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593 lines
22 KiB
Python
593 lines
22 KiB
Python
import itertools as it
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import numpy as np
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import operator as op
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import aggdraw
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import copy
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import time
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from PIL import Image
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from colour import Color
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from scipy.spatial.distance import pdist
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from constants import *
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from mobject.types.image_mobject import AbstractImageMobject
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from mobject.mobject import Mobject
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from mobject.types.point_cloud_mobject import PMobject
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from mobject.types.vectorized_mobject import VMobject
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from utils.color import color_to_int_rgba
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from utils.color import rgb_to_hex
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from utils.config_ops import digest_config
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from utils.images import get_full_raster_image_path
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from utils.iterables import batch_by_property
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from utils.iterables import list_difference_update
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from utils.iterables import remove_list_redundancies
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from utils.simple_functions import fdiv
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from utils.space_ops import angle_of_vector
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class Camera(object):
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CONFIG = {
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"background_image": None,
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"pixel_shape": (DEFAULT_PIXEL_HEIGHT, DEFAULT_PIXEL_WIDTH),
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# Note: frame_shape will be resized to match pixel_shape
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"frame_shape": (FRAME_HEIGHT, FRAME_WIDTH),
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"space_center": ORIGIN,
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"background_color": BLACK,
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"background_alpha": 0, # Out of rgb_max_val
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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_rgb_coords": 4,
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"pixel_array_dtype": 'uint8',
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"use_z_coordinate_for_display_order": False,
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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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}
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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.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_shape):
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self.pixel_shape = tuple(new_shape)
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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 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 pixel_shape, where fixed_dimension determines
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whether frame_shape[0] (height) or frame_shape[1] (width)
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remains fixed while the other changes accordingly.
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"""
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frame_height, frame_width = self.frame_shape
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pixel_height, pixel_width = self.pixel_shape
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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.frame_shape = (frame_height, frame_width)
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def init_background(self):
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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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height, width = self.pixel_shape
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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, alpha=self.background_alpha
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)
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self.background = np.zeros(
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list(self.pixel_shape) + [self.n_rgb_coords],
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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):
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return Image.fromarray(
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self.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: (
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f * self.rgb_max_val).astype(self.pixel_array_dtype),
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2,
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retval)
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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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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(self, mobjects, 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.submobject_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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):
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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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if self.use_z_coordinate_for_display_order:
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# Should perhaps think about what happens here when include_submobjects is False,
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# (for now, the onus is then on the caller to ensure this is handled correctly by
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# passing us an appropriately pre-flattened list of mobjects if need be)
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return sorted(
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mobjects,
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lambda a, b: cmp(self.z_buff_func(a), self.z_buff_func(b))
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)
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else:
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return mobjects
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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: 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)
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# Methods associated with svg rendering
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def get_aggdraw_canvas(self):
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if not hasattr(self, "canvas") or not self.canvas:
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self.reset_aggdraw_canvas()
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return self.canvas
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def reset_aggdraw_canvas(self):
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image = Image.fromarray(self.pixel_array, mode=self.image_mode)
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self.canvas = aggdraw.Draw(image)
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def display_multiple_vectorized_mobjects(self, vmobjects):
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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)
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else:
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self.display_multiple_non_background_colored_vmobjects(batch)
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def display_multiple_non_background_colored_vmobjects(self, vmobjects):
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self.reset_aggdraw_canvas()
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canvas = self.get_aggdraw_canvas()
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for vmobject in vmobjects:
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self.display_vectorized(vmobject, canvas)
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canvas.flush()
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def display_vectorized(self, vmobject, canvas=None):
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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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canvas = canvas or self.get_aggdraw_canvas()
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pen, fill = self.get_pen_and_fill(vmobject)
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pathstring = self.get_pathstring(vmobject)
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symbol = aggdraw.Symbol(pathstring)
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canvas.symbol((0, 0), symbol, pen, fill)
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def get_pen_and_fill(self, vmobject):
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stroke_width = max(vmobject.get_stroke_width(), 0)
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if stroke_width == 0:
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pen = None
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else:
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stroke_rgb = self.get_stroke_rgb(vmobject)
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stroke_hex = rgb_to_hex(stroke_rgb)
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pen = aggdraw.Pen(stroke_hex, stroke_width)
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fill_opacity = int(self.rgb_max_val * vmobject.get_fill_opacity())
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if fill_opacity == 0:
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fill = None
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else:
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fill_rgb = self.get_fill_rgb(vmobject)
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fill_hex = rgb_to_hex(fill_rgb)
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fill = aggdraw.Brush(fill_hex, fill_opacity)
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return (pen, fill)
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def color_to_hex_l(self, color):
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try:
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return color.get_hex_l()
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except:
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return Color(BLACK).get_hex_l()
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def get_stroke_rgb(self, vmobject):
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return vmobject.get_stroke_rgb()
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def get_fill_rgb(self, vmobject):
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return vmobject.get_fill_rgb()
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def get_pathstring(self, vmobject):
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result = ""
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for mob in [vmobject] + vmobject.get_subpath_mobjects():
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points = mob.points
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# points = self.adjust_out_of_range_points(points)
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if len(points) == 0:
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continue
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coords = self.points_to_pixel_coords(points)
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coord_strings = coords.flatten().astype(str)
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# Start new path string with M
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coord_strings[0] = "M" + coord_strings[0]
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# The C at the start of every 6th number communicates
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# that the following 6 define a cubic Bezier
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coord_strings[2::6] = map(
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lambda s: "C" + str(s), coord_strings[2::6])
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# Possibly finish with "Z"
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if vmobject.mark_paths_closed:
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coord_strings[-1] = coord_strings[-1] + " Z"
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result += " ".join(coord_strings)
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return result
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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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long_name = "background_colored_vmobject_displayer"
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if not hasattr(self, long_name):
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setattr(self, long_name, BackgroundColoredVMobjectDisplayer(self))
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return getattr(self, long_name)
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def display_multiple_background_colored_vmobject(self, cvmobjects):
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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(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):
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for pmobject in pmobjects:
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self.display_point_cloud(
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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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)
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def display_point_cloud(self, points, rgbas, thickness):
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if len(points) == 0:
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return
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pixel_coords = self.points_to_pixel_coords(points)
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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 = self.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, pw = self.pixel_shape
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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 = self.pixel_array.reshape((ph * pw, rgba_len))
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new_pa[indices] = rgbas
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self.pixel_array = new_pa.reshape((ph, pw, rgba_len))
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def display_multiple_image_mobjects(self, image_mobjects):
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for image_mobject in image_mobjects:
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self.display_image_mobject(image_mobject)
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def display_image_mobject(self, image_mobject):
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corner_coords = self.points_to_pixel_coords(image_mobject.points)
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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
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pixel_width = int(pdist([ul_coords, ur_coords]))
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pixel_height = int(pdist([ul_coords, dl_coords]))
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sub_image = sub_image.resize(
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(pixel_width, pixel_height), resample=Image.BICUBIC
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)
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# Rotate
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angle = angle_of_vector(right_vect)
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adjusted_angle = -int(360 * angle / TAU)
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if adjusted_angle != 0:
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sub_image = sub_image.rotate(
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adjusted_angle, resample=Image.BICUBIC, expand=1
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)
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# TODO, there is no accounting for a shear...
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# Paste into an image as large as the camear's pixel array
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h, w = self.pixel_shape
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full_image = Image.fromarray(
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np.zeros((h, w)),
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mode="RGBA"
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)
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new_ul_coords = center_coords - np.array(sub_image.size) / 2
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full_image.paste(
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sub_image,
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box=(
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new_ul_coords[0],
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new_ul_coords[1],
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new_ul_coords[0] + sub_image.size[0],
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new_ul_coords[1] + sub_image.size[1],
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)
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)
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# Paint on top of existing pixel array
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self.overlay_PIL_image(full_image)
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def overlay_rgba_array(self, arr):
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self.overlay_PIL_image(Image.fromarray(arr, mode="RGBA"))
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def overlay_PIL_image(self, image):
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self.pixel_array = np.array(
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Image.alpha_composite(self.get_image(), image)
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)
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def adjust_out_of_range_points(self, points):
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if not np.any(points > self.max_allowable_norm):
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return points
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norms = np.apply_along_axis(np.linalg.norm, 1, points)
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violator_indices = norms > self.max_allowable_norm
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violators = points[violator_indices, :]
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violator_norms = norms[violator_indices]
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reshaped_norms = np.repeat(
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violator_norms.reshape((len(violator_norms), 1)),
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points.shape[1], 1
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)
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rescaled = self.max_allowable_norm * violators / reshaped_norms
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points[violator_indices] = rescaled
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return points
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def points_to_pixel_coords(self, points):
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shifted_points = points - self.space_center
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result = np.zeros((len(points), 2))
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ph, pw = self.pixel_shape
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sh, sw = self.frame_shape
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width_mult = pw / sw
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width_add = pw / 2
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height_mult = ph / sh
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height_add = ph / 2
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# Flip on y-axis as you go
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height_mult *= -1
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result[:, 0] = shifted_points[:, 0] * width_mult + width_add
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result[:, 1] = shifted_points[:, 1] * height_mult + height_add
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return result.astype('int')
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def on_screen_pixels(self, pixel_coords):
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return reduce(op.and_, [
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pixel_coords[:, 0] >= 0,
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pixel_coords[:, 0] < self.pixel_shape[1],
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pixel_coords[:, 1] >= 0,
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pixel_coords[:, 1] < self.pixel_shape[0],
|
|
])
|
|
|
|
def adjusted_thickness(self, thickness):
|
|
big_shape = PRODUCTION_QUALITY_CAMERA_CONFIG["pixel_shape"]
|
|
factor = sum(big_shape) / sum(self.pixel_shape)
|
|
return 1 + (thickness - 1) / factor
|
|
|
|
def get_thickening_nudges(self, thickness):
|
|
_range = 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))
|
|
|
|
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.frame_shape)[::-1]
|
|
full_pixel_dims = np.array(self.pixel_shape)[::-1]
|
|
|
|
# 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.pixel_shape)[
|
|
::-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.init_canvas()
|
|
|
|
def init_canvas(self):
|
|
self.pixel_array = np.zeros(
|
|
self.camera.pixel_array.shape,
|
|
dtype=self.camera.pixel_array_dtype,
|
|
)
|
|
self.reset_canvas()
|
|
|
|
def reset_canvas(self):
|
|
image = Image.fromarray(self.pixel_array, mode=self.camera.image_mode)
|
|
self.canvas = aggdraw.Draw(image)
|
|
|
|
def resize_background_array(
|
|
self, background_array,
|
|
new_width, new_height,
|
|
mode="RGBA"
|
|
):
|
|
image = Image.fromarray(background_array, mode=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)
|
|
array = np.array(image)
|
|
|
|
camera = self.camera
|
|
if not np.all(camera.pixel_array.shape == array.shape):
|
|
array = self.resize_background_array_to_match(
|
|
array, camera.pixel_array)
|
|
|
|
self.file_name_to_pixel_array_map[file_name] = array
|
|
return 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)
|
|
for cvmobject in batch:
|
|
self.camera.display_vectorized(cvmobject, self.canvas)
|
|
self.canvas.flush()
|
|
new_array = np.array(
|
|
(background_array * self.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.pixel_array[:, :] = 0
|
|
self.reset_canvas()
|
|
return curr_array
|