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709 lines
26 KiB
Python
709 lines
26 KiB
Python
from functools import reduce
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import itertools as it
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import operator as op
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import time
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import copy
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from PIL import Image
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from scipy.spatial.distance import pdist
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import cairo
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import numpy as np
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from manimlib.constants import *
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from manimlib.mobject.types.image_mobject import AbstractImageMobject
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from manimlib.mobject.mobject import Mobject
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from manimlib.mobject.types.point_cloud_mobject import PMobject
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from manimlib.mobject.types.vectorized_mobject import VMobject
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from manimlib.utils.color import color_to_int_rgba
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from manimlib.utils.config_ops import digest_config
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from manimlib.utils.images import get_full_raster_image_path
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from manimlib.utils.iterables import batch_by_property
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from manimlib.utils.iterables import list_difference_update
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from manimlib.utils.iterables import remove_list_redundancies
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from manimlib.utils.simple_functions import fdiv
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from manimlib.utils.space_ops import angle_of_vector
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from manimlib.utils.space_ops import get_norm
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class Camera(object):
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CONFIG = {
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"background_image": None,
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"pixel_height": DEFAULT_PIXEL_HEIGHT,
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"pixel_width": DEFAULT_PIXEL_WIDTH,
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# Note: frame height and width will be resized to match
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# the pixel aspect ratio
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"frame_height": FRAME_HEIGHT,
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"frame_width": FRAME_WIDTH,
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"frame_center": ORIGIN,
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"background_color": BLACK,
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"background_opacity": 1,
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# Points in vectorized mobjects with norm greater
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# than this value will be rescaled.
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"max_allowable_norm": FRAME_WIDTH,
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"image_mode": "RGBA",
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"n_channels": 4,
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"pixel_array_dtype": 'uint8',
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# z_buff_func is only used if the flag above is set to True.
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# round z coordinate to nearest hundredth when comparring
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"z_buff_func": lambda m: np.round(m.get_center()[2], 2),
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"cairo_line_width_multiple": 0.01,
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}
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def __init__(self, background=None, **kwargs):
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digest_config(self, kwargs, locals())
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self.rgb_max_val = np.iinfo(self.pixel_array_dtype).max
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self.pixel_array_to_cairo_context = {}
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self.init_background()
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self.resize_frame_shape()
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self.reset()
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def __deepcopy__(self, memo):
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# This is to address a strange bug where deepcopying
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# will result in a segfault, which is somehow related
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# to the aggdraw library
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self.canvas = None
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return copy.copy(self)
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def reset_pixel_shape(self, new_height, new_width):
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self.pixel_width = new_width
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self.pixel_height = new_height
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self.init_background()
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self.resize_frame_shape()
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self.reset()
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def get_pixel_height(self):
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return self.pixel_height
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def get_pixel_width(self):
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return self.pixel_width
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def get_frame_height(self):
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return self.frame_height
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def get_frame_width(self):
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return self.frame_width
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def get_frame_center(self):
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return self.frame_center
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def set_frame_height(self, frame_height):
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self.frame_height = frame_height
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def set_frame_width(self, frame_width):
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self.frame_width = frame_width
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def set_frame_center(self, frame_center):
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self.frame_center = frame_center
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def resize_frame_shape(self, fixed_dimension=0):
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"""
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Changes frame_shape to match the aspect ratio
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of the pixels, where fixed_dimension determines
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whether frame_height or frame_width
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remains fixed while the other changes accordingly.
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"""
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pixel_height = self.get_pixel_height()
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pixel_width = self.get_pixel_width()
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frame_height = self.get_frame_height()
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frame_width = self.get_frame_width()
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aspect_ratio = fdiv(pixel_width, pixel_height)
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if fixed_dimension == 0:
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frame_height = frame_width / aspect_ratio
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else:
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frame_width = aspect_ratio * frame_height
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self.set_frame_height(frame_height)
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self.set_frame_width(frame_width)
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def init_background(self):
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height = self.get_pixel_height()
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width = self.get_pixel_width()
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if self.background_image is not None:
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path = get_full_raster_image_path(self.background_image)
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image = Image.open(path).convert(self.image_mode)
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# TODO, how to gracefully handle backgrounds
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# with different sizes?
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self.background = np.array(image)[:height, :width]
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self.background = self.background.astype(self.pixel_array_dtype)
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else:
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background_rgba = color_to_int_rgba(
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self.background_color, self.background_opacity
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)
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self.background = np.zeros(
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(height, width, self.n_channels),
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dtype=self.pixel_array_dtype
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)
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self.background[:, :] = background_rgba
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def get_image(self, pixel_array=None):
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if pixel_array is None:
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pixel_array = self.pixel_array
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return Image.fromarray(
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pixel_array,
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mode=self.image_mode
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)
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def get_pixel_array(self):
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return self.pixel_array
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def convert_pixel_array(self, pixel_array, convert_from_floats=False):
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retval = np.array(pixel_array)
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if convert_from_floats:
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retval = np.apply_along_axis(
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lambda f: (f * self.rgb_max_val).astype(self.pixel_array_dtype),
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2,
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retval
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)
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return retval
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def set_pixel_array(self, pixel_array, convert_from_floats=False):
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converted_array = self.convert_pixel_array(
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pixel_array, convert_from_floats)
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if not (hasattr(self, "pixel_array") and self.pixel_array.shape == converted_array.shape):
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self.pixel_array = converted_array
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else:
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# Set in place
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self.pixel_array[:, :, :] = converted_array[:, :, :]
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def set_background(self, pixel_array, convert_from_floats=False):
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self.background = self.convert_pixel_array(
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pixel_array, convert_from_floats)
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# TODO, this should live in utils, not as a method of Camera
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def make_background_from_func(self, coords_to_colors_func):
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"""
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Sets background by using coords_to_colors_func to determine each pixel's color. Each input
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to coords_to_colors_func is an (x, y) pair in space (in ordinary space coordinates; not
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pixel coordinates), and each output is expected to be an RGBA array of 4 floats.
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"""
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print("Starting set_background; for reference, the current time is ", time.strftime("%H:%M:%S"))
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coords = self.get_coords_of_all_pixels()
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new_background = np.apply_along_axis(
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coords_to_colors_func,
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2,
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coords
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)
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print("Ending set_background; for reference, the current time is ", time.strftime("%H:%M:%S"))
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return self.convert_pixel_array(new_background, convert_from_floats=True)
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def set_background_from_func(self, coords_to_colors_func):
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self.set_background(
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self.make_background_from_func(coords_to_colors_func))
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def reset(self):
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self.set_pixel_array(self.background)
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return self
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####
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def extract_mobject_family_members(
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self, mobjects,
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only_those_with_points=False):
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if only_those_with_points:
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method = Mobject.family_members_with_points
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else:
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method = Mobject.get_family
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return remove_list_redundancies(list(
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it.chain(*[
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method(m)
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for m in mobjects
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if not (isinstance(m, VMobject) and m.is_subpath)
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])
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))
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def get_mobjects_to_display(
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self, mobjects,
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include_submobjects=True,
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excluded_mobjects=None):
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if include_submobjects:
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mobjects = self.extract_mobject_family_members(
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mobjects, only_those_with_points=True,
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)
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if excluded_mobjects:
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all_excluded = self.extract_mobject_family_members(
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excluded_mobjects
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)
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mobjects = list_difference_update(mobjects, all_excluded)
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return mobjects
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def is_in_frame(self, mobject):
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fc = self.get_frame_center()
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fh = self.get_frame_height()
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fw = self.get_frame_width()
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return not reduce(op.or_, [
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mobject.get_right()[0] < fc[0] - fw,
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mobject.get_bottom()[1] > fc[1] + fh,
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mobject.get_left()[0] > fc[0] + fw,
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mobject.get_top()[1] < fc[1] - fh,
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])
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def capture_mobject(self, mobject, **kwargs):
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return self.capture_mobjects([mobject], **kwargs)
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def capture_mobjects(self, mobjects, **kwargs):
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mobjects = self.get_mobjects_to_display(mobjects, **kwargs)
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# Organize this list into batches of the same type, and
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# apply corresponding function to those batches
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type_func_pairs = [
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(VMobject, self.display_multiple_vectorized_mobjects),
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(PMobject, self.display_multiple_point_cloud_mobjects),
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(AbstractImageMobject, self.display_multiple_image_mobjects),
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(Mobject, lambda batch, pa: batch), # Do nothing
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]
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def get_mobject_type(mobject):
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for mobject_type, func in type_func_pairs:
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if isinstance(mobject, mobject_type):
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return mobject_type
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raise Exception(
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"Trying to display something which is not of type Mobject"
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)
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batch_type_pairs = batch_by_property(mobjects, get_mobject_type)
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# Display in these batches
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for batch, batch_type in batch_type_pairs:
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# check what the type is, and call the appropriate function
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for mobject_type, func in type_func_pairs:
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if batch_type == mobject_type:
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func(batch, self.pixel_array)
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# Methods associated with svg rendering
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def get_cached_cairo_context(self, pixel_array):
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return self.pixel_array_to_cairo_context.get(
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id(pixel_array), None
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)
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def cache_cairo_context(self, pixel_array, ctx):
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self.pixel_array_to_cairo_context[id(pixel_array)] = ctx
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def get_cairo_context(self, pixel_array):
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cached_ctx = self.get_cached_cairo_context(pixel_array)
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if cached_ctx:
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return cached_ctx
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pw = self.get_pixel_width()
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ph = self.get_pixel_height()
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fw = self.get_frame_width()
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fh = self.get_frame_height()
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fc = self.get_frame_center()
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surface = cairo.ImageSurface.create_for_data(
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pixel_array,
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cairo.FORMAT_ARGB32,
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pw, ph
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)
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ctx = cairo.Context(surface)
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ctx.scale(pw, ph)
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ctx.set_matrix(cairo.Matrix(
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fdiv(pw, fw), 0,
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0, -fdiv(ph, fh),
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(pw / 2) - fc[0] * fdiv(pw, fw),
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(ph / 2) + fc[1] * fdiv(ph, fh),
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))
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self.cache_cairo_context(pixel_array, ctx)
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return ctx
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def display_multiple_vectorized_mobjects(self, vmobjects, pixel_array):
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if len(vmobjects) == 0:
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return
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batch_file_pairs = batch_by_property(
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vmobjects,
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lambda vm: vm.get_background_image_file()
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)
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for batch, file_name in batch_file_pairs:
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if file_name:
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self.display_multiple_background_colored_vmobject(batch, pixel_array)
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else:
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self.display_multiple_non_background_colored_vmobjects(batch, pixel_array)
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def display_multiple_non_background_colored_vmobjects(self, vmobjects, pixel_array):
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ctx = self.get_cairo_context(pixel_array)
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for vmobject in vmobjects:
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self.display_vectorized(vmobject, ctx)
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def display_vectorized(self, vmobject, ctx):
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if vmobject.is_subpath:
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# Subpath vectorized mobjects are taken care
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# of by their parent
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return
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self.set_cairo_context_path(ctx, vmobject)
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self.apply_stroke(ctx, vmobject, background=True)
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self.apply_fill(ctx, vmobject)
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self.apply_stroke(ctx, vmobject)
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return self
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def set_cairo_context_path(self, ctx, vmobject):
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ctx.new_path()
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for vmob in it.chain([vmobject], vmobject.get_subpath_mobjects()):
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points = self.transform_points_pre_display(
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vmob, vmob.points
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)
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if np.any(np.isnan(points)) or np.any(points == np.inf):
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points = np.zeros((1, 3))
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ctx.new_sub_path()
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ctx.move_to(*points[0][:2])
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for triplet in zip(points[1::3], points[2::3], points[3::3]):
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ctx.curve_to(*it.chain(*[
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point[:2] for point in triplet
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]))
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if vmob.is_closed():
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ctx.close_path()
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return self
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def set_cairo_context_color(self, ctx, rgbas, vmobject):
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if len(rgbas) == 1:
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# Use reversed rgb because cairo surface is
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# encodes it in reverse order
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ctx.set_source_rgba(
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*rgbas[0][2::-1], rgbas[0][3]
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)
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else:
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points = vmobject.get_gradient_start_and_end_points()
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points = self.transform_points_pre_display(
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vmobject, points
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)
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pat = cairo.LinearGradient(*it.chain(*[
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point[:2] for point in points
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]))
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step = 1.0 / (len(rgbas) - 1)
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offsets = np.arange(0, 1 + step, step)
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for rgba, offset in zip(rgbas, offsets):
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pat.add_color_stop_rgba(
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offset, *rgba[2::-1], rgba[3]
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)
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ctx.set_source(pat)
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return self
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def apply_fill(self, ctx, vmobject):
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self.set_cairo_context_color(
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ctx, self.get_fill_rgbas(vmobject), vmobject
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)
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ctx.fill_preserve()
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return self
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def apply_stroke(self, ctx, vmobject, background=False):
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width = vmobject.get_stroke_width(background)
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if width == 0:
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return self
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self.set_cairo_context_color(
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ctx,
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self.get_stroke_rgbas(vmobject, background=background),
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vmobject
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)
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ctx.set_line_width(
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width * self.cairo_line_width_multiple *
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# This ensures lines have constant width
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# as you zoom in on them.
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(self.get_frame_width() / FRAME_WIDTH)
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)
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ctx.stroke_preserve()
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return self
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def get_stroke_rgbas(self, vmobject, background=False):
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return vmobject.get_stroke_rgbas(background)
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def get_fill_rgbas(self, vmobject):
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return vmobject.get_fill_rgbas()
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def get_background_colored_vmobject_displayer(self):
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# Quite wordy to type out a bunch
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bcvd = "background_colored_vmobject_displayer"
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if not hasattr(self, bcvd):
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setattr(self, bcvd, BackgroundColoredVMobjectDisplayer(self))
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return getattr(self, bcvd)
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def display_multiple_background_colored_vmobject(self, cvmobjects, pixel_array):
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displayer = self.get_background_colored_vmobject_displayer()
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cvmobject_pixel_array = displayer.display(*cvmobjects)
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self.overlay_rgba_array(pixel_array, cvmobject_pixel_array)
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return self
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# Methods for other rendering
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def display_multiple_point_cloud_mobjects(self, pmobjects, pixel_array):
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for pmobject in pmobjects:
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self.display_point_cloud(
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pmobject,
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pmobject.points,
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pmobject.rgbas,
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self.adjusted_thickness(pmobject.stroke_width),
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pixel_array,
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)
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def display_point_cloud(self, pmobject, points, rgbas, thickness, pixel_array):
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if len(points) == 0:
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return
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pixel_coords = self.points_to_pixel_coords(
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pmobject, points
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)
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pixel_coords = self.thickened_coordinates(
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pixel_coords, thickness
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)
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rgba_len = pixel_array.shape[2]
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rgbas = (self.rgb_max_val * rgbas).astype(self.pixel_array_dtype)
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target_len = len(pixel_coords)
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factor = target_len // len(rgbas)
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rgbas = np.array([rgbas] * factor).reshape((target_len, rgba_len))
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on_screen_indices = self.on_screen_pixels(pixel_coords)
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pixel_coords = pixel_coords[on_screen_indices]
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rgbas = rgbas[on_screen_indices]
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ph = self.get_pixel_height()
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pw = self.get_pixel_width()
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flattener = np.array([1, pw], dtype='int')
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flattener = flattener.reshape((2, 1))
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indices = np.dot(pixel_coords, flattener)[:, 0]
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indices = indices.astype('int')
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new_pa = pixel_array.reshape((ph * pw, rgba_len))
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new_pa[indices] = rgbas
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pixel_array[:, :] = new_pa.reshape((ph, pw, rgba_len))
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def display_multiple_image_mobjects(self, image_mobjects, pixel_array):
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for image_mobject in image_mobjects:
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self.display_image_mobject(image_mobject, pixel_array)
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def display_image_mobject(self, image_mobject, pixel_array):
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corner_coords = self.points_to_pixel_coords(
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image_mobject, image_mobject.points
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)
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ul_coords, ur_coords, dl_coords = corner_coords
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right_vect = ur_coords - ul_coords
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down_vect = dl_coords - ul_coords
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center_coords = ul_coords + (right_vect + down_vect) / 2
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sub_image = Image.fromarray(
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image_mobject.get_pixel_array(),
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mode="RGBA"
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)
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# Reshape
|
|
pixel_width = max(int(pdist([ul_coords, ur_coords])), 1)
|
|
pixel_height = max(int(pdist([ul_coords, dl_coords])), 1)
|
|
sub_image = sub_image.resize(
|
|
(pixel_width, pixel_height), resample=Image.BICUBIC
|
|
)
|
|
|
|
# Rotate
|
|
angle = angle_of_vector(right_vect)
|
|
adjusted_angle = -int(360 * angle / TAU)
|
|
if adjusted_angle != 0:
|
|
sub_image = sub_image.rotate(
|
|
adjusted_angle, resample=Image.BICUBIC, expand=1
|
|
)
|
|
|
|
# TODO, there is no accounting for a shear...
|
|
|
|
# Paste into an image as large as the camear's pixel array
|
|
full_image = Image.fromarray(
|
|
np.zeros((self.get_pixel_height(), self.get_pixel_width())),
|
|
mode="RGBA"
|
|
)
|
|
new_ul_coords = center_coords - np.array(sub_image.size) / 2
|
|
new_ul_coords = new_ul_coords.astype(int)
|
|
full_image.paste(
|
|
sub_image,
|
|
box=(
|
|
new_ul_coords[0],
|
|
new_ul_coords[1],
|
|
new_ul_coords[0] + sub_image.size[0],
|
|
new_ul_coords[1] + sub_image.size[1],
|
|
)
|
|
)
|
|
# Paint on top of existing pixel array
|
|
self.overlay_PIL_image(pixel_array, full_image)
|
|
|
|
def overlay_rgba_array(self, pixel_array, new_array):
|
|
self.overlay_PIL_image(
|
|
pixel_array,
|
|
self.get_image(new_array),
|
|
)
|
|
|
|
def overlay_PIL_image(self, pixel_array, image):
|
|
pixel_array[:, :] = np.array(
|
|
Image.alpha_composite(
|
|
self.get_image(pixel_array),
|
|
image
|
|
),
|
|
dtype='uint8'
|
|
)
|
|
|
|
def adjust_out_of_range_points(self, points):
|
|
if not np.any(points > self.max_allowable_norm):
|
|
return points
|
|
norms = np.apply_along_axis(get_norm, 1, points)
|
|
violator_indices = norms > self.max_allowable_norm
|
|
violators = points[violator_indices, :]
|
|
violator_norms = norms[violator_indices]
|
|
reshaped_norms = np.repeat(
|
|
violator_norms.reshape((len(violator_norms), 1)),
|
|
points.shape[1], 1
|
|
)
|
|
rescaled = self.max_allowable_norm * violators / reshaped_norms
|
|
points[violator_indices] = rescaled
|
|
return points
|
|
|
|
def transform_points_pre_display(self, mobject, points):
|
|
# Subclasses (like ThreeDCamera) may want to
|
|
# adjust points before they're shown
|
|
return points
|
|
|
|
def points_to_pixel_coords(self, mobject, points):
|
|
points = self.transform_points_pre_display(
|
|
mobject, points
|
|
)
|
|
shifted_points = points - self.get_frame_center()
|
|
|
|
result = np.zeros((len(points), 2))
|
|
pixel_height = self.get_pixel_height()
|
|
pixel_width = self.get_pixel_width()
|
|
frame_height = self.get_frame_height()
|
|
frame_width = self.get_frame_width()
|
|
width_mult = pixel_width / frame_width
|
|
width_add = pixel_width / 2
|
|
height_mult = pixel_height / frame_height
|
|
height_add = pixel_height / 2
|
|
# Flip on y-axis as you go
|
|
height_mult *= -1
|
|
|
|
result[:, 0] = shifted_points[:, 0] * width_mult + width_add
|
|
result[:, 1] = shifted_points[:, 1] * height_mult + height_add
|
|
return result.astype('int')
|
|
|
|
def on_screen_pixels(self, pixel_coords):
|
|
return reduce(op.and_, [
|
|
pixel_coords[:, 0] >= 0,
|
|
pixel_coords[:, 0] < self.get_pixel_width(),
|
|
pixel_coords[:, 1] >= 0,
|
|
pixel_coords[:, 1] < self.get_pixel_height(),
|
|
])
|
|
|
|
def adjusted_thickness(self, thickness):
|
|
# TODO: This seems...unsystematic
|
|
big_sum = op.add(
|
|
PRODUCTION_QUALITY_CAMERA_CONFIG["pixel_height"],
|
|
PRODUCTION_QUALITY_CAMERA_CONFIG["pixel_width"],
|
|
)
|
|
this_sum = op.add(
|
|
self.get_pixel_height(),
|
|
self.get_pixel_width(),
|
|
)
|
|
factor = fdiv(big_sum, this_sum)
|
|
return 1 + (thickness - 1) / factor
|
|
|
|
def get_thickening_nudges(self, thickness):
|
|
thickness = int(thickness)
|
|
_range = list(range(-thickness // 2 + 1, thickness // 2 + 1))
|
|
return np.array(list(it.product(_range, _range)))
|
|
|
|
def thickened_coordinates(self, pixel_coords, thickness):
|
|
nudges = self.get_thickening_nudges(thickness)
|
|
pixel_coords = np.array([
|
|
pixel_coords + nudge
|
|
for nudge in nudges
|
|
])
|
|
size = pixel_coords.size
|
|
return pixel_coords.reshape((size // 2, 2))
|
|
|
|
# TODO, reimplement using cairo matrix
|
|
def get_coords_of_all_pixels(self):
|
|
# These are in x, y order, to help me keep things straight
|
|
full_space_dims = np.array([
|
|
self.get_frame_width(),
|
|
self.get_frame_height()
|
|
])
|
|
full_pixel_dims = np.array([
|
|
self.get_pixel_width(),
|
|
self.get_pixel_height()
|
|
])
|
|
|
|
# These are addressed in the same y, x order as in pixel_array, but the values in them
|
|
# are listed in x, y order
|
|
uncentered_pixel_coords = np.indices(
|
|
[self.get_pixel_height(), self.get_pixel_width()]
|
|
)[::-1].transpose(1, 2, 0)
|
|
uncentered_space_coords = fdiv(
|
|
uncentered_pixel_coords * full_space_dims,
|
|
full_pixel_dims)
|
|
# Could structure above line's computation slightly differently, but figured (without much
|
|
# thought) multiplying by frame_shape first, THEN dividing by pixel_shape, is probably
|
|
# better than the other order, for avoiding underflow quantization in the division (whereas
|
|
# overflow is unlikely to be a problem)
|
|
|
|
centered_space_coords = (
|
|
uncentered_space_coords - fdiv(full_space_dims, 2)
|
|
)
|
|
|
|
# Have to also flip the y coordinates to account for pixel array being listed in
|
|
# top-to-bottom order, opposite of screen coordinate convention
|
|
centered_space_coords = centered_space_coords * (1, -1)
|
|
|
|
return centered_space_coords
|
|
|
|
|
|
class BackgroundColoredVMobjectDisplayer(object):
|
|
def __init__(self, camera):
|
|
self.camera = camera
|
|
self.file_name_to_pixel_array_map = {}
|
|
self.pixel_array = np.array(camera.get_pixel_array())
|
|
self.reset_pixel_array()
|
|
|
|
def reset_pixel_array(self):
|
|
self.pixel_array[:, :] = 0
|
|
|
|
def resize_background_array(
|
|
self, background_array,
|
|
new_width, new_height,
|
|
mode="RGBA"
|
|
):
|
|
image = Image.fromarray(background_array)
|
|
image = image.convert(mode)
|
|
resized_image = image.resize((new_width, new_height))
|
|
return np.array(resized_image)
|
|
|
|
def resize_background_array_to_match(self, background_array, pixel_array):
|
|
height, width = pixel_array.shape[:2]
|
|
mode = "RGBA" if pixel_array.shape[2] == 4 else "RGB"
|
|
return self.resize_background_array(background_array, width, height, mode)
|
|
|
|
def get_background_array(self, file_name):
|
|
if file_name in self.file_name_to_pixel_array_map:
|
|
return self.file_name_to_pixel_array_map[file_name]
|
|
full_path = get_full_raster_image_path(file_name)
|
|
image = Image.open(full_path)
|
|
back_array = np.array(image)
|
|
|
|
pixel_array = self.pixel_array
|
|
if not np.all(pixel_array.shape == back_array.shape):
|
|
back_array = self.resize_background_array_to_match(
|
|
back_array, pixel_array
|
|
)
|
|
|
|
self.file_name_to_pixel_array_map[file_name] = back_array
|
|
return back_array
|
|
|
|
def display(self, *cvmobjects):
|
|
batch_image_file_pairs = batch_by_property(
|
|
cvmobjects, lambda cv: cv.get_background_image_file()
|
|
)
|
|
curr_array = None
|
|
for batch, image_file in batch_image_file_pairs:
|
|
background_array = self.get_background_array(image_file)
|
|
pixel_array = self.pixel_array
|
|
self.camera.display_multiple_non_background_colored_vmobjects(
|
|
batch, pixel_array
|
|
)
|
|
new_array = np.array(
|
|
(background_array * pixel_array.astype('float') / 255),
|
|
dtype=self.camera.pixel_array_dtype
|
|
)
|
|
if curr_array is None:
|
|
curr_array = new_array
|
|
else:
|
|
curr_array = np.maximum(curr_array, new_array)
|
|
self.reset_pixel_array()
|
|
return curr_array
|