) in faces:
images.append(image[y: y+h, x: x+w])
labels.append(label)
return images, labels
def classify_image(image):
image_pil = image.convert('L')
image = numpy.array(image_pil, 'uint8')
faces = frontal_face_cascade.detectMultiScale(image)
faces = classification_utils.group_faces(faces)
(x, y, w, h) = faces[0]
predicted_label_code, confidence = recognizer.predict(image[y: y+h, x: x+w])
print(predicted_label_code)
print("Classified:", classification_utils.lookup_label(predicted_label_code), "with confidence", confidence)
print("")
return classification_utils.lookup_label(predicted_label_code)
def classify_from_webcam():
while True:
video_capture = cv2.VideoCapture(0)
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = frontal_face_cascade.detectMultiScale(gray)
faces = classification_utils.group_faces(faces)
for (x, y, w, h) in faces:
cv2.rectangle(gray , (x, y), (x+w, y+h), (0, 255, 0), 2)
predicted_label_code, confidence = recognizer.predict(gray[y: y+h, x: x+w])
print("******************************", classification_utils.lookup_label(predicted_label_code))
cv2.imshow('ImageWindow', gray)
cv2.waitKey(1)
time.sleep(1)
