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312 lines
No EOL
18 KiB
Python
312 lines
No EOL
18 KiB
Python
from models import ConverterBase
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from facelib import LandmarksProcessor
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from facelib import FaceType
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import cv2
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import numpy as np
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from utils import image_utils
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from utils.console_utils import *
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'''
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default_mode = {1:'overlay',
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2:'hist-match',
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3:'hist-match-bw',
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4:'seamless',
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5:'seamless-hist-match',
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6:'raw'}
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'''
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class ConverterMasked(ConverterBase):
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#override
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def __init__(self, predictor,
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predictor_input_size=0,
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output_size=0,
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face_type=FaceType.FULL,
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default_mode = 4,
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base_erode_mask_modifier = 0,
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base_blur_mask_modifier = 0,
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default_erode_mask_modifier = 0,
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default_blur_mask_modifier = 0,
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clip_hborder_mask_per = 0,
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**in_options):
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super().__init__(predictor)
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self.predictor_input_size = predictor_input_size
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self.output_size = output_size
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self.face_type = face_type
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self.clip_hborder_mask_per = clip_hborder_mask_per
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mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) seamless hist match, (6) raw. Default - %d : " % (default_mode) , default_mode)
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mode_dict = {1:'overlay',
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2:'hist-match',
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3:'hist-match-bw',
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4:'seamless',
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5:'seamless-hist-match',
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6:'raw'}
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self.mode = mode_dict.get (mode, mode_dict[default_mode] )
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if self.mode == 'raw':
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mode = input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ", 2)
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self.raw_mode = {1:'rgb',
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2:'rgb-mask',
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3:'mask-only',
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4:'predicted-only'}.get (mode, 'rgb-mask')
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if self.mode != 'raw':
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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self.masked_hist_match = input_bool("Masked hist match? (y/n skip:y) : ", True)
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
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self.hist_match_threshold = np.clip ( input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255)
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self.use_predicted_mask = input_bool("Use predicted mask? (y/n skip:y) : ", True)
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if self.mode != 'raw':
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self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( input_int ("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200)
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self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( input_int ("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200)
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self.seamless_erode_mask_modifier = 0
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if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
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self.seamless_erode_mask_modifier = np.clip ( input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100)
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self.output_face_scale = np.clip ( 1.0 + input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5)
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self.color_transfer_mode = input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
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if self.mode != 'raw':
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self.final_image_color_degrade_power = np.clip ( input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
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self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False)
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print ("")
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#override
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def get_mode(self):
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return ConverterBase.MODE_FACE
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#override
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def dummy_predict(self):
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self.predictor ( np.zeros ( (self.predictor_input_size,self.predictor_input_size,4), dtype=np.float32 ) )
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#override
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def convert_face (self, img_bgr, img_face_landmarks, debug):
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if debug:
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debugs = [img_bgr.copy()]
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.output_size, face_type=self.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale)
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4 )
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4 )
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predictor_input_bgr = cv2.resize (dst_face_bgr, (self.predictor_input_size,self.predictor_input_size))
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predictor_input_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size))
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predictor_input_mask_a = np.expand_dims (predictor_input_mask_a_0, -1)
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predicted_bgra = self.predictor ( np.concatenate( (predictor_input_bgr, predictor_input_mask_a), -1) )
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prd_face_bgr = np.clip (predicted_bgra[:,:,0:3], 0, 1.0 )
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prd_face_mask_a_0 = np.clip (predicted_bgra[:,:,3], 0.0, 1.0)
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if not self.use_predicted_mask:
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prd_face_mask_a_0 = predictor_input_mask_a_0
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prd_face_mask_a_0[ prd_face_mask_a_0 < 0.001 ] = 0.0
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prd_face_mask_a = np.expand_dims (prd_face_mask_a_0, axis=-1)
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prd_face_mask_aaa = np.repeat (prd_face_mask_a, (3,), axis=-1)
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img_prd_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_LANCZOS4 )
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img_prd_face_mask_aaa = np.clip (img_prd_face_mask_aaa, 0.0, 1.0)
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img_face_mask_aaa = img_prd_face_mask_aaa
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if debug:
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debugs += [img_face_mask_aaa.copy()]
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img_face_mask_aaa [ img_face_mask_aaa <= 0.1 ] = 0.0
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img_face_mask_flatten_aaa = img_face_mask_aaa.copy()
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img_face_mask_flatten_aaa[img_face_mask_flatten_aaa > 0.9] = 1.0
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maxregion = np.argwhere(img_face_mask_flatten_aaa==1.0)
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out_img = img_bgr.copy()
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if self.mode == 'raw':
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if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask':
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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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if self.raw_mode == 'rgb-mask':
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out_img = np.concatenate ( [out_img, np.expand_dims (img_face_mask_aaa[:,:,0],-1)], -1 )
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if self.raw_mode == 'mask-only':
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out_img = img_face_mask_aaa
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if self.raw_mode == 'predicted-only':
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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(out_img.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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else:
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if maxregion.size != 0:
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miny,minx = maxregion.min(axis=0)[:2]
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maxy,maxx = maxregion.max(axis=0)[:2]
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if debug:
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print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy ) )
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lenx = maxx - minx
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leny = maxy - miny
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if lenx >= 4 and leny >= 4:
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masky = int(minx+(lenx//2))
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maskx = int(miny+(leny//2))
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lowest_len = min (lenx, leny)
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if debug:
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print ("lowest_len = %f" % (lowest_len) )
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img_mask_blurry_aaa = img_face_mask_aaa
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if self.erode_mask_modifier != 0:
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
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if debug:
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print ("erode_size = %d" % (ero) )
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if ero > 0:
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img_mask_blurry_aaa = cv2.erode(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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img_mask_blurry_aaa = cv2.dilate(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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if self.seamless_erode_mask_modifier != 0:
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.seamless_erode_mask_modifier )
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if debug:
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print ("seamless_erode_size = %d" % (ero) )
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if ero > 0:
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img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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if self.clip_hborder_mask_per > 0: #clip hborder before blur
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prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=np.float32)
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prd_border_size = int ( prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per )
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prd_hborder_rect_mask_a[:,0:prd_border_size,:] = 0
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prd_hborder_rect_mask_a[:,-prd_border_size:,:] = 0
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prd_hborder_rect_mask_a = np.expand_dims(cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
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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_LANCZOS4 )
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img_prd_hborder_rect_mask_a = np.expand_dims (img_prd_hborder_rect_mask_a, -1)
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img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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if debug:
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debugs += [img_mask_blurry_aaa.copy()]
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if self.blur_mask_modifier > 0:
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blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
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if debug:
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print ("blur_size = %d" % (blur) )
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if blur > 0:
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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if debug:
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debugs += [img_mask_blurry_aaa.copy()]
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if self.color_transfer_mode is not None:
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if self.color_transfer_mode == 'rct':
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if debug:
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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_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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prd_face_bgr = image_utils.reinhard_color_transfer ( np.clip( (prd_face_bgr*255).astype(np.uint8), 0, 255),
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np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255) )
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prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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if debug:
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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_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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elif self.color_transfer_mode == 'lct':
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if debug:
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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_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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prd_face_bgr = image_utils.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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prd_face_bgr = np.clip( prd_face_bgr, 0.0, 1.0)
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if debug:
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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_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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if self.mode == 'hist-match-bw':
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prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY)
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prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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if debug:
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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_LANCZOS4, cv2.BORDER_TRANSPARENT ) ]
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hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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if self.masked_hist_match:
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hist_mask_a *= prd_face_mask_a
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hist_match_1 = prd_face_bgr*hist_mask_a + (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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hist_match_1[ hist_match_1 > 1.0 ] = 1.0
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hist_match_2 = dst_face_bgr*hist_mask_a + (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
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hist_match_2[ hist_match_1 > 1.0 ] = 1.0
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prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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if self.mode == 'hist-match-bw':
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prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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out_img = np.clip(out_img, 0.0, 1.0)
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if debug:
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debugs += [out_img.copy()]
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if self.mode == 'overlay':
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pass
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if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
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out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) , 0, 1.0 )
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if debug:
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debugs += [out_img.copy()]
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out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_mask_flatten_aaa*255).astype(np.uint8), (masky,maskx) , cv2.NORMAL_CLONE )
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out_img = out_img.astype(dtype=np.float32) / 255.0
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if debug:
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debugs += [out_img.copy()]
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
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if self.mode == 'seamless-hist-match':
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
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new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
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new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
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if self.final_image_color_degrade_power != 0:
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if debug:
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debugs += [out_img.copy()]
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out_img_reduced = image_utils.reduce_colors(out_img, 256)
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if self.final_image_color_degrade_power == 100:
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out_img = out_img_reduced
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else:
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alpha = self.final_image_color_degrade_power / 100.0
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out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
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if self.alpha:
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out_img = np.concatenate ( [out_img, np.expand_dims (img_mask_blurry_aaa[:,:,0],-1)], -1 )
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out_img = np.clip (out_img, 0.0, 1.0 )
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if debug:
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debugs += [out_img.copy()]
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return debugs if debug else out_img
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