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execution.py
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execution.py
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#!usr/bin/env python3
"""
File name: execution.py
Author: Peter Maldonado
Date created: 3/05/2019
Date last modified: 3/11/2019
Python Version: 3.7
This module contains the methods need to triangulate an image using Delaunay
Triangulation as the underlying algorithm.
"""
import argparse
import os
import sys
import numpy as np
import tqdm
from numpy.random import randint, uniform, choice
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from scipy.spatial import KDTree, Delaunay
from scipy.signal import convolve2d
import skimage.restoration
from skimage.color import rgb2gray, gray2rgb, rgb2lab
from skimage.draw import polygon, polygon_perimeter, circle_perimeter
from skimage.feature import canny
from skimage.filters import gaussian, scharr
from skimage.filters.rank import entropy
from skimage.io import imread, imsave, imshow
from skimage.morphology import disk, dilation
from skimage.restoration import denoise_bilateral
from skimage.transform import pyramid_reduce
from skimage.util import img_as_ubyte, invert, img_as_float64
import time
import triangle_util
def visualize_sample(img, weights, sample_points):
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
sharex=True, sharey=True)
ax1.imshow(img, cmap='gray')
ax1.axis('off')
ax2.imshow(weights, cmap='gray')
ax2.axis('off')
heatmap = gray2rgb(img_as_float64(weights))
for point in sample_points:
rr, cc = circle_perimeter(point[0], point[1], 2, shape=weights.shape)
heatmap[rr, cc, 0] = 1
ax3.imshow(heatmap)
ax3.axis('off')
fig.tight_layout()
plt.show()
def generate_sample_points(img, max_points):
'''
Generates samples points for triangulation of a given image.
Parameters
----------
img : np.array
The image to sample.
Returns
-------
list :
The list of points to triangulate.
'''
width = img.shape[0]
height = img.shape[1]
n = min(round(height * width * args.rate), max_points)
print("Preprocessing...")
t0 = time.perf_counter()
if args.process == 'approx-canny':
weights = approx_canny(img, args.blur)
elif args.process == 'edge-entropy':
weights = edge_entropy(img)
t1 = time.perf_counter()
if args.time:
print(f"Preprocess timer: {round(t1 - t0, 3)} seconds.")
print("Sampling...")
t0 = time.perf_counter()
if args.sample == 'threshold':
threshold = args.threshold
sample_points = threshold_sample(n, weights, threshold)
elif args.sample == 'disk':
sample_points = poisson_disk_sample(n, weights)
t1 = time.perf_counter()
if args.time:
print(f"Sample timer: {round(t1 - t0, 3)} seconds.")
if args.debug:
visualize_sample(img, weights, sample_points)
corners = np.array([[0, 0], [0, height - 1], [width - 1, 0], [width - 1, height - 1]])
return np.append(sample_points, corners, axis=0)
def approx_canny(img, blur):
'''
Weights pixels based on an approximate canny edge-detection algorithm.
Parameters
----------
img : ndarray
Image to weight.
blur : int
Blur radius for pre-processing.
Returns
-------
ndarray :
Noramlized weight matrix for pixel sampling.
'''
edge_threshold = 3 / 256
gray_img = rgb2gray(img)
blur_filt = np.ones(shape=(2 * blur + 1, 2 * blur + 1)) / ((2 * blur + 1) ** 2)
blurred = convolve2d(gray_img, blur_filt, mode='same', boundary='symm')
edge_filt = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
edge = convolve2d(blurred, edge_filt, mode='same', boundary='symm')
for idx, val in np.ndenumerate(edge):
if val < edge_threshold:
edge[idx] = 0
dense_filt = np.ones((3, 3))
dense = convolve2d(edge, dense_filt, mode='same', boundary='symm')
dense /= np.amax(dense)
if args.debug:
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
sharex=True, sharey=True)
ax1.imshow(blurred)
ax1.axis('off')
ax2.imshow(edge)
ax2.axis('off')
ax3.imshow(dense)
ax3.axis('off')
fig.tight_layout()
plt.show()
return dense
def edge_entropy(img, bal=0.1):
'''
Weights pixels based on a weighted edge-detection and entropy balance.
Parameters
----------
img : ndarray
Image to weight.
bal : float (optional)
How much to value entropy (bal) versus edge-detection (1 - bal)
Returns
-------
ndarray :
Noramlized weight matrix for pixel sampling.
'''
dn_img = skimage.restoration.denoise_tv_bregman(img, 0.1)
img_gray = rgb2gray(dn_img)
img_lab = rgb2lab(dn_img)
entropy_img = gaussian(img_as_float64(dilation(entropy(img_as_ubyte(img_gray), disk(5)), disk(5))))
edges_img = dilation(np.mean(np.array([scharr(img_lab[:, :, channel]) for channel in range(3)]), axis=0), disk(3))
weight = (bal * entropy_img) + ((1 - bal) * edges_img)
weight /= np.mean(weight)
weight /= np.amax(weight)
if args.debug:
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
sharex=True, sharey=True)
ax1.imshow(entropy_img)
ax1.axis('off')
ax2.imshow(edges_img)
ax2.axis('off')
ax3.imshow(weight)
ax3.axis('off')
fig.tight_layout()
plt.show()
return weight
def poisson_disk_sample(n, weights, k=16):
'''
Performs weighted poisson disk sampling over a region.
Algorithm based on
https://www.cs.ubc.ca/~rbridson/docs/bridson-siggraph07-poissondisk.pdf
Weighted approach inspired by
https://codegolf.stackexchange.com/questions/50299/draw-an-image-as-a-voronoi-map
Parameters
----------
n : int
The number of points to sample.
weights : np.array
Weights of grid to sample over. Assumes weights are normalized.
k : int (optional)
The number of attempts to sample an annulus before removing center.
Returns
-------
ist :
List of sampled points
'''
width = weights.shape[0]
height = weights.shape[1]
c = np.log10(width * height) / 2
max_rad = min(width, height) / 4
avg_rad = np.sqrt((height * width) / ((1 / c) * n * np.pi))
min_rad = avg_rad / 4
weights /= np.mean(weights)
rads = np.clip(avg_rad / (weights + 0.01), min_rad, max_rad)
if args.debug:
print(f"Weights: [{np.min(weights)}, {np.max(weights)}]" \
f" and Radii: [{rads.min()}, {rads.max()}]")
first = (randint(width), randint(height))
queue = [first]
sample_points = [first]
tree = KDTree(sample_points)
def in_bounds(point):
return 0 <= point[0] < width and 0 <= point[1] < height
def has_neighbor(new_point, rads, tree):
return len(tree.query_ball_point(new_point, rads[new_point])) > 0
while queue and len(sample_points) < n:
idx = randint(len(queue))
point = queue[idx]
success = False
for it in range(k):
new_point = get_point_near(point, rads, max_rad)
if (in_bounds(new_point) and not
has_neighbor(new_point, rads, tree)):
queue.append(new_point)
sample_points.append(new_point)
tree = KDTree(sample_points)
success = True
break
if not success:
queue.pop(idx)
print(f"Goal points: {n}")
print(f"Generated {len(sample_points)} sample points with disk sampling.")
print(f"{len(set(sample_points))} unique points.")
return np.array(list(sample_points))
def get_point_near(point, rads, max_rad):
'''
Randomly samples an annulus near a given point using a uniform
distribution.
Parameters
----------
point : (int, int)
The point to sample nearby.
rads : np.array
The lower bound for the random search.
max_rad : int
The upper bound for the random search.
Returns
-------
(int, int) :
The nearby point.
'''
rad = uniform(rads[point], max_rad)
theta = uniform(0, 2 * np.pi)
new_point = (point[0] + rad * np.cos(theta),
point[1] + rad * np.sin(theta))
return (int(new_point[0]), int(new_point[1]))
def threshold_sample(n, weights, threshold):
'''
Sample the weighted points uniformly above a certain threshold.
Parameters
----------
n : int
The number of points to sample.
weights : np.array
Weights of grid to sample over. Assumes weights are normalized.
threshold : float
The threshold to ignore points
Returns
-------
list :
The list of points to triangulate.
'''
candidates = np.array([idx for idx, weight in np.ndenumerate(weights) if weight >= threshold])
if candidates.shape[0] < n:
raise ValueError(f"Not enough candidate points for threshold {threshold}. "
f"Only {candidates.shape[0]} available.")
print(f"Generated {n} sample points with threshold sampling.")
return candidates[choice(candidates.shape[0], size=n, replace=False)]
def render_gradients(triangles, img, *, edge_angle=None):
layers = []
for triangle in triangles:
for i in range(len(triangle)):
triangle[i] = triangle[i][::-1]
centroid_x, centroid_y = triangle_util.centroid(*triangle)
color = img[centroid_y][centroid_x]
converted = triangle_util.convert_triangle(triangle, color, edge_angle=edge_angle)
for layer in converted:
layers.append(layer)
layers.reverse()
return layers
def execute(app_args):
print(f"Running {__name__} with arguments: {app_args}")
if app_args.seed is not None:
np.random.seed(app_args.seed)
print(f"Using seed {np.random.get_state()[1][0]}.")
img = imread(app_args.img)[:, :, :3]
sample_points = generate_sample_points(img, app_args.max_points)
triangulation = Delaunay(sample_points)
triangles = sample_points[triangulation.simplices]
layers = render_gradients(triangles, img, edge_angle=app_args.edge_angle)
layer_string = ',\n'.join(layers)
rule = f"""html, body {{
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
margin: 0;
}}
.frame {{
width: {len(img[0])}px;
height: {len(img)}px;
background: {layer_string};
background-repeat: no-repeat;
}}"""
return rule
def execute_many(app_args):
print(f"Running {__name__} in directory mode with arguments: {app_args}")
dir_path = app_args.img
listing = list(os.scandir(dir_path))
total_frames = len(listing)
frames = []
initial_frame = "none"
w = 0
h = 0
for idx, path in enumerate(tqdm.tqdm(listing)):
img_path = os.path.join(path)
try:
img = imread(img_path)[:, :, :3]
sample_points = generate_sample_points(img, app_args.max_points)
triangulation = Delaunay(sample_points)
triangles = sample_points[triangulation.simplices]
layers = render_gradients(triangles, img, edge_angle=app_args.edge_angle)
layer_string = ',\n'.join(layers)
w = len(img[0])
h = len(img)
except Exception as e:
print(e, file=sys.stderr)
layer_string = "none"
if idx == app_args.thumbnail:
initial_frame = layer_string
frame = f"""
{round(idx / total_frames * 100, 3)}% {{
background: {layer_string};
background-repeat: no-repeat;
}}
"""
frames.append(frame)
frame_string = '\n'.join(frames)
rule = f"""html, body {{
width: 100%;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
margin: 0;
}}
.frame {{
width: {w}px;
height: {h}px;
background: {initial_frame};
background-repeat: no-repeat;
}}
.executor {{
animation: play {total_frames / args.fps}s step-end forwards;
}}
@keyframes play {{
{frame_string}
}}"""
return rule
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='''Perform delaunay triangulation
on a given image to create a low-polygon approximation.''')
parser.add_argument('img', help="The image to triangulate.",
type=str)
parser.add_argument('-directory', help="Use @keyframe animation.",
action='store_true', default=False)
parser.add_argument('-fps', help="Frames per second.",
type=int, default=24)
parser.add_argument('-sample', help="Sampling method for candidate points.",
type=str, default="threshold", choices=['disk', 'threshold'])
parser.add_argument('-process', help="Pre-processing method to use.",
type=str, default='approx-canny', choices=['approx-canny',
'edge-entropy'])
parser.add_argument('-rate', help="Desired ratio of sample points to pixels.",
type=float, default=0.03)
parser.add_argument('-blur', help="Blur radius for approximate canny.",
type=int, default=2)
parser.add_argument('-threshold', help='Threshold for threshold sampling.',
type=float, default=0.02)
parser.add_argument('-max-points', help="Max number of sample points.",
type=int, default=5000)
parser.add_argument('-seed', help="Seed for random number generation.",
type=int, default=None)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--time', help="Display timer for each section.",
action='store_true')
parser.add_argument('-thumbnail', help="Frame number to pick thumbnail.",
type=int, default=None)
parser.add_argument('-edge-angle', help="Extra angle around the cone to fill up gaps in the output.", type=float,
default=0.025)
parser.add_argument('-rounding', help="Round CSS gradients to max decimal places.", type=int, default=3)
args = parser.parse_args()
if args.directory:
rule = execute_many(args)
else:
rule = execute(args)
with open("styles.css", "w") as f:
f.write(rule)