File size: 5,515 Bytes
b004d6f | 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 | import numpy as np
# import matlab.engine
from .SSP.ssp import get_ssp_depth
c = 3e8
class SinglePhotonImaging:
def __init__(self, lr: int, lc: int, tp: float):
"""
初始化深度估计器
参数:
lr: 图像行数
lc: 图像列数
tp: 时间bin持续时间 (秒)
"""
self.lr = lr
self.lc = lc
self.tp = tp
def _spad2tof(self, spad: np.ndarray) -> np.ndarray:
"""
将稀疏SPAD数据转换为时间飞行(TOF)对象数组 [内部方法]
参数:
spad: 稀疏SPAD数据矩阵 [N, M]
返回:
np.ndarray: TOF对象数组 [lr, lc]，每个元素为时间bin数组
"""
rows, cols = spad.nonzero()
values = spad.data
tof = np.empty((self.lr, self.lc), dtype=np.ndarray)
for i in range(self.lr * self.lc):
indices = np.where(rows == i)[0]
ph = []
for j in indices:
ph += [int(cols[j])] * int(values[j])
ph = np.array([ph]).T
x = i % self.lc
y = i // self.lc
tof[x, y] = ph
return tof
def _time_to_depth(self, time_data: np.ndarray, time_bin: float) -> np.ndarray:
"""统一时间到深度转换"""
return time_data * c / 2 * time_bin
def max_hist(self, spads: np.ndarray) -> np.ndarray:
"""
通过滑动窗口均值法获取深度图
参数:
spads: 稀疏SPAD数据矩阵 [N, T]
返回:
np.ndarray: 深度图矩阵 [lc, lr] (单位:米)
"""
print("Max Hist Processing...")
spads_dense = spads.todense()
max_histogram_mean = np.empty(self.lr * self.lc, dtype=float)
for i in range(self.lr * self.lc):
data = np.ascontiguousarray(spads_dense[i, :]).ravel()
max_index = np.argmax(data)
max_histogram_mean[i] = max_index
depth = self._time_to_depth(
max_histogram_mean.reshape(self.lr, self.lc).T, self.tp
)
return depth
# def shin(self, spads: np.ndarray) -> np.ndarray:
# """
# 使用Shin算法估计深度图
# 参数:
# spads: 稀疏SPAD数据矩阵 [N, T]
# 返回:
# np.ndarray: 深度图矩阵 [lc, lr] (单位:米)
# """
# print("Shin Processing...")
# tofs = self._spad2tof(spads)
# eng = matlab.engine.start_matlab()
# eng.addpath(r"matlab\fcns_Shin")
# matlab_tofs = eng.eval("cell(1, 3136)", nargout=1)
# matlab_tofs = [tof for row in tofs for tof in row]
# tof_shin = np.array(eng.cal_Shin(matlab_tofs))
# eng.quit()
# depth = self._time_to_depth(tof_shin, self.tp)
# return depth
# def rapp(
# self, spads: np.ndarray, signal_per_pixel: float, frame_num: int
# ) -> np.ndarray:
# """
# 使用Rapp算法估计深度图
# 参数:
# spads: 稀疏SPAD数据矩阵 [N, T]
# signal_per_pixel: 每像素平均信号光子数
# frame_num: 总帧数
# 返回:
# np.ndarray: 深度图矩阵 [lc, lr] (单位:米)
# """
# print("Rapp Processing...")
# binnum = spads.shape[1]
# tofs = self._spad2tof(spads)
# sbr = 0.2
# perframenum = 50.0
# numFrames = perframenum * frame_num
# eng = matlab.engine.start_matlab()
# eng.addpath(r"matlab\fcns_Rapp")
# matlab_tofs = eng.eval("cell(1, 3136)", nargout=1)
# matlab_tofs = [tof for row in tofs for tof in row]
# tof_rapp = np.array(
# eng.cal_Rapp_py(
# matlab_tofs,
# signal_per_pixel,
# numFrames,
# sbr,
# float(self.tp),
# float(binnum),
# )
# )
# eng.quit()
# depth = self._time_to_depth(tof_rapp, self.tp)
# return depth
# def li(self, spads: np.ndarray) -> np.ndarray:
# """
# 使用Li算法估计深度图
# 参数:
# spads: 稀疏SPAD数据矩阵 [N, T]
# 返回:
# np.ndarray: 深度图矩阵 [lc, lr] (单位:米)
# """
# print("Li Processing...")
# eng = matlab.engine.start_matlab()
# eng.addpath(r"matlab\fcns_Li", nargout=0)
# # 直接使用原始SPAD数据
# spads = spads.todense()
# tof_li = np.array(
# eng.cal_Li_py(
# spads,
# float(self.tp),
# float(spads.shape[1]),
# nargout=1,
# )
# )
# eng.quit()
# depth = self._time_to_depth(tof_li, self.tp)
# return depth
def ssp(self, spads: np.ndarray) -> np.ndarray:
"""
使用SSP算法估计深度图
参数:
spads: 稀疏SPAD数据矩阵 [N, T]
返回:
np.ndarray: 深度图矩阵 [lc, lr] (单位:米)
"""
# print("SSP Processing...")
tp = self.tp
tr = spads.shape[1]
depth = get_ssp_depth(spads, tr, tp, self.lr, self.lc)
return depth
|