2.1.0 (#253)
* Operating system specific installer options * Update dependencies * Sorting before NMS according to the standard * Minor typing fix * Change the wording * Update preview.py (#222) Added a release listener to the preview frame slider, this will update the frame preview with the latest frame * Combine preview slider listener * Remove change listener * Introduce multi source (#223) * Implement multi source * Adjust face enhancer and face debugger to multi source * Implement multi source to UI * Implement multi source to UI part2 * Implement multi source to UI part3 * Implement multi source to UI part4 * Some cleanup * Add face occluder (#225) (#226) * Add face occluder (#225) * add face-occluder (commandline only) * review 1 * Update face_masker.py * Update face_masker.py * Add gui & fix typing * Minor naming cleanup * Minor naming cleanup part2 --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Update usage information * Fix averaged normed_embedding * Remove blur from face occluder, enable accelerators * Switch to RANSAC with 100 threshold * Update face_enhancer.py (#229) * Update face_debugger.py (#230) * Split utilities (#232) * Split utilities * Split utilities part2 * Split utilities part3 * Split utilities part4 * Some cleanup * Implement log level support (#233) * Implement log level support * Fix testing * Implement debug logger * Implement debug logger * Fix alignment offset (#235) * Update face_helper.py * fix 2 * Enforce virtual environment via installer * Enforce virtual environment via installer * Enforce virtual environment via installer * Enforce virtual environment via installer * Feat/multi process reference faces (#239) * Multi processing aware reference faces * First clean up and joining of files * Finalize the face store * Reduce similar face detection to one set, use __name__ for scopes in logger * Rename to face_occluder * Introduce ModelSet type * Improve webcam error handling * Prevent null pointer on is_image() and is_video() * Prevent null pointer on is_image() and is_video() * Fix find similar faces * Fix find similar faces * Fix process_images for face enhancer * Bunch of minor improvements * onnxruntime for ROCM under linux * Improve mask related naming * Fix falsy import * Fix typo * Feat/face parser refactoring (#247) * Face parser update (#244) * face-parser * Update face_masker.py * update debugger * Update globals.py * Update face_masker.py * Refactor code to split occlusion from region * fix (#246) * fix * fix debugger resolution * flip input to horizontal * Clean up UI * Reduce the regions to inside face only * Reduce the regions to inside face only --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Fix enhancer, remove useless dest in add_argument() * Prevent unselect of the face_mask_regions via UI * Prepare next release * Shorten arguments that have choices and nargs * Add missing clear to face debugger --------- Co-authored-by: Mathias <github@feroc.de> Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com>
This commit is contained in:
Henry Ruhs
@@ -1,14 +1,14 @@
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from typing import Any, Dict, Tuple, List
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from functools import lru_cache
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from cv2.typing import Size
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from functools import lru_cache
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import cv2
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import numpy
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from facefusion.typing import Bbox, Kps, Frame, Matrix, Template, Padding
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from facefusion.typing import Bbox, Kps, Frame, Mask, Matrix, Template
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TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
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{
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'arcface_v1': numpy.array(
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'arcface_112_v1': numpy.array(
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[
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[ 39.7300, 51.1380 ],
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[ 72.2700, 51.1380 ],
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@@ -16,7 +16,7 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
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[ 42.4630, 87.0100 ],
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[ 69.5370, 87.0100 ]
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]),
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'arcface_v2': numpy.array(
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'arcface_112_v2': numpy.array(
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[
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[ 38.2946, 51.6963 ],
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[ 73.5318, 51.5014 ],
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@@ -24,7 +24,15 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
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[ 41.5493, 92.3655 ],
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[ 70.7299, 92.2041 ]
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]),
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'ffhq': numpy.array(
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'arcface_128_v2': numpy.array(
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[
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[ 46.2946, 51.6963 ],
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[ 81.5318, 51.5014 ],
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[ 64.0252, 71.7366 ],
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[ 49.5493, 92.3655 ],
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[ 78.7299, 92.2041 ]
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]),
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'ffhq_512': numpy.array(
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[
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[ 192.98138, 239.94708 ],
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[ 318.90277, 240.1936 ],
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@@ -37,39 +45,23 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
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def warp_face(temp_frame : Frame, kps : Kps, template : Template, size : Size) -> Tuple[Frame, Matrix]:
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normed_template = TEMPLATES.get(template) * size[1] / size[0]
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affine_matrix = cv2.estimateAffinePartial2D(kps, normed_template, method = cv2.LMEDS)[0]
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affine_matrix = cv2.estimateAffinePartial2D(kps, normed_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
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crop_frame = cv2.warpAffine(temp_frame, affine_matrix, (size[1], size[1]), borderMode = cv2.BORDER_REPLICATE)
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return crop_frame, affine_matrix
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def paste_back(temp_frame : Frame, crop_frame: Frame, affine_matrix : Matrix, face_mask_blur : float, face_mask_padding : Padding) -> Frame:
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def paste_back(temp_frame : Frame, crop_frame: Frame, crop_mask : Mask, affine_matrix : Matrix) -> Frame:
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inverse_matrix = cv2.invertAffineTransform(affine_matrix)
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temp_frame_size = temp_frame.shape[:2][::-1]
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mask_size = tuple(crop_frame.shape[:2])
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mask_frame = create_static_mask_frame(mask_size, face_mask_blur, face_mask_padding)
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inverse_mask_frame = cv2.warpAffine(mask_frame, inverse_matrix, temp_frame_size).clip(0, 1)
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inverse_crop_mask = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size).clip(0, 1)
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inverse_crop_frame = cv2.warpAffine(crop_frame, inverse_matrix, temp_frame_size, borderMode = cv2.BORDER_REPLICATE)
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paste_frame = temp_frame.copy()
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paste_frame[:, :, 0] = inverse_mask_frame * inverse_crop_frame[:, :, 0] + (1 - inverse_mask_frame) * temp_frame[:, :, 0]
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paste_frame[:, :, 1] = inverse_mask_frame * inverse_crop_frame[:, :, 1] + (1 - inverse_mask_frame) * temp_frame[:, :, 1]
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paste_frame[:, :, 2] = inverse_mask_frame * inverse_crop_frame[:, :, 2] + (1 - inverse_mask_frame) * temp_frame[:, :, 2]
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paste_frame[:, :, 0] = inverse_crop_mask * inverse_crop_frame[:, :, 0] + (1 - inverse_crop_mask) * temp_frame[:, :, 0]
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paste_frame[:, :, 1] = inverse_crop_mask * inverse_crop_frame[:, :, 1] + (1 - inverse_crop_mask) * temp_frame[:, :, 1]
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paste_frame[:, :, 2] = inverse_crop_mask * inverse_crop_frame[:, :, 2] + (1 - inverse_crop_mask) * temp_frame[:, :, 2]
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return paste_frame
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@lru_cache(maxsize = None)
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def create_static_mask_frame(mask_size : Size, face_mask_blur : float, face_mask_padding : Padding) -> Frame:
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mask_frame = numpy.ones(mask_size, numpy.float32)
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blur_amount = int(mask_size[0] * 0.5 * face_mask_blur)
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blur_area = max(blur_amount // 2, 1)
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mask_frame[:max(blur_area, int(mask_size[1] * face_mask_padding[0] / 100)), :] = 0
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mask_frame[-max(blur_area, int(mask_size[1] * face_mask_padding[2] / 100)):, :] = 0
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mask_frame[:, :max(blur_area, int(mask_size[0] * face_mask_padding[3] / 100))] = 0
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mask_frame[:, -max(blur_area, int(mask_size[0] * face_mask_padding[1] / 100)):] = 0
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if blur_amount > 0:
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mask_frame = cv2.GaussianBlur(mask_frame, (0, 0), blur_amount * 0.25)
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return mask_frame
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@lru_cache(maxsize = None)
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def create_static_anchors(feature_stride : int, anchor_total : int, stride_height : int, stride_width : int) -> numpy.ndarray[Any, Any]:
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y, x = numpy.mgrid[:stride_height, :stride_width][::-1]
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