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Update datasets.py #24

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78 changes: 57 additions & 21 deletions src/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -317,46 +317,82 @@ def rectify_events(self, x: np.ndarray, y: np.ndarray):
return rectify_map[y, x]

def get_data(self, index) -> Dict[str, any]:
ts_start: int = self.timestamps_flow[index] - self.delta_t_us
ts_end: int = self.timestamps_flow[index]
#ts_start: int = self.timestamps_flow[index] - self.delta_t_us
#ts_end: int = self.timestamps_flow[index]
ts_start1: int = self.timestamps_flow[index] - self.delta_t_us
ts_end1: int = self.timestamps_flow[index]
ts_start2: int = self.timestamps_flow[index + 1] - self.delta_t_us
ts_end2: int = self.timestamps_flow[index + 1]

file_index = self.indices[index]
#file_index = self.indices[index]
file_index1 = self.indices[index]
file_index2 = self.indices[index + 1]

output = {
'file_index': file_index,
'timestamp': self.timestamps_flow[index],
#'file_index': file_index,
'file_index1': file_index1,
'file_index2': file_index2,
#'timestamp': self.timestamps_flow[index],
'timestamp1': self.timestamps_flow[index],
'timestamp2': self.timestamps_flow[index + 1],
'seq_name': self.seq_name
}
# Save sample for benchmark submission
output['save_submission'] = file_index in self.idx_to_visualize
output['visualize'] = self.visualize_samples
event_data = self.event_slicer.get_events(
ts_start, ts_end)
p = event_data['p']
t = event_data['t']
x = event_data['x']
y = event_data['y']

#event_data = self.event_slicer.get_events(ts_start, ts_end)
event_data1 = self.event_slicer.get_events(ts_start1, ts_end1)
event_data2 = self.event_slicer.get_events(ts_start2, ts_end2)

#p = event_data['p']
#t = event_data['t']
#x = event_data['x']
#y = event_data['y']

#event_data1 = self.event_slicer.get_events(ts_start1, ts_end1)
p1 = event_data1['p']
t1 = event_data1['t']
x1 = event_data1['x']
y1 = event_data1['y']

#event_data2 = self.event_slicer.get_events(ts_start2, ts_end2)
p2 = event_data2['p']
t2 = event_data2['t']
x2 = event_data2['x']
y2 = event_data2['y']

p = np.concatenate((p1, p2))
t = np.concatenate((t1, t2))
x = np.concatenate((x1, x2))
y = np.concatenate((y1, y2))


xy_rect = self.rectify_events(x, y)
x_rect = xy_rect[:, 0]
y_rect = xy_rect[:, 1]

if self.voxel_grid is None:
raise NotImplementedError
else:
event_representation = self.events_to_voxel_grid(
p, t, x_rect, y_rect)
event_representation = self.events_to_voxel_grid(p, t, x_rect, y_rect)
output['event_volume'] = event_representation
output['name_map'] = self.name_idx

if self.load_gt:
output['flow_gt'
] = [torch.tensor(x) for x in self.load_flow(self.flow_png[index])]

output['flow_gt'
][0] = torch.moveaxis(output['flow_gt'][0], -1, 0)
output['flow_gt'
][1] = torch.unsqueeze(output['flow_gt'][1], 0)
if self.load_gt:
#output['flow_gt'] = [torch.tensor(x) for x in self.load_flow(self.flow_png[index])]
flow_gt1 = [torch.tensor(x) for x in self.load_flow(self.flow_png[index])]
flow_gt2 = [torch.tensor(x) for x in self.load_flow(self.flow_png[index + 1])]

#output['flow_gt'][0] = torch.moveaxis(output['flow_gt'][0], -1, 0)
#output['flow_gt'][1] = torch.unsqueeze(output['flow_gt'][1], 0)
flow_gt1[0] = torch.moveaxis(flow_gt1[0], -1, 0)
flow_gt1[1] = torch.unsqueeze(flow_gt1[1], 0)
flow_gt2[0] = torch.moveaxis(flow_gt2[0], -1, 0)
flow_gt2[1] = torch.unsqueeze(flow_gt2[1], 0)

output['flow_gt'] = [flow_gt1, flow_gt2]

return output

def __getitem__(self, idx):
Expand Down