mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 13:32:09 -07:00
438213e97c
sort by final : now you can specify target number of images, converter: fix seamless mask and exception, huge refactoring
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
from .Converter import Converter
|
|
from facelib import LandmarksProcessor
|
|
from facelib import FaceType
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from utils import image_utils
|
|
|
|
'''
|
|
predictor_func:
|
|
input: [predictor_input_size, predictor_input_size, BGR]
|
|
output: [predictor_input_size, predictor_input_size, BGR]
|
|
'''
|
|
|
|
class ConverterImage(Converter):
|
|
|
|
#override
|
|
def __init__(self, predictor_func,
|
|
predictor_input_size=0,
|
|
output_size=0):
|
|
|
|
super().__init__(predictor_func, Converter.TYPE_IMAGE)
|
|
|
|
self.predictor_input_size = predictor_input_size
|
|
self.output_size = output_size
|
|
|
|
#override
|
|
def dummy_predict(self):
|
|
self.predictor_func ( np.zeros ( (self.predictor_input_size, self.predictor_input_size,3), dtype=np.float32) )
|
|
|
|
#override
|
|
def convert_image (self, img_bgr, img_landmarks, debug):
|
|
img_size = img_bgr.shape[1], img_bgr.shape[0]
|
|
|
|
predictor_input_bgr = cv2.resize ( img_bgr, (self.predictor_input_size, self.predictor_input_size), cv2.INTER_LANCZOS4 )
|
|
predicted_bgr = self.predictor_func ( predictor_input_bgr )
|
|
|
|
output = cv2.resize ( predicted_bgr, (self.output_size, self.output_size), cv2.INTER_LANCZOS4 )
|
|
if debug:
|
|
return (predictor_input_bgr,output,)
|
|
return output
|