Spaces:
Running
Running
File size: 7,513 Bytes
07f408f 681fa96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION, run_script
import comfy.model_management as model_management
import numpy as np
import torch
from einops import rearrange
import os, sys
import subprocess, threading
import scipy.ndimage
import cv2
import torch.nn.functional as F
def install_deps():
try:
import mediapipe
except ImportError:
run_script([sys.executable, '-s', '-m', 'pip', 'install', 'mediapipe'])
run_script([sys.executable, '-s', '-m', 'pip', 'install', '--upgrade', 'protobuf'])
try:
import trimesh
except ImportError:
run_script([sys.executable, '-s', '-m', 'pip', 'install', 'trimesh[easy]'])
#Sauce: https://github.com/comfyanonymous/ComfyUI/blob/8c6493578b3dda233e9b9a953feeaf1e6ca434ad/comfy_extras/nodes_mask.py#L309
def expand_mask(mask, expand, tapered_corners):
c = 0 if tapered_corners else 1
kernel = np.array([[c, 1, c],
[1, 1, 1],
[c, 1, c]])
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = []
for m in mask:
output = m.numpy()
for _ in range(abs(expand)):
if expand < 0:
output = scipy.ndimage.grey_erosion(output, footprint=kernel)
else:
output = scipy.ndimage.grey_dilation(output, footprint=kernel)
output = torch.from_numpy(output)
out.append(output)
return torch.stack(out, dim=0)
class Mesh_Graphormer_Depth_Map_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return define_preprocessor_inputs(
mask_bbox_padding=("INT", {"default": 30, "min": 0, "max": 100}),
resolution=INPUT.RESOLUTION(),
mask_type=INPUT.COMBO(["based_on_depth", "tight_bboxes", "original"]),
mask_expand=INPUT.INT(default=5, min=-MAX_RESOLUTION, max=MAX_RESOLUTION),
rand_seed=INPUT.INT(default=88, min=0, max=0xffffffffffffffff),
detect_thr=INPUT.FLOAT(default=0.6, min=0.1),
presence_thr=INPUT.FLOAT(default=0.6, min=0.1)
)
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("IMAGE", "INPAINTING_MASK")
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
def execute(self, image, mask_bbox_padding=30, mask_type="based_on_depth", mask_expand=5, resolution=512, rand_seed=88, detect_thr=0.6, presence_thr=0.6, **kwargs):
install_deps()
from custom_controlnet_aux.mesh_graphormer import MeshGraphormerDetector
model = kwargs["model"] if "model" in kwargs \
else MeshGraphormerDetector.from_pretrained(detect_thr=detect_thr, presence_thr=presence_thr).to(model_management.get_torch_device())
depth_map_list = []
mask_list = []
for single_image in image:
np_image = np.asarray(single_image.cpu() * 255., dtype=np.uint8)
depth_map, mask, info = model(np_image, output_type="np", detect_resolution=resolution, mask_bbox_padding=mask_bbox_padding, seed=rand_seed)
if mask_type == "based_on_depth":
H, W = mask.shape[:2]
mask = cv2.resize(depth_map.copy(), (W, H))
mask[mask > 0] = 255
elif mask_type == "tight_bboxes":
mask = np.zeros_like(mask)
hand_bboxes = (info or {}).get("abs_boxes") or []
for hand_bbox in hand_bboxes:
x_min, x_max, y_min, y_max = hand_bbox
mask[y_min:y_max+1, x_min:x_max+1, :] = 255 #HWC
mask = mask[:, :, :1]
depth_map_list.append(torch.from_numpy(depth_map.astype(np.float32) / 255.0))
mask_list.append(torch.from_numpy(mask.astype(np.float32) / 255.0))
depth_maps, masks = torch.stack(depth_map_list, dim=0), rearrange(torch.stack(mask_list, dim=0), "n h w 1 -> n 1 h w")
return depth_maps, expand_mask(masks, mask_expand, tapered_corners=True)
def normalize_size_base_64(w, h):
short_side = min(w, h)
remainder = short_side % 64
return short_side - remainder + (64 if remainder > 0 else 0)
class Mesh_Graphormer_With_ImpactDetector_Depth_Map_Preprocessor:
@classmethod
def INPUT_TYPES(s):
types = define_preprocessor_inputs(
# Impact pack
bbox_threshold=INPUT.FLOAT(default=0.5, min=0.1),
bbox_dilation=INPUT.INT(default=10, min=-512, max=512),
bbox_crop_factor=INPUT.FLOAT(default=3.0, min=1.0, max=10.0),
drop_size=INPUT.INT(default=10, min=1, max=MAX_RESOLUTION),
# Mesh Graphormer
mask_bbox_padding=INPUT.INT(default=30, min=0, max=100),
mask_type=INPUT.COMBO(["based_on_depth", "tight_bboxes", "original"]),
mask_expand=INPUT.INT(default=5, min=-MAX_RESOLUTION, max=MAX_RESOLUTION),
rand_seed=INPUT.INT(default=88, min=0, max=0xffffffffffffffff),
resolution=INPUT.RESOLUTION()
)
types["required"]["bbox_detector"] = ("BBOX_DETECTOR", )
return types
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("IMAGE", "INPAINTING_MASK")
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
def execute(self, image, bbox_detector, bbox_threshold=0.5, bbox_dilation=10, bbox_crop_factor=3.0, drop_size=10, resolution=512, **mesh_graphormer_kwargs):
install_deps()
from custom_controlnet_aux.mesh_graphormer import MeshGraphormerDetector
mesh_graphormer_node = Mesh_Graphormer_Depth_Map_Preprocessor()
model = MeshGraphormerDetector.from_pretrained(detect_thr=0.6, presence_thr=0.6).to(model_management.get_torch_device())
mesh_graphormer_kwargs["model"] = model
frames = image
depth_maps, masks = [], []
for idx in range(len(frames)):
frame = frames[idx:idx+1,...] #Impact Pack's BBOX_DETECTOR only supports single batch image
bbox_detector.setAux('face') # make default prompt as 'face' if empty prompt for CLIPSeg
_, segs = bbox_detector.detect(frame, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size)
bbox_detector.setAux(None)
n, h, w, _ = frame.shape
depth_map, mask = torch.zeros_like(frame), torch.zeros(n, 1, h, w)
for i, seg in enumerate(segs):
x1, y1, x2, y2 = seg.crop_region
cropped_image = frame[:, y1:y2, x1:x2, :] # Never use seg.cropped_image to handle overlapping area
mesh_graphormer_kwargs["resolution"] = 0 #Disable resizing
sub_depth_map, sub_mask = mesh_graphormer_node.execute(cropped_image, **mesh_graphormer_kwargs)
depth_map[:, y1:y2, x1:x2, :] = sub_depth_map
mask[:, :, y1:y2, x1:x2] = sub_mask
depth_maps.append(depth_map)
masks.append(mask)
return (torch.cat(depth_maps), torch.cat(masks))
NODE_CLASS_MAPPINGS = {
"MeshGraphormer-DepthMapPreprocessor": Mesh_Graphormer_Depth_Map_Preprocessor,
"MeshGraphormer+ImpactDetector-DepthMapPreprocessor": Mesh_Graphormer_With_ImpactDetector_Depth_Map_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
"MeshGraphormer-DepthMapPreprocessor": "MeshGraphormer Hand Refiner",
"MeshGraphormer+ImpactDetector-DepthMapPreprocessor": "MeshGraphormer Hand Refiner With External Detector"
} |