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
|
import gdb
import numpy as np
from enum import Enum
np.set_printoptions(suppress=True) # prevent numpy exponential notation on print, default False
# np.set_printoptions(threshold=sys.maxsize)
def conv(obj, t):
return gdb.parse_and_eval(f'({t})({obj})')
def booli(obj):
return conv(str(obj).lower(), 'bool')
def stri(obj):
s = f'"{obj}"'
return conv(s.translate(s.maketrans('\n', ' ')), 'char*')
class MagicValues(Enum):
MAGIC_VAL = 0x42FF0000
AUTO_STEP = 0
CONTINUOUS_FLAG = 1 << 14
SUBMATRIX_FLAG = 1 << 15
class MagicMasks(Enum):
MAGIC_MASK = 0xFFFF0000
TYPE_MASK = 0x00000FFF
DEPTH_MASK = 7
class Depth(Enum):
CV_8U = 0
CV_8S = 1
CV_16U = 2
CV_16S = 3
CV_32S = 4
CV_32F = 5
CV_64F = 6
CV_16F = 7
def create_enum(n):
def make_type(depth, cn):
return depth.value + ((cn - 1) << 3)
defs = [(f'{depth.name}C{i}', make_type(depth, i)) for depth in Depth for i in range(1, n + 1)]
return Enum('Type', defs)
Type = create_enum(512)
class Flags:
def depth(self):
return Depth(self.flags & MagicMasks.DEPTH_MASK.value)
def dtype(self):
depth = self.depth()
ret = None
if depth == Depth.CV_8U:
ret = (np.uint8, 'uint8_t')
elif depth == Depth.CV_8S:
ret = (np.int8, 'int8_t')
elif depth == Depth.CV_16U:
ret = (np.uint16, 'uint16_t')
elif depth == Depth.CV_16S:
ret = (np.int16, 'int16_t')
elif depth == Depth.CV_32S:
ret = (np.int32, 'int32_t')
elif depth == Depth.CV_32F:
ret = (np.float32, 'float')
elif depth == Depth.CV_64F:
ret = (np.float64, 'double')
elif depth == Depth.CV_16F:
ret = (np.float16, 'float16')
return ret
def type(self):
return Type(self.flags & MagicMasks.TYPE_MASK.value)
def channels(self):
return ((self.flags & (511 << 3)) >> 3) + 1
def is_continuous(self):
return (self.flags & MagicValues.CONTINUOUS_FLAG.value) != 0
def is_submatrix(self):
return (self.flags & MagicValues.SUBMATRIX_FLAG.value) != 0
def __init__(self, flags):
self.flags = flags
def __iter__(self):
return iter({
'type': stri(self.type().name),
'is_continuous': booli(self.is_continuous()),
'is_submatrix': booli(self.is_submatrix())
}.items())
class Size:
def __init__(self, ptr):
self.ptr = ptr
def dims(self):
return int((self.ptr - 1).dereference())
def to_numpy(self):
return np.array([int(self.ptr[i]) for i in range(self.dims())], dtype=np.int64)
def __iter__(self):
return iter({'size': stri(self.to_numpy())}.items())
class Mat:
def __init__(self, m, size, flags):
(dtype, ctype) = flags.dtype()
elsize = np.dtype(dtype).itemsize
shape = size.to_numpy()
steps = np.asarray([int(m['step']['p'][i]) for i in range(len(shape))], dtype=np.int64)
ptr = m['data']
# either we are default-constructed or sizes are zero
if int(ptr) == 0 or np.prod(shape * steps) == 0:
self.mat = np.array([])
self.view = self.mat
return
# we don't want to show excess brackets
if flags.channels() != 1:
shape = np.append(shape, flags.channels())
steps = np.append(steps, elsize)
# get the length of contiguous array from data to the last element of the matrix
length = 1 + np.sum((shape - 1) * steps) // elsize
if dtype != np.float16:
# read all elements into self.mat
ctype = gdb.lookup_type(ctype)
ptr = ptr.cast(ctype.array(length - 1).pointer()).dereference()
self.mat = np.array([ptr[i] for i in range(length)], dtype=dtype)
else:
# read as uint16_t and then reinterpret the bytes as float16
u16 = gdb.lookup_type('uint16_t')
ptr = ptr.cast(u16.array(length - 1).pointer()).dereference()
self.mat = np.array([ptr[i] for i in range(length)], dtype=np.uint16)
self.mat = self.mat.view(np.float16)
# numpy will do the heavy lifting of strided access
self.view = np.lib.stride_tricks.as_strided(self.mat, shape=shape, strides=steps)
def __iter__(self):
return iter({'data': stri(self.view)}.items())
class MatPrinter:
"""Print a cv::Mat"""
def __init__(self, mat):
self.mat = mat
def views(self):
m = self.mat
flags = Flags(int(m['flags']))
size = Size(m['size']['p'])
data = Mat(m, size, flags)
for x in [flags, size, data]:
for k, v in x:
yield 'view_' + k, v
def real(self):
m = self.mat
for field in m.type.fields():
k = field.name
v = m[k]
yield k, v
# TODO: add an enum in interface.h with all cv::Mat element types and use that instead
# yield 'test', gdb.parse_and_eval(f'(cv::MatTypes)0')
def children(self): # TODO: hide real members under new child somehow
yield from self.views()
yield from self.real()
def get_type(val):
# Get the type.
vtype = val.type
# If it points to a reference, get the reference.
if vtype.code == gdb.TYPE_CODE_REF:
vtype = vtype.target()
# Get the unqualified type, stripped of typedefs.
vtype = vtype.unqualified().strip_typedefs()
# Get the type name.
typename = vtype.tag
return typename
def mat_printer(val):
typename = get_type(val)
if typename is None:
return None
if str(typename) == 'cv::Mat':
return MatPrinter(val)
gdb.pretty_printers.append(mat_printer)
|
