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 check_trainer_accuracy():
testRecognizer = cv2.createLBPHFaceRecognizer(neighbors=5)
(train_images, train_labels) = get_images_and_labels(TRAINING_DIR)
test_images = []
test_labels = []
for i in range(0, len(train_images)/10):
index = random.randrange(len(train_images)-1)
test_images.append(train_images[index])
test_labels.append(train_labels[index])
del train_images[index]
del train_labels[index]
train_label_codes = []
for label in train_labels:
train_label_codes.append(classification_utils.lookup_code(label))
testRecognizer.train(train_images, numpy.array(train_label_codes))
testRecognizer.save("recognizer.dat")
num_correct = 0;
for i in range(0, len(test_images)):
predicted_label_code, confidence = testRecognizer.predict(test_images[i])
actual_label = test_labels[i]
predicted_label = classification_utils.lookup_label(predicted_label_code)
if predicted_label == actual_label:
num_correct += 1
print("Correctly predicted with confidence", confidence)
else:
print("Incorrectly recognized", actual_label, "as", predicted_label)
print("Predicted with", float(num_correct)/float(len(test_images)), "accuracy")
def test_classification(path):
image_path = os.path.join(path)
image_pil = Image.open(image_path).convert('L')
image = numpy.array(image_pil, 'uint8')
face = frontal_face_cascade.detectMultiScale(image)
(x, y, w, h) = face[0]
predicted_label_code, confidence = recognizer.predict(image[y: y+h, x: x+w])
actual_label = os.path.split(image_path)[1].split('-')[0]
predicted_label = classification_utils.lookup_label(predicted_label_code)
if predicted_label == actual_label:
print("Correctly predicted with confidence", confidence)
else:
print("Incorrectly recognized as", predicted_label)
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)
