File size: 10,854 Bytes
5fc3d65 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 |
import torch
import torch.nn as nn
import numpy as np
import os
def nor(img):
return (img - np.min(img)) / (np.max(img) - np.min(img))
def baocun(filename=""):
sz = os.path.getsize(filename)
frame_sz = 1000 * 1024 // 8 #每一帧占多少字节
data_len = sz // frame_sz #有多少帧
frame_b = [] #按帧保存
with open(filename, 'rb') as fu:
for i in range(data_len):
a = fu.read(frame_sz)
frame_b.append(a)
print("len(frame_b) ",len(frame_b))
for i in range(0, len(frame_b)-41,5):
data = frame_b[i:i+41]
save_path = os.path.join("./bubble/","%s_%d.dat"%("1600",i))
with open(save_path, "wb") as fr:
for j in range(len(data)):
fr.write(data[j])
def load_vidar_dat(filename, frame_cnt=None, width=640, height=480, reverse_spike=True):
'''
output: <class 'numpy.ndarray'> (frame_cnt, height, width) {0，1} float32
'''
array = np.fromfile(filename, dtype=np.uint8)
len_per_frame = height * width // 8
framecnt = frame_cnt if frame_cnt != None else len(array) // len_per_frame
spikes = []
for i in range(framecnt):
compr_frame = array[i * len_per_frame: (i + 1) * len_per_frame]
blist = []
for b in range(8):
blist.append(np.right_shift(np.bitwise_and(
compr_frame, np.left_shift(1, b)), b))
frame_ = np.stack(blist).transpose()
frame_ = frame_.reshape((height, width), order='C')
if reverse_spike:
frame_ = np.flipud(frame_)
spikes.append(frame_)
return np.array(spikes).astype(np.float32)
def middleTFI(spike, middle, window=50):
'''
左右估计tfi
input: <class 'numpy.ndarray'> (frame_cnt, h, w) {0，1} float32
output: <class 'numpy.ndarray'> (h, w), (0,1) float32
'''
C, H, W = spike.shape
lindex, rindex = np.zeros([H, W]), np.zeros([H, W])
l, r = middle+1, middle+1
for r in range(middle+1, middle + window+1): #往左包括自己50个,往右不包括自己也是50个
l = l - 1
if l>=0:
newpos = spike[l, :, :]*(1 - np.sign(lindex))
distance = l*newpos
lindex += distance
if r<C:
newpos = spike[r, :, :]*(1 - np.sign(rindex))
distance = r*newpos
rindex += distance
if l<0 and r>=C:
break
rindex[rindex==0] = window+middle
lindex[lindex==0] = middle-window
interval = rindex - lindex
tfi = 1.0 / interval
return tfi.astype(np.float32) #numpy.zeros 默认float64
def middleISI(spike, middle, window=50):
'''
左右估计tfi
input: <class 'numpy.ndarray'> (frame_cnt, h, w) {0，1} float32
output: <class 'numpy.ndarray'> (h, w), (0,1) float32
'''
C, H, W = spike.shape
lindex, rindex = np.zeros([H, W]), np.zeros([H, W])
l, r = middle+1, middle+1
for r in range(middle+1, middle + window+1): #往左包括自己50个,往右不包括自己也是50个
l = l - 1
if l>=0:
newpos = spike[l, :, :]*(1 - np.sign(lindex))
distance = l*newpos
lindex += distance
if r<C:
newpos = spike[r, :, :]*(1 - np.sign(rindex))
distance = r*newpos
rindex += distance
if l<0 and r>=C:
break
rindex[rindex==0] = window+middle
lindex[lindex==0] = middle-window
interval = rindex - lindex
interval = nor(interval)
return interval.astype(np.float32) #numpy.zeros 默认float64
def middleTFP(spike, middle, window=50):
'''
左右估计tfp
input: <class 'numpy.ndarray'> (frame_cnt, h, w) {0，1} float32
output: <class 'numpy.ndarray'> (h, w), (0,1) float32
'''
C, _, _ = spike.shape
l,r = max(middle-window+1,0),min(middle+window+1,C)
tfp = np.mean(spike[l:r],axis=0) #往左包括自己50个,往右不包括自己也是50个
return tfp
class torch_filter(nn.Module):
def __init__(self, filter_weight, is_grad=False):
super(torch_filter, self).__init__()
assert type(filter_weight) == np.ndarray
k=filter_weight.shape[0]
filter=torch.tensor(filter_weight).unsqueeze(dim=0).unsqueeze(dim=0)
# filters = torch.cat([filter, filter, filter], dim=0)
self.conv = nn.Conv2d(1, 1, kernel_size=k, bias=False, padding=int((k-1)/2))
self.conv.weight.data.copy_(filter)
self.conv.requires_grad_(is_grad)
def forward(self,x):
output = self.conv(x)
output = torch.clip(output, 0, 1)
return output
class GradFilter_Torch(nn.Module):
def __init__(self, type='sobel', is_grad=False):
super(GradFilter_Torch, self).__init__()
# assert type(filter_weight) == np.ndarray
if type == 'sobel':
weight1 = np.array(
[
[-1, 0, 1],
[-2, 0, 2],
[-1, 0, 1],
]
)
weight2 = np.array(
[
[1, 2, 1],
[0, 0, 0],
[-1, -2, -1],
]
)
elif type == 'scharr':
weight1 = np.array(
[
[-3, 0, 3],
[-10, 0, 10],
[-3, 0, 3],
]
)
weight2 = np.array(
[
[3, 10, 3],
[0, 0, 0],
[-3, -10, -3],
]
)
k=weight1.shape[0]
filter1=torch.tensor(weight1).unsqueeze(dim=0).unsqueeze(dim=0)
filter2=torch.tensor(weight2).unsqueeze(dim=0).unsqueeze(dim=0)
self.conv1 = nn.Conv2d(1, 1, kernel_size=k, bias=False, padding=int((k-1)/2))
self.conv1.weight.data.copy_(filter1)
self.conv1.requires_grad_(is_grad)
self.conv2 = nn.Conv2d(1, 1, kernel_size=k, bias=False, padding=int((k-1)/2))
self.conv2.weight.data.copy_(filter2)
self.conv2.requires_grad_(is_grad)
def forward(self,x):
output1 = self.conv1(x)
output2 = self.conv2(x)
# output = torch.clip(output, 0, 1)
return output1+output2
def tfi_torch(spk_seq, device=torch.device("cuda")):
b, n, h, w = spk_seq.shape
last_index = torch.zeros((b, h, w)).to(device).float()
cur_index = torch.zeros((b, h, w)).to(device).float()
c_frames = torch.zeros_like(spk_seq).to(device).float()
for i in range(n - 1):
last_index = cur_index
cur_index = spk_seq[:,i+1,:,:] * (i + 1) + (1 - spk_seq[:,i+1,:,:]) * last_index
c_frames[:,i,:,:] = cur_index - last_index
last_frame = c_frames[:,n-1,:,:]
last_frame[last_frame==0] = n
c_frames[:,n-1,:,:] = last_frame
last_interval = n * torch.ones((b, h, w)).to(device).float()
for i in range(n - 2, -1, -1):
last_interval = spk_seq[:,i+1,:,:] * c_frames[:,i,:,:] + (1 - spk_seq[:,i+1,:,:]) * last_interval
tmp_frame = c_frames[:,i,:,:]
tmp_frame[tmp_frame==0] = last_interval[tmp_frame==0]
c_frames[:,i,:,:] = tmp_frame
return 1. / c_frames
def interval_torch(spk_seq, device=torch.device("cuda")):
b, n, h, w = spk_seq.shape
last_index = torch.zeros((b, h, w)).to(device).float()
cur_index = torch.zeros((b, h, w)).to(device).float()
c_frames = torch.zeros_like(spk_seq).to(device).float()
for i in range(n - 1):
last_index = cur_index
cur_index = spk_seq[:,i+1,:,:] * (i + 1) + (1 - spk_seq[:,i+1,:,:]) * last_index
c_frames[:,i,:,:] = cur_index - last_index
last_frame = c_frames[:,n-1,:,:]
last_frame[last_frame==0] = n
c_frames[:,n-1,:,:] = last_frame
last_interval = n * torch.ones((b, h, w)).to(device).float()
for i in range(n - 2, -1, -1):
last_interval = spk_seq[:,i+1,:,:] * c_frames[:,i,:,:] + (1 - spk_seq[:,i+1,:,:]) * last_interval
tmp_frame = c_frames[:,i,:,:]
tmp_frame[tmp_frame==0] = last_interval[tmp_frame==0]
c_frames[:,i,:,:] = tmp_frame
return c_frames
def tfp_torch(spk_seq, wsize, device):
b, n, h, w = spk_seq.shape
clips = torch.zeros((b, n - wsize + 1, wsize, h, w)).to(device).float()
for i in range(n - wsize + 1):
clips[:,i,:,:,:] = spk_seq[:,i:i+wsize,:,:]
c_frames = torch.mean(clips, axis=2)
return c_frames
def tfi_np(spk_seq, gamma):
n, h, w = spk_seq.shape
last_index = np.zeros((1, h, w))
cur_index = np.zeros((1, h, w))
c_frames = np.zeros_like(spk_seq).astype(np.float64)
for i in range(n - 1):
last_index = cur_index
cur_index = spk_seq[i+1,:,:] * (i + 1) + (1 - spk_seq[i+1,:,:]) * last_index
c_frames[i,:,:] = cur_index - last_index
last_frame = c_frames[n-1:,:]
last_frame[last_frame==0] = n
c_frames[n-1,:,:] = last_frame
last_interval = n * np.ones((1, h, w))
for i in range(n - 2, -1, -1):
last_interval = spk_seq[i+1,:,:] * c_frames[i,:,:] + (1 - spk_seq[i+1,:,:]) * last_interval
tmp_frame = np.expand_dims(c_frames[i,:,:], 0)
tmp_frame[tmp_frame==0] = last_interval[tmp_frame==0]
c_frames[i] = tmp_frame
return ((1 / c_frames)**gamma * 255).astype(np.uint8)
def tfp_np(spk_seq, win_size, gamma):
half_win = win_size // 2
n, h, w = spk_seq.shape
c_frames = np.zeros((n - win_size + 1, h, w)).astype(np.float64)
for i in range(half_win, n - half_win):
c_frame = np.mean(spk_seq[i - half_win:i+half_win+1,:,:], axis=0)
c_frames[i-half_win,:,:] = c_frame
return (c_frames**gamma * 255).astype(np.uint8)
def interval_np(spk_seq, offset=0, type=np.uint16):
n, h, w = spk_seq.shape
last_index = np.zeros((1, h, w))
cur_index = np.zeros((1, h, w))
c_frames = np.zeros_like(spk_seq).astype(np.float64)
for i in range(n - 1):
last_index = cur_index
cur_index = spk_seq[i+1,:,:] * (i + 1) + (1 - spk_seq[i+1,:,:]) * last_index
c_frames[i,:,:] = cur_index - last_index
last_frame = c_frames[n-1:,:]
last_frame[last_frame==0] = n + 1
c_frames[n-1,:,:] = last_frame
last_interval = (n + 1) * np.ones((1, h, w))
for i in range(n - 2, -1, -1):
last_interval = spk_seq[i+1,:,:] * c_frames[i,:,:] + (1 - spk_seq[i+1,:,:]) * last_interval
tmp_frame = np.expand_dims(c_frames[i,:,:], 0)
tmp_frame[tmp_frame==0] = last_interval[tmp_frame==0]
c_frames[i] = tmp_frame
c_frames = c_frames + offset
return c_frames.astype(type)
|