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merge into: nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-5
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nodes/TRI3D-LC-tri3d-comfyui-nodes:main nodes/TRI3D-LC-tri3d-comfyui-nodes:saveflattened nodes/TRI3D-LC-tri3d-comfyui-nodes:levind_abhi nodes/TRI3D-LC-tri3d-comfyui-nodes:idmvton nodes/TRI3D-LC-tri3d-comfyui-nodes:cloth_seg_debug nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-5 nodes/TRI3D-LC-tri3d-comfyui-nodes:face-pose nodes/TRI3D-LC-tri3d-comfyui-nodes:ram-changes2 nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-2 nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-3
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pull from: nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-3
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nodes/TRI3D-LC-tri3d-comfyui-nodes:main nodes/TRI3D-LC-tri3d-comfyui-nodes:saveflattened nodes/TRI3D-LC-tri3d-comfyui-nodes:levind_abhi nodes/TRI3D-LC-tri3d-comfyui-nodes:idmvton nodes/TRI3D-LC-tri3d-comfyui-nodes:cloth_seg_debug nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-5 nodes/TRI3D-LC-tri3d-comfyui-nodes:face-pose nodes/TRI3D-LC-tri3d-comfyui-nodes:ram-changes2 nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-2 nodes/TRI3D-LC-tri3d-comfyui-nodes:aravind-3
8 Commits
aravind-5 .. aravind-3
Author SHA1 Message Date
aravind 369fd22121 Added recolor node in RGB space 2024-01-13 14:53:31 +05:30
aravind 8942c94604 Added recolor in LAB color space 2024-01-12 14:38:59 +05:30
aravind 50d4816fd7 Added recoloring in LAB and HSV space 2024-01-12 14:23:13 +05:30
aravind 266446b8d6 Trying to fix the destructive color changes 2024-01-12 13:24:24 +05:30
aravind d8273743df Trying to fix the destructive color changes 2024-01-12 12:26:23 +05:30
aravind 475ab259b1 Got recoloring node working 2024-01-10 16:28:29 +05:30
aravind 740c063011 Started work on recoloring node 2024-01-10 16:18:21 +05:30
aravind 8bec52e912 Started work on recoloring node 2024-01-10 16:15:15 +05:30
12 changed files with 146 additions and 4980 deletions
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.env_sample
-1
View File
@@ -1 +0,0 @@
CLIPDROP_API_KEY=
.gitignore
-1
View File
@@ -5,6 +5,5 @@ venv
.DS_Store
checkpoints/
checkpoint/
.env
dwpose/keypoints/
__init__.py
+144 -577
View File
@@ -1,5 +1,5 @@
# v1.1.0
import cv2, json, os, math, pathlib, requests, io
import cv2, json, os, math
import numpy as np
import torch
import hashlib
@@ -8,51 +8,6 @@ import folder_paths
from PIL import Image, ImageOps
def from_torch_image(image):
image = image.squeeze().cpu().numpy() * 255.0
image = np.clip(image, 0, 255).astype(np.uint8)
return image
def to_torch_image(image):
image = image.astype(dtype=np.float32)
image /= 255.0
image = torch.from_numpy(image)[
None,
]
image = image.unsqueeze(0)
return image
def get_bounding_box(mask_input):
rows = np.any(mask_input, axis=1)
cols = np.any(mask_input, axis=0)
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
return rmin, cmin, rmax, cmax
def extract_box_from_image(image, mask):
y_min, x_min, y_max, x_max = get_bounding_box(mask_input=mask)
return image[y_min:y_max + 1, x_min:x_max + 1, :]
def stitch_back_box_to_image(image_original, mask_original, image_patch):
y_min, x_min, y_max, x_max = get_bounding_box(mask_input=mask_original)
image_original[y_min:y_max + 1, x_min:x_max + 1] = image_patch
return image_original
def do_work(image, external_file_name):
exec(open(external_file_name, 'r').read())
return image
def do_work_with_mask(image, mask, external_file_name):
exec(open(external_file_name, 'r').read())
return image
class TRI3DATRParseBatch:
def __init__(self):
@@ -1063,7 +1018,7 @@ class TRI3DDWPose_Preprocessor:
CATEGORY = "TRI3D"
def estimate_pose(self, images, detect_hand, detect_body, detect_face,
filename_path):
**kwargs):
from .dwpose import DwposeDetector
detect_hand = detect_hand == "enable"
@@ -1089,7 +1044,8 @@ class TRI3DDWPose_Preprocessor:
include_face=detect_face,
include_body=detect_body)
cur_file_dir = os.path.dirname(os.path.realpath(__file__))
save_file_path = os.path.join(cur_file_dir, filename_path)
save_file_path = os.path.join(cur_file_dir,
kwargs['filename_path'])
json.dump(pose_dict, open(save_file_path, 'w'))
np_result = cv2.resize(np_result, (W, H),
interpolation=cv2.INTER_AREA)
@@ -1148,19 +1104,21 @@ class TRI3DPoseAdaption:
def INPUT_TYPES(s):
return {
"required": {
"input_pose_json_file": ("STRING",{"default" : "dwpose/keypoints/input.json"}),
"ref_pose_json_file": ("STRING",{"default" : "dwpose/keypoints/ref-pose.json"}),
"image_angle": (["front", "back","back_fixed_kid","back_fixed","back_fixed_left","back_fixed_right"], {"default": "front"}),
"input_pose_json_file": ("STRING", {
"default": "dwpose/keypoints"
}),
"ref_pose_json_file": ("STRING", {
"default": "dwpose/keypoints"
}),
"image_angle": (["front", "side", "back"], {
"default": "front"
}),
"rotation_threshold": ("FLOAT", {
"default": 5.0,
"min": 0.0,
"max": 15.0,
"step": 0.01
}),
"garment_category":(["no_sleeve_garment", "half_sleeve_garment", "full_sleeve_garment", \
"shorts", "trouser"], {"default": "no_sleeve_garment"})
})
}
}
@@ -1169,10 +1127,7 @@ class TRI3DPoseAdaption:
CATEGORY = "TRI3D"
def main(self, input_pose_json_file, ref_pose_json_file, image_angle,
rotation_threshold, garment_category):
print(image_angle, "image_angle")
print(garment_category, "garment_category")
rotation_threshold):
from .dwpose import comfy_utils
if image_angle == "front":
@@ -1188,30 +1143,26 @@ class TRI3DPoseAdaption:
ref_height = ref_pose['height']
ref_width = ref_pose['width']
ref_keypoints = ref_pose['keypoints']
similar_torso = None
#check torso similarity
if garment_category not in ["shorts", "trouser"]:
ls_angle_1, rs_angle_1, torso_angle_1 = comfy_utils.get_torso_angles(
input_keypoints)
ls_angle_2, rs_angle_2, torso_angle_2 = comfy_utils.get_torso_angles(
ref_keypoints)
ls_angle_1, rs_angle_1, torso_angle_1 = comfy_utils.get_torso_angles(
input_keypoints)
ls_angle_2, rs_angle_2, torso_angle_2 = comfy_utils.get_torso_angles(
ref_keypoints)
ls_angle_diff = abs(ls_angle_2 - ls_angle_1)
rs_angle_diff = abs(rs_angle_2 - rs_angle_1)
torso_angle_diff = abs(torso_angle_2 - torso_angle_1)
ls_angle_diff = abs(ls_angle_2 - ls_angle_1)
rs_angle_diff = abs(rs_angle_2 - rs_angle_1)
torso_angle_diff = abs(torso_angle_2 - torso_angle_1)
similar_torso = False if (
ls_angle_diff >= rotation_threshold) | (
rs_angle_diff >= rotation_threshold) | (
torso_angle_diff >= rotation_threshold) else True
similar_torso = False if (ls_angle_diff >= rotation_threshold) | (
rs_angle_diff >= rotation_threshold) | (
torso_angle_diff >= rotation_threshold) else True
if similar_torso == False:
canvas = torch.from_numpy(
canvas.astype(np.float32) / 255.0)[
None,
]
return (canvas, similar_torso)
if similar_torso == False:
canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
None,
]
return (canvas, similar_torso)
#Hands
if input_keypoints[4] == [-1, -1]:
@@ -1222,21 +1173,17 @@ class TRI3DPoseAdaption:
input_keypoints[88:] = ref_keypoints[
88:] #replace hands with reference hands
if garment_category not in [
"half_sleeve_garment", "full_sleeve_garment"
]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 2,
3) # rotate left elbow
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 2,
3) # rotate left elbow
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 2, 5, 2,
ref_keypoints, input_keypoints, 1, 2, 2,
3) #scaling w.r.t to shoulder to elbow ratio of ref pose
prev_lw = input_keypoints[4]
if garment_category != "full_sleeve_garment":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 3,
4) #rotate left wrist
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 3,
4) #rotate left wrist
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 2, 3, 3,
4) #scaling w.r.t to elbow to wrist ratio of ref pose
@@ -1254,21 +1201,17 @@ class TRI3DPoseAdaption:
ref_keypoints, input_keypoints, 3, 4,
109) #scaling left hand w.r.t left wrist
if garment_category not in [
"half_sleeve_garment", "full_sleeve_garment"
]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 5,
6) #rotate right elbow
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 5,
6) #rotate right elbow
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 2, 5, 5,
ref_keypoints, input_keypoints, 1, 5, 5,
6) #scaling w.r.t to shoulder to elbow ratio of ref pose
prev_rw = input_keypoints[7]
if garment_category != "full_sleeve_garment":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 6,
7) #rotate right wrist
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 6,
7) #rotate right wrist
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 5, 6, 6,
7) #scaling w.r.t to elbow to wrist ratio of ref pose
@@ -1287,33 +1230,26 @@ class TRI3DPoseAdaption:
88) #scaling right hand w.r.t right wrist
#legs
if garment_category not in ["trouser", "shorts"]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 8,
9) #rotate left knee
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 8,
9) #rotate left knee
input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
1, 8, 8, 9) #scale left knee
if garment_category != "trouser":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 9,
10) #rotate left foot
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 9,
10) #rotate left foot
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 8, 9, 9,
10) #scaling w.r.t to knee to foot ratio of ref pose
if garment_category not in ["trouser", "shorts"]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 11,
12) #rotate right knee
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 11,
12) #rotate right knee
input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
1, 11, 11,
12) #scale right knee
if garment_category != "trouser":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 12,
13) #rotate right foot
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 12,
13) #rotate right foot
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 11, 12, 12,
13) #scaling w.r.t to knee to foot ratio of ref pose
@@ -1370,7 +1306,7 @@ class TRI3DPoseAdaption:
]
return (canvas, similar_torso)
if 'back' in image_angle:
if image_angle == "back":
input_pose = json.load(open(input_pose_json_file))
input_height = input_pose['height']
@@ -1381,47 +1317,28 @@ class TRI3DPoseAdaption:
canvas = np.zeros(shape=(input_height, input_width, 3),
dtype=np.uint8)
if 'back_fixed' in image_angle:
back_pose_dir = pathlib.Path().resolve(
) / 'custom_nodes/tri3d-comfyui-nodes/samples/back_poses/'
back_pose_dictionary = {
'back_fixed_kid': 'backpose_kid.json',
'back_fixed': 'backpose.json',
'back_fixed_left': 'left_backpose.json',
'back_fixed_right': 'right_backpose.json'
}
ref_pose_json_file = back_pose_dir / back_pose_dictionary[
image_angle]
# /home/ubuntu/GITHUB/comfyanonymous/ComfyUI/custom_nodes/tri3d-comfyui-nodes
print(ref_pose_json_file)
ref_pose = json.load(open(ref_pose_json_file))
ref_keypoints = ref_pose['keypoints']
similar_torso = None
#check torso similarity
if garment_category not in ["shorts", "trouser"]:
ls_angle_1, rs_angle_1, torso_angle_1 = comfy_utils.get_torso_angles(
input_keypoints)
ls_angle_2, rs_angle_2, torso_angle_2 = comfy_utils.get_torso_angles(
ref_keypoints)
ls_angle_1, rs_angle_1, torso_angle_1 = comfy_utils.get_torso_angles(
input_keypoints)
ls_angle_2, rs_angle_2, torso_angle_2 = comfy_utils.get_torso_angles(
ref_keypoints)
ls_angle_diff = abs(ls_angle_2 - ls_angle_1)
rs_angle_diff = abs(rs_angle_2 - rs_angle_1)
torso_angle_diff = abs(torso_angle_2 - torso_angle_1)
ls_angle_diff = abs(ls_angle_2 - ls_angle_1)
rs_angle_diff = abs(rs_angle_2 - rs_angle_1)
torso_angle_diff = abs(torso_angle_2 - torso_angle_1)
similar_torso = False if (ls_angle_diff >= 5) | (
rs_angle_diff >= 5) | (torso_angle_diff >= 5) else True
similar_torso = False if (ls_angle_diff >= 5) | (
rs_angle_diff >= 5) | (torso_angle_diff >= 5) else True
if similar_torso == False:
canvas = torch.from_numpy(
canvas.astype(np.float32) / 255.0)[
None,
]
return (canvas, similar_torso)
if similar_torso == False:
canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
None,
]
return (canvas, similar_torso)
#Removing the face points if existed
null_indices = [
@@ -1431,16 +1348,12 @@ class TRI3DPoseAdaption:
for i in null_indices:
input_keypoints[i] = [-1, -1]
all_x = [i[0] for i in input_keypoints if i[0] != -1]
min_width = min(all_x)
max_width = max(all_x)
if input_pose_type == "front_pose":
#flip horizontally
for i in range(len(input_keypoints)):
x, y = input_keypoints[i]
if input_keypoints[i] == [-1, -1]: continue
input_keypoints[i] = [(max_width - x) + min_width, y]
input_keypoints[i] = [input_width - x, y]
#Hands
if input_keypoints[4] == [-1, -1]:
@@ -1450,21 +1363,17 @@ class TRI3DPoseAdaption:
input_keypoints[88:] = ref_keypoints[
88:] #replace hands with reference hands
if garment_category not in [
"half_sleeve_garment", "full_sleeve_garment"
]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 2,
3) # rotate left elbow
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 2,
3) # rotate left elbow
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 2, 5, 2,
ref_keypoints, input_keypoints, 1, 2, 2,
3) #scaling w.r.t to shoulder to elbow ratio of ref pose
prev_lw = input_keypoints[4]
if garment_category != "full_sleeve_garment":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 3,
4) #rotate left wrist
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 3,
4) #rotate left wrist
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 2, 3, 3,
4) #scaling w.r.t to elbow to wrist ratio of ref pose
@@ -1482,21 +1391,17 @@ class TRI3DPoseAdaption:
ref_keypoints, input_keypoints, 3, 4,
109) #scaling left hand w.r.t left wrist
if garment_category not in [
"half_sleeve_garment", "full_sleeve_garment"
]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 5,
6) #rotate right elbow
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 5,
6) #rotate right elbow
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 2, 5, 5,
ref_keypoints, input_keypoints, 1, 5, 5,
6) #scaling w.r.t to shoulder to elbow ratio of ref pose
prev_rw = input_keypoints[7]
if garment_category != "full_sleeve_garment":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 6,
7) #rotate right wrist
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 6,
7) #rotate right wrist
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 5, 6, 6,
7) #scaling w.r.t to elbow to wrist ratio of ref pose
@@ -1515,35 +1420,26 @@ class TRI3DPoseAdaption:
88) #scaling right hand w.r.t right wrist
#legs
if garment_category not in ["trouser", "shorts"]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 8,
9) #rotate left knee
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 8,
9) #rotate left knee
input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
1, 8, 8, 9) #scale left knee
if garment_category != "trouser":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 9,
10) #rotate left foot
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 9,
10) #rotate left foot
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 8, 9, 9,
10) #scaling w.r.t to knee to foot ratio of ref pose
if garment_category not in [
"half_sleeve_garment", "full_sleeve_garment"
]:
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 11,
12) #rotate right knee
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 11,
12) #rotate right knee
input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
1, 11, 11,
12) #scale right knee
if garment_category != "trouser":
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 12,
13) #rotate right foot
input_keypoints = comfy_utils.rotate(ref_keypoints,
input_keypoints, 12,
13) #rotate right foot
input_keypoints = comfy_utils.scale(
ref_keypoints, input_keypoints, 11, 12, 12,
13) #scaling w.r.t to knee to foot ratio of ref pose
@@ -1715,194 +1611,6 @@ class TRI3DFaceRecognise:
return ({"overlap (float)": s}, )
class FloatToImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", {
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.01
})
}
}
CATEGORY = "TRI3D"
RETURN_TYPES = ("IMAGE", )
FUNCTION = "load_image"
def load_image(self, value):
def render_float(float_input):
latex_expression = '$' + str(float_input) + '$'
import matplotlib.pyplot as plt
fig = plt.figure(
figsize=(10, 4)) # Dimensions of figsize are in inches
text = fig.text(
x=0.5, # x-coordinate to place the text
y=0.5, # y-coordinate to place the text
s=latex_expression,
horizontalalignment="center",
verticalalignment="center",
fontsize=32,
)
import tempfile
path_file_image_output = tempfile.NamedTemporaryFile(
).name + '.png'
plt.savefig(path_file_image_output)
import cv2
image = cv2.imread(path_file_image_output, cv2.IMREAD_COLOR)
import os
# os.unlink(path_file_image_output)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def to_torch_image(image):
import numpy as np
import torch
image = image.astype(dtype=np.float32)
image /= 255.0
image = torch.from_numpy(image)[
None,
]
image = image.unsqueeze(0)
return image
image = render_float(float_input=value)
image = to_torch_image(image)
return image
class TRI3D_recolor_LAB_manual:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image_input": ("IMAGE", ),
"mask_input": ("IMAGE", ),
"factor_mean": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}),
"factor_sigma": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}),
}
}
RETURN_TYPES = ("IMAGE", )
FUNCTION = "recolor"
CATEGORY = "TRI3D"
def recolor(self, image_input, mask_input, factor_mean, factor_sigma):
def get_mu_sigma(array_input, mask_input):
import numpy as np
import math
array_input = array_input.astype(dtype=np.float32).flatten()
mask_input = mask_input.flatten()
sum = np.sum(mask_input)
mean = np.sum(array_input * mask_input) / sum
array_input -= mean
array_input *= mask_input
sigma = math.sqrt(np.sum(np.square(array_input)) / sum)
return mean, sigma
def do_recolor(image, mask, mean, sigma):
import cv2
import numpy as np
import math
image_original = image.copy()
mask = (mask > 127).astype(dtype=np.uint8)
sum = np.sum(mask.flatten())
for i in range(3):
image[:, :, i] *= mask
image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
image = image.astype(dtype=np.float32)
for i in range(1):
mu_1, sigma_1 = get_mu_sigma(array_input=image[:, :, i],
mask_input=mask)
image[:, :, i] = (((image[:, :, i] - mu_1) / sigma_1) *
(sigma * sigma_1)) + (mean * mu_1)
image = np.clip(image, 0, 255)
image = image.astype(dtype=np.uint8)
image = cv2.cvtColor(image, cv2.COLOR_LAB2BGR)
for i in range(3):
image_original[:, :,
i] = (image_original[:, :, i] *
(1 - mask)) + (image[:, :, i] * mask)
return image_original
def from_torch_image(image):
image = image.squeeze().cpu().numpy() * 255.0
image = np.clip(image, 0, 255).astype(np.uint8)
return image
def to_torch_image(image):
import numpy as np
import torch
image = image.astype(dtype=np.float32)
image /= 255.0
image = torch.from_numpy(image)[
None,
]
image = image.unsqueeze(0)
return image
image = from_torch_image(image=image_input)
mask = from_torch_image(image=mask_input)[:, :, 0]
image_output = do_recolor(image=image,
mask=mask,
mean=factor_mean,
sigma=factor_sigma)
image_output = to_torch_image(image=image_output)
return image_output
class TRI3D_recolor_LAB:
@classmethod
@@ -2273,205 +1981,74 @@ class TRI3D_recolor:
return image_output
class TRI3D_image_mask_2_box:
def __init__(self):
pass
class FloatToImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", ),
"mask": ("MASK", ),
},
}
FUNCTION = "run"
RETURN_TYPES = ("IMAGE", )
CATEGORY = "HackNode"
def run(self, image, mask):
image = from_torch_image(image)
mask = from_torch_image(mask)
image = extract_box_from_image(image, mask)
image = to_torch_image(image)
return image
class TRI3D_image_mask_box_2_image:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", ),
"mask": ("MASK", ),
"box": ("IMAGE", ),
},
}
FUNCTION = "run"
RETURN_TYPES = ("IMAGE", )
CATEGORY = "HackNode"
def run(self, image, mask, box):
image = from_torch_image(image)
mask = from_torch_image(mask)
box = from_torch_image(box)
image = stitch_back_box_to_image(image, mask, box)
image = to_torch_image(image)
return image
class TRI3D_clipdrop_bgremove_api:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", )
},
}
FUNCTION = "run"
RETURN_TYPES = ("IMAGE", )
CATEGORY = "TRI3D"
def run(self, image):
image = from_torch_image(image)
print(image.shape)
_, enc_image = cv2.imencode('.jpg', image)
from dotenv import load_dotenv
load_dotenv()
CLIPDROP_API_KEY = os.getenv('CLIPDROP_API_KEY')
# CLIPDROP_API_KEY = os.environ.get('CLIPDROP_API_KEY')
r = requests.post('https://clipdrop-api.co/remove-background/v1',
files={
'image_file':
("mannequin.jpg", enc_image.tobytes(),
'image/jpeg'),
},
headers={'x-api-key': CLIPDROP_API_KEY})
if (r.ok):
pass
else:
r.raise_for_status()
output = np.array(Image.open(io.BytesIO(r.content)))
output = cv2.cvtColor(output, cv2.COLOR_BGRA2RGBA)
# print("decoded output",output.shape)
# mask = output[:,:,3]
# output = output[:,:,0:3]
output = torch.from_numpy(output.astype(np.float32) / 255.0)[
None,
]
# print("converted image to torch")
# print(output.shape)
# mask = torch.from_numpy(mask.astype(np.float32)/255.0)[None,]
# print("converted mask to torch")
# print(mask.shape)
return output,
class TRI3DAdjustNeck:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"posemap_json_file_path": ("STRING", {
"default":
"dwpose/keypoints/input.json"
}),
# "age_group" :(["10-12 yrs"],{"default":"10-12 yrs"}),
"neck_shoulder_ratio": ("FLOAT", {
"default": 0.7,
"value": ("FLOAT", {
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.01
}),
"save_json_file_path": ("STRING", {
"default":
"dwpose/keypoints/output.json"
})
},
}
}
FUNCTION = "run"
RETURN_TYPES = ("IMAGE", "STRING")
CATEGORY = "TRI3D"
RETURN_TYPES = ("IMAGE", )
FUNCTION = "load_image"
def run(self, posemap_json_file_path, neck_shoulder_ratio,
save_json_file_path):
from .dwpose import comfy_utils
def load_image(self, value):
# age_to_ratio = {"10-12 yrs": 0.65}
def render_float(float_input):
latex_expression = '$' + str(float_input) + '$'
import matplotlib.pyplot as plt
input_pose = json.load(open(posemap_json_file_path))
input_height = input_pose['height']
input_width = input_pose['width']
input_keypoints = input_pose['keypoints']
fig = plt.figure(
figsize=(10, 4)) # Dimensions of figsize are in inches
ref_x1, ref_y1 = input_keypoints[2] #left shoulder
ref_x2, ref_y2 = input_keypoints[5] #right_shoulder
text = fig.text(
x=0.5, # x-coordinate to place the text
y=0.5, # y-coordinate to place the text
s=latex_expression,
horizontalalignment="center",
verticalalignment="center",
fontsize=32,
)
x1, y1 = input_keypoints[1] #neck
x2, y2 = input_keypoints[0] #nose
prev_nose = input_keypoints[0]
import tempfile
path_file_image_output = tempfile.NamedTemporaryFile(
).name + '.png'
ref_len = np.linalg.norm(
np.array([ref_x1, ref_y1]) - np.array(
[ref_x2, ref_y2])) #ref body part length i.e. shoulder length
targ_len = np.linalg.norm(np.array([x1, y1]) - np.array([
x2, y2
])) #targ body part length i.e. neck length - neck to nose length
plt.savefig(path_file_image_output)
input_targ_ref_len = targ_len / ref_len #neck to shoulder ratio
print("neck to shoulder ratio found", input_targ_ref_len)
print("neck to shoulder ratio target", neck_shoulder_ratio)
#scale the coords
# scale = age_to_ratio[age_group] / input_targ_ref_len
scale = neck_shoulder_ratio / input_targ_ref_len
import cv2
image = cv2.imread(path_file_image_output, cv2.IMREAD_COLOR)
x2_scaled = x1 + (x2 - x1) * scale
y2_scaled = y1 + (y2 - y1) * scale
input_keypoints[0] = [x2_scaled, y2_scaled]
import os
# os.unlink(path_file_image_output)
#changing face points to w.r.t to new nose point after rotation
input_keypoints[14:18] = comfy_utils.move(prev_nose,
input_keypoints[0],
input_keypoints[14:18])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
canvas = np.zeros(shape=(input_height, input_width, 3), dtype=np.uint8)
canvas = comfy_utils.draw_bodypose(canvas, input_keypoints)
canvas = comfy_utils.draw_handpose(
canvas, input_keypoints[88:109]) #right hand
canvas = comfy_utils.draw_handpose(canvas,
input_keypoints[109:]) #left hand
canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
None,
]
def to_torch_image(image):
output_pose = {
"height": input_height,
"width": input_width,
"keypoints": input_keypoints
}
cur_file_dir = os.path.dirname(os.path.realpath(__file__))
save_json_file_path = os.path.join(cur_file_dir, save_json_file_path)
json.dump(output_pose, open(save_json_file_path, 'w'))
return (canvas, save_json_file_path)
import numpy as np
import torch
image = image.astype(dtype=np.float32)
image /= 255.0
image = torch.from_numpy(image)[
None,
]
image = image.unsqueeze(0)
return image
image = render_float(float_input=value)
image = to_torch_image(image)
return image
# A dictionary that contains all nodes you want to export with their names
@@ -2493,14 +2070,9 @@ NODE_CLASS_MAPPINGS = {
"tri3d-recolor-mask": TRI3D_recolor,
"tri3d-recolor-mask-LAB_space": TRI3D_recolor_LAB,
"tri3d-recolor-mask-RGB_space": TRI3D_recolor_RGB,
"tri3d-image-mask-2-box": TRI3D_image_mask_2_box,
"tri3d-image-mask-box-2-image": TRI3D_image_mask_box_2_image,
"tri3d-clipdrop-bgremove-api": TRI3D_clipdrop_bgremove_api,
"tri3d-adjust-neck": TRI3DAdjustNeck,
"tri3d_recolor_lab_manual": TRI3D_recolor_LAB_manual,
}
VERSION = "2.4.2"
VERSION = "1.7.0"
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"tri3d-atr-parse-batch": "ATR Parse Batch" + " v" + VERSION,
@@ -2521,9 +2093,4 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"tri3d-recolor-mask": "Recolor mask HSV space" + " v" + VERSION,
"tri3d-recolor-mask-LAB_space": "Recolor mask LAB space" + " v" + VERSION,
"tri3d-recolor-mask-RGB_space": "Recolor mask RGB space" + " v" + VERSION,
"tri3d--image-mask-2-box": "Extract box from image" + " v" + VERSION,
"tri3d-image-mask-box-2-image": "Stitch box to image" + " v" + VERSION,
"tri3d-clipdrop-bgremove-api": "RemBG ClipDrop" + " v" + VERSION,
"tri3d-adjust-neck": "Adjust Neck" + " v" + VERSION,
"tri3d_recolor_lab_manual": "Adjust color manually" + " v" + VERSION,
}
samples/1.6_backpose_adaption/backpose.json
+2 -2
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@@ -67,11 +67,11 @@
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[
samples/1.7.2_garment_pose_lock/garment_pose_lock_workflow.json
-1242
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samples/1.7.2_garment_pose_lock/mannequin_pose.json
-1
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samples/1.7.2_garment_pose_lock/ref_pose.json
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samples/back_poses/backpose.json
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samples/back_poses/backpose_kid.json
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