如何在列表中保存英特尔实感图像（pyrealsense2）
How to save Intel Realsense images in list (pyrealsense2)
我正在尝试将 Intel Realsense D435i 相机的深度和彩色图像保存在包含 300 张图像的列表中。然后我将使用多处理将这 300 张图像保存到我的磁盘上。但是每次我尝试时，程序都会成功地在列表中附加 15 张图像，然后出现此错误：
Frame didn't arrived within 5000
我确定我在 python 3.6 上安装了 64 位版本，并且当我不尝试将图像保存在列表中时，相机的流式传输非常好。真实感查看器也很好用。我也尝试过不同的分辨率和帧率，但它似乎也不起作用。有趣的是，如果我只保存彩色图像，我不会得到相同的错误，而是会在列表中一遍又一遍地得到相同的彩色图像。
if __name__ == '__main__':
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
profile = pipeline.start(config)
depth_sensor = profile.get_device().first_depth_sensor()
depth_sensor.set_option(
rs.option.visual_preset, 3
) # Set high accuracy for depth sensor
depth_scale = depth_sensor.get_depth_scale()
align_to = rs.stream.color
align = rs.align(align_to)
# Init variables
im_count = 0
image_chunk = []
image_chunk2 = []
# sentinel = True
try:
while True:
# Wait for a coherent pair of frames: depth and color
frames = pipeline.wait_for_frames()
aligned_frames = align.process(frames)
aligned_depth_frame = aligned_frames.get_depth_frame()
color_frame = aligned_frames.get_color_frame()
if not aligned_depth_frame or not color_frame:
print("problem here")
raise RuntimeError("Could not acquire depth or color frames.")
depth_image = np.asanyarray(aligned_depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
image_chunk.append(color_image)
image_chunk2.append(depth_image)
except Exception as e:
print(e)
finally:
# Stop streaming
pipeline.stop()
我只是需要它连续保存300张图像，仅此而已，所以我很困惑是什么导致了这个问题。
按住相框会锁定内存，最终会达到极限，从而无法获取更多图像。即使您正在创建图像，数据仍然来自框架。您需要在创建图像后克隆图像以将 link 释放到框架的内存中。
depth_image = np.asanyarray(aligned_depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
depth_image = depth_image.copy()
color_image = color_image.copy()
image_chunk.append(color_image)
image_chunk2.append(depth_image)
在此处阅读有关帧和内存管理的更多信息：
https://dev.intelrealsense.com/docs/frame-management
我创建了一个包装器 class 以从框架集中提取以后无法重新创建的各种元素。它有点沉重，但展示了一些可能对其他人有帮助的常见操作：
colorizer = None
align_to_depth = None
align_to_color = None
pointcloud = rs.pointcloud()
class IntelD435ImagePacket:
"""
Class that contains image and associated processing data.
"""
@property
def frame_id(self):
return self._frame_id
@property
def timestamp(self):
return self._timestamp
@property
def image_color(self):
return self._image_color
@property
def image_depth(self):
return self._image_depth
@property
def image_color_aligned(self):
return self._image_color_aligned
@property
def image_depth_aligned(self):
return self._image_depth_aligned
@property
def image_depth_colorized(self):
if not self._image_depth_colorized:
self._image_depth_colorized = cv2.applyColorMap(self.image_depth, cv2.COLORMAP_JET);
return self._image_depth_colorized
@property
def intrinsics(self):
return self._intrinsics
@property
def pointcloud(self):
return self._pointcloud
@property
def pointcloud_texture(self):
return self._pointcloud_texture
def _rs_intrinsics_to_opencv_matrix(self, rs_intrinsics):
fx = rs_intrinsics.fx
fy = rs_intrinsics.fy
cx = rs_intrinsics.ppx
cy = rs_intrinsics.ppy
s = 0 # skew
return np.array([fx, s, cx,
0, fy, cy,
0, 0, 1]).reshape(3, 3)
def __init__(self, frame_set, frame_id=None, timestamp=None, *args, **kwargs):
global colorizer
if not colorizer:
colorizer = rs.colorizer()
colorizer.set_option(rs.option.color_scheme, 0)
global align_to_depth
if not align_to_depth:
align_to_depth = rs.align(rs.stream.depth)
global align_to_color
if not align_to_color:
align_to_color = rs.align(rs.stream.color)
global pointcloud
if not pointcloud:
pointcloud = rs.pointcloud()
# Get intrinsics
profile = frame_set.get_profile()
video_stream_profile = profile.as_video_stream_profile()
rs_intrinsics = video_stream_profile.get_intrinsics()
self._intrinsics = self._rs_intrinsics_to_opencv_matrix(rs_intrinsics)
# Get pointcloud
depth_frame = frame_set.get_depth_frame()
color_frame = frame_set.get_color_frame()
pointcloud.map_to(color_frame)
points = pointcloud.calculate(depth_frame)
vtx = np.asanyarray(points.get_vertices())
points_arr = vtx.view(np.float32).reshape(vtx.shape + (-1,)).copy()
self._pointcloud = points_arr
# Get pointcloud texture mapping
tex = np.asanyarray(points.get_texture_coordinates())
color_map_arr = tex.view(np.float32).reshape(tex.shape + (-1,)).copy()
self._pointcloud_texture = color_map_arr
# Extract color image
color_frame = frame_set.get_color_frame()
self._image_color = np.asanyarray(color_frame.get_data()).copy()
# Extract depth image
depth_frame = frame_set.get_depth_frame()
self._image_depth = np.asanyarray(depth_frame.get_data()).copy()
# Align the color frame to depth frame and extract color image
color_frame_aligned = align_to_depth.process(frame_set).get_color_frame()
self._image_color_aligned = np.asanyarray(color_frame_aligned.get_data()).copy()
# Align the depth frame to color frame and extract depth image
depth_frame_aligned = align_to_color.process(frame_set).get_depth_frame()
self._image_depth_aligned = np.asanyarray(depth_frame_aligned.get_data()).copy()
self._image_depth_colorized = None
if frame_id:
self._frame_id = frame_id
else:
self._frame_id = frame_set.frame_number
if timestamp:
self._timestamp = timestamp
else:
self._timestamp = frame_set.timestamp
self.__dict__.update(kwargs)
我正在尝试将 Intel Realsense D435i 相机的深度和彩色图像保存在包含 300 张图像的列表中。然后我将使用多处理将这 300 张图像保存到我的磁盘上。但是每次我尝试时，程序都会成功地在列表中附加 15 张图像，然后出现此错误：
Frame didn't arrived within 5000
我确定我在 python 3.6 上安装了 64 位版本，并且当我不尝试将图像保存在列表中时，相机的流式传输非常好。真实感查看器也很好用。我也尝试过不同的分辨率和帧率，但它似乎也不起作用。有趣的是，如果我只保存彩色图像，我不会得到相同的错误，而是会在列表中一遍又一遍地得到相同的彩色图像。
if __name__ == '__main__':
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
profile = pipeline.start(config)
depth_sensor = profile.get_device().first_depth_sensor()
depth_sensor.set_option(
rs.option.visual_preset, 3
) # Set high accuracy for depth sensor
depth_scale = depth_sensor.get_depth_scale()
align_to = rs.stream.color
align = rs.align(align_to)
# Init variables
im_count = 0
image_chunk = []
image_chunk2 = []
# sentinel = True
try:
while True:
# Wait for a coherent pair of frames: depth and color
frames = pipeline.wait_for_frames()
aligned_frames = align.process(frames)
aligned_depth_frame = aligned_frames.get_depth_frame()
color_frame = aligned_frames.get_color_frame()
if not aligned_depth_frame or not color_frame:
print("problem here")
raise RuntimeError("Could not acquire depth or color frames.")
depth_image = np.asanyarray(aligned_depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
image_chunk.append(color_image)
image_chunk2.append(depth_image)
except Exception as e:
print(e)
finally:
# Stop streaming
pipeline.stop()
我只是需要它连续保存300张图像，仅此而已，所以我很困惑是什么导致了这个问题。
按住相框会锁定内存，最终会达到极限，从而无法获取更多图像。即使您正在创建图像，数据仍然来自框架。您需要在创建图像后克隆图像以将 link 释放到框架的内存中。
depth_image = np.asanyarray(aligned_depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
depth_image = depth_image.copy()
color_image = color_image.copy()
image_chunk.append(color_image)
image_chunk2.append(depth_image)
在此处阅读有关帧和内存管理的更多信息： https://dev.intelrealsense.com/docs/frame-management
我创建了一个包装器 class 以从框架集中提取以后无法重新创建的各种元素。它有点沉重，但展示了一些可能对其他人有帮助的常见操作：
colorizer = None
align_to_depth = None
align_to_color = None
pointcloud = rs.pointcloud()
class IntelD435ImagePacket:
"""
Class that contains image and associated processing data.
"""
@property
def frame_id(self):
return self._frame_id
@property
def timestamp(self):
return self._timestamp
@property
def image_color(self):
return self._image_color
@property
def image_depth(self):
return self._image_depth
@property
def image_color_aligned(self):
return self._image_color_aligned
@property
def image_depth_aligned(self):
return self._image_depth_aligned
@property
def image_depth_colorized(self):
if not self._image_depth_colorized:
self._image_depth_colorized = cv2.applyColorMap(self.image_depth, cv2.COLORMAP_JET);
return self._image_depth_colorized
@property
def intrinsics(self):
return self._intrinsics
@property
def pointcloud(self):
return self._pointcloud
@property
def pointcloud_texture(self):
return self._pointcloud_texture
def _rs_intrinsics_to_opencv_matrix(self, rs_intrinsics):
fx = rs_intrinsics.fx
fy = rs_intrinsics.fy
cx = rs_intrinsics.ppx
cy = rs_intrinsics.ppy
s = 0 # skew
return np.array([fx, s, cx,
0, fy, cy,
0, 0, 1]).reshape(3, 3)
def __init__(self, frame_set, frame_id=None, timestamp=None, *args, **kwargs):
global colorizer
if not colorizer:
colorizer = rs.colorizer()
colorizer.set_option(rs.option.color_scheme, 0)
global align_to_depth
if not align_to_depth:
align_to_depth = rs.align(rs.stream.depth)
global align_to_color
if not align_to_color:
align_to_color = rs.align(rs.stream.color)
global pointcloud
if not pointcloud:
pointcloud = rs.pointcloud()
# Get intrinsics
profile = frame_set.get_profile()
video_stream_profile = profile.as_video_stream_profile()
rs_intrinsics = video_stream_profile.get_intrinsics()
self._intrinsics = self._rs_intrinsics_to_opencv_matrix(rs_intrinsics)
# Get pointcloud
depth_frame = frame_set.get_depth_frame()
color_frame = frame_set.get_color_frame()
pointcloud.map_to(color_frame)
points = pointcloud.calculate(depth_frame)
vtx = np.asanyarray(points.get_vertices())
points_arr = vtx.view(np.float32).reshape(vtx.shape + (-1,)).copy()
self._pointcloud = points_arr
# Get pointcloud texture mapping
tex = np.asanyarray(points.get_texture_coordinates())
color_map_arr = tex.view(np.float32).reshape(tex.shape + (-1,)).copy()
self._pointcloud_texture = color_map_arr
# Extract color image
color_frame = frame_set.get_color_frame()
self._image_color = np.asanyarray(color_frame.get_data()).copy()
# Extract depth image
depth_frame = frame_set.get_depth_frame()
self._image_depth = np.asanyarray(depth_frame.get_data()).copy()
# Align the color frame to depth frame and extract color image
color_frame_aligned = align_to_depth.process(frame_set).get_color_frame()
self._image_color_aligned = np.asanyarray(color_frame_aligned.get_data()).copy()
# Align the depth frame to color frame and extract depth image
depth_frame_aligned = align_to_color.process(frame_set).get_depth_frame()
self._image_depth_aligned = np.asanyarray(depth_frame_aligned.get_data()).copy()
self._image_depth_colorized = None
if frame_id:
self._frame_id = frame_id
else:
self._frame_id = frame_set.frame_number
if timestamp:
self._timestamp = timestamp
else:
self._timestamp = frame_set.timestamp
self.__dict__.update(kwargs)