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SDK Core Python bindings

In this notebook, we describe some of the processes available in Python in the module metavision_sdk_core and present the results visually

import metavision_sdk_core
from metavision_sdk_base import EventCD
from metavision_core.event_io import RawReader
from metavision_sdk_core import BaseFrameGenerationAlgorithm

# if the file doesn't exist, it will be downloaded from Prophesee's public sample server
from metavision_core.utils import get_sample

%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = [11, 7]
import os

Load automotive sequence

sequence_filename_raw = "driving_sample.raw"
get_sample(sequence_filename_raw, folder=".")
assert os.path.isfile(sequence_filename_raw)

mv_raw = RawReader(sequence_filename_raw)
height, width = mv_raw.get_size()
assert (width, height) == (1280, 720)

ev = mv_raw.load_delta_t(10000)

image = np.zeros((height, width, 3), dtype=np.uint8)
BaseFrameGenerationAlgorithm.generate_frame(ev, image)
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf941f05f8>

../../../../_images/metavision_sdk_core_bindings_4_1.png

ROIFilterAlgorithm

Output absolute coordinates (default)

Coordinates of output events are unchanged (expressed in the frame’s coordinate system)

roi_filter = metavision_sdk_core.RoiFilterAlgorithm(750, 250, 1000, 450)
filtered_events_buffer = roi_filter.get_empty_output_buffer()
assert filtered_events_buffer.numpy().dtype == EventCD

roi_filter.process_events(ev, filtered_events_buffer)
filtered_events_np = filtered_events_buffer.numpy()


assert filtered_events_np["x"].min() >= 750
assert filtered_events_np["x"].max() <= 1000
assert filtered_events_np["y"].min() >= 250
assert filtered_events_np["y"].max() <= 450

BaseFrameGenerationAlgorithm.generate_frame(filtered_events_np, image)
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf940f64a8>

../../../../_images/metavision_sdk_core_bindings_6_1.png

Output relative coordinates

Coordinates are expressed in the coordinate system of the ROI (top left of the ROI region is (0,0))

roi_filter_relative = metavision_sdk_core.RoiFilterAlgorithm(750, 250, 999, 449, output_relative_coordinates=True)
filtered_events_buffer = roi_filter_relative.get_empty_output_buffer()
assert filtered_events_buffer.numpy().dtype == EventCD

roi_filter_relative.process_events(ev, filtered_events_buffer)
filtered_events_np = filtered_events_buffer.numpy()


assert filtered_events_np["x"].min() >= 0
assert filtered_events_np["x"].max() < 250
assert filtered_events_np["y"].min() >= 0
assert filtered_events_np["y"].max() < 200

image_small = np.zeros((201, 251, 3), dtype=np.uint8)
BaseFrameGenerationAlgorithm.generate_frame(filtered_events_np, image_small)
plt.imshow(image_small[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf940657b8>

../../../../_images/metavision_sdk_core_bindings_8_1.png

FlipXAlgorithm / FlipYAlgorithm

flip_x = metavision_sdk_core.FlipXAlgorithm(width - 1)
flipped_events_buffer = flip_x.get_empty_output_buffer()

flip_x.process_events(ev, flipped_events_buffer)
plt.title("Events flipped horizontally")
BaseFrameGenerationAlgorithm.generate_frame(flipped_events_buffer.numpy(), image)
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf9403fc50>

../../../../_images/metavision_sdk_core_bindings_10_1.png

Processing in-place

Calling process_events() does not change the input events. To perform processing in-place, use the method process_events_() instead.

plt.title("Input events")
BaseFrameGenerationAlgorithm.generate_frame(ev, image)
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf8ef86c88>

../../../../_images/metavision_sdk_core_bindings_12_1.png

flip_y = metavision_sdk_core.FlipYAlgorithm(height - 1)
flip_y.process_events_(ev)
# input events have been processed in-place
plt.title("Events flipped vertically")
BaseFrameGenerationAlgorithm.generate_frame(ev, image)
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf8ef6fc50>

../../../../_images/metavision_sdk_core_bindings_13_1.png

Load hand-spinner sequence

sequence_filename_raw = "spinner.raw"
get_sample(sequence_filename_raw, folder=".")
assert os.path.isfile(sequence_filename_raw)

mv_raw = RawReader(sequence_filename_raw)
height, width = mv_raw.get_size()
assert (width, height) == (640, 480)

ev = mv_raw.load_delta_t(5000) # 5 ms

image = np.zeros((height, width, 3), dtype=np.uint8)
BaseFrameGenerationAlgorithm.generate_frame(ev, image)

plt.title("Input events")
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf8eec0ac8>

../../../../_images/metavision_sdk_core_bindings_15_1.png

PolarityInverterAlgorithm

This is used to invert the polarity of events

polarity_inverter = metavision_sdk_core.PolarityInverterAlgorithm()
filtered_events_buf = polarity_inverter.get_empty_output_buffer()

polarity_inverter.process_events(ev, filtered_events_buf)
BaseFrameGenerationAlgorithm.generate_frame(filtered_events_buf.numpy(), image)
plt.title("Inverted polarity")
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf94132f60>

../../../../_images/metavision_sdk_core_bindings_17_1.png

PolarityFilterAlgorithm

This is used to filter the events by polarity

polarity_filter = metavision_sdk_core.PolarityFilterAlgorithm(1)
filtered_events_buf = polarity_filter.get_empty_output_buffer() # keep only positive events

polarity_filter.process_events(ev, filtered_events_buf)
BaseFrameGenerationAlgorithm.generate_frame(filtered_events_buf.numpy(), image)
plt.title("Keep only positive events")
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf9419b668>

../../../../_images/metavision_sdk_core_bindings_19_1.png

polarity_filter.polarity = 0 # keep only negative events
polarity_filter.process_events(ev, filtered_events_buf)
BaseFrameGenerationAlgorithm.generate_frame(filtered_events_buf.numpy(), image)
plt.title("Keep only negative events")
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf8eefbc50>

../../../../_images/metavision_sdk_core_bindings_20_1.png

Load counting sequence and select a Region Of Interest

sequence_filename_raw = "80_balls.raw"
get_sample(sequence_filename_raw, folder=".")
assert os.path.isfile(sequence_filename_raw)

mv_raw = RawReader(sequence_filename_raw)
height, width = mv_raw.get_size()
assert (width, height) == (640, 480)

ev = mv_raw.load_n_events(3000) # discard first 3000 events

ev = mv_raw.load_n_events(3000) # process next 3000 events
ev["t"] -= ev["t"].min()

image = np.zeros((height, width, 3), dtype=np.uint8)
BaseFrameGenerationAlgorithm.generate_frame(ev, image)

plt.title("Input events")
plt.imshow(image[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf8eebd550>

../../../../_images/metavision_sdk_core_bindings_22_1.png

roi_filter = metavision_sdk_core.RoiFilterAlgorithm(300, 0, 350, 50, output_relative_coordinates=True)
ev_roi_buffer = roi_filter.get_empty_output_buffer()
roi_filter.process_events(ev, ev_roi_buffer)
roi_width, roi_height = 51, 51

image_roi = np.zeros((roi_height, roi_width, 3), dtype=np.uint8)
BaseFrameGenerationAlgorithm.generate_frame(ev_roi_buffer.numpy(), image_roi)

plt.title("Input events in ROI")
plt.imshow(image_roi[:,:,::-1])

<matplotlib.image.AxesImage at 0x7faf8ee16a90>

../../../../_images/metavision_sdk_core_bindings_23_1.png

TimeSurfaceProducerAlgorithm

We can compute a Time-Surface on this chunk of events. It can be computed either by separating the positive and negative events, or by merging them in the same channel.

Separating positive and negative events

last_processed_timestamp = 0
time_surface_double_channel = metavision_sdk_core.MostRecentTimestampBuffer(rows=roi_width,
                                                                            cols=roi_height,
                                                                            channels=2)

ts_prod_double_channel = metavision_sdk_core.TimeSurfaceProducerAlgorithmSplitPolarities(width=roi_width,
                                                                                         height=roi_height)
def callback_double_channel(ts, time_surface):
    global last_processed_timestamp
    global time_surface_double_channel
    last_processed_timestamp = ts
    time_surface_double_channel.numpy()[...] = time_surface.numpy()[...]
ts_prod_double_channel.set_output_callback(callback_double_channel)

ts_prod_double_channel.process_events(ev_roi_buffer)
plt.title("TimeSurface positive events")
plt.imshow(time_surface_double_channel.numpy()[:,:,1])

<matplotlib.image.AxesImage at 0x7faf8ed7e780>

../../../../_images/metavision_sdk_core_bindings_25_1.png

plt.title("TimeSurface negative events")
plt.imshow(time_surface_double_channel.numpy()[:,:,0])

<matplotlib.image.AxesImage at 0x7faf8ed6b5c0>

../../../../_images/metavision_sdk_core_bindings_26_1.png

Merging positive and negative events

last_processed_timestamp = 0
time_surface_single_channel = metavision_sdk_core.MostRecentTimestampBuffer(rows=roi_height,
                                                                            cols=roi_width,
                                                                            channels=1)

ts_prod_single_channel = metavision_sdk_core.TimeSurfaceProducerAlgorithmMergePolarities(width=roi_width,
                                                                                         height=roi_height)
def callback_single_channel(ts, time_surface):
    global last_processed_timestamp
    global time_surface_single_channel
    last_processed_timestamp = ts
    time_surface_single_channel.numpy()[...] = time_surface.numpy()[...]
ts_prod_single_channel.set_output_callback(callback_single_channel)

ts_prod_single_channel.process_events(ev_roi_buffer)
plt.title("TimeSurface")
min_time = time_surface_single_channel.numpy()[time_surface_single_channel.numpy() != 0].min()
plt.imshow(time_surface_single_channel.numpy())

<matplotlib.image.AxesImage at 0x7faf8ed23f98>

../../../../_images/metavision_sdk_core_bindings_28_1.png

Note

This tutorial was created using Jupiter Notebooks

Download the source code.