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fixed error "Failed to get convolution algorithm" on some systems

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fixed error "dll load failed" on some systems
Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
This commit is contained in:
iperov 2019-08-11 11:17:22 +04:00
parent 582c974851
commit b72d5a3f9a
15 changed files with 367 additions and 222 deletions
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21
mainscripts/Extractor.py
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@ -6,6 +6,7 @@ import multiprocessing
import shutil
from pathlib import Path
import numpy as np
import math
import mathlib
import imagelib
import cv2
@ -21,6 +22,8 @@ from nnlib import nnlib
from joblib import Subprocessor
from interact import interact as io
DEBUG = False
class ExtractSubprocessor(Subprocessor):
class Data(object):
def __init__(self, filename=None, rects=None, landmarks = None, landmarks_accurate=True, pitch_yaw_roll=None, final_output_files = None):
@ -44,6 +47,11 @@ class ExtractSubprocessor(Subprocessor):
self.cpu_only = client_dict['device_type'] == 'CPU'
self.final_output_path = Path(client_dict['final_output_dir']) if 'final_output_dir' in client_dict.keys() else None
self.debug_dir = client_dict['debug_dir']
#transfer and set stdin in order to work code.interact in debug subprocess
stdin_fd = client_dict['stdin_fd']
if stdin_fd is not None and DEBUG:
sys.stdin = os.fdopen(stdin_fd)
self.cached_image = (None, None)
@ -224,10 +232,12 @@ class ExtractSubprocessor(Subprocessor):
rect = np.array(rect)
if self.face_type == FaceType.MARK_ONLY:
image_to_face_mat = None
face_image = image
face_image_landmarks = image_landmarks
else:
image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)
face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
@ -239,8 +249,8 @@ class ExtractSubprocessor(Subprocessor):
if landmarks_area > 4*rect_area: #get rid of faces which umeyama-landmark-area > 4*detector-rect-area
continue
if self.debug_dir is not None:
LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True)
if self.debug_dir is not None:
LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True)
if src_dflimg is not None and filename_path.suffix == '.jpg':
#if extracting from dflimg and jpg copy it in order not to lose quality
@ -296,7 +306,7 @@ class ExtractSubprocessor(Subprocessor):
self.devices = ExtractSubprocessor.get_devices_for_config(self.manual, self.type, multi_gpu, cpu_only)
no_response_time_sec = 60 if not self.manual else 999999
no_response_time_sec = 60 if not self.manual and not DEBUG else 999999
super().__init__('Extractor', ExtractSubprocessor.Cli, no_response_time_sec)
#override
@ -342,7 +352,8 @@ class ExtractSubprocessor(Subprocessor):
'image_size': self.image_size,
'face_type': self.face_type,
'debug_dir': self.debug_dir,
'final_output_dir': str(self.final_output_path)}
'final_output_dir': str(self.final_output_path),
'stdin_fd': sys.stdin.fileno() }
for (device_idx, device_type, device_name, device_total_vram_gb) in self.devices:
@ -620,7 +631,7 @@ class ExtractSubprocessor(Subprocessor):
return [ (i, 'CPU', 'CPU%d' % (i), 0 ) for i in range( min(8, multiprocessing.cpu_count() // 2) ) ]
elif type == 'final':
return [ (i, 'CPU', 'CPU%d' % (i), 0 ) for i in range(min(8, multiprocessing.cpu_count())) ]
return [ (i, 'CPU', 'CPU%d' % (i), 0 ) for i in (range(min(8, multiprocessing.cpu_count())) if not DEBUG else [0]) ]
class DeletedFilesSearcherSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):