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https://github.com/iperov/DeepFaceLive
synced 2025-07-07 21:42:09 -07:00
import cupy on demand , because it eats almost 2Gb paging file in every subprocess
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iperov
parent
1a47d99278
commit
b546c735a1
3 changed files with 56 additions and 50 deletions
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@ -1,7 +1,6 @@
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import time
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from enum import IntEnum
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import cupy as cp
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import numexpr as ne
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import numpy as np
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from xlib import cupy as lib_cp
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@ -38,6 +37,8 @@ class FaceMergerWorker(BackendWorker):
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def get_control_sheet(self) -> 'Sheet.Worker': return super().get_control_sheet()
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def on_start(self, weak_heap : BackendWeakHeap, reemit_frame_signal : BackendSignal, bc_in : BackendConnection, bc_out : BackendConnection):
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self.weak_heap = weak_heap
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self.reemit_frame_signal = reemit_frame_signal
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self.bc_in = bc_in
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@ -95,6 +96,9 @@ class FaceMergerWorker(BackendWorker):
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if device != 'CPU':
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self.is_gpu = True
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global cp
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import cupy as cp # BUG eats 1.8Gb paging file per process, so import on demand
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cp.cuda.Device( device.get_index() ).use()
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self.cp_mask_clip_kernel = cp.ElementwiseKernel('T x', 'T z', 'z = x < 0.004 ? 0 : x > 1.0 ? 1.0 : x', 'mask_clip_kernel')
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@ -1,6 +1,6 @@
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from typing import List
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import cupy as cp
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class CuPyDeviceInfo:
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@ -55,6 +55,7 @@ def get_available_devices() -> List[CuPyDeviceInfo]:
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"""
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global _cupy_devices
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if _cupy_devices is None:
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import cupy as cp # BUG eats 1.8Gb paging file per process, so import on demand
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devices = []
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for i in range (cp.cuda.runtime.getDeviceCount()):
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@ -1,14 +1,9 @@
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from enum import IntEnum
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from typing import Tuple, Union
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import cupy as cp
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import cupyx.scipy.ndimage
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import cv2
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import numexpr as ne
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import numpy as np
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import scipy
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import scipy.ndimage
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class ImageProcessor:
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"""
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@ -24,13 +19,20 @@ class ImageProcessor:
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for cupy you should set device before using ImageProcessor
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"""
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def __init__(self, img : Union[np.ndarray,cp.ndarray], copy=False):
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self._xp = xp = cp.get_array_module(img)
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def __init__(self, img : Union[np.ndarray,'cp.ndarray'], copy=False):
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if copy and xp == np:
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if img.__class__ == np.ndarray:
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self._xp = np
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import scipy
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import scipy.ndimage
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self._sp = scipy
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if copy:
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img = img.copy()
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self._sp = cupyx.scipy if xp == cp else scipy
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else:
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import cupy as cp # BUG eats 1.8Gb paging file per process, so import on demand
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import cupyx.scipy.ndimage
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self._xp = cp
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self._sp = cupyx.scipy
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ndim = img.ndim
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if ndim not in [2,3,4]:
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@ -145,10 +147,10 @@ class ImageProcessor:
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if scale != 1.0:
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img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) )
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if self._xp == cp:
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img = sp.ndimage.zoom(img, (scale, scale, 1.0), order=1)
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else:
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if self._xp == np:
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img = cv2.resize (img, ( int(W*scale), int(H*scale) ), interpolation=ImageProcessor.Interpolation.LINEAR)
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else:
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img = sp.ndimage.zoom(img, (scale, scale, 1.0), order=1)
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H,W,_ = img.shape
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img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) )
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@ -191,16 +193,16 @@ class ImageProcessor:
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N,H,W,C = img.shape
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img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) )
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if xp == cp:
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W_lr = max(4, round(W*(1.0-power)))
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H_lr = max(4, round(H*(1.0-power)))
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img = sp.ndimage.zoom(img, (H_lr/H, W_lr/W, 1), order=_scipy_order[interpolation])
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img = sp.ndimage.zoom(img, (H/img.shape[0], W/img.shape[1], 1), order=_scipy_order[interpolation])
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else:
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if xp == np:
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W_lr = max(4, int(W*(1.0-power)))
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H_lr = max(4, int(H*(1.0-power)))
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img = cv2.resize (img, (W_lr,H_lr), interpolation=_cv_inter[interpolation])
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img = cv2.resize (img, (W,H) , interpolation=_cv_inter[interpolation])
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else:
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W_lr = max(4, round(W*(1.0-power)))
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H_lr = max(4, round(H*(1.0-power)))
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img = sp.ndimage.zoom(img, (H_lr/H, W_lr/W, 1), order=_scipy_order[interpolation])
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img = sp.ndimage.zoom(img, (H/img.shape[0], W/img.shape[1], 1), order=_scipy_order[interpolation])
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img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) )
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@ -226,12 +228,12 @@ class ImageProcessor:
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img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) )
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if xp == cp:
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img_blur = sp.ndimage.median_filter(img, size=(size,size,1) )
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img = img*(1.0-power) + img_blur*power
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else:
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if xp == np:
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img_blur = cv2.medianBlur(img, size)
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img = ne.evaluate('img*(1.0-power) + img_blur*power')
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else:
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img_blur = sp.ndimage.median_filter(img, size=(size,size,1) )
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img = img*(1.0-power) + img_blur*power
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img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) )
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self._img = img
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@ -262,18 +264,18 @@ class ImageProcessor:
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if erode > 0:
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el = xp.asarray(cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)))
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iterations = max(1,erode//2)
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if self._xp == cp:
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img = sp.ndimage.binary_erosion(img, el[...,None], iterations = iterations, brute_force=True ).astype(dtype)
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else:
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if self._xp == np:
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img = cv2.erode(img, el, iterations = iterations )
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else:
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img = sp.ndimage.binary_erosion(img, el[...,None], iterations = iterations, brute_force=True ).astype(dtype)
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elif erode < 0:
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el = xp.asarray(cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)))
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iterations = max(1,-erode//2)
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if self._xp == cp:
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img = sp.ndimage.binary_dilation(img, el[...,None], iterations = iterations, brute_force=True).astype(dtype)
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else:
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if self._xp == np:
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img = cv2.dilate(img, el, iterations = iterations )
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else:
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img = sp.ndimage.binary_dilation(img, el[...,None], iterations = iterations, brute_force=True).astype(dtype)
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if fade_to_border:
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h_clip_size = H + blur // 2
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@ -285,10 +287,10 @@ class ImageProcessor:
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if blur > 0:
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sigma = blur * 0.125 * 2
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if self._xp == cp:
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img = sp.ndimage.gaussian_filter(img, (sigma, sigma,0), mode='constant')
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else:
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if self._xp == np:
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img = cv2.GaussianBlur(img, (0, 0), sigma)
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else:
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img = sp.ndimage.gaussian_filter(img, (sigma, sigma,0), mode='constant')
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#if img.ndim == 2:
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# img = img[...,None]
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@ -370,11 +372,11 @@ class ImageProcessor:
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N,H,W,C = img.shape
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img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) )
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if xp == cp:
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raise
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else:
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if xp == np:
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blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
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img = cv2.addWeighted(img, 1.0 + (0.5 * factor), blur, -(0.5 * factor), 0)
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else:
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raise
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img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) )
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@ -478,10 +480,10 @@ class ImageProcessor:
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img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) )
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if self._xp == cp:
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img = sp.ndimage.zoom(img, (TW/W, TH/H, 1), order=_scipy_order[interpolation])
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else:
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if self._xp == np:
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img = cv2.resize (img, (TW, TH), interpolation=_cv_inter[interpolation])
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else:
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img = sp.ndimage.zoom(img, (TW/W, TH/H, 1), order=_scipy_order[interpolation])
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img = img.reshape( (TH,TW,N,C) ).transpose( (2,0,1,3) )
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@ -503,9 +505,11 @@ class ImageProcessor:
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if interpolation is None:
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interpolation = ImageProcessor.Interpolation.LINEAR
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if xp == cp:
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if xp == np:
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img = cv2.warpAffine(img, mat, (out_width, out_height), flags=_cv_inter[interpolation] )
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else:
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# AffineMat inverse
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xp_mat = cp.get_array_module(mat)
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xp_mat = xp.get_array_module(mat)
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mat = xp_mat.linalg.inv(xp_mat.concatenate( ( mat, xp_mat.array([[0,0,1]], xp_mat.float32)), 0) )[0:2,:]
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mx, my = xp.meshgrid( xp.arange(0, out_width, dtype=xp.float32), xp.arange(0, out_height, dtype=xp.float32) )
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@ -514,9 +518,6 @@ class ImageProcessor:
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mat_coords = xp.matmul (xp.asarray(mat), coords.reshape( (3,-1) ) ).reshape( (2,out_height,out_width))
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img = xp.concatenate([sp.ndimage.map_coordinates( img[...,c], mat_coords[::-1,...], order=_scipy_order[interpolation], mode='opencv' )[...,None] for c in range(N*C) ], -1)
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else:
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img = cv2.warpAffine(img, mat, (out_width, out_height), flags=_cv_inter[interpolation] )
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img = img.reshape( (out_height,out_width,N,C) ).transpose( (2,0,1,3) )
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self._img = img
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return self
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