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cv_imshow.py
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# Copyright (c) 2012-2017 Renato Florentino Garcia <[email protected]>
# Stefano Pellegrini <[email protected]>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the authors nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHORS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import gdb
import struct
import sys
try:
from PIL import Image
except ModuleNotFoundError:
import Image
if sys.version_info >= (3, 0):
_range = range
_to_string = lambda data: str(data)
else:
_range = xrange
_to_string = lambda data: unicode(data).encode('utf-8')
def chunker(seq, size):
return (seq[pos:pos + size] for pos in _range(0, len(seq), size))
class cv_imshow(gdb.Command):
"""Diplays the content of an opencv image"""
def __init__(self):
super(cv_imshow, self).__init__('cv_imshow',
gdb.COMMAND_SUPPORT,
gdb.COMPLETE_FILENAME)
def invoke(self, arg, from_tty):
# Access the variable from gdb.
frame = gdb.selected_frame()
val = frame.read_var(arg)
if str(val.type.strip_typedefs()) == 'IplImage *':
img_info = self.get_iplimage_info(val)
else:
img_info = self.get_cvmat_info(val)
if img_info:
self.show_image(*img_info)
@staticmethod
def get_cvmat_info(val):
flags = val['flags']
depth = flags & 7
channels = 1 + (flags >> 3) & 63
if depth == 0:
cv_type_name = 'CV_8U'
data_symbol = 'B'
elif depth == 1:
cv_type_name = 'CV_8S'
data_symbol = 'b'
elif depth == 2:
cv_type_name = 'CV_16U'
data_symbol = 'H'
elif depth == 3:
cv_type_name = 'CV_16S'
data_symbol = 'h'
elif depth == 4:
cv_type_name = 'CV_32S'
data_symbol = 'i'
elif depth == 5:
cv_type_name = 'CV_32F'
data_symbol = 'f'
elif depth == 6:
cv_type_name = 'CV_64F'
data_symbol = 'd'
else:
gdb.write('Unsupported cv::Mat depth\n', gdb.STDERR)
return
rows = val['rows']
cols = val['cols']
line_step = val['step']['p'][0]
gdb.write(cv_type_name + ' with ' + str(channels) + ' channels, ' +
str(rows) + ' rows and ' + str(cols) + ' cols\n')
data_address = _to_string(val['data']).split()[0]
data_address = int(data_address, 16)
return (cols, rows, channels, line_step, data_address, data_symbol)
@staticmethod
def get_iplimage_info(val):
depth = val['depth']
channels = val['nChannels']
if depth == 0x8:
cv_type_name = 'IPL_DEPTH_8U'
data_symbol = 'B'
elem_size = 1
elif depth == -0x7FFFFFF8:
cv_type_name = 'IPL_DEPTH_8S'
data_symbol = 'b'
elem_size = 1
elif depth == 0x10:
cv_type_name = 'IPL_DEPTH_16U'
data_symbol = 'H'
elem_size = 2
elif depth == -0x7FFFFFF0:
cv_type_name = 'IPL_DEPTH_16S'
data_symbol = 'h'
elem_size = 2
elif depth == -0x7FFFFFE0:
cv_type_name = 'IPL_DEPTH_32S'
data_symbol = 'i'
elem_size = 4
elif depth == 0x20:
cv_type_name = 'IPL_DEPTH_32F'
data_symbol = 'f'
elem_size = 4
elif depth == 0x40:
cv_type_name = 'IPL_DEPTH_64F'
data_symbol = 'd'
elem_size = 8
else:
gdb.write('Unsupported IplImage depth\n', gdb.STDERR)
return
rows = val['height'] if str(val['roi']) == '0x0' else val['roi']['height']
cols = val['width'] if str(val['roi']) == '0x0' else val['roi']['width']
line_step = val['widthStep']
gdb.write(cv_type_name + ' with ' + str(channels) + ' channels, ' +
str(rows) + ' rows and ' + str(cols) + ' cols\n')
data_address = _to_string(val['imageData']).split()[0]
data_address = int(data_address, 16)
if str(val['roi']) != '0x0':
x_offset = int(val['roi']['xOffset'])
y_offset = int(val['roi']['yOffset'])
data_address += line_step * y_offset + x_offset * elem_size * channels
return (cols, rows, channels, line_step, data_address, data_symbol)
@staticmethod
def show_image(width, height, n_channel, line_step, data_address, data_symbol):
""" Copies the image data to a PIL image and shows it.
Args:
width: The image width, in pixels.
height: The image height, in pixels.
n_channel: The number of channels in image.
line_step: The offset to change to pixel (i+1, j) being
in pixel (i, j), in bytes.
data_address: The address of image data in memory.
data_symbol: Python struct module code to the image data type.
"""
width = int(width)
height = int(height)
n_channel = int(n_channel)
line_step = int(line_step)
data_address = int(data_address)
infe = gdb.inferiors()
memory_data = infe[0].read_memory(data_address, line_step * height)
# Calculate the memory padding to change to the next image line.
# Either due to memory alignment or a ROI.
if data_symbol in ('b', 'B'):
elem_size = 1
elif data_symbol in ('h', 'H'):
elem_size = 2
elif data_symbol in ('i', 'f'):
elem_size = 4
elif data_symbol == 'd':
elem_size = 8
padding = line_step - width * n_channel * elem_size
# Format memory data to load into the image.
image_data = []
if n_channel == 1:
mode = 'L'
fmt = '%d%s%dx' % (width, data_symbol, padding)
for line in chunker(memory_data, line_step):
image_data.extend(struct.unpack(fmt, line))
elif n_channel == 3:
mode = 'RGB'
fmt = '%d%s%dx' % (width * 3, data_symbol, padding)
for line in chunker(memory_data, line_step):
image_data.extend(struct.unpack(fmt, line))
else:
gdb.write('Only 1 or 3 channels supported\n', gdb.STDERR)
return
# Fit the opencv elemente data in the PIL element data
if data_symbol == 'b':
image_data = [i+128 for i in image_data]
elif data_symbol == 'H':
image_data = [i >> 8 for i in image_data]
elif data_symbol == 'h':
image_data = [(i+32768) >> 8 for i in image_data]
elif data_symbol == 'i':
image_data = [(i+2147483648) >> 24 for i in image_data]
elif data_symbol in ('f', 'd'):
# A float image is discretized in 256 bins for display.
max_image_data = max(image_data)
min_image_data = min(image_data)
img_range = max_image_data - min_image_data
if img_range > 0:
image_data = [int(255 * (i - min_image_data) / img_range)
for i in image_data]
else:
image_data = [0 for i in image_data]
if n_channel == 3:
# OpenCV stores the channels in BGR mode. Convert to RGB while packing.
image_data = list(zip(*[image_data[i::3] for i in [2, 1, 0]]))
# Show image.
img = Image.new(mode, (width, height))
img.putdata(image_data)
img.show("Image")
cv_imshow()