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 insert_lbp_features(path, table):
name2vector = {}
img_paths = load_images(path)
desc = LocalBinaryPatterns(510, 8)
for img_path in img_paths:
hist = get_lbp_predictions(img_path, desc)
name2vector[img_path.split('/')[-1]] = hist
db = DBObject(db=DB_NAME, user=USER, password=PASSWORD)
save_to_db(table, name2vector, db)
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)
def run_iteration(iteration, hash_map):
lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
images = [os.path.join(root, name) for root, dirs, files in os.walk("../training_images")
for name in files if name.endswith((".jpeg", ".jpg"))]
#Spliting it into training and testing groups
training, testing = train_test_split(images, test_size = 0.25)
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if imagePath in hash_map:
hist = hash_map[imagePath]
else:
hist = lbp.compute(gray)
hash_map[imagePath] = hist
print str(iteration) + " DEBUG(Training): Computed LBP Histogram for " + imagePath
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("SVM")
print "\n\n" + str(iteration) + " DEBUG: Training Classifier"
classifier.train(data, labels)
print "\n\n" + str(iteration) + " DEBUG: Trained Classifier\n\n"
#Testing Phase
data = []
labels = []
for imagePath in testing:
#Load the image, convert to grayscale, describe it and classify it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if imagePath in hash_map:
hist = hash_map[imagePath]
else:
hist = lbp.compute(gray)
hash_map[imagePath] = hist
print str(iteration) + " DEBUG(Testing): Computed LBP Histogram for " + imagePath
data.append(hist)
labels.append(imagePath.split("/")[-2])
print "\n\n" + str(iteration) + " DEBUG: Forming predictions"
predictions = classifier.predict(data)
counter = 0
print "\n\n" + str(iteration) + " DEBUG: Printing predictions\n\n"
for index, prediction in enumerate(predictions):
print "Name -> " + testing[index] + " Actual -> " + labels[index] + " Prediction -> " + prediction
if labels[index] == prediction:
counter = counter + 1
accuracy = (float(counter)/float(len(predictions))) * 100.0
print "\n\n" + str(iteration) + " The Classifier Accuracy was " + str(accuracy) + "%"
return accuracy
from local_binary_patterns import LocalBinaryPatterns
from classifier import Classifier
import os
import cv2
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt
#Caputuring the Image
#---To be done---
lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
training = [
os.path.join(root, name)
for root, dirs, files in os.walk("../training_images") for name in files
if name.endswith((".jpeg", ".jpg"))
]
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = lbp.compute(gray)
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()
from local_binary_patterns import LocalBinaryPatterns
from classifier import Classifier
import os
import cv2
from sklearn.cross_validation import train_test_split
#Caputuring the Image
#---To be done---
lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
training = [os.path.join(root, name) for root, dirs, files in os.walk("../training_images")
for name in files if name.endswith((".jpeg", ".jpg"))]
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = lbp.compute(gray)
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("Chi-Squared")
classifier.train(data, labels)
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'))
from local_binary_patterns import LocalBinaryPatterns
import os
import cv2
import matplotlib.pyplot as plt
from skimage import feature
lbp = LocalBinaryPatterns(24, 8)
data = []
#Finding all images
images = [
os.path.join(root, name) for root, dirs, files in os.walk("../images")
for name in files if name.endswith((".jpeg", ".jpg"))
]
#Training Phase
for imagePath in images:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
lbp = feature.local_binary_pattern(gray, 24, 8, method="uniform")
cv2.imshow("LBP", lbp)
cv2.waitKey(0)
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()