151 lines
3.9 KiB
Python
151 lines
3.9 KiB
Python
from __future__ import division
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from itertools import groupby
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from PIL import Image
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Image.MAX_IMAGE_PIXELS = 1000000000
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import axi
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import math
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import numpy as np
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import sys
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LNG1 = -125
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LNG2 = -100
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LAT1 = 49
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LAT2 = 31
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WIDTH = 13
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HEIGHT = 8.5
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LANDSCAPE = True
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ROWS = LAT1 - LAT2
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if not LANDSCAPE:
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WIDTH, HEIGHT = HEIGHT, WIDTH
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def remove_flats(path):
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# return [list(path)]
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paths = []
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for k, g in groupby(path, lambda p: p[1] > 0):
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if k:
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paths.append(list(g))
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return paths
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def crop(im):
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w, h = im.size
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lng1 = LNG1 + 180
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lng2 = LNG2 + 180
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lat1 = 90 - LAT1
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lat2 = 90 - LAT2
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pix_per_lng = int(w / 360)
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pix_per_lat = int(h / 180)
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x1 = lng1 * pix_per_lng
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x2 = lng2 * pix_per_lng
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y1 = lat1 * pix_per_lat
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y2 = lat2 * pix_per_lat
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return im.crop((x1, y1, x2, y2))
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def circle(cx, cy, r, n):
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points = []
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for i in range(n + 1):
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a = 2 * math.pi * i / n
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x = cx + math.cos(a) * r
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y = cy + math.sin(a) * r
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points.append((x, y))
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return points
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def lat_label(text, y):
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d = axi.Drawing(axi.text(text, axi.FUTURAL))
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d = d.scale_to_fit_height(0.1)
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d = d.move(WIDTH + 1 / 8, y, 0, 1)
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# d.paths.append(circle(12.125 + d.width + 1 / 16, y - d.height, 1 / 48, 36))
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d = d.join_paths(0.01)
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d = d.simplify_paths(0.001)
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paths = d.paths
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# paths.append([(WIDTH, y), (WIDTH + 1 / 16, y)])
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return paths
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def lng_label(text, x):
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d = axi.Drawing(axi.text(text, axi.FUTURAL))
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d = d.scale_to_fit_height(0.1)
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d = d.move(x, HEIGHT + 0.125, 0.5, 1)
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# d.paths.append(circle(x + d.width / 2 + 1 / 16, 8.5 + 0.125 - d.height, 1 / 48, 36))
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d = d.join_paths(0.01)
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d = d.simplify_paths(0.001)
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paths = d.paths
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paths.append([(x, HEIGHT - 1 / 8), (x, HEIGHT - 1 / 16)])
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return paths
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def vertical_stack(ds, spacing=0):
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result = axi.Drawing()
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y = 0
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for d in ds:
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d = d.origin().translate(-d.width / 2, y)
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result.add(d)
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y += d.height + spacing
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return result
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def title():
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d = axi.Drawing(axi.text('Topography of the Western United States', axi.FUTURAM))
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d = d.scale_to_fit_height(0.25)
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d = d.join_paths(0.01)
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d = d.simplify_paths(0.001)
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return d
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def main():
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paths = []
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im = Image.open(sys.argv[1])
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im = im.convert('L')
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im = crop(im)
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# im.save('crop.png')
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print im.size
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w, h = im.size
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data = np.asarray(im)
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data = data / np.amax(data)
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# data = data ** 0.5
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lines_per_row = int(h / ROWS)
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for j in range(0, ROWS, 1):
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y0 = j * lines_per_row
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y1 = y0 + lines_per_row
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d = data[y0:y1]
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for q in range(0, 101, 25):
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print j, q
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values = np.percentile(d, q, axis=0) * 1.2
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path = enumerate(values)
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for path in remove_flats(path):
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x = np.array([p[0] for p in path]) * WIDTH / w
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y = (j - np.array([p[1] for p in path])) * HEIGHT / ROWS
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path = zip(x, y)
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path = axi.simplify_paths([path], 0.005)[0]
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paths.append(path)
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lat = LAT1 + (LAT2 - LAT1) * j / (ROWS)
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paths.extend(lat_label('%g' % lat, j * HEIGHT / ROWS))
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for lng in range(LNG1, LNG2 + 1):
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x = (lng - LNG1) / (LNG2 - LNG1) * WIDTH
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paths.extend(lng_label('%g' % abs(lng), x))
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d = axi.Drawing(paths)
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print len(d.paths)
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print 'joining paths'
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d = d.join_paths(0.01)
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print len(d.paths)
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print 'sorting paths'
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d = d.sort_paths()
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print 'joining paths'
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d = d.join_paths(0.01)
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print len(d.paths)
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d = vertical_stack([title(), d], 0.25)
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# d = d.rotate(180)
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d = d.rotate_and_scale_to_fit(12, 8.5, step=90)
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im = d.render(
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scale=109 * 1, line_width=0.3/25.4,
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)#show_axi_bounds=False, use_axi_bounds=False)
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im.write_to_png('out.png')
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# d = d.rotate_and_scale_to_fit(12, 8.5, step=90)
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d.dump('out.axi')
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if __name__ == '__main__':
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main()
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