def __init__(self, path):
ds = np.load(path)
self.train = ds["train"]
self.test = ds["test"]
self.val = ds["val"]
print("dataset loaded:", path)
self.og = ObjGenerator(MAX_GRID, 1.0)
self.paths = []
self.sample_idx = [
0, 0, 0, 0
] # corresponding to train labeled, train unlabeled, test, val
get_data_dir(),
"list-v2-snek_{}-grid_{}-final.pkl".format(SNEK_LEN_2, GRID_SIZE_2))
SNEK_LEN_3 = 14
GRID_SIZE_3 = 6
OUT_PATH_3 = os.path.join(
get_data_dir(),
"list-v2-snek_{}-grid_{}-final.pkl".format(SNEK_LEN_3, GRID_SIZE_3))
SNEK_LEN_4 = 15
GRID_SIZE_4 = 8
OUT_PATH_4 = os.path.join(
get_data_dir(),
"list-v2-snek_{}-grid_{}-final.pkl".format(SNEK_LEN_4, GRID_SIZE_4))
og1 = ObjGenerator(GRID_SIZE_1, 1.0)
og2 = ObjGenerator(GRID_SIZE_2, 1.0)
og3 = ObjGenerator(GRID_SIZE_3, 1.0)
og4 = ObjGenerator(GRID_SIZE_4, 1.0)
with open(OUT_PATH_1, "rb") as output_file:
final1 = pickle.load(output_file)
with open(OUT_PATH_2, "rb") as output_file:
final2 = pickle.load(output_file)
with open(OUT_PATH_3, "rb") as output_file:
final3 = pickle.load(output_file)
with open(OUT_PATH_4, "rb") as output_file:
final4 = pickle.load(output_file)
OUT_PATH = os.path.join(get_data_dir(), "object-{:06d}.obj")
# the size of the snake is randomly sampled
# from this interval
SNEK_LEN_MIN = 5
SNEK_LEN_MAX = 10
GRID_SIZE = 4 # the grid for the snake is N x N x N
CUBE_SIZE = 1.0 # cubes are unit-sized
### IMPORTANT
OBJECTS_TO_GENERATE = 1000
if __name__ == '__main__':
og = ObjGenerator(GRID_SIZE, CUBE_SIZE)
## test with one cube
# v, f = make_cube(CUBE_SIZE)
# plot_cube(v, f)
# test with one grid
g = og.walk_snek(SNEK_LEN_MIN, SNEK_LEN_MAX)
print(g)
v, f = og.grid_to_cubes(g)
og.write_obj(g, v, f, 0, OUT_PATH)
og.plot_cube(v, f)
# v, f = og.grid_to_cubes(np.flip(g,axis=0))
# og.plot_cube(v, f)
#
# from: https://open.spotify.com/user/spotify/playlist/37i9dQZEVXcHz1uKVd65vg?si=FIkd0LBAS9en1sRXxkH57A
IN_PATH = os.path.expanduser("~/data/ig/aggregated-question.pkl")
PARAMS = [(11, 5), (13, 5), (14, 6), (15, 8)]
MAX_GRID = 8
with open(IN_PATH, "rb") as output_file:
refs = pickle.load(output_file)
cola = pickle.load(output_file)
colb = pickle.load(output_file)
colc = pickle.load(output_file)
cold = pickle.load(output_file)
answ = pickle.load(output_file)
og = ObjGenerator(MAX_GRID, 1.0)
def plot_obj_from_list(in_list):
obj, idx = in_list
grid = np.array(obj).reshape(
(PARAMS[idx][1], PARAMS[idx][1], PARAMS[idx][1]))
uniform_grid = embed_in_bigger(grid, MAX_GRID)
v, f = og.grid_to_cubes(uniform_grid)
og.plot_cube(v, f)
# plt.show()
def embed_in_bigger(grid, bigger_size):
if grid.shape[0] == bigger_size:
return grid
import pickle
import os
import matplotlib.pyplot as plt
from inversegraphics_generator.obj_generator import ObjGenerator
from inversegraphics_generator.iqtest_objs import get_data_dir
SNEK_LEN = 9
GRID_SIZE = 4
OUT_PATH = os.path.join(
get_data_dir(), "tree-v2-snek_{}-grid_{}.pkl".format(SNEK_LEN, GRID_SIZE))
print("loading file:", OUT_PATH)
og = ObjGenerator(GRID_SIZE, 1.0)
def getLeafs(node):
if len(node.children) == 0:
return node
else:
out = [getLeafs(child) for child in node.children]
out_flat = []
for child in out: # each layer removes some mess
if type([]) == type(child):
for child2 in child:
out_flat.append(child2)
else:
out_flat.append(child)
return out_flat
root = pickle.load(open(OUT_PATH, "rb"))