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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
76ca79216e
Removed the wait at first launch for most graphics cards. Increased speed of training by 10-20%, but you have to retrain all models from scratch. SAEHD: added option 'use float16' Experimental option. Reduces the model size by half. Increases the speed of training. Decreases the accuracy of the model. The model may collapse or not train. Model may not learn the mask in large resolutions. true_face_training option is replaced by "True face power". 0.0000 .. 1.0 Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination. Comparison - https://i.imgur.com/czScS9q.png
57 lines
No EOL
2.3 KiB
Python
57 lines
No EOL
2.3 KiB
Python
import numpy as np
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import cv2
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from core import randomex
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def gen_warp_params (source, flip, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_seed=None ):
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h,w,c = source.shape
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if (h != w):
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raise ValueError ('gen_warp_params accepts only square images.')
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if rnd_seed != None:
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rnd_state = np.random.RandomState (rnd_seed)
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else:
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rnd_state = np.random
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rotation = rnd_state.uniform( rotation_range[0], rotation_range[1] )
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scale = rnd_state.uniform(1 +scale_range[0], 1 +scale_range[1])
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tx = rnd_state.uniform( tx_range[0], tx_range[1] )
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ty = rnd_state.uniform( ty_range[0], ty_range[1] )
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p_flip = flip and rnd_state.randint(10) < 4
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#random warp by grid
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cell_size = [ w // (2**i) for i in range(1,4) ] [ rnd_state.randint(3) ]
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cell_count = w // cell_size + 1
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grid_points = np.linspace( 0, w, cell_count)
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mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
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mapy = mapx.T
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mapx[1:-1,1:-1] = mapx[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
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mapy[1:-1,1:-1] = mapy[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
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half_cell_size = cell_size // 2
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mapx = cv2.resize(mapx, (w+cell_size,)*2 )[half_cell_size:-half_cell_size-1,half_cell_size:-half_cell_size-1].astype(np.float32)
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mapy = cv2.resize(mapy, (w+cell_size,)*2 )[half_cell_size:-half_cell_size-1,half_cell_size:-half_cell_size-1].astype(np.float32)
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#random transform
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random_transform_mat = cv2.getRotationMatrix2D((w // 2, w // 2), rotation, scale)
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random_transform_mat[:, 2] += (tx*w, ty*w)
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params = dict()
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params['mapx'] = mapx
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params['mapy'] = mapy
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params['rmat'] = random_transform_mat
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params['w'] = w
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params['flip'] = p_flip
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return params
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def warp_by_params (params, img, warp, transform, flip, is_border_replicate):
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if warp:
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img = cv2.remap(img, params['mapx'], params['mapy'], cv2.INTER_CUBIC )
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if transform:
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img = cv2.warpAffine( img, params['rmat'], (params['w'], params['w']), borderMode=(cv2.BORDER_REPLICATE if is_border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
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if flip and params['flip']:
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img = img[:,::-1,...]
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return img |