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import argparse
import json
import logging
import os
from os.path import join
import cv2
import matplotlib as mpl
import matplotlib.cm as cm
import numpy as np
import torch
import matplotlib.pyplot as plt
def save_vidar_dat(save_path, SpikeSeq, filpud=True, delete_if_exists=True):
if delete_if_exists:
if os.path.exists(save_path):
os.remove(save_path)
sfn, h, w = SpikeSeq.shape
assert (h * w) % 8 == 0
base = np.power(2, np.linspace(0, 7, 8))
fid = open(save_path, 'ab')
for img_id in range(sfn):
if filpud:
spike = np.flipud(SpikeSeq[img_id, :, :])
else:
spike = SpikeSeq[img_id, :, :]
spike = spike.flatten()
spike = spike.reshape([int(h*w/8), 8])
data = spike * base
data = np.sum(data, axis=1).astype(np.uint8)
fid.write(data.tobytes())
fid.close()
return
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 RawToSpike(video_seq, h, w, flipud=True):
video_seq = np.array(video_seq).astype(np.uint8)
img_size = h*w
img_num = len(video_seq)//(img_size//8)
SpikeMatrix = np.zeros([img_num, h, w], np.uint8)
pix_id = np.arange(0,h*w)
pix_id = np.reshape(pix_id, (h, w))
comparator = np.left_shift(1, np.mod(pix_id, 8))
byte_id = pix_id // 8
for img_id in np.arange(img_num):
id_start = int(img_id)*int(img_size)//8
id_end = int(id_start) + int(img_size)//8
cur_info = video_seq[id_start:id_end]
data = cur_info[byte_id]
result = np.bitwise_and(data, comparator)
if flipud:
SpikeMatrix[img_id, :, :] = np.flipud((result == comparator))
else:
SpikeMatrix[img_id, :, :] = (result == comparator)
return SpikeMatrix
def spikes_to_middletfi(spike, middle, window=50):
C, H, W = spike.shape
lindex, rindex = torch.zeros([H, W]), torch.zeros([H, W])
l, r = middle+1, middle+1
for r in range(middle+1, middle + window+1):
l = l - 1
if l>=0:
newpos = spike[l, :, :]*(1 - torch.sign(lindex))
distance = l*newpos
lindex += distance
if r<C:
newpos = spike[r, :, :]*(1 - torch.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
tfi = tfi.unsqueeze(0)
return tfi.float()
def spikes_to_tfp(spike, idx, halfwsize):
# real size of window == 2*halfwsize+1
spike_ = spike[idx-halfwsize:idx+halfwsize]
tfp_img = torch.mean(spike_, axis=0)
spike_min, spike_max = torch.min(tfp_img), torch.max(tfp_img)
tfp_img = (tfp_img - spike_min) / (spike_max - spike_min)
return tfp_img
if __name__ == "__main__":
spike_path = 'spike_0000000082.npy'
spike_path = 'spike_0000000276.npy'
for i in ["08", "26", "28"]:
spike_path = f'C:/Users/lze/Desktop/dat/MDE_Dataset/Outdoor-Spike/seq_{i}.dat'
f = open(spike_path, 'rb')
spike_seq = f.read()
spike_seq = np.frombuffer(spike_seq, 'b')
spikes = RawToSpike(spike_seq, 250, 400)
spikes = spikes.astype(np.float32)
spikes = torch.from_numpy(spikes)
f.close()
if i == "08":
spikes = spikes[:, 15:-16, 89:-92]
elif i == "26":
spikes = spikes[:, 18:-18, 87:-99]
elif i == "28":
spikes = spikes[:, 13:-13, 88:-88]
print(spikes.shape)
quit()
tfp = spikes_to_tfp(spikes, 10000, 100)
print(tfp.shape)
frame_to_plot = tfp.numpy() # 将torch张量转换为numpy数组
plt.imshow(frame_to_plot, cmap='gray') # 使用灰度色图显示
plt.savefig(f'{i}.png')
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