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https://github.com/iperov/DeepFaceLab.git
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57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
import numpy as np
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import cv2
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from utils import random_utils
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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] + random_utils.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] + random_utils.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 |