''' Created on May 8, 2014 @author: eran ''' from adiencealign.cascade_detection.cascade_face_finder import CascadeFaceFinder from adiencealign.affine_alignment.affine_aligner import AffineAligner import glob import os from adiencealign.landmarks_detection.landmarks_detector import detect_landmarks from adiencealign.common.landmarks import read_fidu, unwarp_fidu, draw_fidu import cv2 class CascadeFaceAligner(object): ''' classdocs ''' def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml', lbp_file = '../resources/lbpcascade_frontalface.xml', fidu_model_file = '../resources/model_ang_0.txt', fidu_exec_dir = '../resources/'): ''' Constructor ''' self.face_finder = CascadeFaceFinder(haar_file = haar_file, lbp_file = lbp_file) self.aligner = AffineAligner(fidu_model_file = fidu_model_file) self.fidu_exec_dir = fidu_exec_dir self.valid_angles = [-45,-30,-15,0,15,30,45] def detect_faces(self, input_folder, output_folder, mark_dones = True): ''' mark_dones - if True, will create a hidden file, marking this file as done with a hidden file, starting with '.done.' ''' input_files1 = glob.glob(os.path.join(input_folder, '*.jpg')) input_files2 = glob.glob(os.path.join(input_folder, '*.png')) input_files = input_files1 + input_files2 N = len(input_files) for n_file, input_file in enumerate(input_files): a,b = os.path.split(input_file) done_file = os.path.join(a, '.done.' + b.rsplit('.',1)[0]) if os.path.exists(done_file): continue print("... processing", input_file) target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt') faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file ) sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file, faces_file = faces_file, target_folder = output_folder, img_type = 'jpg') #touch open(done_file,'w').close() print("Detected on %d / %d files" %(n_file, N)) def align_faces(self, input_images, output_path, fidu_max_size = None, fidu_min_size = None, is_align = True, is_draw_fidu = False, delete_no_fidu = False): ''' input_images - can be either a folder (all *.jpgs in it) or a list of filenames , fidu_max_size = None, fidu_min_size = None): ''' if type(input_images) == type(''): input_images = glob.glob(os.path.join(input_images, '*.jpg')) for input_image in input_images: detect_landmarks(fname = os.path.abspath(input_image), max_size = fidu_max_size, min_size = fidu_min_size, fidu_exec_dir = self.fidu_exec_dir) fidu_file = input_image.rsplit('.',1)[0] + '.cfidu' fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file) if not (fidu_score is not None and yaw_angle in self.valid_angles): # skip face if delete_no_fidu: os.remove(fidu_file) os.remove(input_image) continue if is_align: # save the aligned image sub_img = cv2.imread(input_image) _, base_fname = os.path.split(input_image) aligned_img, R = self.aligner.align(sub_img, fidu_points) aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.png') cv2.imwrite(aligned_img_file, aligned_img) # save a copy of the aligned image, with the landmarks drawn if is_draw_fidu: aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.withpoints.png') fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R) draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3) cv2.imwrite(aligned_img_file, aligned_img) # # def align_faces2(self, input_folder, output_folder, detect_landmarks = True, delete_no_fidu = True, is_align = True, is_draw_fidu = False, fidu_max_size = None, fidu_min_size = None): # ''' # delete_no_fidu - deletes the .cfidu and sub_img if no fidu was found # is_draw_fidu - creates a copy of the aligned images with the original fiducial points on it # ''' # input_files = glob.glob(os.path.join(input_folder, '*')) # for input_file in input_files: # print "... processing", input_file # target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt') # # faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file ) # sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file, # faces_file = faces_file, # target_folder = output_folder, # img_type = 'jpg') # # for sub_image_file in sub_images_files: # detect_landmarks(fname = os.path.abspath(sub_image_file), # max_size = fidu_max_size, # min_size = fidu_min_size, # fidu_exec_dir = self.fidu_exec_dir) # # fidu_file = sub_image_file.rsplit('.',1)[0] + '.cfidu' # fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file) # # if not (fidu_score is not None and yaw_angle in self.valid_angles): # # skip face # os.remove(fidu_file) # os.remove(sub_image_file) # continue # # if is_align: # # save the aligned image # sub_img = cv2.imread(sub_image_file) # aligned_img, R = self.aligner.align(sub_img, fidu_points) # aligned_img_file = sub_image_file.rsplit('.',1)[0] + '.aligned.png' # cv2.imwrite(aligned_img_file, aligned_img) # # # save a copy of the aligned image, with the landmarks drawn # if is_draw_fidu: # aligned_img_file = sub_image_file.rsplit('.',1)[0] + '.aligned.withpoints.png' # fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R) # draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3) # cv2.imwrite(aligned_img_file, aligned_img)