def recognition_image(image):
desc = LocalBinaryPatterns(24, 8)
model = pickle.load(open("models/model.save", 'rb'))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(gray)
prediction = model.predict([hist])[0]
return prediction
def get_dataset(image_path):
dataset = []
labels = []
desc = LocalBinaryPatterns(24, 8)
for img in image_path:
image = cv.imread(img)
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
hist = desc.describe(gray)
dataset.append(hist)
label = img.split(os.path.sep)[-2]
labels.append(label)
dataset = np.array(dataset, dtype="float")
dataset = normalize(dataset)
labels = np.array(labels)
return dataset, labels
def recognition_images():
# initialize the local binary patterns descriptor along with
# the data and label lists
desc = LocalBinaryPatterns(24, 8)
model = pickle.load(open("models/model.save", 'rb'))
# loop over the testing images
for imagePath in paths.list_images("faces/testing"):
# load the image, convert it to grayscale, describe it,
# and classify it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(gray)
prediction = model.predict([hist])[0]
# display the image and the prediction
cv2.putText(image, prediction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0,
(0, 0, 255), 3)
cv2.imshow("Image", image)
cv2.waitKey(0)
from local_binary_patterns import LocalBinaryPatterns
from sklearn.svm import LinearSVC
from imutils import paths
import pickle
#opencv
import cv2
# initialize the local binary patterns descriptor along with
# the data and label lists
desc = LocalBinaryPatterns(24, 8)
data = []
labels = []
# loop over the training images
for imagePath in paths.list_images("faces/training"):
# load the image, convert it to grayscale, and describe it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(gray)
# extract the label from the image path, then update the
# label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
# train a Linear SVM on the data
model = LinearSVC(C=100.0, random_state=42)
model.fit(data, labels)
pickle.dump(model, open("model.save", 'wb'))