ProgrammerParamesh/VirtualDress

1import pdb
2
3import numpy as np
4import cv2
5from PIL import Image, ImageDraw
6
7label_map = {
8    "background": 0,
9    "hat": 1,
10    "hair": 2,
11    "sunglasses": 3,
12    "upper_clothes": 4,
13    "skirt": 5,
14    "pants": 6,
15    "dress": 7,
16    "belt": 8,
17    "left_shoe": 9,
18    "right_shoe": 10,
19    "head": 11,
20    "left_leg": 12,
21    "right_leg": 13,
22    "left_arm": 14,
23    "right_arm": 15,
24    "bag": 16,
25    "scarf": 17,
26}
27
28def extend_arm_mask(wrist, elbow, scale):
29  wrist = elbow + scale * (wrist - elbow)
30  return wrist
31
32def hole_fill(img):
33    img = np.pad(img[1:-1, 1:-1], pad_width = 1, mode = 'constant', constant_values=0)
34    img_copy = img.copy()
35    mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8)
36
37    cv2.floodFill(img, mask, (0, 0), 255)
38    img_inverse = cv2.bitwise_not(img)
39    dst = cv2.bitwise_or(img_copy, img_inverse)
40    return dst
41
42def refine_mask(mask):
43    contours, hierarchy = cv2.findContours(mask.astype(np.uint8),
44                                           cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
45    area = []
46    for j in range(len(contours)):
47        a_d = cv2.contourArea(contours[j], True)
48        area.append(abs(a_d))
49    refine_mask = np.zeros_like(mask).astype(np.uint8)
50    if len(area) != 0:
51        i = area.index(max(area))
52        cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1)
53
54    return refine_mask
55
56def get_mask_location(model_type, category, model_parse: Image.Image, keypoint: dict, width=384,height=512):
57    im_parse = model_parse.resize((width, height), Image.NEAREST)
58    parse_array = np.array(im_parse)
59
60    if model_type == 'hd':
61        arm_width = 60
62    elif model_type == 'dc':
63        arm_width = 45
64    else:
65        raise ValueError("model_type must be \'hd\' or \'dc\'!")
66
67    parse_head = (parse_array == 1).astype(np.float32) + \
68                 (parse_array == 3).astype(np.float32) + \
69                 (parse_array == 11).astype(np.float32)
70
71    parser_mask_fixed = (parse_array == label_map["left_shoe"]).astype(np.float32) + \
72                        (parse_array == label_map["right_shoe"]).astype(np.float32) + \
73                        (parse_array == label_map["hat"]).astype(np.float32) + \
74                        (parse_array == label_map["sunglasses"]).astype(np.float32) + \
75                        (parse_array == label_map["bag"]).astype(np.float32)
76
77    parser_mask_changeable = (parse_array == label_map["background"]).astype(np.float32)
78
79    arms_left = (parse_array == 14).astype(np.float32)
80    arms_right = (parse_array == 15).astype(np.float32)
81    arms = arms_left + arms_right
82
83    if category == 'dresses':
84        parse_mask = (parse_array == 7).astype(np.float32) + \
85                     (parse_array == 4).astype(np.float32) + \
86                     (parse_array == 5).astype(np.float32) + \
87                     (parse_array == 6).astype(np.float32)
88
89        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
90
91    elif category == 'upper_body':
92        parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
93        parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \
94                                        (parse_array == label_map["pants"]).astype(np.float32)
95        parser_mask_fixed += parser_mask_fixed_lower_cloth
96        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
97    elif category == 'lower_body':
98        parse_mask = (parse_array == 6).astype(np.float32) + \
99                     (parse_array == 12).astype(np.float32) + \
100                     (parse_array == 13).astype(np.float32) + \
101                     (parse_array == 5).astype(np.float32)
102        parser_mask_fixed += (parse_array == label_map["upper_clothes"]).astype(np.float32) + \
103                             (parse_array == 14).astype(np.float32) + \
104                             (parse_array == 15).astype(np.float32)
105        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
106    else:
107        raise NotImplementedError
108
109    # Load pose points
110    pose_data = keypoint["pose_keypoints_2d"]
111    pose_data = np.array(pose_data)
112    pose_data = pose_data.reshape((-1, 2))
113
114    im_arms_left = Image.new('L', (width, height))
115    im_arms_right = Image.new('L', (width, height))
116    arms_draw_left = ImageDraw.Draw(im_arms_left)
117    arms_draw_right = ImageDraw.Draw(im_arms_right)
118    if category == 'dresses' or category == 'upper_body':
119        shoulder_right = np.multiply(tuple(pose_data[2][:2]), height / 512.0)
120        shoulder_left = np.multiply(tuple(pose_data[5][:2]), height / 512.0)
121        elbow_right = np.multiply(tuple(pose_data[3][:2]), height / 512.0)
122        elbow_left = np.multiply(tuple(pose_data[6][:2]), height / 512.0)
123        wrist_right = np.multiply(tuple(pose_data[4][:2]), height / 512.0)
124        wrist_left = np.multiply(tuple(pose_data[7][:2]), height / 512.0)
125        ARM_LINE_WIDTH = int(arm_width / 512 * height)
126        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]
127        size_right = [shoulder_right[0] - ARM_LINE_WIDTH // 2, shoulder_right[1] - ARM_LINE_WIDTH // 2, shoulder_right[0] + ARM_LINE_WIDTH // 2,
128                      shoulder_right[1] + ARM_LINE_WIDTH // 2]
129        
130
131        if wrist_right[0] <= 1. and wrist_right[1] <= 1.:
132            im_arms_right = arms_right
133        else:
134            wrist_right = extend_arm_mask(wrist_right, elbow_right, 1.2)
135            arms_draw_right.line(np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
136            arms_draw_right.arc(size_right, 0, 360, 'white', ARM_LINE_WIDTH // 2)
137
138        if wrist_left[0] <= 1. and wrist_left[1] <= 1.:
139            im_arms_left = arms_left
140        else:
141            wrist_left = extend_arm_mask(wrist_left, elbow_left, 1.2)
142            arms_draw_left.line(np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
143            arms_draw_left.arc(size_left, 0, 360, 'white', ARM_LINE_WIDTH // 2)
144
145        hands_left = np.logical_and(np.logical_not(im_arms_left), arms_left)
146        hands_right = np.logical_and(np.logical_not(im_arms_right), arms_right)
147        parser_mask_fixed += hands_left + hands_right
148
149    parser_mask_fixed = np.logical_or(parser_mask_fixed, parse_head)
150    parse_mask = cv2.dilate(parse_mask, np.ones((5, 5), np.uint16), iterations=5)
151    if category == 'dresses' or category == 'upper_body':
152        neck_mask = (parse_array == 18).astype(np.float32)
153        neck_mask = cv2.dilate(neck_mask, np.ones((5, 5), np.uint16), iterations=1)
154        neck_mask = np.logical_and(neck_mask, np.logical_not(parse_head))
155        parse_mask = np.logical_or(parse_mask, neck_mask)
156        arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=4)
157        parse_mask += np.logical_or(parse_mask, arm_mask)
158
159    parse_mask = np.logical_and(parser_mask_changeable, np.logical_not(parse_mask))
160
161    parse_mask_total = np.logical_or(parse_mask, parser_mask_fixed)
162    inpaint_mask = 1 - parse_mask_total
163    img = np.where(inpaint_mask, 255, 0)
164    dst = hole_fill(img.astype(np.uint8))
165    dst = refine_mask(dst)
166    inpaint_mask = dst / 255 * 1
167    mask = Image.fromarray(inpaint_mask.astype(np.uint8) * 255)
168    mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127)
169
170    return mask, mask_gray
171