mirror of
https://github.com/iperov/DeepFaceLab.git
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db83a21244
Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher. New default values with new model: Archi : ‘liae-ud’ AdaBelief : enabled
340 lines
18 KiB
Python
340 lines
18 KiB
Python
import collections
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import math
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from enum import IntEnum
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import cv2
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import numpy as np
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from core import imagelib
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from core.cv2ex import *
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from core.imagelib import sd
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from facelib import FaceType, LandmarksProcessor
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class SampleProcessor(object):
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class SampleType(IntEnum):
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NONE = 0
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IMAGE = 1
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FACE_IMAGE = 2
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FACE_MASK = 3
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LANDMARKS_ARRAY = 4
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PITCH_YAW_ROLL = 5
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PITCH_YAW_ROLL_SIGMOID = 6
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class ChannelType(IntEnum):
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NONE = 0
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BGR = 1 #BGR
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G = 2 #Grayscale
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GGG = 3 #3xGrayscale
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class FaceMaskType(IntEnum):
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NONE = 0
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FULL_FACE = 1 # mask all hull as grayscale
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EYES = 2 # mask eyes hull as grayscale
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EYES_MOUTH = 3 # eyes and mouse
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class Options(object):
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def __init__(self, random_flip = True, rotation_range=[-10,10], scale_range=[-0.05, 0.05], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05] ):
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self.random_flip = random_flip
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self.rotation_range = rotation_range
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self.scale_range = scale_range
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self.tx_range = tx_range
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self.ty_range = ty_range
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@staticmethod
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def process (samples, sample_process_options, output_sample_types, debug, ct_sample=None):
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SPST = SampleProcessor.SampleType
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SPCT = SampleProcessor.ChannelType
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SPFMT = SampleProcessor.FaceMaskType
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sample_rnd_seed = np.random.randint(0x80000000)
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outputs = []
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for sample in samples:
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sample_face_type = sample.face_type
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sample_bgr = sample.load_bgr()
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sample_landmarks = sample.landmarks
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ct_sample_bgr = None
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h,w,c = sample_bgr.shape
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def get_full_face_mask():
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xseg_mask = sample.get_xseg_mask()
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if xseg_mask is not None:
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if xseg_mask.shape[0] != h or xseg_mask.shape[1] != w:
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xseg_mask = cv2.resize(xseg_mask, (w,h), interpolation=cv2.INTER_CUBIC)
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xseg_mask = imagelib.normalize_channels(xseg_mask, 1)
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return np.clip(xseg_mask, 0, 1)
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else:
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full_face_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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return np.clip(full_face_mask, 0, 1)
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def get_eyes_mask():
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eyes_mask = LandmarksProcessor.get_image_eye_mask (sample_bgr.shape, sample_landmarks)
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return np.clip(eyes_mask, 0, 1)
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def get_eyes_mouth_mask():
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eyes_mask = LandmarksProcessor.get_image_eye_mask (sample_bgr.shape, sample_landmarks)
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mouth_mask = LandmarksProcessor.get_image_mouth_mask (sample_bgr.shape, sample_landmarks)
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mask = eyes_mask + mouth_mask
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return np.clip(mask, 0, 1)
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is_face_sample = sample_landmarks is not None
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if debug and is_face_sample:
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LandmarksProcessor.draw_landmarks (sample_bgr, sample_landmarks, (0, 1, 0))
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params_per_resolution = {}
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warp_rnd_state = np.random.RandomState (sample_rnd_seed-1)
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for opts in output_sample_types:
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resolution = opts.get('resolution', None)
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if resolution is None:
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continue
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params_per_resolution[resolution] = imagelib.gen_warp_params(resolution,
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sample_process_options.random_flip,
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rotation_range=sample_process_options.rotation_range,
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scale_range=sample_process_options.scale_range,
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tx_range=sample_process_options.tx_range,
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ty_range=sample_process_options.ty_range,
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rnd_state=warp_rnd_state)
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outputs_sample = []
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for opts in output_sample_types:
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sample_type = opts.get('sample_type', SPST.NONE)
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channel_type = opts.get('channel_type', SPCT.NONE)
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resolution = opts.get('resolution', 0)
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nearest_resize_to = opts.get('nearest_resize_to', None)
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warp = opts.get('warp', False)
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transform = opts.get('transform', False)
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motion_blur = opts.get('motion_blur', None)
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gaussian_blur = opts.get('gaussian_blur', None)
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random_bilinear_resize = opts.get('random_bilinear_resize', None)
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random_rgb_levels = opts.get('random_rgb_levels', False)
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random_hsv_shift = opts.get('random_hsv_shift', False)
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random_circle_mask = opts.get('random_circle_mask', False)
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normalize_tanh = opts.get('normalize_tanh', False)
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ct_mode = opts.get('ct_mode', None)
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data_format = opts.get('data_format', 'NHWC')
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if sample_type == SPST.FACE_MASK or sample_type == SPST.IMAGE:
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border_replicate = False
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elif sample_type == SPST.FACE_IMAGE:
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border_replicate = True
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border_replicate = opts.get('border_replicate', border_replicate)
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borderMode = cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT
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if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK:
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if not is_face_sample:
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raise ValueError("face_samples should be provided for sample_type FACE_*")
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if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK:
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face_type = opts.get('face_type', None)
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face_mask_type = opts.get('face_mask_type', SPFMT.NONE)
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if face_type is None:
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raise ValueError("face_type must be defined for face samples")
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if sample_type == SPST.FACE_MASK:
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if face_mask_type == SPFMT.FULL_FACE:
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img = get_full_face_mask()
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elif face_mask_type == SPFMT.EYES:
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img = get_eyes_mask()
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elif face_mask_type == SPFMT.EYES_MOUTH:
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mask = get_full_face_mask().copy()
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mask[mask != 0.0] = 1.0
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img = get_eyes_mouth_mask()*mask
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else:
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img = np.zeros ( sample_bgr.shape[0:2]+(1,), dtype=np.float32)
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if sample_face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
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img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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img = cv2.resize( img, (resolution,resolution), interpolation=cv2.INTER_LINEAR )
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else:
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if face_type != sample_face_type:
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR )
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else:
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if w != resolution:
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img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_LINEAR )
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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if face_mask_type == SPFMT.EYES_MOUTH:
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div = img.max()
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if div != 0.0:
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img = img / div # normalize to 1.0 after warp
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if len(img.shape) == 2:
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img = img[...,None]
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if channel_type == SPCT.G:
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out_sample = img.astype(np.float32)
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else:
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raise ValueError("only channel_type.G supported for the mask")
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elif sample_type == SPST.FACE_IMAGE:
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img = sample_bgr
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if random_rgb_levels:
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random_mask = sd.random_circle_faded ([w,w], rnd_state=np.random.RandomState (sample_rnd_seed) ) if random_circle_mask else None
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img = imagelib.apply_random_rgb_levels(img, mask=random_mask, rnd_state=np.random.RandomState (sample_rnd_seed) )
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if random_hsv_shift:
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random_mask = sd.random_circle_faded ([w,w], rnd_state=np.random.RandomState (sample_rnd_seed+1) ) if random_circle_mask else None
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img = imagelib.apply_random_hsv_shift(img, mask=random_mask, rnd_state=np.random.RandomState (sample_rnd_seed+1) )
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if face_type != sample_face_type:
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
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else:
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if w != resolution:
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img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
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# Apply random color transfer
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if ct_mode is not None and ct_sample is not None:
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if ct_sample_bgr is None:
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ct_sample_bgr = ct_sample.load_bgr()
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img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), interpolation=cv2.INTER_LINEAR ) )
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate)
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img = np.clip(img.astype(np.float32), 0, 1)
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if motion_blur is not None:
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random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+2)) if random_circle_mask else None
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img = imagelib.apply_random_motion_blur(img, *motion_blur, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+2) )
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if gaussian_blur is not None:
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random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+3)) if random_circle_mask else None
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img = imagelib.apply_random_gaussian_blur(img, *gaussian_blur, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+3) )
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if random_bilinear_resize is not None:
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random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+4)) if random_circle_mask else None
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img = imagelib.apply_random_bilinear_resize(img, *random_bilinear_resize, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+4) )
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# Transform from BGR to desired channel_type
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if channel_type == SPCT.BGR:
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out_sample = img
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elif channel_type == SPCT.G:
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out_sample = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[...,None]
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elif channel_type == SPCT.GGG:
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out_sample = np.repeat ( np.expand_dims(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),-1), (3,), -1)
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# Final transformations
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if nearest_resize_to is not None:
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out_sample = cv2_resize(out_sample, (nearest_resize_to,nearest_resize_to), interpolation=cv2.INTER_NEAREST)
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if not debug:
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if normalize_tanh:
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out_sample = np.clip (out_sample * 2.0 - 1.0, -1.0, 1.0)
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if data_format == "NCHW":
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out_sample = np.transpose(out_sample, (2,0,1) )
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elif sample_type == SPST.IMAGE:
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img = sample_bgr
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=True)
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img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
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out_sample = img
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if data_format == "NCHW":
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out_sample = np.transpose(out_sample, (2,0,1) )
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elif sample_type == SPST.LANDMARKS_ARRAY:
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l = sample_landmarks
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l = np.concatenate ( [ np.expand_dims(l[:,0] / w,-1), np.expand_dims(l[:,1] / h,-1) ], -1 )
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l = np.clip(l, 0.0, 1.0)
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out_sample = l
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elif sample_type == SPST.PITCH_YAW_ROLL or sample_type == SPST.PITCH_YAW_ROLL_SIGMOID:
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pitch,yaw,roll = sample.get_pitch_yaw_roll()
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if params_per_resolution[resolution]['flip']:
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yaw = -yaw
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if sample_type == SPST.PITCH_YAW_ROLL_SIGMOID:
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pitch = np.clip( (pitch / math.pi) / 2.0 + 0.5, 0, 1)
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yaw = np.clip( (yaw / math.pi) / 2.0 + 0.5, 0, 1)
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roll = np.clip( (roll / math.pi) / 2.0 + 0.5, 0, 1)
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out_sample = (pitch, yaw)
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else:
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raise ValueError ('expected sample_type')
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outputs_sample.append ( out_sample )
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outputs += [outputs_sample]
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return outputs
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"""
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STRUCT = 4 #mask structure as grayscale
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elif face_mask_type == SPFMT.STRUCT:
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if sample.eyebrows_expand_mod is not None:
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample_landmarks)
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close_sample = sample.close_target_list[ np.random.randint(0, len(sample.close_target_list)) ] if sample.close_target_list is not None else None
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close_sample_bgr = close_sample.load_bgr() if close_sample is not None else None
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if debug and close_sample_bgr is not None:
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LandmarksProcessor.draw_landmarks (close_sample_bgr, close_sample.landmarks, (0, 1, 0))
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RANDOM_CLOSE = 0x00000040, #currently unused
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MORPH_TO_RANDOM_CLOSE = 0x00000080, #currently unused
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if f & SPTF.RANDOM_CLOSE != 0:
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img_type += 10
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elif f & SPTF.MORPH_TO_RANDOM_CLOSE != 0:
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img_type += 20
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if img_type >= 10 and img_type <= 19: #RANDOM_CLOSE
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img_type -= 10
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img = close_sample_bgr
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cur_sample = close_sample
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elif img_type >= 20 and img_type <= 29: #MORPH_TO_RANDOM_CLOSE
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img_type -= 20
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res = sample.shape[0]
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s_landmarks = sample.landmarks.copy()
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d_landmarks = close_sample.landmarks.copy()
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idxs = list(range(len(s_landmarks)))
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#remove landmarks near boundaries
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for i in idxs[:]:
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s_l = s_landmarks[i]
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d_l = d_landmarks[i]
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if s_l[0] < 5 or s_l[1] < 5 or s_l[0] >= res-5 or s_l[1] >= res-5 or \
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d_l[0] < 5 or d_l[1] < 5 or d_l[0] >= res-5 or d_l[1] >= res-5:
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idxs.remove(i)
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#remove landmarks that close to each other in 5 dist
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for landmarks in [s_landmarks, d_landmarks]:
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for i in idxs[:]:
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s_l = landmarks[i]
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for j in idxs[:]:
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if i == j:
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continue
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s_l_2 = landmarks[j]
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diff_l = np.abs(s_l - s_l_2)
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if np.sqrt(diff_l.dot(diff_l)) < 5:
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idxs.remove(i)
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break
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s_landmarks = s_landmarks[idxs]
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d_landmarks = d_landmarks[idxs]
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s_landmarks = np.concatenate ( [s_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] )
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d_landmarks = np.concatenate ( [d_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] )
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img = imagelib.morph_by_points (sample_bgr, s_landmarks, d_landmarks)
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cur_sample = close_sample
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else:
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"""
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