def predict_fiducial():
svm = mlpy.LibSvm.load_model("svm.model")
image = "/home/varun/Projects/FEAR/Dataset/cohn-kanade-images/S005/001/S005_001_00000001.png"
landmarks = "/home/varun/Projects/FEAR/Dataset/Landmarks/S005/001/S005_001_00000001_landmarks.txt"
points = utils.get_points(landmarks)
gabor = fear.get_gabor_images(image)
x = pack_gabor_images(gabor)
y = svm.pred(x)
img = cv2.imread(image)
(h, w, c) = img.shape
# utils.mark(image, points[0][0], points[0][1])
print y[points[0][0] * h + points[0][1]]
# for i in range(len(x)):
for i in y:
# y = svm.pred(numpy.array(x[i]))
if i > 0.0:
print i
# utils.mark(image, i/490, i%640)
print y
def predict_fiducial():
svm = mlpy.LibSvm.load_model('svm.model')
image = "/home/varun/Projects/FEAR/Dataset/cohn-kanade-images/S005/001/S005_001_00000001.png"
landmarks = "/home/varun/Projects/FEAR/Dataset/Landmarks/S005/001/S005_001_00000001_landmarks.txt"
points = utils.get_points(landmarks)
gabor = fear.get_gabor_images(image)
x = pack_gabor_images(gabor)
y = svm.pred(x)
img = cv2.imread(image)
(h, w, c) = img.shape
#utils.mark(image, points[0][0], points[0][1])
print y[points[0][0]*h + points[0][1]]
#for i in range(len(x)):
for i in y:
#y = svm.pred(numpy.array(x[i]))
if i > 0.0:
print i
#utils.mark(image, i/490, i%640)
print y
def show_dataset(): root_dir = '/home/varun/Projects/FEAR/Dataset/' image_root = root_dir + 'cohn-kanade-images' landmarks_root = root_dir + 'Landmarks' image1_list = os.listdir(image_root) image1_list.sort() landmark1_list = os.listdir(landmarks_root) landmark1_list.sort() #print len(image1_list) for f1 in image1_list[:1]: image1 = os.path.join(image_root, f1) landmark1 = os.path.join(landmarks_root, f1) image2_list = os.listdir(image1) image2_list.sort() landmark2_list = os.listdir(landmark1) landmark2_list.sort() for f2 in image2_list: image2 = os.path.join(image1, f2) landmark2 = os.path.join(landmark1, f2) images = os.listdir(image2) images.sort() landmarks = os.listdir(landmark2) landmarks.sort() for i in range(len(images)): image_file = os.path.join(image2, images[i]) landmark_file = os.path.join(landmark2, landmarks[i]) print "File: %s" % image_file points = utils.get_points(landmark_file) gabors = fear.get_gabor_images(image_file) train_SVMs(gabors, points)
def show_dataset():
root_dir = "/home/varun/Projects/FEAR/Dataset/"
image_root = root_dir + "cohn-kanade-images"
landmarks_root = root_dir + "Landmarks"
image1_list = os.listdir(image_root)
image1_list.sort()
landmark1_list = os.listdir(landmarks_root)
landmark1_list.sort()
# print len(image1_list)
for f1 in image1_list[:1]:
image1 = os.path.join(image_root, f1)
landmark1 = os.path.join(landmarks_root, f1)
image2_list = os.listdir(image1)
image2_list.sort()
landmark2_list = os.listdir(landmark1)
landmark2_list.sort()
for f2 in image2_list:
image2 = os.path.join(image1, f2)
landmark2 = os.path.join(landmark1, f2)
images = os.listdir(image2)
images.sort()
landmarks = os.listdir(landmark2)
landmarks.sort()
for i in range(len(images)):
image_file = os.path.join(image2, images[i])
landmark_file = os.path.join(landmark2, landmarks[i])
print "File: %s" % image_file
points = utils.get_points(landmark_file)
gabors = fear.get_gabor_images(image_file)
train_SVMs(gabors, points)
