def DetectGrasps(self, cloud, viewPoints, viewPointIndices, nSamples,
scoreThresh, minWidth, maxWidth, tablePosition,
tableUpAxis, tableFingerLength, offsets):
'''Calls the DetectGrasps Matlab script.'''
# short circuit the process if there are no points in the cloud
if cloud.shape[0] == 0:
return []
mCloud = matlab.double(cloud.T.tolist())
mViewPoints = matlab.double(viewPoints.T.tolist())
mViewPointIndices = matlab.int32(viewPointIndices.tolist(),
size=(len(viewPointIndices), 1))
mTablePosition = matlab.double(tablePosition.tolist(), size=(3, 1))
plotBitmap = matlab.logical([False, False, False, False, False])
mGrasps = self.eng.DetectGrasps(mCloud, mViewPoints, mViewPointIndices,
nSamples, scoreThresh, minWidth,
maxWidth, mTablePosition, tableUpAxis,
tableFingerLength, plotBitmap)
return self.UnpackGrasps(mGrasps, offsets)
def _convert_dict_dtypes(dictionary):
"""
takes in dictionary of numpy/python dtypes, and returns
dictionary of nearly equivalent MATLAB dtypes.
type 'list' is currently unsupported if they are mixed dtype.
(though this could be easily implemented using MATLAB cell,
it would not work well in the context of FEM_VGPy)
"""
dict_out = {}
for key in dictionary.keys():
# walk through all keys, checking the type
value = dictionary[key]
# first if-elif ladder (initial checks)
if value is None:
#don't add it to the output
continue
elif type(value) is str:
# matlab can automatically convert str to char
dict_out[key] = value
continue
elif type(value) is list:
# attempt to convert this to a tuple. element
# dtypes will be taken care of in 2nd ladder
value = tuple(value)
# second if-elif ladder (convert dtypes)
if type(value) is tuple:
#iterable type; convert to array
if type(value[0]) is int:
dict_out[key] = matlab.int32(value)
elif type(value[0]) is float:
dict_out[key] = matlab.double(value)
elif type(value) is numpy.ndarray:
# we want to convert to an equivalent matlab matrix...
#convert data to equivalent list
data_list = value.tolist()
#change dtype to matlab double and save
dict_out[key] = matlab.double(data_list)
elif type(value) is float:
# matlab considers floats to be matrices as well.
dict_out[key] = matlab.double([value])
elif type(value) is int:
# matlab considers ints to be matrices as well.
dict_out[key] = matlab.int32([value])
elif type(value) is dict:
# use recursion to take care of this
dict_out[key] = _convert_dict_dtypes(value)
else:
# undefined. alert user, then save as-is.
# if there is a problem, matlab engine will
# throw the proper exceptions
print "\n!!! undefined type ",
print type(value),
print "... saving anyway\n"
dict_out[key] = value
return dict_out
def plot_test(eng):
print "Plot Testing Begin"
eng.workspace['data'] = \
eng.randi(matlab.double([1,100]),matlab.double([30,2]))
eng.eval("plot(data(:,1),'ro-')")
eng.hold('on', nargout=0)
eng.eval("plot(data(:,2),'bx--')")
print "Plot testing end"
if __name__ == '__main__':
# basic_test(eng)
I = eng.imread('rice.png')
mapping = eng.getmapping(8, 'riu2')
lbpMap = eng.lbp(I, 2, 8, 0, 0)
H1 = eng.lbp(I, 1, 8, mapping, 'h')
print H1
eng.subplot(2, 1, 1)
eng.stem(H1)
H2 = eng.lbp(I)
eng.subplot(2, 1, 2)
eng.stem(H2)
# a = matlab.double([1,4,9,16,25],[3,4,5,6,7,8])
SP = matlab.int32([[-1, -1], [-1, 0], [-1, 1], [0, -1], [-0, 1], [1, -1],
[1, 0], [1, 1]])
I2 = eng.lbp(I, SP, 0, 'i')
eng.quit()
# correct the values of updated input image
input_img.data.clamp_(0, 1)
def unload(tensor):
# Convert an image from tensor to PIL image
image = tensor.cpu().clone()
image = image.squeeze(0)
image_pil = t_func.to_pil_image(image)
return image_pil
output = unload(input_img)
save_path = args.output
plt.imsave(save_path, output)
print("Save image to {}".format(save_path))
print("Post processing")
inimg = np.array(unload(content_img))
inimg_mat = matlab.int32(inimg.tolist())
outimg = np.array(output)
outimg_mat = matlab.int32(outimg.tolist())
eng = start_matlab()
processed_img = inimg - np.asarray(eng.RF(inimg_mat, args.post_s, args.post_r, args.post_it, inimg_mat)) + \
np.asarray(eng.RF(outimg_mat, args.post_s, args.post_r, args.post_it, inimg_mat))
processed_img = np.uint8(np.clip(processed_img, 0, 255))
output_rt, output_ext = os.path.splitext(args.output)
save_path = output_rt + "_post" + output_ext
print("Save post processed image to {}".format(save_path))
plt.imsave(save_path, Image.fromarray(processed_img))
partial_index_1,
partial_index_2,
inlier_index,
descriptor_categories=descriptor_categories)
# inlier未更新的版本
# descriptor_final = create_descriptor.create_descriptor(np.array(n_pre_matches_r), np.array(n_pre_matches_s), des_1, des_2, partial_index_1, partial_index_2,
# descriptor_categories=descriptor_categories)
print("KNN结束")
# network筛选出可靠的inlier
predict = ornet.predict(descriptor_final)
inlier_index = np.where(np.argmax(predict, axis=1) == 0)[0]
print("inlier数量：", len(inlier_index))
# 转换类型以便于喂入matlab +1 是因为matalb从1开始索引
n_pre_matches_s, param = eng.featureTransform(n_pre_matches_s,
n_pre_matches_r,
matlab.int32((inlier_index +
1).tolist()),
nargout=2)
img_1 = img_1[:, :, [2, 1, 0]]
img_2 = img_2[:, :, [2, 1, 0]]
img_1 = np.array(img_1) / 255.0
img_2 = np.array(img_2) / 255.0
img_1 = matlab.double(img_1.tolist())
img_2 = matlab.double(img_2.tolist())
pre_matches_r = matlab.double(pre_matches_r.tolist())
pre_matches_s = matlab.double(pre_matches_s.tolist())
eng.demonstration(img_2,
img_1,
pre_matches_s,
pre_matches_r,
matlab.int32((inlier_index + 1).tolist()),
print "Basic Testing End"
def plot_test(eng):
print "Plot Testing Begin"
eng.workspace['data'] = \
eng.randi(matlab.double([1,100]),matlab.double([30,2]))
eng.eval("plot(data(:,1),'ro-')")
eng.hold('on',nargout=0)
eng.eval("plot(data(:,2),'bx--')")
print "Plot testing end"
if __name__=='__main__':
# basic_test(eng)
I=eng.imread('rice.png')
mapping=eng.getmapping(8,'riu2')
lbpMap = eng.lbp(I, 2, 8, 0, 0)
H1=eng.lbp(I,1,8,mapping,'h')
print H1
eng.subplot(2,1,1)
eng.stem(H1)
H2=eng.lbp(I)
eng.subplot(2,1,2)
eng.stem(H2)
# a = matlab.double([1,4,9,16,25],[3,4,5,6,7,8])
SP=matlab.int32([[-1,-1],[-1,0],[-1,1],[0,-1],[-0,1],[1,-1],[1,0],[1,1]])
I2=eng.lbp(I,SP,0,'i')
eng.quit()
def _convert_dict_dtypes(cls, dictionary):
"""
takes in dictionary of numpy/python dtypes, and returns
dictionary of nearly equivalent MATLAB dtypes.
type 'list' is currently unsupported if they are mixed dtype.
(though this could be easily implemented using MATLAB cell,
it would not work well in the context of FEM_VGPy)
"""
dict_out = {}
for key in dictionary.keys():
# walk through all keys
value = dictionary[key]
# first if-elif ladder (initial checks)
if '__' in (key[0:2], key[-2:]):
# this is a python construct,
# don't add it to the output.
continue
elif value is None:
# this is a python construct,
# don't add it to the output.
continue
elif type(value) is str:
# matlab can automatically convert str to char
dict_out[key] = value
continue
elif type(value) is bool:
# matlab can automatically handle bool
dict_out[key] = value
continue
elif type(value) is list:
# attempt to convert this to a tuple. element
# dtypes will be taken care of in 2nd ladder
value = tuple(value)
# second if-elif ladder (convert dtypes)
if type(value) is tuple:
# iterable type; convert to array
if (type(value[0]) is int) or (type(value[0]) is numpy.int_):
dict_out[key] = matlab.int32(value)
elif (type(value[0]) is float) or (type(value[0]) is numpy.float_):
dict_out[key] = matlab.double(value)
elif type(value) is numpy.ndarray:
# we want to convert to an equivalent matlab matrix...
# convert data to equivalent list
data_list = value.tolist()
# change to matlab equivalent and save
if (value.dtype == 'int32') or (value.dtype == 'int64') or (value.dtype == 'int'):
# for python 2.7, int64 is always treated as int32 in matlab.
dict_out[key] = matlab.int32(data_list)
else:
dict_out[key] = matlab.double(data_list)
elif type(value) is float:
# matlab considers floats to be matrices as well.
dict_out[key] = matlab.double([value])
elif type(value) is int:
# matlab considers ints to be matrices as well.
dict_out[key] = matlab.int32([value])
elif type(value) is dict:
# use recursion to take care of this
dict_out[key] = cls._convert_dict_dtypes(value)
elif type(value) is unicode:
# convert this to a string representation
dict_out[key] = str(value)
else:
# undefined. alert user, then save as-is.
# if there is a problem, matlab engine will
# throw the proper exceptions
print "\n!!! undefined type ",
print type(value),
print "... saving anyway\n"
dict_out[key] = value
return dict_out
