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import os, json
from os.path import join as opj
import numpy as np
from scipy.ndimage import binary_dilation
import cv2
from PIL import Image, ImageDraw
import torchvision.transforms as T
from ..utils import split_procidx
def remove_postfix(fn):
splits = fn.split("_")
if len(splits) > 3:
return fn
else:
return splits[0]
def get_validation_pairs(
data_root_dir,
is_paired,
data_type,
category=None,
proc_idx=0,
n_proc=1,
n_samples=None,
prompt_version=None,
text_file_postfix=None,
use_dc_cloth=False,
test_file_postfix=None,
**kwargs,
):
img_names = []
c_names = []
full_texts = []
clothing_texts = []
is_vitonhd = category is None
assert data_type in ["train", "test"]
if data_type == "train":
pair_postfix = "pairs"
else:
if is_paired:
pair_postfix = "pairs"
else:
pair_postfix = "unpairs"
if test_file_postfix is not None:
print(f"pair postfix : {test_file_postfix}")
pair_postfix = test_file_postfix
if not is_vitonhd:
assert category in ["upper_body", "lower_body", "dresses"]
data_root_dir = opj(data_root_dir, category)
if not use_dc_cloth:
txt_path = opj(data_root_dir, f"{data_type}_{pair_postfix}.txt")
else:
txt_path = opj(data_root_dir, f"{data_type}_{pair_postfix}_dc.txt")
with open(txt_path, "r") as f:
for line in f.readlines():
img_name, c_name = line.strip().split()
img_names.append(img_name)
c_names.append(c_name)
img_names, c_names = map(list, zip(*sorted(zip(img_names, c_names))))
img_names = split_procidx(img_names, n_proc, proc_idx)
c_names = split_procidx(c_names, n_proc, proc_idx)
img_names = img_names[:n_samples]
c_names = c_names[:n_samples]
prompter = Prompter(category="upper_body" if is_vitonhd else category, version=prompt_version, data_type=data_type)
if text_file_postfix is not None:
if is_vitonhd: # vitonhd
textfile_bn = f"{data_type}_{text_file_postfix}"
else:
textfile_bn = text_file_postfix
with open(opj(data_root_dir, textfile_bn), "rb") as f:
text_dict = json.load(f)
if text_file_postfix is None:
full_texts = ["" for _ in range(len(img_names))]
clothing_texts = ["" for _ in range(len(img_names))]
else:
for img_name, c_name in zip(img_names, c_names):
if (category == "upper_body") and ("012143_0" in img_name): # error
continue
img_fn = os.path.splitext(img_name)[0]
c_fn = os.path.splitext(c_name)[0]
if not is_vitonhd:
img_fn = remove_postfix(img_fn)
c_fn = remove_postfix(c_fn)
person_dict = text_dict[img_fn]["person"]
clothing_dict = text_dict[c_fn]["clothing"]
if "person" in text_dict[c_fn].keys():
clothing_person_dict = text_dict[c_fn]["person"]
else:
clothing_person_dict = person_dict
full_txt, clothing_txt = prompter.generate(person_dict, clothing_dict, clothing_person_dict)
full_texts.append(full_txt)
clothing_texts.append(clothing_txt)
return img_names, c_names, full_texts, clothing_texts
class Prompter:
def __init__(self, category, version, data_type="train"):
assert category in ["upper_body", "lower_body", "dresses"]
self.category = category
self.version = version
self.data_type = data_type
print(f"category : {self.category}, version : {self.version}")
@staticmethod
def create_prompt_v12(person_dict, clothing_dict, clothing_person_dict):
clothing_template = "a {category}, {material}, with {sleeve}, {neckline}"
full_template = "a {body_shape} {gender} wears {fit_of_clothing}, {category} ({material}), {neckline}, {sleeve_rolling_style}, {tucking_style}. With {hair_length} hair, {pose} with hands {hand_pose}"
# tucking style
if "crop" in clothing_dict["upper cloth length"]:
tucking_style = "untucked"
else:
tucking_style = person_dict["tucking style"]
if "short" in clothing_dict["sleeve"]:
sleeve_rolling_style = "short sleeve"
else:
sleeve_rolling_style = clothing_person_dict["sleeve rolling style"]
clothing_prompt = clothing_template.format(
category=clothing_dict['upper cloth category'],
material=clothing_dict["material"],
sleeve=clothing_dict["sleeve"],
neckline=clothing_dict["neckline"],
).lower()
full_prompt = full_template.format(
gender=person_dict["gender"],
body_shape=person_dict['body shape'],
hair_length=person_dict['hair length'],
pose=person_dict["pose"],
hand_pose=person_dict['hand pose'],
fit_of_clothing=person_dict['fit of upper cloth'],
sleeve_rolling_style=sleeve_rolling_style,
tucking_style=tucking_style,
category=clothing_dict['upper cloth category'],
material=clothing_dict["material"],
neckline=clothing_dict["neckline"],
).lower()
return full_prompt, clothing_prompt
def generate(self, person_dict, clothing_dict, clothing_person_dict):
full_prompt, clothing_prompt = self.create_prompt_v12(person_dict, clothing_dict, clothing_person_dict)
return full_prompt, clothing_prompt
class IdentityTransform:
def __call__(self, x):
return x
def get_transform(txt_lst, **kwargs):
trans_lst = []
for tr in txt_lst:
tr = tr.lower()
if tr == "hflip":
trans_lst.append(T.RandomHorizontalFlip())
elif tr == "randomresizedcrop":
trans_lst.append(T.RandomResizedCrop((kwargs["img_h"], kwargs["img_w"]), scale=(0.8, 1)))
elif tr == "resize":
trans_lst.append(T.Resize((kwargs["img_h"], kwargs["img_w"]), antialias=True))
elif tr == "randomresizedcrop_dynamic":
trans_lst.append(T.RandomResizedCrop((kwargs["img_h"], kwargs["img_w"]), scale=(0.5, 1), ratio=(0.3,2)))
elif tr == "randomaffine":
trans_lst.append(T.RandomAffine(degrees=0, translate=(0,0), scale=(0.8, 1.2)))
elif tr == "randomaffine_dynamic":
trans_lst.append(T.RandomAffine(degrees=(-30,30), translate=(0.1, 0.2), scale=(0.8, 1.2), fill=246))
elif tr == "rotate":
trans_lst.append(T.RandomAffine(degrees=(-30,30), fill=246))
elif tr == "colorjitter":
trans_lst.append(T.ColorJitter(
brightness=(0.8,1.2),
contrast=(0.8,1.2),
saturation=(0.8,1.2),
hue=(-0.1,0.1),
))
elif tr == "colorjitter2":
trans_lst.append(T.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1,
))
elif tr == "elastictransform":
trans_lst.append(T.ElasticTransform())
elif tr == "identity":
trans_lst.append(IdentityTransform())
else:
raise NotImplementedError(tr)
return trans_lst
label_map = {
"background": 0,
"hat": 1,
"hair": 2,
"sunglasses": 3,
"upper_clothes": 4,
"skirt": 5,
"pants": 6,
"dress": 7,
"belt": 8,
"left_shoe": 9,
"right_shoe": 10,
"head": 11,
"left_leg": 12,
"right_leg": 13,
"left_arm": 14,
"right_arm": 15,
"bag": 16,
"scarf": 17,
}
def extend_arm_mask(wrist, elbow, scale):
wrist = elbow + scale * (wrist - elbow)
return wrist
def hole_fill(img):
img = np.pad(img[1:-1, 1:-1], pad_width=1, mode='constant', constant_values=0)
img_copy = img.copy()
mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8)
cv2.floodFill(img, mask, (0, 0), 255)
img_inverse = cv2.bitwise_not(img)
dst = cv2.bitwise_or(img_copy, img_inverse)
return dst
def refine_mask(mask):
contours, hierarchy = cv2.findContours(mask.astype(np.uint8),
cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
area = []
for j in range(len(contours)):
a_d = cv2.contourArea(contours[j], True)
area.append(abs(a_d))
refine_mask = np.zeros_like(mask).astype(np.uint8)
if len(area) != 0:
i = area.index(max(area))
cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1)
return refine_mask
def get_mask_location(
model_type,
category,
model_parse: Image.Image,
keypoint: dict,
width=384, height=512,
radius=5,
version=None,
only_cloth=False,
only_cloth_arm=False,
only_cloth_armneck_with_dilate=False,
densepose=None,
use_pad=False,
):
if category is None or category == "": category = "upper_body"
im_parse = model_parse.resize((width, height), Image.NEAREST)
parse_array = np.array(im_parse)
if model_type == 'hd':
arm_width = 60
elif model_type == 'dc':
arm_width = 45
else:
raise ValueError("model_type must be \'hd\' or \'dc\'!")
parse_head = (parse_array == 1).astype(np.float32) + \
(parse_array == 3).astype(np.float32) + \
(parse_array == 11).astype(np.float32)
parser_mask_fixed = (parse_array == label_map["left_shoe"]).astype(np.float32) + \
(parse_array == label_map["right_shoe"]).astype(np.float32) + \
(parse_array == label_map["hat"]).astype(np.float32) + \
(parse_array == label_map["sunglasses"]).astype(np.float32) + \
(parse_array == label_map["bag"]).astype(np.float32)
parser_mask_changeable = (parse_array == label_map["background"]).astype(np.float32)
arms_left = (parse_array == 14).astype(np.float32)
arms_right = (parse_array == 15).astype(np.float32)
if category == 'dresses':
parse_mask = (parse_array == 7).astype(np.float32) + \
(parse_array == 4).astype(np.float32) + \
(parse_array == 5).astype(np.float32) + \
(parse_array == 6).astype(np.float32)
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
elif category == 'upper_body':
parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \
(parse_array == label_map["pants"]).astype(np.float32)
parser_mask_fixed_nolower = parser_mask_fixed.copy()
parser_mask_fixed += parser_mask_fixed_lower_cloth
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
elif category == 'lower_body':
parse_mask = (parse_array == 6).astype(np.float32) + \
(parse_array == 12).astype(np.float32) + \
(parse_array == 13).astype(np.float32) + \
(parse_array == 5).astype(np.float32)
parser_mask_fixed += (parse_array == label_map["upper_clothes"]).astype(np.float32) + \
(parse_array == 14).astype(np.float32) + \
(parse_array == 15).astype(np.float32)
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
else:
raise NotImplementedError(f"category {category}")
if only_cloth:
mask = Image.fromarray(parse_mask.astype(np.uint8) * 255)
mask_gray = Image.fromarray(parse_mask.astype(np.uint8) * 127)
if use_pad:
dil_mask = binary_dilation(mask, iterations=30)
mask = Image.fromarray(dil_mask.astype(np.uint8)*255)
mask_gray = Image.fromarray(dil_mask.astype(np.uint8)*127)
return mask, mask_gray
elif only_cloth_arm:
parse_mask = parse_mask + (parse_array == 14).astype(np.float32) + (parse_array == 15).astype(np.float32)
mask = Image.fromarray(parse_mask.astype(np.uint8) * 255)
mask_gray = Image.fromarray(parse_mask.astype(np.uint8) * 127)
return mask, mask_gray
elif only_cloth_armneck_with_dilate:
parse_mask = parse_mask + (parse_array == 14).astype(np.float32) + (parse_array == 15).astype(np.float32)
parse_mask = cv2.dilate(parse_mask.astype(np.uint8), np.ones((3,3), dtype=np.uint8), iterations=3)
parse_mask = np.logical_or(parse_mask, (parse_array == 18).astype(np.float32))
mask = Image.fromarray(parse_mask.astype(np.uint8) * 255)
mask_gray = Image.fromarray(parse_mask.astype(np.uint8) * 127)
return mask, mask_gray
# Load pose points
pose_data = keypoint["pose_keypoints_2d"]
pose_data = np.array(pose_data)
pose_data = pose_data.reshape((-1, 2))
im_arms_left = Image.new('L', (width, height))
im_arms_right = Image.new('L', (width, height))
arms_draw_left = ImageDraw.Draw(im_arms_left)
arms_draw_right = ImageDraw.Draw(im_arms_right)
if category == 'dresses' or category == 'upper_body':
shoulder_right = np.multiply(tuple(pose_data[2][:2]), height / 512.0)
shoulder_left = np.multiply(tuple(pose_data[5][:2]), height / 512.0)
elbow_right = np.multiply(tuple(pose_data[3][:2]), height / 512.0)
elbow_left = np.multiply(tuple(pose_data[6][:2]), height / 512.0)
wrist_right = np.multiply(tuple(pose_data[4][:2]), height / 512.0)
wrist_left = np.multiply(tuple(pose_data[7][:2]), height / 512.0)
hip_right = np.multiply(tuple(pose_data[8][:2]), height / 512.0)
hip_left = np.multiply(tuple(pose_data[11][:2]), height / 512.0)
ARM_LINE_WIDTH = int(arm_width / 512 * height)
size_left = [shoulder_left[0] - ARM_LINE_WIDTH // 2, shoulder_left[1] - ARM_LINE_WIDTH // 2, shoulder_left[0] + ARM_LINE_WIDTH // 2, shoulder_left[1] + ARM_LINE_WIDTH // 2]
size_right = [shoulder_right[0] - ARM_LINE_WIDTH // 2, shoulder_right[1] - ARM_LINE_WIDTH // 2, shoulder_right[0] + ARM_LINE_WIDTH // 2,
shoulder_right[1] + ARM_LINE_WIDTH // 2]
if wrist_right[0] <= 1. and wrist_right[1] <= 1.:
im_arms_right = arms_right
else:
wrist_right = extend_arm_mask(wrist_right, elbow_right, 1.2)
arms_draw_right.line(np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
arms_draw_right.arc(size_right, 0, 360, 'white', ARM_LINE_WIDTH // 2)
if wrist_left[0] <= 1. and wrist_left[1] <= 1.:
im_arms_left = arms_left
else:
wrist_left = extend_arm_mask(wrist_left, elbow_left, 1.2)
arms_draw_left.line(np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
arms_draw_left.arc(size_left, 0, 360, 'white', ARM_LINE_WIDTH // 2)
if version == "v9":
pass
else:
hands_left = np.logical_and(np.logical_not(im_arms_left), arms_left)
hands_right = np.logical_and(np.logical_not(im_arms_right), arms_right)
hands = hands_left + hands_right
parser_mask_fixed += hands
if category == 'upper_body':
parser_mask_fixed_nolower += hands
parser_mask_fixed = np.logical_or(parser_mask_fixed, parse_head)
if category == 'upper_body':
parser_mask_fixed_nolower = np.logical_or(parser_mask_fixed_nolower, parse_head)
parse_mask = cv2.dilate(parse_mask, np.ones((radius, radius), np.uint16), iterations=5)
if category == 'dresses' or category == 'upper_body':
neck_mask = (parse_array == 18).astype(np.float32)
neck_mask = cv2.dilate(neck_mask, np.ones((radius, radius), np.uint16), iterations=1)
neck_mask = np.logical_and(neck_mask, np.logical_not(parse_head))
parse_mask = np.logical_or(parse_mask, neck_mask)
if version == 'v7':
arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=15)
else:
arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=4)
parse_mask += np.logical_or(parse_mask, arm_mask)
parse_mask = np.logical_and(parser_mask_changeable, np.logical_not(parse_mask))
parse_mask_total = np.logical_or(parse_mask, parser_mask_fixed)
inpaint_mask = 1 - parse_mask_total
img = np.where(inpaint_mask, 255, 0)
dst = hole_fill(img.astype(np.uint8))
dst = refine_mask(dst)
if version not in ["v5", "v6"]:
inpaint_mask = dst / 255 * 1
if version is None or "official" in version:
pass
elif version == 'v9':
inpaint_mask = dst
hip_x1, hip_y1 = hip_left.astype(np.int64)
hip_x2, hip_y2 = hip_right.astype(np.int64)
inpaint_mask[hip_y1, hip_x1] = 255
inpaint_mask[hip_y2, hip_x2] = 255
coords = np.column_stack(np.where(inpaint_mask == 255))
y_min, x_min = coords.min(axis=0)
y_max, x_max = coords.max(axis=0)
inpaint_mask[y_min:y_max+1, x_min:x_max+1] = 255
inpaint_mask = np.logical_and(inpaint_mask, np.logical_not(parser_mask_fixed_nolower))
inpaint_mask = hole_fill(inpaint_mask.astype(np.uint8))
inpaint_mask = np.logical_and(inpaint_mask, np.logical_not(parse_head))
inpaint_mask = inpaint_mask.astype(np.uint8)
else:
raise NotImplementedError(f"upper body version {version}")
mask = Image.fromarray(inpaint_mask.astype(np.uint8) * 255)
mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127)
return mask, mask_gray |