def farthestSampling(file_names, NUM_POINT):
file_indexs = np.arange(0, len(file_names))
inputData = dict()
inputLabel = dict()
for index in range (len(file_indexs)):
current_data, current_label = utils.loadDataFile(file_names[file_indexs[index]])
current_data = current_data[:,0:NUM_POINT,:]
current_label = np.squeeze(current_label)
current_label= np.int_(current_label)
inputData.update({index : current_data})
inputLabel.update({index : current_label})
return inputData, inputLabel
def uniformSampling(file_names, NUM_POINT):
file_indexs = np.arange(0, len(file_names))
inputData = dict()
inputLabel = dict()
for index in range (len(file_indexs)):
current_data, current_label = utils.loadDataFile(file_names[file_indexs[index]])
current_label = np.squeeze(current_label)
current_label= np.int_(current_label)
output = np.zeros((len(current_data), NUM_POINT, 3))
for i,object_xyz in enumerate (current_data):
samples_index=np.random.choice(2048, NUM_POINT, replace=False)
output[i] = object_xyz[samples_index]
inputData.update({index : output})
inputLabel.update({index : current_label})
return inputData, inputLabel
def farthestSampling(file_names, NUM_POINT):
#Description: load point cloud data into dictionary format {key: batch index, value: batch data or labels}
# containing each batch of the data (data prepared by farthest sampling)
#input: (1)file name (2) point number
#return: (1) input data dictionary (2) input data label
file_indexs = np.arange(0, len(file_names))
inputData = dict()
inputLabel = dict()
for index in range (len(file_indexs)):
current_data, current_label = utils.loadDataFile(file_names[file_indexs[index]])
current_data = current_data[:,0:NUM_POINT,:]
current_label = np.squeeze(current_label)
current_label= np.int_(current_label)
inputData.update({index : current_data})
inputLabel.update({index : current_label})
return inputData, inputLabel
def farthestSampling(file_names, NUM_POINT):
#Description: load point cloud data into dictionary format {key: batch index, value: batch data or labels}
# containing each batch of the data (data prepared by farthest sampling)
#input: (1)file name (2) point number
#return: (1) input data dictionary (2) input data label
file_indexs = np.arange(0, len(file_names))
inputData = dict()
inputLabel = dict()
for index in range (len(file_indexs)):
current_data, current_label = utils.loadDataFile(file_names[file_indexs[index]])
current_data = current_data[:,0:NUM_POINT,:]
current_label = np.squeeze(current_label)
current_label= np.int_(current_label)
inputData.update({index : current_data})
inputLabel.update({index : current_label})
return inputData, inputLabel
def uniformSampling(file_names, NUM_POINT):
#Description: load point cloud data into dictionary format {key: batch index, value: batch data or labels}
# containing each batch of the data (data prepared by uniform sampling)
#input: (1)file name (2) point number
#return: (1) input data dictionary (2) input data label
file_indexs = np.arange(0, len(file_names))
inputData = dict()
inputLabel = dict()
for index in range (len(file_indexs)):
current_data, current_label = utils.loadDataFile(file_names[file_indexs[index]])
current_label = np.squeeze(current_label)
current_label= np.int_(current_label)
output = np.zeros((len(current_data), NUM_POINT, 3))
for i,object_xyz in enumerate (current_data):
samples_index=np.random.choice(2048, NUM_POINT, replace=False) #随机从2048个点中取NUM_POINT个点的ID
output[i] = object_xyz[samples_index]
inputData.update({index : output})
inputLabel.update({index : current_label})
return inputData, inputLabel
def uniformSampling(file_names, NUM_POINT):
#Description: load point cloud data into dictionary format {key: batch index, value: batch data or labels}
# containing each batch of the data (data prepared by uniform sampling)
#input: (1)file name (2) point number
#return: (1) input data dictionary (2) input data label
file_indexs = np.arange(0, len(file_names))
inputData = dict()
inputLabel = dict()
for index in range (len(file_indexs)):
current_data, current_label = utils.loadDataFile(file_names[file_indexs[index]])
current_label = np.squeeze(current_label)
current_label= np.int_(current_label)
output = np.zeros((len(current_data), NUM_POINT, 3))
for i,object_xyz in enumerate (current_data):
samples_index=np.random.choice(2048, NUM_POINT, replace=False)
output[i] = object_xyz[samples_index]
inputData.update({index : output})
inputLabel.update({index : current_label})
return inputData, inputLabel
