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Zero
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import numpy as np
import cv2
from PIL import Image, ImageDraw
import torch
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):
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)
arms = arms_left + arms_right
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 += 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
# 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)
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)
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)
parser_mask_fixed += hands_left + hands_right
parser_mask_fixed = np.logical_or(parser_mask_fixed, parse_head)
parse_mask = cv2.dilate(parse_mask, np.ones((5, 5), 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((5, 5), 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)
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)
inpaint_mask = dst / 255 * 1
mask = Image.fromarray(inpaint_mask.astype(np.uint8) * 255)
mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127)
return mask, mask_gray
def get_tensor(img, h, w, is_mask=False):
img = np.array(img.resize((w, h))).astype(np.float32)
if not is_mask:
img = (img / 127.5) - 1.0
else:
img = (img < 128).astype(np.float32)[:,:,None]
return torch.from_numpy(img)[None]
def get_batch(image, cloth, densepose, agn_img, agn_mask, img_h, img_w):
batch = dict()
batch["image"] = get_tensor(image, img_h, img_w)
batch["cloth"] = get_tensor(cloth, img_h, img_w)
batch["image_densepose"] = get_tensor(densepose, img_h, img_w)
batch["agn"] = get_tensor(agn_img, img_h, img_w)
batch["agn_mask"] = get_tensor(agn_mask, img_h, img_w, is_mask=True)
batch["txt"] = ""
return batch
def tensor2img(x):
'''
x : [BS x c x H x W] or [c x H x W]
'''
if x.ndim == 3:
x = x.unsqueeze(0)
BS, C, H, W = x.shape
x = x.permute(0,2,3,1).reshape(-1, W, C).detach().cpu().numpy()
x = np.clip(x, -1, 1)
x = (x+1)/2
x = np.uint8(x*255.0)
if x.shape[-1] == 1:
x = np.concatenate([x,x,x], axis=-1)
return x
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