from enum import IntEnum import numpy as np import cv2 from utils import image_utils from facelib import LandmarksProcessor from facelib import FaceType class SampleProcessor(object): class TypeFlags(IntEnum): SOURCE = 0x00000001, WARPED = 0x00000002, WARPED_TRANSFORMED = 0x00000004, TRANSFORMED = 0x00000008, LANDMARKS_ARRAY = 0x00000010, #currently unused RANDOM_CLOSE = 0x00000020, MORPH_TO_RANDOM_CLOSE \ = 0x00000040, FACE_ALIGN_HALF = 0x00000100, FACE_ALIGN_FULL = 0x00000200, FACE_ALIGN_HEAD = 0x00000400, FACE_ALIGN_AVATAR = 0x00000800, FACE_MASK_FULL = 0x00001000, FACE_MASK_EYES = 0x00002000, MODE_BGR = 0x01000000, #BGR MODE_G = 0x02000000, #Grayscale MODE_GGG = 0x04000000, #3xGrayscale MODE_M = 0x08000000, #mask only MODE_BGR_SHUFFLE = 0x10000000, #BGR shuffle class Options(object): def __init__(self, random_flip = True, normalize_tanh = False, rotation_range=[-10,10], scale_range=[-0.05, 0.05], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05]): self.random_flip = random_flip self.normalize_tanh = normalize_tanh self.rotation_range = rotation_range self.scale_range = scale_range self.tx_range = tx_range self.ty_range = ty_range @staticmethod def process (sample, sample_process_options, output_sample_types, debug): sample_bgr = sample.load_bgr() h,w,c = sample_bgr.shape is_face_sample = sample.landmarks is not None if debug and is_face_sample: LandmarksProcessor.draw_landmarks (sample_bgr, sample.landmarks, (0, 1, 0)) close_sample = sample.close_target_list[ np.random.randint(0, len(sample.close_target_list)) ] if sample.close_target_list is not None else None close_sample_bgr = close_sample.load_bgr() if close_sample is not None else None if debug and close_sample_bgr is not None: LandmarksProcessor.draw_landmarks (close_sample_bgr, close_sample.landmarks, (0, 1, 0)) params = image_utils.gen_warp_params(sample_bgr, sample_process_options.random_flip, rotation_range=sample_process_options.rotation_range, scale_range=sample_process_options.scale_range, tx_range=sample_process_options.tx_range, ty_range=sample_process_options.ty_range ) images = [[None]*3 for _ in range(30)] sample_rnd_seed = np.random.randint(0x80000000) outputs = [] for sample_type in output_sample_types: f = sample_type[0] size = sample_type[1] random_sub_size = 0 if len (sample_type) < 3 else min( sample_type[2] , size) if f & SampleProcessor.TypeFlags.SOURCE != 0: img_type = 0 elif f & SampleProcessor.TypeFlags.WARPED != 0: img_type = 1 elif f & SampleProcessor.TypeFlags.WARPED_TRANSFORMED != 0: img_type = 2 elif f & SampleProcessor.TypeFlags.TRANSFORMED != 0: img_type = 3 elif f & SampleProcessor.TypeFlags.LANDMARKS_ARRAY != 0: img_type = 4 else: raise ValueError ('expected SampleTypeFlags type') if f & SampleProcessor.TypeFlags.RANDOM_CLOSE != 0: img_type += 10 elif f & SampleProcessor.TypeFlags.MORPH_TO_RANDOM_CLOSE != 0: img_type += 20 face_mask_type = 0 if f & SampleProcessor.TypeFlags.FACE_MASK_FULL != 0: face_mask_type = 1 elif f & SampleProcessor.TypeFlags.FACE_MASK_EYES != 0: face_mask_type = 2 target_face_type = -1 if f & SampleProcessor.TypeFlags.FACE_ALIGN_HALF != 0: target_face_type = FaceType.HALF elif f & SampleProcessor.TypeFlags.FACE_ALIGN_FULL != 0: target_face_type = FaceType.FULL elif f & SampleProcessor.TypeFlags.FACE_ALIGN_HEAD != 0: target_face_type = FaceType.HEAD elif f & SampleProcessor.TypeFlags.FACE_ALIGN_AVATAR != 0: target_face_type = FaceType.AVATAR if img_type == 4: l = sample.landmarks l = np.concatenate ( [ np.expand_dims(l[:,0] / w,-1), np.expand_dims(l[:,1] / h,-1) ], -1 ) l = np.clip(l, 0.0, 1.0) img = l else: if images[img_type][face_mask_type] is None: if img_type >= 10 and img_type <= 19: #RANDOM_CLOSE img_type -= 10 img = close_sample_bgr cur_sample = close_sample elif img_type >= 20 and img_type <= 29: #MORPH_TO_RANDOM_CLOSE img_type -= 20 res = sample.shape[0] s_landmarks = sample.landmarks.copy() d_landmarks = close_sample.landmarks.copy() idxs = list(range(len(s_landmarks))) #remove landmarks near boundaries for i in idxs[:]: s_l = s_landmarks[i] d_l = d_landmarks[i] if s_l[0] < 5 or s_l[1] < 5 or s_l[0] >= res-5 or s_l[1] >= res-5 or \ d_l[0] < 5 or d_l[1] < 5 or d_l[0] >= res-5 or d_l[1] >= res-5: idxs.remove(i) #remove landmarks that close to each other in 5 dist for landmarks in [s_landmarks, d_landmarks]: for i in idxs[:]: s_l = landmarks[i] for j in idxs[:]: if i == j: continue s_l_2 = landmarks[j] diff_l = np.abs(s_l - s_l_2) if np.sqrt(diff_l.dot(diff_l)) < 5: idxs.remove(i) break s_landmarks = s_landmarks[idxs] d_landmarks = d_landmarks[idxs] s_landmarks = np.concatenate ( [s_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] ) d_landmarks = np.concatenate ( [d_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] ) img = image_utils.morph_by_points (sample_bgr, s_landmarks, d_landmarks) cur_sample = close_sample else: img = sample_bgr cur_sample = sample if is_face_sample: if face_mask_type == 1: img = np.concatenate( (img, LandmarksProcessor.get_image_hull_mask (img.shape, cur_sample.landmarks) ), -1 ) elif face_mask_type == 2: mask = LandmarksProcessor.get_image_eye_mask (img.shape, cur_sample.landmarks) mask = np.expand_dims (cv2.blur (mask, ( w // 32, w // 32 ) ), -1) mask[mask > 0.0] = 1.0 img = np.concatenate( (img, mask ), -1 ) images[img_type][face_mask_type] = image_utils.warp_by_params (params, img, (img_type==1 or img_type==2), (img_type==2 or img_type==3), img_type != 0, face_mask_type == 0) img = images[img_type][face_mask_type] if is_face_sample and target_face_type != -1: if target_face_type > sample.face_type: raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_face_type) ) img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, size, target_face_type), (size,size), flags=cv2.INTER_LANCZOS4 ) else: img = cv2.resize( img, (size,size), cv2.INTER_LANCZOS4 ) if random_sub_size != 0: sub_size = size - random_sub_size rnd_state = np.random.RandomState (sample_rnd_seed+random_sub_size) start_x = rnd_state.randint(sub_size+1) start_y = rnd_state.randint(sub_size+1) img = img[start_y:start_y+sub_size,start_x:start_x+sub_size,:] img_bgr = img[...,0:3] img_mask = img[...,3:4] if f & SampleProcessor.TypeFlags.MODE_BGR != 0: img = img elif f & SampleProcessor.TypeFlags.MODE_BGR_SHUFFLE != 0: img_bgr = np.take (img_bgr, np.random.permutation(img_bgr.shape[-1]), axis=-1) img = np.concatenate ( (img_bgr,img_mask) , -1 ) elif f & SampleProcessor.TypeFlags.MODE_G != 0: img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 ) elif f & SampleProcessor.TypeFlags.MODE_GGG != 0: img = np.concatenate ( ( np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1), img_mask), -1) elif is_face_sample and f & SampleProcessor.TypeFlags.MODE_M != 0: if face_mask_type== 0: raise ValueError ('no face_mask_type defined') img = img_mask else: raise ValueError ('expected SampleTypeFlags mode') if not debug and sample_process_options.normalize_tanh: img = img * 2.0 - 1.0 outputs.append ( img ) if debug: result = [] for output in outputs: if output.shape[2] < 4: result += [output,] elif output.shape[2] == 4: result += [output[...,0:3]*output[...,3:4],] return result else: return outputs