def ConvertMaskedFace(cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks): #if debug: # debugs = [img_bgr.copy()] img_size = img_bgr.shape[1], img_bgr.shape[0] img_face_mask_a = LandmarksProcessor.get_image_hull_mask( img_bgr.shape, img_face_landmarks) if cfg.mode == 'original': if cfg.export_mask_alpha: img_bgr = np.concatenate([img_bgr, img_face_mask_a], -1) return img_bgr, img_face_mask_a out_img = img_bgr.copy() out_merging_mask = None output_size = cfg.predictor_input_shape[0] if cfg.super_resolution_mode != 0: output_size *= 2 face_mat = LandmarksProcessor.get_transform_mat(img_face_landmarks, output_size, face_type=cfg.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, output_size, face_type=cfg.face_type, scale=1.0 + 0.01 * cfg.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC) dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC) predictor_input_bgr = cv2.resize(dst_face_bgr, cfg.predictor_input_shape[0:2]) if cfg.predictor_masked: prd_face_bgr, prd_face_mask_a_0 = cfg.predictor_func( predictor_input_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0) prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0) else: predicted = cfg.predictor_func(predictor_input_bgr) prd_face_bgr = np.clip(predicted, 0, 1.0) prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, cfg.predictor_input_shape[0:2]) if cfg.super_resolution_mode: #if debug: # tmp = cv2.resize (prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC) # debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = cfg.superres_func(cfg.super_resolution_mode, prd_face_bgr) #if debug: # debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if cfg.predictor_masked: prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) else: prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) if cfg.mask_mode == 2: #dst prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) elif cfg.mask_mode >= 3 and cfg.mask_mode <= 7: if cfg.mask_mode == 3 or cfg.mask_mode == 5 or cfg.mask_mode == 6: prd_face_fanseg_bgr = cv2.resize(prd_face_bgr, (cfg.fanseg_input_size, ) * 2) prd_face_fanseg_mask = cfg.fanseg_extract_func( FaceType.FULL, prd_face_fanseg_bgr) FAN_prd_face_mask_a_0 = cv2.resize(prd_face_fanseg_mask, (output_size, output_size), cv2.INTER_CUBIC) if cfg.mask_mode >= 4 or cfg.mask_mode <= 7: full_face_fanseg_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, cfg.fanseg_input_size, face_type=FaceType.FULL) dst_face_fanseg_bgr = cv2.warpAffine(img_bgr, full_face_fanseg_mat, (cfg.fanseg_input_size, ) * 2, flags=cv2.INTER_CUBIC) dst_face_fanseg_mask = cfg.fanseg_extract_func( FaceType.FULL, dst_face_fanseg_bgr) if cfg.face_type == FaceType.FULL: FAN_dst_face_mask_a_0 = cv2.resize(dst_face_fanseg_mask, (output_size, output_size), cv2.INTER_CUBIC) elif cfg.face_type == FaceType.HALF: half_face_fanseg_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, cfg.fanseg_input_size, face_type=FaceType.HALF) fanseg_rect_corner_pts = np.array( [[0, 0], [cfg.fanseg_input_size - 1, 0], [0, cfg.fanseg_input_size - 1]], dtype=np.float32) a = LandmarksProcessor.transform_points(fanseg_rect_corner_pts, half_face_fanseg_mat, invert=True) b = LandmarksProcessor.transform_points( a, full_face_fanseg_mat) m = cv2.getAffineTransform(b, fanseg_rect_corner_pts) FAN_dst_face_mask_a_0 = cv2.warpAffine( dst_face_fanseg_mask, m, (cfg.fanseg_input_size, ) * 2, flags=cv2.INTER_CUBIC) FAN_dst_face_mask_a_0 = cv2.resize(FAN_dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) else: raise ValueError("cfg.face_type unsupported") if cfg.mask_mode == 3: #FAN-prd prd_face_mask_a_0 = FAN_prd_face_mask_a_0 elif cfg.mask_mode == 4: #FAN-dst prd_face_mask_a_0 = FAN_dst_face_mask_a_0 elif cfg.mask_mode == 5: prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif cfg.mask_mode == 6: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif cfg.mask_mode == 7: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0 prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = prd_face_mask_a_0[..., np.newaxis] prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1) img_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC) img_face_mask_aaa = np.clip(img_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 #get rid of noise #if debug: # debugs += [img_face_mask_aaa.copy()] if 'raw' in cfg.mode: face_corner_pts = np.array( [[0, 0], [output_size - 1, 0], [output_size - 1, output_size - 1], [0, output_size - 1]], dtype=np.float32) square_mask = np.zeros(img_bgr.shape, dtype=np.float32) cv2.fillConvexPoly(square_mask, \ LandmarksProcessor.transform_points (face_corner_pts, face_output_mat, invert=True ).astype(np.int), \ (1,1,1) ) if cfg.mode == 'raw-rgb': out_merging_mask = square_mask if cfg.mode == 'raw-rgb' or cfg.mode == 'raw-rgb-mask': out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) if cfg.mode == 'raw-rgb-mask': out_img = np.concatenate( [out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1) out_merging_mask = square_mask elif cfg.mode == 'raw-mask-only': out_img = img_face_mask_aaa out_merging_mask = img_face_mask_aaa elif cfg.mode == 'raw-predicted-only': out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) out_merging_mask = square_mask out_img = np.clip(out_img, 0.0, 1.0) else: #averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask ar = [] for i in range(1, 10): maxregion = np.argwhere(img_face_mask_aaa > i / 10.0) if maxregion.size != 0: miny, minx = maxregion.min(axis=0)[:2] maxy, maxx = maxregion.max(axis=0)[:2] lenx = maxx - minx leny = maxy - miny if min(lenx, leny) >= 4: ar += [[lenx, leny]] if len(ar) > 0: lenx, leny = np.mean(ar, axis=0) lowest_len = min(lenx, leny) #if debug: # io.log_info ("lenx/leny:(%d/%d) " % (lenx, leny ) ) # io.log_info ("lowest_len = %f" % (lowest_len) ) if cfg.erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * cfg.erode_mask_modifier) #if debug: # io.log_info ("erode_size = %d" % (ero) ) if ero > 0: img_face_mask_aaa = cv2.erode(img_face_mask_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_mask_aaa = cv2.dilate(img_face_mask_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if cfg.clip_hborder_mask_per > 0: #clip hborder before blur prd_hborder_rect_mask_a = np.ones(prd_face_mask_a.shape, dtype=np.float32) prd_border_size = int(prd_hborder_rect_mask_a.shape[1] * cfg.clip_hborder_mask_per) prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0 prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0 prd_hborder_rect_mask_a[-prd_border_size:, :, :] = 0 prd_hborder_rect_mask_a = np.expand_dims( cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size)), -1) img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC) img_prd_hborder_rect_mask_a = np.expand_dims( img_prd_hborder_rect_mask_a, -1) img_face_mask_aaa *= img_prd_hborder_rect_mask_a img_face_mask_aaa = np.clip(img_face_mask_aaa, 0, 1.0) #if debug: # debugs += [img_face_mask_aaa.copy()] if cfg.blur_mask_modifier > 0: blur = int(lowest_len * 0.10 * 0.01 * cfg.blur_mask_modifier) #if debug: # io.log_info ("blur_size = %d" % (blur) ) if blur > 0: img_face_mask_aaa = cv2.blur(img_face_mask_aaa, (blur, blur)) img_face_mask_aaa = np.clip(img_face_mask_aaa, 0, 1.0) #if debug: # debugs += [img_face_mask_aaa.copy()] if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: #if debug: # debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = imagelib.reinhard_color_transfer( np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=prd_face_mask_a, target_mask=prd_face_mask_a) prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) #if debug: # debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] elif cfg.color_transfer_mode == 2: #if debug: # debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = imagelib.linear_color_transfer( prd_face_bgr, dst_face_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0) #if debug: # debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if cfg.mode == 'hist-match-bw': prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY) prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1), (3, ), -1) if cfg.mode == 'hist-match' or cfg.mode == 'hist-match-bw': #if debug: # debugs += [ cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) if cfg.masked_hist_match: hist_mask_a *= prd_face_mask_a white = (1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1 = prd_face_bgr * hist_mask_a + white hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + white hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = imagelib.color_hist_match( hist_match_1, hist_match_2, cfg.hist_match_threshold) #if cfg.masked_hist_match: # prd_face_bgr -= white if cfg.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) out_img = np.clip(out_img, 0.0, 1.0) #if debug: # debugs += [out_img.copy()] if cfg.mode == 'overlay': pass if 'seamless' in cfg.mode: #mask used for cv2.seamlessClone img_face_seamless_mask_a = None img_face_mask_a = img_face_mask_aaa[..., 0:1] for i in range(1, 10): a = img_face_mask_a > i / 10.0 if len(np.argwhere(a)) == 0: continue img_face_seamless_mask_a = img_face_mask_aaa[..., 0:1].copy() img_face_seamless_mask_a[a] = 1.0 img_face_seamless_mask_a[img_face_seamless_mask_a <= i / 10.0] = 0.0 break try: #calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering (not flickering) l, t, w, h = cv2.boundingRect( (img_face_seamless_mask_a * 255).astype(np.uint8)) s_maskx, s_masky = int(l + w / 2), int(t + h / 2) out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), img_bgr_uint8, (img_face_seamless_mask_a * 255).astype(np.uint8), (s_maskx, s_masky), cv2.NORMAL_CLONE) out_img = out_img.astype(dtype=np.float32) / 255.0 except Exception as e: #seamlessClone may fail in some cases e_str = traceback.format_exc() if 'MemoryError' in e_str: raise Exception( "Seamless fail: " + e_str ) #reraise MemoryError in order to reprocess this data by other processes else: print("Seamless fail: " + e_str) #if debug: # debugs += [out_img.copy()] out_img = img_bgr * (1 - img_face_mask_aaa) + (out_img * img_face_mask_aaa) out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size)) if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] face_mask_aaa = cv2.warpAffine(img_face_mask_aaa, face_mat, (output_size, output_size)) out_face_bgr = imagelib.reinhard_color_transfer( np.clip((out_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=face_mask_aaa, target_mask=face_mask_aaa) out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] elif cfg.color_transfer_mode == 2: #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] out_face_bgr = imagelib.linear_color_transfer( out_face_bgr, dst_face_bgr) out_face_bgr = np.clip(out_face_bgr, 0.0, 1.0) #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if cfg.mode == 'seamless-hist-match': out_face_bgr = imagelib.color_hist_match( out_face_bgr, dst_face_bgr, cfg.hist_match_threshold) cfg_mp = cfg.motion_blur_power / 100.0 if cfg_mp != 0: k_size = int(frame_info.motion_power * cfg_mp) if k_size >= 1: k_size = np.clip(k_size + 1, 2, 50) if cfg.super_resolution_mode: k_size *= 2 out_face_bgr = imagelib.LinearMotionBlur( out_face_bgr, k_size, frame_info.motion_deg) if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0: out_face_bgr = cfg.sharpen_func(out_face_bgr, cfg.sharpen_mode, 3, cfg.sharpen_amount) new_out = cv2.warpAffine(out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) out_img = np.clip( img_bgr * (1 - img_face_mask_aaa) + (new_out * img_face_mask_aaa), 0, 1.0) if cfg.color_degrade_power != 0: #if debug: # debugs += [out_img.copy()] out_img_reduced = imagelib.reduce_colors(out_img, 256) if cfg.color_degrade_power == 100: out_img = out_img_reduced else: alpha = cfg.color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if cfg.export_mask_alpha: out_img = np.concatenate( [out_img, img_face_mask_aaa[:, :, 0:1]], -1) out_merging_mask = img_face_mask_aaa #if debug: # debugs += [out_img.copy()] return out_img, out_merging_mask
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_FULL_NO_ALIGN: FaceType.FULL_NO_ALIGN } 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') def do_transform(img, 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) if len(mask.shape) == 2: mask = mask[..., np.newaxis] img = np.concatenate((img, mask), -1) return img 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) if sample.face_type == FaceType.MARK_ONLY: if mask is not None: img = np.concatenate((img, mask), -1) else: img = do_transform(img, mask) 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)) if sample.face_type == FaceType.MARK_ONLY: img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat( sample.landmarks, sample.shape[0], ft), (sample.shape[0], sample.shape[0]), flags=cv2.INTER_CUBIC) mask = img[..., 3:4] if img.shape[2] > 3 else None img = img[..., 0:3] img = do_transform(img, mask) img = cv2.resize(img, (resolution, resolution), cv2.INTER_CUBIC) else: 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 and ct_sample is not None: if ct_sample_bgr is None: ct_sample_bgr = ct_sample.load_bgr() ct_sample_bgr_resized = cv2.resize( ct_sample_bgr, (resolution, resolution), cv2.INTER_LINEAR) 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 = np.take(img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-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
def process(samples, sample_process_options, output_sample_types, debug, ct_sample=None): SPTF = SampleProcessor.Types sample_rnd_seed = np.random.randint(0x80000000) outputs = [] for sample in samples: 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, rnd_seed=sample_rnd_seed) outputs_sample = [] for opts in output_sample_types: resolution = opts.get('resolution', 0) types = opts.get('types', []) border_replicate = opts.get('border_replicate', True) 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) gaussian_blur = opts.get('gaussian_blur', None) ct_mode = opts.get('ct_mode', 'None') 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') def do_transform(img, 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, border_replicate) if mask is not None: mask = imagelib.warp_by_params( params, mask, warp, transform, flip, False) if len(mask.shape) == 2: mask = mask[..., np.newaxis] return img, mask img = sample_bgr ### Prepare a mask mask = None if is_face_sample: mask = sample.load_fanseg_mask( ) #using fanseg_mask if exist if mask is None: if sample.eyebrows_expand_mod is not None: mask = LandmarksProcessor.get_image_hull_mask( img.shape, sample.landmarks, eyebrows_expand_mod=sample. eyebrows_expand_mod) else: mask = LandmarksProcessor.get_image_hull_mask( img.shape, sample.landmarks) if sample.ie_polys is not None: sample.ie_polys.overlay_mask(mask) ################## if motion_blur is not None: chance, mb_max_size = motion_blur chance = np.clip(chance, 0, 100) if np.random.randint(100) < chance: img = imagelib.LinearMotionBlur( img, np.random.randint(mb_max_size) + 1, np.random.randint(360)) if gaussian_blur is not None: chance, kernel_max_size = gaussian_blur chance = np.clip(chance, 0, 100) if np.random.randint(100) < chance: img = cv2.GaussianBlur( img, (np.random.randint(kernel_max_size) * 2 + 1, ) * 2, 0) if is_face_sample and target_face_type != SPTF.NONE: target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[ target_face_type] if target_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, target_ft)) if sample.face_type == FaceType.MARK_ONLY: #first warp to target facetype img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat( sample.landmarks, sample.shape[0], target_ft), (sample.shape[0], sample.shape[0]), flags=cv2.INTER_CUBIC) mask = cv2.warpAffine( mask, LandmarksProcessor.get_transform_mat( sample.landmarks, sample.shape[0], target_ft), (sample.shape[0], sample.shape[0]), flags=cv2.INTER_CUBIC) #then apply transforms img, mask = do_transform(img, mask) img = np.concatenate((img, mask), -1) img = cv2.resize(img, (resolution, resolution), cv2.INTER_CUBIC) else: img, mask = do_transform(img, mask) mat = LandmarksProcessor.get_transform_mat( sample.landmarks, resolution, target_ft) img = cv2.warpAffine( img, mat, (resolution, resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC) mask = cv2.warpAffine( mask, mat, (resolution, resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_CUBIC) img = np.concatenate((img, mask[..., None]), -1) else: img, mask = do_transform(img, mask) img = np.concatenate((img, mask), -1) 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).astype(np.float32) img_bgr = img[..., 0:3] img_mask = img[..., 3:4] if ct_mode is not None and ct_sample is not None: if ct_sample_bgr is None: ct_sample_bgr = ct_sample.load_bgr() ct_sample_bgr_resized = cv2.resize( ct_sample_bgr, (resolution, resolution), cv2.INTER_LINEAR) if ct_mode == 'lct': img_bgr = imagelib.linear_color_transfer( img_bgr, ct_sample_bgr_resized) img_bgr = np.clip(img_bgr, 0.0, 1.0) elif ct_mode == 'rct': img_bgr = imagelib.reinhard_color_transfer( np.clip((img_bgr * 255).astype(np.uint8), 0, 255), np.clip((ct_sample_bgr_resized * 255).astype( np.uint8), 0, 255)) img_bgr = np.clip( img_bgr.astype(np.float32) / 255.0, 0.0, 1.0) elif ct_mode == 'mkl': img_bgr = imagelib.color_transfer_mkl( img_bgr, ct_sample_bgr_resized) elif ct_mode == 'idt': img_bgr = imagelib.color_transfer_idt( img_bgr, ct_sample_bgr_resized) elif ct_mode == 'sot': img_bgr = imagelib.color_transfer_sot( 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 = np.take(img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1) elif mode_type == SPTF.MODE_BGR_RANDOM_HSV_SHIFT: rnd_state = np.random.RandomState(sample_rnd_seed) hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv) h = (h + rnd_state.randint(360)) % 360 s = np.clip(s + rnd_state.random() - 0.5, 0, 1) v = np.clip(v + rnd_state.random() - 0.5, 0, 1) hsv = cv2.merge([h, s, v]) img = np.clip(cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR), 0, 1) elif mode_type == SPTF.MODE_G: img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)[..., None] elif mode_type == SPTF.MODE_GGG: img = np.repeat( np.expand_dims( cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY), -1), (3, ), -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_sample.append(img) outputs += [outputs_sample] return outputs
def process(sample, sample_process_options, output_sample_types, debug): SPTF = SampleProcessor.TypeFlags 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 = 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) 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 & SPTF.SOURCE != 0: img_type = 0 elif f & SPTF.WARPED != 0: img_type = 1 elif f & SPTF.WARPED_TRANSFORMED != 0: img_type = 2 elif f & SPTF.TRANSFORMED != 0: img_type = 3 elif f & SPTF.LANDMARKS_ARRAY != 0: img_type = 4 else: raise ValueError('expected SampleTypeFlags type') if f & SPTF.RANDOM_CLOSE != 0: img_type += 10 elif f & SPTF.MORPH_TO_RANDOM_CLOSE != 0: img_type += 20 face_mask_type = 0 if f & SPTF.FACE_MASK_FULL != 0: face_mask_type = 1 elif f & SPTF.FACE_MASK_EYES != 0: face_mask_type = 2 target_face_type = -1 if f & SPTF.FACE_TYPE_HALF != 0: target_face_type = FaceType.HALF elif f & SPTF.FACE_TYPE_FULL != 0: target_face_type = FaceType.FULL elif f & SPTF.FACE_TYPE_HEAD != 0: target_face_type = FaceType.HEAD elif f & SPTF.FACE_TYPE_AVATAR != 0: target_face_type = FaceType.AVATAR apply_motion_blur = f & SPTF.OPT_APPLY_MOTION_BLUR != 0 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 = imagelib.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 apply_motion_blur and sample_process_options.motion_blur is not None: chance, mb_range = sample_process_options.motion_blur if np.random.randint(100) < chance: dim = mb_range[np.random.randint( len(mb_range))] img = imagelib.LinearMotionBlur( img, dim, np.random.randint(180)) if face_mask_type == 1: 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) img = np.concatenate((img, mask), -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] = imagelib.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_CUBIC) else: img = cv2.resize(img, (size, size), cv2.INTER_CUBIC) 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 & SPTF.MODE_BGR != 0: img = img_bgr elif f & SPTF.MODE_BGR_SHUFFLE != 0: 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 f & SPTF.MODE_G != 0: img = np.concatenate((np.expand_dims( cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY), -1), img_mask), -1) elif f & SPTF.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 & SPTF.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: if sample_process_options.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
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) outputs = [] for opts in output_sample_types: resolution = opts.get('resolution', 0) types = opts.get('types', []) border_replicate = opts.get('border_replicate', True) 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', ColorTransferMode.NONE) 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') def do_transform(img, 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, border_replicate) if mask is not None: mask = imagelib.warp_by_params(params, mask, warp, transform, flip, False) if len(mask.shape) == 2: mask = mask[..., np.newaxis] img = np.concatenate((img, mask), -1) return img img = sample_bgr ### Prepare a mask mask = None if is_face_sample: mask = sample.load_fanseg_mask( ) #using fanseg_mask if exist if mask is None: if sample.eyebrows_expand_mod is not None: mask = LandmarksProcessor.get_image_hull_mask( img.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod) else: mask = LandmarksProcessor.get_image_hull_mask( img.shape, sample.landmarks) if sample.ie_polys is not None: sample.ie_polys.overlay_mask(mask) ################## if motion_blur is not None: chance, mb_max_size = motion_blur chance = np.clip(chance, 0, 100) if np.random.randint(100) < chance: img = imagelib.LinearMotionBlur( img, np.random.randint(mb_max_size) + 1, np.random.randint(360)) if is_face_sample and target_face_type != SPTF.NONE: target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[ target_face_type] if target_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, target_ft)) if sample.face_type == FaceType.MARK_ONLY: #first warp to target facetype img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat( sample.landmarks, sample.shape[0], target_ft), (sample.shape[0], sample.shape[0]), flags=cv2.INTER_CUBIC) mask = cv2.warpAffine( mask, LandmarksProcessor.get_transform_mat( sample.landmarks, sample.shape[0], target_ft), (sample.shape[0], sample.shape[0]), flags=cv2.INTER_CUBIC) #then apply transforms img = do_transform(img, mask) img = cv2.resize(img, (resolution, resolution), cv2.INTER_CUBIC) else: img = do_transform(img, mask) img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat( sample.landmarks, resolution, target_ft), (resolution, resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC) else: img = do_transform(img, mask) 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 and ct_sample is not None: if ct_sample_bgr is None: ct_sample_bgr = ct_sample.load_bgr() if apply_ct == ColorTransferMode.LCT: img_bgr = imagelib.linear_color_transfer( img_bgr, ct_sample_bgr) elif ColorTransferMode.RCT <= apply_ct <= ColorTransferMode.MASKED_RCT_PAPER_CLIP: ct_options = { ColorTransferMode.RCT: (False, False, False), ColorTransferMode.RCT_CLIP: (False, False, True), ColorTransferMode.RCT_PAPER: (False, True, False), ColorTransferMode.RCT_PAPER_CLIP: (False, True, True), ColorTransferMode.MASKED_RCT: (True, False, False), ColorTransferMode.MASKED_RCT_CLIP: (True, False, True), ColorTransferMode.MASKED_RCT_PAPER: (True, True, False), ColorTransferMode.MASKED_RCT_PAPER_CLIP: (True, True, True), } use_masks, use_paper, use_clip = ct_options[apply_ct] if not use_masks: img_bgr = imagelib.reinhard_color_transfer( img_bgr, ct_sample_bgr, clip=use_clip, preserve_paper=use_paper) else: if ct_sample_mask is None: ct_sample_mask = ct_sample.load_mask() img_bgr = imagelib.reinhard_color_transfer( img_bgr, ct_sample_bgr, clip=use_clip, preserve_paper=use_paper, source_mask=img_mask, target_mask=ct_sample_mask) 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 = np.take(img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1) elif mode_type == SPTF.MODE_LAB_RAND_TRANSFORM: rnd_state = np.random.RandomState(sample_rnd_seed) img = random_color_transform(img_bgr, rnd_state) 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