Refactor image simulation for speed
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Julian Stirling
parent
70a049e52f
commit
80c78fbc5e
1 changed files with 64 additions and 37 deletions
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@ -44,7 +44,9 @@ BG_COLOR = [220, 215, 217]
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# Random Number Generator
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RNG = np.random.default_rng()
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DOWNSAMPLE = 2
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LOW_MAG_DOWNSAMPLE = 8
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# Upsample for sprites and then downsample to create sharp edges for each sprite
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# as these are small and calculated once there is almost no performance penalty
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# for a nice gain in quality.
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@ -60,20 +62,24 @@ COLOUR_REGEX = re.compile(r"^#([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})$")
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@overload
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def _downsample_shape(shape: tuple[int, int]) -> tuple[int, int]: ...
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def _downsample_shape(
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shape: tuple[int, int], factor: float | int
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) -> tuple[int, int]: ...
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@overload
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def _downsample_shape(shape: tuple[int, int, int]) -> tuple[int, int, int]: ...
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def _downsample_shape(
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shape: tuple[int, int, int], factor: float | int
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) -> tuple[int, int, int]: ...
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def _downsample_shape(
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shape: tuple[int, int] | tuple[int, int, int],
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shape: tuple[int, int] | tuple[int, int, int], factor: float | int
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) -> tuple[int, int] | tuple[int, int, int]:
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if len(shape) == 2:
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return (shape[0] // DOWNSAMPLE, shape[1] // DOWNSAMPLE)
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return (int(shape[0] // factor), int(shape[1] // factor))
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if len(shape) == 3:
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return (shape[0] // DOWNSAMPLE, shape[1] // DOWNSAMPLE, shape[2])
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return (int(shape[0] // factor), int(shape[1] // factor), shape[2])
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raise ValueError("Shape should be a 2 or 3 element tuple.")
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@ -127,11 +133,6 @@ class SimulatedCamera(BaseCamera):
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raise ValueError("Objective must be one of 4, 10, 20, 40, 60, 100.")
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self._objective = value
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@property
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def magnification_scale(self) -> float:
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"""Magnification scale relative to 40x objective."""
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return self._objective / 40.0
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def __init__(
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self,
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thing_server_interface: lt.ThingServerInterface,
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@ -150,10 +151,9 @@ class SimulatedCamera(BaseCamera):
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"""
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super().__init__(thing_server_interface)
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self.shape = shape
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self.ds_shape = _downsample_shape(shape)
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self.glyph_size = 105 // DOWNSAMPLE
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self.canvas_shape = _downsample_shape(canvas_shape)
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self.canvas_shape = _downsample_shape(canvas_shape, DOWNSAMPLE)
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self.low_mag_canvas_shape = _downsample_shape(canvas_shape, LOW_MAG_DOWNSAMPLE)
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self.frame_interval = frame_interval
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self._capture_thread: Optional[Thread] = None
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self._capture_enabled = False
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@ -275,6 +275,10 @@ class SimulatedCamera(BaseCamera):
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self.blank_canvas[:, :, 0] *= BG_COLOR[0]
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self.blank_canvas[:, :, 1] *= BG_COLOR[1]
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self.blank_canvas[:, :, 2] *= BG_COLOR[2]
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self.blank_canvas_low_mag = np.ones(self.low_mag_canvas_shape, dtype=np.int16)
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self.blank_canvas_low_mag[:, :, 0] *= BG_COLOR[0]
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self.blank_canvas_low_mag[:, :, 1] *= BG_COLOR[1]
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self.blank_canvas_low_mag[:, :, 2] *= BG_COLOR[2]
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new_canvas = self.blank_canvas.copy()
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for blob_x, blob_y, sprite_index in self.blobs:
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@ -282,6 +286,17 @@ class SimulatedCamera(BaseCamera):
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new_canvas, self.sprites[int(sprite_index)], int(blob_y), int(blob_x)
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)
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self.canvas = np.clip(new_canvas, 0, 255)
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# Create a further downsized canvas for low mag. This has a minimal memory
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# footprint bus speeds up indexing the canvas when simulation low magnification
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# objectives
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self.canvas_low_mag = fast_resize_and_blur(
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self.canvas, sigma=0, shape=self.low_mag_canvas_shape
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)
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# Check edge pixels are blank as these are repeated for finite samples.
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self.canvas_low_mag[0, :, :] = self.blank_canvas_low_mag[0, :, :]
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self.canvas_low_mag[-1, :, :] = self.blank_canvas_low_mag[-1, :, :]
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self.canvas_low_mag[:, 0, :] = self.blank_canvas_low_mag[:, 0, :]
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self.canvas_low_mag[:, -1, :] = self.blank_canvas_low_mag[:, -1, :]
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def draw_sprite_on_canvas(
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self, canvas: np.ndarray, sprite: np.ndarray, centre_y: int, centre_x: int
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@ -317,24 +332,33 @@ class SimulatedCamera(BaseCamera):
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:param pos: a 3-item tuple containing the x,y,z coordinates of the 'stage'
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"""
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canvas_width, canvas_height, _ = self.canvas_shape
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canvas_width, canvas_height, _ = self.low_mag_canvas_shape
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# Base image size
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base_width, base_height, _ = self.ds_shape
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# Scale crop size inversely with magnification
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scale = self.magnification_scale
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image_width = int(base_width / scale)
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image_height = int(base_height / scale)
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objective_downsample = self.objective / 40
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if objective_downsample >= 0.4:
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canvas = self.canvas if self._show_sample else self.blank_canvas
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canvas_width, canvas_height, _ = self.canvas_shape
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canvas_ds = DOWNSAMPLE
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img_downsample = DOWNSAMPLE * objective_downsample
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else:
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canvas = (
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self.canvas_low_mag if self._show_sample else self.blank_canvas_low_mag
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)
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canvas_width, canvas_height, _ = self.low_mag_canvas_shape
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canvas_ds = LOW_MAG_DOWNSAMPLE
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img_downsample = LOW_MAG_DOWNSAMPLE * objective_downsample
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image_width, image_height, _ = _downsample_shape(self.shape, img_downsample)
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im_pos = (
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pos[0] * RATIO[0] / DOWNSAMPLE,
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pos[1] * RATIO[1] / DOWNSAMPLE,
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pos[0] * RATIO[0] / canvas_ds,
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pos[1] * RATIO[1] / canvas_ds,
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pos[2] * RATIO[2],
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)
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top_left = (
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int(im_pos[0]) - image_width // 2 + self.canvas_shape[0] // 2,
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int(im_pos[1]) - image_height // 2 + self.canvas_shape[1] // 2,
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int(im_pos[0]) - image_width // 2 + canvas_width // 2,
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int(im_pos[1]) - image_height // 2 + canvas_height // 2,
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)
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x_indices = np.arange(top_left[0], top_left[0] + image_width)
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@ -353,18 +377,20 @@ class SimulatedCamera(BaseCamera):
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x_indices = np.clip(x_indices, 0, canvas_width - 1)
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y_indices = np.clip(y_indices, 0, canvas_height - 1)
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z_indices = np.arange(self.ds_shape[2])
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canvas = self.canvas if self._show_sample else self.blank_canvas
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z_indices = np.arange(self.shape[2])
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# Use npx to make each 1d index list 3D
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focused_np_img = canvas[np.ix_(x_indices, y_indices, z_indices)]
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np_img = fast_pil_blur(focused_np_img, sigma=np.abs(im_pos[2]) / 5)
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# Add noise and convert to uint8
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np_img += RNG.normal(scale=self.noise_level, size=np_img.shape).astype("int16")
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np_img = fast_resize_and_blur(
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focused_np_img, sigma=np.abs(im_pos[2]) / 5, shape=self.shape
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)
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# Generate random noise by repeating 500 noise points, as the speed rather
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# than randomness is important for simulation.
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noise = RNG.normal(scale=self.noise_level, size=500).astype("int16")
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np_img += np.resize(noise, np_img.shape)
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# Clip then convert to uint8
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np.clip(np_img, 0, 255, out=np_img)
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pl_img = Image.fromarray(np_img.astype("uint8"))
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return pl_img.resize((self.shape[1], self.shape[0]), Image.Resampling.BILINEAR)
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return Image.fromarray(np_img.astype("uint8"))
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def set_led(self, led_on: bool = True) -> None:
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"""Set the simulated LED to on or off."""
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@ -570,13 +596,14 @@ def _frame2bytes(frame: Image.Image) -> bytes:
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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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def fast_resize_and_blur(
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array: np.ndarray, sigma: float, shape: tuple[int, ...]
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) -> 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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img_pil = img_pil.resize((shape[1], shape[0]), Image.Resampling.BILINEAR)
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if sigma > 0.5:
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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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