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Do video cropping on the cropped node too

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kijai
2024-07-09 20:19:53 +03:00
parent 9c190804a7
commit 4e19dbd6d1
6 changed files with 101 additions and 108 deletions
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liveportrait/live_portrait_pipeline.py
+13 -19
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@@ -61,9 +61,9 @@ class LivePortraitPipeline(object):
):
inference_cfg = self.live_portrait_wrapper.cfg
I_p_lst = []
I_p_paste_lst = []
driving_lmk_lst = []
cropped_image_list = []
composited_image_list = []
driving_landmark_list = []
out_mask_list = []
R_d_0, x_d_0_info = None, None
@@ -72,7 +72,7 @@ class LivePortraitPipeline(object):
pbar = comfy.utils.ProgressBar(total_frames)
if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
driving_lmk_lst = self.cropper.get_retargeting_lmk_info(driving_images_np)
driving_landmark_list = self.cropper.get_retargeting_lmk_info(driving_images_np)
for i in tqdm(range(total_frames), desc='Animating...', total=total_frames):
source_frame_rgb = self._get_source_frame(
@@ -80,12 +80,8 @@ class LivePortraitPipeline(object):
)
driving_frame = driving_images_np[i]
crop_info = self.cropper.crop_single_image(source_frame_rgb, draw_keypoints=False)
source_lmk = crop_info["lmk_crop"]
_, img_crop_256x256 = (
crop_info["img_crop"],
crop_info["img_crop_256x256"],
)
source_lmk = crop_info[i]["lmk_crop"]
img_crop_256x256 = crop_info[i]["img_crop_256x256"]
if inference_cfg.flag_do_crop:
I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256)
@@ -126,10 +122,10 @@ class LivePortraitPipeline(object):
)[0]
if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
# driving_lmk_lst = self.cropper.get_retargeting_lmk_info([driving_frame])
# driving_landmark_list = self.cropper.get_retargeting_lmk_info([driving_frame])
input_eye_ratio_lst, input_lip_ratio_lst = (
self.live_portrait_wrapper.calc_retargeting_ratio(
source_lmk, driving_lmk_lst
source_lmk, driving_landmark_list
)
)
@@ -251,12 +247,12 @@ class LivePortraitPipeline(object):
out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
I_p_i = self.live_portrait_wrapper.parse_output(out["out"])[0]
I_p_lst.append(I_p_i)
cropped_image_list.append(I_p_i)
# Transform and blend
I_p_i_to_ori = _transform_img(
I_p_i,
crop_info["M_c2o"],
crop_info[i]["M_c2o"],
dsize=(source_frame_rgb.shape[1], source_frame_rgb.shape[0]),
)
@@ -265,18 +261,16 @@ class LivePortraitPipeline(object):
inference_cfg.mask_crop = cv2.imread(os.path.join(script_directory, "utils", "resources", "mask_template.png"), cv2.IMREAD_COLOR)
mask_ori = _transform_img(
inference_cfg.mask_crop,
crop_info["M_c2o"],
crop_info[i]["M_c2o"],
dsize=(source_frame_rgb.shape[1], source_frame_rgb.shape[0]),
)
mask_ori = mask_ori.astype(np.float32) / 255.0
I_p_i_to_ori_blend = np.clip(
mask_ori * I_p_i_to_ori + (1 - mask_ori) * source_frame_rgb, 0, 255
).astype(np.uint8)
else:
I_p_i_to_ori_blend = I_p_i_to_ori
I_p_paste_lst.append(I_p_i_to_ori_blend)
composited_image_list.append(I_p_i_to_ori_blend)
out_mask_list.append(mask_ori)
pbar.update(1)
return I_p_lst, I_p_paste_lst, out_mask_list
return cropped_image_list, composited_image_list, out_mask_list
liveportrait/utils/crop.py
+1 -1
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@@ -4,7 +4,7 @@
cropping function and the related preprocess functions for cropping
"""
import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) # NOTE: enforce single thread
import cv2#; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) # NOTE: enforce single thread
import numpy as np
from math import sin, cos, acos, degrees
liveportrait/utils/cropper.py
+12 -59
View File
@@ -3,11 +3,11 @@
import numpy as np
from typing import List, Union, Tuple
from dataclasses import dataclass, field
import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
import cv2#; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
from .landmark_runner import LandmarkRunner
from .face_analysis_diy import FaceAnalysisDIY
from .crop import crop_image, crop_image_by_bbox, parse_bbox_from_landmark, average_bbox_lst
from .crop import crop_image
import folder_paths
import os
@@ -15,8 +15,8 @@ script_directory = os.path.dirname(os.path.abspath(__file__))
@dataclass
class Trajectory:
start: int = -1 # 起始帧 闭区间
end: int = -1 # 结束帧 闭区间
start: int = -1
end: int = -1
lmk_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list
bbox_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # bbox list
frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame list
@@ -48,9 +48,11 @@ class Cropper(object):
if hasattr(self.crop_cfg, k):
setattr(self.crop_cfg, k, v)
def crop_single_image(self, img_rgb, draw_keypoints, **kwargs):
def crop_single_image(self, img_rgb, **kwargs):
direction = kwargs.get('direction', 'large-small')
src_face = self.face_analysis_wrapper.get(
img_rgb,
flag_do_landmark_2d_106=True,
@@ -62,10 +64,9 @@ class Cropper(object):
#elif len(src_face) > 1:
# print(f'More than one face detected in the image, only pick one face by rule {direction}.')
src_face = src_face[self.crop_cfg.face_index]
src_face = src_face[self.crop_cfg.face_index] # choose the index if multiple faces detected
pts = src_face.landmark_2d_106
# crop the face
ret_dct = crop_image(
img_rgb, # ndarray
@@ -78,67 +79,19 @@ class Cropper(object):
ret_dct['img_crop_256x256'] = cv2.resize(ret_dct['img_crop'], (256, 256), interpolation=cv2.INTER_AREA)
ret_dct['pt_crop_256x256'] = ret_dct['pt_crop'] * 256 / kwargs.get('dsize', 512)
input_image_size = img_rgb.shape[:2]
ret_dct['input_image_size'] = input_image_size
recon_ret = self.landmark_runner.run(img_rgb, pts)
lmk = recon_ret['pts']
ret_dct['lmk_crop'] = lmk
# Draw each landmark as a circle
if draw_keypoints:
print("Drawing keypoints...")
height, width = img_rgb.shape[:2]
blank_image = np.zeros((height, width, 3), dtype=np.uint8) * 255
for (x, y) in lmk:
# Ensure the coordinates are within the dimensions of the blank image
if 0 <= x < width and 0 <= y < height:
cv2.circle(blank_image, (int(x), int(y)), radius=2, color=(0, 0, 255))
keypoints_image = cv2.cvtColor(blank_image, cv2.COLOR_BGR2RGB)
return ret_dct, keypoints_image
else:
return ret_dct
def get_retargeting_lmk_info(self, driving_rgb_lst):
# TODO: implement a tracking-based version
driving_lmk_lst = []
for driving_image in driving_rgb_lst:
ret_dct = self.crop_single_image(driving_image, draw_keypoints=False)
ret_dct = self.crop_single_image(driving_image)
driving_lmk_lst.append(ret_dct['lmk_crop'])
return driving_lmk_lst
def make_video_clip(self, driving_rgb_lst, output_path, output_fps=30, **kwargs):
trajectory = Trajectory()
direction = kwargs.get('direction', 'large-small')
for idx, driving_image in enumerate(driving_rgb_lst):
if idx == 0 or trajectory.start == -1:
src_face = self.face_analysis_wrapper.get(
driving_image,
flag_do_landmark_2d_106=True,
direction=direction
)
if len(src_face) == 0:
# No face detected in the driving_image
continue
elif len(src_face) > 1:
print(f'More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.')
src_face = src_face[0]
pts = src_face.landmark_2d_106
lmk_203 = self.landmark_runner(driving_image, pts)['pts']
trajectory.start, trajectory.end = idx, idx
else:
lmk_203 = self.face_recon_wrapper(driving_image, trajectory.lmk_lst[-1])['pts']
trajectory.end = idx
trajectory.lmk_lst.append(lmk_203)
ret_bbox = parse_bbox_from_landmark(lmk_203, scale=self.crop_cfg.globalscale, vy_ratio=elf.crop_cfg.vy_ratio)['bbox']
bbox = [ret_bbox[0, 0], ret_bbox[0, 1], ret_bbox[2, 0], ret_bbox[2, 1]] # 4,
trajectory.bbox_lst.append(bbox) # bbox
trajectory.frame_rgb_lst.append(driving_image)
global_bbox = average_bbox_lst(trajectory.bbox_lst)
for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)):
ret_dct = crop_image_by_bbox(
frame_rgb, global_bbox, lmk=lmk,
dsize=self.video_crop_cfg.dsize, flag_rot=self.video_crop_cfg.flag_rot, borderValue=self.video_crop_cfg.borderValue
)
frame_rgb_crop = ret_dct['img_crop']
liveportrait/utils/face_analysis_diy.py
+17 -2
View File
@@ -1,15 +1,30 @@
# coding: utf-8
"""
face detectoin and alignment using InsightFace
face detection and alignment using InsightFace
"""
from insightface.utils import transform
#patch Insightface function to get rid of the annoying warnings
def patched_estimate_affine_matrix_3d23d(X, Y):
''' Using least-squares solution
Args:
X: [n, 3]. 3d points(fixed)
Y: [n, 3]. corresponding 3d points(moving). Y = PX
Returns:
P_Affine: (3, 4). Affine camera matrix (the third row is [0, 0, 0, 1]).
'''
X_homo = np.hstack((X, np.ones([X.shape[0],1]))) # n x 4
P = np.linalg.lstsq(X_homo, Y, rcond=None)[0].T # Affine matrix. 3 x 4
return P
transform.estimate_affine_matrix_3d23d = patched_estimate_affine_matrix_3d23d
import numpy as np
from insightface.app import FaceAnalysis
from insightface.app.common import Face
from .timer import Timer
def sort_by_direction(faces, direction: str = 'large-small', face_center=None):
if len(faces) <= 0:
return faces
liveportrait/utils/landmark_runner.py
+1 -1
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@@ -1,6 +1,6 @@
# coding: utf-8
import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
import cv2#; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
import torch
import numpy as np
import onnxruntime
nodes.py
+54 -23
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@@ -6,6 +6,7 @@ import comfy.model_management as mm
import comfy.utils
import numpy as np
import cv2
from tqdm import tqdm
script_directory = os.path.dirname(os.path.abspath(__file__))
@@ -224,7 +225,7 @@ class LivePortraitProcess:
return {"required": {
"pipeline": ("LIVEPORTRAITPIPE",),
"crop_info": ("CROPINFO", {"default": {}}),
"crop_info": ("CROPINFO", {"default": []}),
"source_image": ("IMAGE",),
"driving_images": ("IMAGE",),
"lip_zero": ("BOOLEAN", {"default": True}),
@@ -267,7 +268,7 @@ class LivePortraitProcess:
self,
source_image: torch.Tensor,
driving_images: torch.Tensor,
crop_info: dict,
crop_info: list,
pipeline: LivePortraitPipeline,
lip_zero: bool,
lip_zero_threshold: float,
@@ -305,13 +306,11 @@ class LivePortraitProcess:
crop_mask = np.repeat(np.atleast_3d(crop_mask), 3, axis=2)
pipeline.live_portrait_wrapper.cfg.mask_crop = crop_mask
pipeline.cropper = crop_info['cropper']
cropped_out_list = []
full_out_list = []
cropped_out_list, full_out_list, out_mask_list = pipeline.execute(
source_np, driving_images_np, crop_info['crop_info'], mismatch_method
source_np, driving_images_np, crop_info, mismatch_method
)
cropped_tensors_out = (
torch.stack([torch.from_numpy(np_array) for np_array in cropped_out_list])
@@ -343,20 +342,15 @@ class LivePortraitCropper:
"vx_ratio": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}),
"vy_ratio": ("FLOAT", {"default": -0.125, "min": -1.0, "max": 1.0, "step": 0.01}),
"face_index": ("INT", {"default": 0, "min": 0, "max": 100}),
},
"optional": {
"onnx_device": (
[
'CPU',
'CUDA',
], {
['CPU', 'CUDA', 'ROCM'], {
"default": 'CPU'
}),
}
},
}
RETURN_TYPES = ("IMAGE", "CROPINFO", "IMAGE",)
RETURN_NAMES = ("cropped_image", "crop_info", "keypoints_image",)
RETURN_TYPES = ("IMAGE", "CROPINFO",)
RETURN_NAMES = ("cropped_image", "crop_info",)
FUNCTION = "process"
CATEGORY = "LivePortrait"
@@ -372,21 +366,56 @@ class LivePortraitCropper:
)
cropper = Cropper(crop_cfg=crop_cfg, provider=onnx_device)
crop_info, keypoints_img = cropper.crop_single_image(source_image_np[0], draw_keypoints=True)
keypoints_image_tensor = torch.from_numpy(keypoints_img) / 255
keypoints_image_tensor = keypoints_image_tensor.unsqueeze(0).cpu().float()
crop_info_list = []
pbar = comfy.utils.ProgressBar(len(source_image_np))
for i in tqdm(range(len(source_image_np)), desc='Detecting and cropping..', total=len(source_image_np)):
crop_info = cropper.crop_single_image(source_image_np[i])
crop_info_list.append(crop_info)
pbar.update(1)
cropped_image = crop_info['img_crop_256x256']
cropped_tensors = torch.from_numpy(cropped_image) / 255
cropped_tensors = cropped_tensors.unsqueeze(0).cpu().float()
cropper_dict = {
"cropper": cropper,
"crop_info": crop_info,
return (cropped_tensors, crop_info_list)
class KeypointsToImage:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"crop_info": ("CROPINFO", {"default": []}),
},
}
return (cropped_tensors, cropper_dict, keypoints_image_tensor)
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("keypoints_image",)
FUNCTION = "drawkeypoints"
CATEGORY = "LivePortrait"
def drawkeypoints(self, crop_info):
height, width = crop_info[0]['input_image_size']
keypoints_img_list = []
pbar = comfy.utils.ProgressBar(len(crop_info))
for crop in crop_info:
keypoints = crop['lmk_crop'].copy()
# Draw each landmark as a circle
blank_image = np.zeros((height, width, 3), dtype=np.uint8) * 255
for (x, y) in keypoints:
# Ensure the coordinates are within the dimensions of the blank image
if 0 <= x < width and 0 <= y < height:
cv2.circle(blank_image, (int(x), int(y)), radius=2, color=(0, 0, 255))
keypoints_image = cv2.cvtColor(blank_image, cv2.COLOR_BGR2RGB)
keypoints_img_list.append(keypoints_image)
pbar.update(1)
keypoints_img_tensor = (
torch.stack([torch.from_numpy(np_array) for np_array in keypoints_img_list]) / 255).float()
return (keypoints_img_tensor,)
class KeypointScaler:
@classmethod
@@ -442,11 +471,13 @@ NODE_CLASS_MAPPINGS = {
"DownloadAndLoadLivePortraitModels": DownloadAndLoadLivePortraitModels,
"LivePortraitProcess": LivePortraitProcess,
"LivePortraitCropper": LivePortraitCropper,
"KeypointScaler": KeypointScaler
"KeypointScaler": KeypointScaler,
"KeypointsToImage": KeypointsToImage
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DownloadAndLoadLivePortraitModels": "(Down)Load LivePortraitModels",
"LivePortraitProcess": "LivePortraitProcess",
"LivePortraitCropper": "LivePortraitCropper",
"KeypointScaler": "KeypointScaler"
"KeypointScaler": "KeypointScaler",
"KeypointsToImage": "LivePortrait KeypointsToImage"
}