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update == 04.20.2019 == (#242)

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* superb improved fanseg

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* added FANseg extractor for src and dst faces to use it in training

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* update to 'partial' func

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* trained FANSeg_256_full_face.h5,
new experimental models: AVATAR, RecycleGAN

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* fix for TCC mode cards(tesla), was conflict with plaidML initialization.

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* update manuals

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iperov 2019-04-20 08:23:08 +04:00 • committed by GitHub
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46
converters/ConverterImage.py
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@ -1,40 +1,50 @@
from .Converter import Converter
from facelib import LandmarksProcessor
from facelib import FaceType
import time
import cv2
import numpy as np
'''
predictor_func:
input: [predictor_input_size, predictor_input_size, BGR]
output: [predictor_input_size, predictor_input_size, BGR]
'''
from facelib import FaceType, LandmarksProcessor
from joblib import SubprocessFunctionCaller
from utils.pickle_utils import AntiPickler
from .Converter import Converter
class ConverterImage(Converter):
#override
def __init__(self, predictor_func,
predictor_input_size=0,
output_size=0):
predictor_input_size=0):
super().__init__(predictor_func, Converter.TYPE_IMAGE)
self.predictor_input_size = predictor_input_size
self.output_size = output_size
#dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ) )
time.sleep(2)
#override
def dummy_predict(self):
self.predictor_func ( np.zeros ( (self.predictor_input_size, self.predictor_input_size,3), dtype=np.float32) )
predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
self.predictor_func_host = AntiPickler(predictor_func_host)
self.predictor_func = predictor_func
#overridable
def on_host_tick(self):
self.predictor_func_host.obj.process_messages()
#override
def convert_image (self, img_bgr, img_landmarks, debug):
def cli_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 )
if debug:
debugs = [predictor_input_bgr]
output = 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
if debug:
debugs += [out_img.copy()]
return debugs if debug else output