Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,794 Bytes
b53c368 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
import numpy as np
import cv2
from PIL import Image, ImageDraw
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)
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
|