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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
refactoring
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Colombo
parent
9365e42f25
commit
54548afe1a
6 changed files with 110 additions and 25 deletions
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@ -2,11 +2,7 @@ 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_state=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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def gen_warp_params (w, flip, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None ):
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if rnd_state is None:
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rnd_state = np.random
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@ -308,6 +308,15 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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mat = cv2.getAffineTransform(l_t,pts2)
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return mat
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def get_rect_from_landmarks(image_landmarks):
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mat = get_transform_mat(image_landmarks, 256, FaceType.FULL_NO_ALIGN)
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g_p = transform_points ( np.float32([(0,0),(255,255) ]) , mat, True)
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(l,t,r,b) = g_p[0][0], g_p[0][1], g_p[1][0], g_p[1][1]
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return (l,t,r,b)
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def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
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if len(lmrks) != 68:
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raise Exception('works only with 68 landmarks')
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@ -649,7 +658,7 @@ def draw_landmarks (image, image_landmarks, color=(0,255,0), draw_circles=True,
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mask = get_image_hull_mask (image.shape, image_landmarks, ie_polys=ie_polys)
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image[...] = ( image * (1-mask) + image * mask / 2 )[...]
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def draw_rect_landmarks (image, rect, image_landmarks, face_size, face_type, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0)):
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def draw_rect_landmarks (image, rect, image_landmarks, face_type, face_size=256, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0)):
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draw_landmarks(image, image_landmarks, color=landmarks_color, transparent_mask=transparent_mask, ie_polys=ie_polys)
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imagelib.draw_rect (image, rect, (255,0,0), 2 )
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@ -248,7 +248,7 @@ class ExtractSubprocessor(Subprocessor):
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continue
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if output_debug_path is not None:
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LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, image_size, face_type, transparent_mask=True)
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LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, face_type, image_size, transparent_mask=True)
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output_path = final_output_path
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if data.force_output_path is not None:
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@ -601,7 +601,7 @@ class ExtractSubprocessor(Subprocessor):
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view_landmarks = LandmarksProcessor.transform_points (view_landmarks, mat)
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landmarks_color = (255,255,0) if self.rect_locked else (0,255,0)
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LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.image_size, self.face_type, landmarks_color=landmarks_color)
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LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.face_type, self.image_size, landmarks_color=landmarks_color)
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self.extract_needed = False
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io.show_image (self.wnd_name, image)
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@ -4,7 +4,7 @@ from pathlib import Path
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import cv2
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from DFLIMG import *
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from facelib import LandmarksProcessor
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from facelib import LandmarksProcessor, FaceType
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from core.imagelib import IEPolys
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from core.interact import interact as io
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from core import pathex
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@ -170,8 +170,16 @@ def add_landmarks_debug_images(input_path):
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if img is not None:
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face_landmarks = dflimg.get_landmarks()
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face_type = FaceType.fromString ( dflimg.get_face_type() )
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if face_type == FaceType.MARK_ONLY:
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rect = dflimg.get_source_rect()
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LandmarksProcessor.draw_rect_landmarks(img, rect, face_landmarks, FaceType.FULL )
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else:
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LandmarksProcessor.draw_landmarks(img, face_landmarks, transparent_mask=True, ie_polys=IEPolys.load(dflimg.get_ie_polys()) )
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output_file = '{}{}'.format( str(Path(str(input_path)) / filepath.stem), '_debug.jpg')
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cv2_imwrite(output_file, img, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
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@ -9,7 +9,7 @@ from DFLIMG import *
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from facelib import FaceType, LandmarksProcessor
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from core.interact import interact as io
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from core.joblib import Subprocessor
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from core import pathex
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from core import pathex, imagelib
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from core.cv2ex import *
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from . import Extractor, Sorter
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@ -392,6 +392,71 @@ def extract_fanseg(input_dir, device_args={} ):
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io.log_info ("Performing extract fanseg for %d files..." % (paths_to_extract_len) )
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data = ExtractSubprocessor ([ ExtractSubprocessor.Data(filename) for filename in paths_to_extract ], 'fanseg', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
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#unused in end user workflow
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def dev_test(input_dir ):
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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output_path = input_path.parent / (input_path.name+'_aligned')
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io.log_info(f'Output dir is % {output_path}')
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if output_path.exists():
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output_images_paths = pathex.get_image_paths(output_path)
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if len(output_images_paths) > 0:
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io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
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for filename in output_images_paths:
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Path(filename).unlink()
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else:
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output_path.mkdir(parents=True, exist_ok=True)
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images_paths = pathex.get_image_paths(input_path)
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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filepath = Path(filepath)
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pts_filepath = filepath.parent / (filepath.stem+'.pts')
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if pts_filepath.exists():
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pts = pts_filepath.read_text()
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pts_lines = pts.split('\n')
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lmrk_lines = None
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for pts_line in pts_lines:
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if pts_line == '{':
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lmrk_lines = []
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elif pts_line == '}':
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break
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else:
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if lmrk_lines is not None:
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lmrk_lines.append (pts_line)
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if lmrk_lines is not None and len(lmrk_lines) == 68:
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try:
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lmrks = [ np.array ( lmrk_line.strip().split(' ') ).astype(np.float32).tolist() for lmrk_line in lmrk_lines]
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except Exception as e:
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print(e)
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print(filepath)
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continue
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rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
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output_filepath = output_path / (filepath.stem+'.jpg')
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img = cv2_imread(filepath)
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img = imagelib.normalize_channels(img, 3)
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cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 95] )
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DFLJPG.embed_data(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
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landmarks=lmrks,
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source_filename=filepath.name,
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source_rect=rect,
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source_landmarks=lmrks
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)
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io.log_info("Done.")
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#unused in end user workflow
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def extract_umd_csv(input_file_csv,
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face_type='full_face',
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@ -464,7 +529,7 @@ def extract_umd_csv(input_file_csv,
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io.log_info ('Faces detected: %d' % (faces_detected) )
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io.log_info ('-------------------------')
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def dev_test(input_dir):
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def dev_test1(input_dir):
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input_path = Path(input_dir)
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dir_names = pathex.get_all_dir_names(input_path)
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@ -51,6 +51,7 @@ class SampleProcessor(object):
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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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ct_sample_bgr = None
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h,w,c = sample_bgr.shape
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@ -60,7 +61,12 @@ class SampleProcessor(object):
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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 = imagelib.gen_warp_params(sample_bgr, sample_process_options.random_flip, rotation_range=sample_process_options.rotation_range, scale_range=sample_process_options.scale_range, tx_range=sample_process_options.tx_range, ty_range=sample_process_options.ty_range )
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if sample_face_type == FaceType.MARK_ONLY:
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warp_resolution = np.max( [ opts.get('resolution', 0) for opts in output_sample_types ] )
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else:
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warp_resolution = w
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params = imagelib.gen_warp_params(warp_resolution, sample_process_options.random_flip, rotation_range=sample_process_options.rotation_range, scale_range=sample_process_options.scale_range, tx_range=sample_process_options.tx_range, ty_range=sample_process_options.ty_range )
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outputs_sample = []
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for opts in output_sample_types:
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@ -124,14 +130,15 @@ class SampleProcessor(object):
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if sample.ie_polys is not None:
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sample.ie_polys.overlay_mask(img)
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if sample.face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], face_type)
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img = cv2.warpAffine( img, mat, (sample.shape[0],sample.shape[0]), flags=cv2.INTER_LINEAR )
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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, img, warp, transform, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LINEAR)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_LINEAR )[...,None]
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else:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LINEAR)
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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=cv2.BORDER_CONSTANT, flags=cv2.INTER_LINEAR )[...,None]
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if channel_type == SPCT.G:
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@ -164,14 +171,14 @@ class SampleProcessor(object):
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if gblur_rnd_chance < chance:
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img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
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if sample.face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], face_type)
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img = cv2.warpAffine( img, mat, (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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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_CUBIC )
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=True)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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else:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=True)
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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=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
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img = np.clip(img.astype(np.float32), 0, 1)
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