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)