Merge branch 'pil-simulation' into 'v3'
Improve simulation frame rate See merge request openflexure/openflexure-microscope-server!419
This commit is contained in:
commit
551e392e91
1 changed files with 39 additions and 22 deletions
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@ -12,11 +12,10 @@ from typing import Literal, Optional, Mapping
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from types import TracebackType
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from threading import Thread
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import time
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import io
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import cv2
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import numpy as np
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from PIL import Image
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from scipy.ndimage import gaussian_filter
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from PIL import Image, ImageFilter
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import labthings_fastapi as lt
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@ -194,7 +193,7 @@ class SimulatedCamera(BaseCamera):
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self.canvas[top:bottom, left:right] -= sprite
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def generate_image(self, pos: tuple[int, int, int]) -> np.ndarray:
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def generate_image(self, pos: tuple[int, int, int]) -> Image:
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"""Generate an image with blobs based on supplied coordinates.
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:param pos: a 3-item tuple containing the x,y,z coordinates of the 'stage'
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@ -214,11 +213,9 @@ class SimulatedCamera(BaseCamera):
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canvas = self.canvas if self._show_sample else self.blank_canvas
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# Use npx to make each 1d index list 3D
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focused_image = canvas[np.ix_(x_indices, y_indices, z_indices)]
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image = gaussian_filter(
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focused_image,
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sigma=np.abs(pos[2]) / 5,
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axes=(0, 1),
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)
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image = fast_pil_blur(focused_image, sigma=np.abs(pos[2]) / 5)
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if image.shape != self.shape:
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raise ValueError(
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f"Image shape {image.shape} does not match intended shape {self.shape}"
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@ -228,7 +225,7 @@ class SimulatedCamera(BaseCamera):
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image += RNG.normal(scale=self.noise_level, size=self.shape).astype("int16")
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image[image < 0] = 0
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image[image > 255] = 255
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return image.astype("uint8")
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return Image.fromarray(image.astype("uint8"))
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def attach_to_server(
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self, server: lt.ThingServer, path: str, setting_storage_path: str
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@ -251,7 +248,7 @@ class SimulatedCamera(BaseCamera):
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self._stage = self._server.things["/stage/"]
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return self._stage.instantaneous_position
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def generate_frame(self) -> np.ndarray:
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def generate_frame(self) -> Image:
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"""Generate a frame with blobs based on the stage coordinates."""
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try:
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pos = self.get_stage_position()
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@ -313,18 +310,18 @@ class SimulatedCamera(BaseCamera):
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def _capture_frames(self) -> None:
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portal = lt.get_blocking_portal(self)
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last_frame_t = time.time()
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while self._capture_enabled:
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time.sleep(self.frame_interval)
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wait_time = last_frame_t - time.time() - self.frame_interval
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if wait_time > 0:
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time.sleep(wait_time)
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last_frame_t = time.time()
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try:
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frame = self.generate_frame()
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jpeg = cv2.imencode(".jpg", frame)[1].tobytes()
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self.mjpeg_stream.add_frame(jpeg, portal)
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# Downsample for lores
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ds_frame = cv2.resize(
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frame, (320, 240), interpolation=cv2.INTER_NEAREST
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)
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jpeg_lores = cv2.imencode(".jpg", ds_frame)[1].tobytes()
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self.lores_mjpeg_stream.add_frame(jpeg_lores, portal)
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self.mjpeg_stream.add_frame(_frame2bytes(frame), portal)
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ds_frame = frame.resize((320, 240), resample=Image.NEAREST)
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self.lores_mjpeg_stream.add_frame(_frame2bytes(ds_frame), portal)
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except Exception as e:
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LOGGER.exception(f"Failed to capture frame: {e}, retrying...")
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@ -353,7 +350,7 @@ class SimulatedCamera(BaseCamera):
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if wait is not None:
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LOGGER.warning("Simulation camera has no wait option. Use None.")
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LOGGER.warning(f"Simulation camera camera doesn't respect {stream_name=}")
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return self.generate_frame()
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return np.array(self.generate_frame())
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def capture_image(
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self,
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@ -370,7 +367,7 @@ class SimulatedCamera(BaseCamera):
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if wait is not None:
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LOGGER.warning("Simulation camera has no wait option. Use None.")
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LOGGER.warning(f"Simulation camera camera doesn't respect {stream_name=}")
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return Image.fromarray(self.generate_frame())
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return self.generate_frame()
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@lt.thing_action
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def full_auto_calibrate(self, portal: lt.deps.BlockingPortal) -> None:
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@ -423,3 +420,23 @@ class SimulatedCamera(BaseCamera):
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def manual_camera_settings(self) -> list[PropertyControl]:
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"""The camera settings to expose as property controls in the settings panel."""
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return [property_control_for(self, "noise_level", label="Noise Level")]
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def _frame2bytes(frame: Image) -> bytes:
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"""Convert frame to bytes."""
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with io.BytesIO() as buf:
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# Save in low quality for speed.
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frame.save(buf, format="JPEG", quality=85)
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return buf.getvalue()
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def fast_pil_blur(array: np.ndarray, sigma: float) -> np.ndarray:
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"""Apply Gaussian blur using PIL (faster than scipy)."""
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if sigma < 0.5:
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return array # no visible blur needed
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img_pil = Image.fromarray(array.astype(np.uint8))
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img_pil = img_pil.filter(ImageFilter.GaussianBlur(radius=sigma))
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# Convert back to NumPy array
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return np.array(img_pil, dtype=array.dtype)
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