File size: 11,808 Bytes
16c8067
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
from __future__ import print_function
import os
import sys
import time
import torch
import math
import numpy as np
import cv2


def _gaussian(
        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
        mean_vert=0.5):
    # handle some defaults
    if width is None:
        width = size
    if height is None:
        height = size
    if sigma_horz is None:
        sigma_horz = sigma
    if sigma_vert is None:
        sigma_vert = sigma
    center_x = mean_horz * width + 0.5
    center_y = mean_vert * height + 0.5
    gauss = np.empty((height, width), dtype=np.float32)
    # generate kernel
    for i in range(height):
        for j in range(width):
            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
    if normalize:
        gauss = gauss / np.sum(gauss)
    return gauss


def draw_gaussian(image, point, sigma):
    # Check if the gaussian is inside
    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
        return image
    size = 6 * sigma + 1
    g = _gaussian(size)
    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
    assert (g_x[0] > 0 and g_y[1] > 0)
    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
    image[image > 1] = 1
    return image


def transform(point, center, scale, resolution, invert=False):
    """Generate and affine transformation matrix.

    Given a set of points, a center, a scale and a targer resolution, the
    function generates and affine transformation matrix. If invert is ``True``
    it will produce the inverse transformation.

    Arguments:
        point {torch.tensor} -- the input 2D point
        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
        scale {float} -- the scale of the face/object
        resolution {float} -- the output resolution

    Keyword Arguments:
        invert {bool} -- define wherever the function should produce the direct or the
        inverse transformation matrix (default: {False})
    """
    _pt = torch.ones(3)
    _pt[0] = point[0]
    _pt[1] = point[1]

    h = 200.0 * scale
    t = torch.eye(3)
    t[0, 0] = resolution / h
    t[1, 1] = resolution / h
    t[0, 2] = resolution * (-center[0] / h + 0.5)
    t[1, 2] = resolution * (-center[1] / h + 0.5)

    if invert:
        t = torch.inverse(t)

    new_point = (torch.matmul(t, _pt))[0:2]

    return new_point.int()


def crop(image, center, scale, resolution=256.0):
    """Center crops an image or set of heatmaps

    Arguments:
        image {numpy.array} -- an rgb image
        center {numpy.array} -- the center of the object, usually the same as of the bounding box
        scale {float} -- scale of the face

    Keyword Arguments:
        resolution {float} -- the size of the output cropped image (default: {256.0})

    Returns:
        [type] -- [description]
    """  # Crop around the center point
    """ Crops the image around the center. Input is expected to be an np.ndarray """
    ul = transform([1, 1], center, scale, resolution, True)
    br = transform([resolution, resolution], center, scale, resolution, True)
    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
    if image.ndim > 2:
        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
                           image.shape[2]], dtype=np.int32)
        newImg = np.zeros(newDim, dtype=np.uint8)
    else:
        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
        newImg = np.zeros(newDim, dtype=np.uint8)
    ht = image.shape[0]
    wd = image.shape[1]
    newX = np.array(
        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
    newY = np.array(
        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
                        interpolation=cv2.INTER_LINEAR)
    return newImg


def get_preds_fromhm(hm, center=None, scale=None):
    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
    and the scale is provided the function will return the points also in
    the original coordinate frame.

    Arguments:
        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]

    Keyword Arguments:
        center {torch.tensor} -- the center of the bounding box (default: {None})
        scale {float} -- face scale (default: {None})
    """
    max, idx = torch.max(
        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
    idx += 1
    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)

    for i in range(preds.size(0)):
        for j in range(preds.size(1)):
            hm_ = hm[i, j, :]
            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
                diff = torch.FloatTensor(
                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
                preds[i, j].add_(diff.sign_().mul_(.25))

    preds.add_(-.5)

    preds_orig = torch.zeros(preds.size())
    if center is not None and scale is not None:
        for i in range(hm.size(0)):
            for j in range(hm.size(1)):
                preds_orig[i, j] = transform(
                    preds[i, j], center, scale, hm.size(2), True)

    return preds, preds_orig

def get_preds_fromhm_batch(hm, centers=None, scales=None):
    """Obtain (x,y) coordinates given a set of N heatmaps. If the centers
    and the scales is provided the function will return the points also in
    the original coordinate frame.

    Arguments:
        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]

    Keyword Arguments:
        centers {torch.tensor} -- the centers of the bounding box (default: {None})
        scales {float} -- face scales (default: {None})
    """
    max, idx = torch.max(
        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
    idx += 1
    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)

    for i in range(preds.size(0)):
        for j in range(preds.size(1)):
            hm_ = hm[i, j, :]
            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
                diff = torch.FloatTensor(
                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
                preds[i, j].add_(diff.sign_().mul_(.25))

    preds.add_(-.5)

    preds_orig = torch.zeros(preds.size())
    if centers is not None and scales is not None:
        for i in range(hm.size(0)):
            for j in range(hm.size(1)):
                preds_orig[i, j] = transform(
                    preds[i, j], centers[i], scales[i], hm.size(2), True)

    return preds, preds_orig

def shuffle_lr(parts, pairs=None):
    """Shuffle the points left-right according to the axis of symmetry
    of the object.

    Arguments:
        parts {torch.tensor} -- a 3D or 4D object containing the
        heatmaps.

    Keyword Arguments:
        pairs {list of integers} -- [order of the flipped points] (default: {None})
    """
    if pairs is None:
        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
                 62, 61, 60, 67, 66, 65]
    if parts.ndimension() == 3:
        parts = parts[pairs, ...]
    else:
        parts = parts[:, pairs, ...]

    return parts


def flip(tensor, is_label=False):
    """Flip an image or a set of heatmaps left-right

    Arguments:
        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]

    Keyword Arguments:
        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
    """
    if not torch.is_tensor(tensor):
        tensor = torch.from_numpy(tensor)

    if is_label:
        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
    else:
        tensor = tensor.flip(tensor.ndimension() - 1)

    return tensor

# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py)


def appdata_dir(appname=None, roaming=False):
    """ appdata_dir(appname=None, roaming=False)

    Get the path to the application directory, where applications are allowed
    to write user specific files (e.g. configurations). For non-user specific
    data, consider using common_appdata_dir().
    If appname is given, a subdir is appended (and created if necessary).
    If roaming is True, will prefer a roaming directory (Windows Vista/7).
    """

    # Define default user directory
    userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
    if userDir is None:
        userDir = os.path.expanduser('~')
        if not os.path.isdir(userDir):  # pragma: no cover
            userDir = '/var/tmp'  # issue #54

    # Get system app data dir
    path = None
    if sys.platform.startswith('win'):
        path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
        path = (path2 or path1) if roaming else (path1 or path2)
    elif sys.platform.startswith('darwin'):
        path = os.path.join(userDir, 'Library', 'Application Support')
    # On Linux and as fallback
    if not (path and os.path.isdir(path)):
        path = userDir

    # Maybe we should store things local to the executable (in case of a
    # portable distro or a frozen application that wants to be portable)
    prefix = sys.prefix
    if getattr(sys, 'frozen', None):
        prefix = os.path.abspath(os.path.dirname(sys.executable))
    for reldir in ('settings', '../settings'):
        localpath = os.path.abspath(os.path.join(prefix, reldir))
        if os.path.isdir(localpath):  # pragma: no cover
            try:
                open(os.path.join(localpath, 'test.write'), 'wb').close()
                os.remove(os.path.join(localpath, 'test.write'))
            except IOError:
                pass  # We cannot write in this directory
            else:
                path = localpath
                break

    # Get path specific for this app
    if appname:
        if path == userDir:
            appname = '.' + appname.lstrip('.')  # Make it a hidden directory
        path = os.path.join(path, appname)
        if not os.path.isdir(path):  # pragma: no cover
            os.mkdir(path)

    # Done
    return path