import collections from enum import IntEnum import cv2 import numpy as np import imagelib from facelib import FaceType, LandmarksProcessor """ output_sample_types = [ {} opts, ... ] opts: 'types' : (S,S,...,S) where S: 'IMG_SOURCE' 'IMG_WARPED' 'IMG_WARPED_TRANSFORMED'' 'IMG_TRANSFORMED' 'IMG_LANDMARKS_ARRAY' #currently unused 'IMG_PITCH_YAW_ROLL' 'FACE_TYPE_HALF' 'FACE_TYPE_FULL' 'FACE_TYPE_HEAD' #currently unused 'FACE_TYPE_AVATAR' #currently unused 'MODE_BGR' #BGR 'MODE_G' #Grayscale 'MODE_GGG' #3xGrayscale 'MODE_M' #mask only 'MODE_BGR_SHUFFLE' #BGR shuffle 'resolution' : N 'motion_blur' : (chance_int, range) - chance 0..100 to apply to face (not mask), and range [1..3] where 3 is highest power of motion blur 'apply_ct' : bool 'normalize_tanh' : bool """ class SampleProcessor(object): class Types(IntEnum): NONE = 0 IMG_TYPE_BEGIN = 1 IMG_SOURCE = 1 IMG_WARPED = 2 IMG_WARPED_TRANSFORMED = 3 IMG_TRANSFORMED = 4 IMG_LANDMARKS_ARRAY = 5 #currently unused IMG_PITCH_YAW_ROLL = 6 IMG_PITCH_YAW_ROLL_SIGMOID = 7 IMG_TYPE_END = 10 FACE_TYPE_BEGIN = 10 FACE_TYPE_HALF = 10 FACE_TYPE_FULL = 11 FACE_TYPE_HEAD = 12 #currently unused FACE_TYPE_AVATAR = 13 #currently unused FACE_TYPE_END = 20 MODE_BEGIN = 40 MODE_BGR = 40 #BGR MODE_G = 41 #Grayscale MODE_GGG = 42 #3xGrayscale MODE_M = 43 #mask only MODE_BGR_SHUFFLE = 44 #BGR shuffle MODE_END = 50 class Options(object): def __init__(self, random_flip = True, 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.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, ct_sample=None): SPTF = SampleProcessor.Types sample_bgr = sample.load_bgr() ct_sample_bgr = None ct_sample_mask = None 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)) params = imagelib.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 ) cached_images = collections.defaultdict(dict) sample_rnd_seed = np.random.randint(0x80000000) SPTF_FACETYPE_TO_FACETYPE = { SPTF.FACE_TYPE_HALF : FaceType.HALF, SPTF.FACE_TYPE_FULL : FaceType.FULL, SPTF.FACE_TYPE_HEAD : FaceType.HEAD, SPTF.FACE_TYPE_AVATAR : FaceType.AVATAR } outputs = [] for opts in output_sample_types: resolution = opts.get('resolution', 0) types = opts.get('types', [] ) random_sub_res = opts.get('random_sub_res', 0) normalize_std_dev = opts.get('normalize_std_dev', False) normalize_vgg = opts.get('normalize_vgg', False) motion_blur = opts.get('motion_blur', None) apply_ct = opts.get('apply_ct', False) normalize_tanh = opts.get('normalize_tanh', False) img_type = SPTF.NONE target_face_type = SPTF.NONE face_mask_type = SPTF.NONE mode_type = SPTF.NONE for t in types: if t >= SPTF.IMG_TYPE_BEGIN and t < SPTF.IMG_TYPE_END: img_type = t elif t >= SPTF.FACE_TYPE_BEGIN and t < SPTF.FACE_TYPE_END: target_face_type = t elif t >= SPTF.MODE_BEGIN and t < SPTF.MODE_END: mode_type = t if img_type == SPTF.NONE: raise ValueError ('expected IMG_ type') if img_type == SPTF.IMG_LANDMARKS_ARRAY: 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 elif img_type == SPTF.IMG_PITCH_YAW_ROLL or img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID: pitch_yaw_roll = sample.pitch_yaw_roll if pitch_yaw_roll is not None: pitch, yaw, roll = pitch_yaw_roll else: pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll (sample.landmarks) if params['flip']: yaw = -yaw if img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID: pitch = (pitch+1.0) / 2.0 yaw = (yaw+1.0) / 2.0 roll = (roll+1.0) / 2.0 img = (pitch, yaw, roll) else: if mode_type == SPTF.NONE: raise ValueError ('expected MODE_ type') img = cached_images.get(img_type, None) if img is None: img = sample_bgr mask = None cur_sample = sample if is_face_sample: if motion_blur is not None: chance, mb_range = motion_blur chance = np.clip(chance, 0, 100) if np.random.randint(100) < chance: mb_range = [3,5,7,9][ : np.clip(mb_range, 0, 3)+1 ] dim = mb_range[ np.random.randint(len(mb_range) ) ] img = imagelib.LinearMotionBlur (img, dim, np.random.randint(180) ) mask = cur_sample.load_fanseg_mask() #using fanseg_mask if exist if mask is None: mask = LandmarksProcessor.get_image_hull_mask (img.shape, cur_sample.landmarks) if cur_sample.ie_polys is not None: cur_sample.ie_polys.overlay_mask(mask) warp = (img_type==SPTF.IMG_WARPED or img_type==SPTF.IMG_WARPED_TRANSFORMED) transform = (img_type==SPTF.IMG_WARPED_TRANSFORMED or img_type==SPTF.IMG_TRANSFORMED) flip = img_type != SPTF.IMG_WARPED img = imagelib.warp_by_params (params, img, warp, transform, flip, True) if mask is not None: mask = imagelib.warp_by_params (params, mask, warp, transform, flip, False)[...,np.newaxis] img = np.concatenate( (img, mask ), -1 ) cached_images[img_type] = img if is_face_sample and target_face_type != SPTF.NONE: ft = SPTF_FACETYPE_TO_FACETYPE[target_face_type] if ft > 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, ft) ) img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, ft), (resolution,resolution), flags=cv2.INTER_CUBIC ) else: img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC ) if random_sub_res != 0: sub_size = resolution - random_sub_res rnd_state = np.random.RandomState (sample_rnd_seed+random_sub_res) 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 = np.clip(img, 0, 1) img_bgr = img[...,0:3] img_mask = img[...,3:4] if apply_ct: if ct_sample_bgr is None: ct_sample_bgr = ct_sample.load_bgr() ct_sample_mask = LandmarksProcessor.get_image_hull_mask (ct_sample_bgr.shape, ct_sample.landmarks) ct_sample_bgr_resized = cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR ) ct_sample_mask_resized = cv2.resize( ct_sample_mask, (resolution,resolution), cv2.INTER_LINEAR )[...,np.newaxis] img_bgr = imagelib.linear_color_transfer (img_bgr, ct_sample_bgr_resized) img_bgr = np.clip( img_bgr, 0.0, 1.0) if normalize_std_dev: img_bgr = (img_bgr - img_bgr.mean( (0,1)) ) / img_bgr.std( (0,1) ) elif normalize_vgg: img_bgr = np.clip(img_bgr*255, 0, 255) img_bgr[:,:,0] -= 103.939 img_bgr[:,:,1] -= 116.779 img_bgr[:,:,2] -= 123.68 if mode_type == SPTF.MODE_BGR: img = img_bgr elif mode_type == SPTF.MODE_BGR_SHUFFLE: rnd_state = np.random.RandomState (sample_rnd_seed) img_bgr = np.take (img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1) img = np.concatenate ( (img_bgr,img_mask) , -1 ) elif mode_type == SPTF.MODE_G: img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 ) elif mode_type == SPTF.MODE_GGG: img = np.concatenate ( ( np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1), img_mask), -1) elif mode_type == SPTF.MODE_M and is_face_sample: img = img_mask if not debug: if normalize_tanh: img = np.clip (img * 2.0 - 1.0, -1.0, 1.0) else: img = np.clip (img, 0.0, 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 """ 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)) RANDOM_CLOSE = 0x00000040, #currently unused MORPH_TO_RANDOM_CLOSE = 0x00000080, #currently unused if f & SPTF.RANDOM_CLOSE != 0: img_type += 10 elif f & SPTF.MORPH_TO_RANDOM_CLOSE != 0: img_type += 20 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 = imagelib.morph_by_points (sample_bgr, s_landmarks, d_landmarks) cur_sample = close_sample else: """