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Running
on
Zero
File size: 3,216 Bytes
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import os, sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
import matplotlib
matplotlib.use('Agg')
import numpy as np
import networkx as nx
from io import BytesIO
from PIL import Image, ImageDraw
from matplotlib import pyplot as plt
from sklearn.decomposition import PCA
from singapo_utils.refs import graph_color_ref
def add_text(text, imgarr):
'''
Function to add text to image
Args:
- text (str): text to add
- imgarr (np.array): image array
Returns:
- img (np.array): image array with text
'''
img = Image.fromarray(imgarr)
I = ImageDraw.Draw(img)
I.text((10, 10), text, fill='black')
return np.asarray(img)
def get_color(ref, n_nodes):
'''
Function to color the nodes
Args:
- ref (list): list of color reference
- n_nodes (int): number of nodes
Returns:
- colors (list): list of colors
'''
N = len(ref)
colors = []
for i in range(n_nodes):
colors.append(np.array([[int(i) for i in ref[i%N][4:-1].split(',')]]) / 255.)
return colors
def make_grid(images, cols=5):
"""
Arrange list of images into a N x cols grid.
Args:
- images (list): List of Numpy arrays representing the images.
- cols (int): Number of columns for the grid.
Returns:
- grid (numpy array): Numpy array representing the image grid.
"""
# Determine the dimensions of each image
img_h, img_w, _ = images[0].shape
rows = len(images) // cols
# Initialize a blank canvas
grid = np.zeros((rows * img_h, cols * img_w, 3), dtype=images[0].dtype)
# Place each image onto the grid
for idx, img in enumerate(images):
y = (idx // cols) * img_h
x = (idx % cols) * img_w
grid[y: y + img_h, x: x + img_w] = img
return grid
def viz_graph(info_dict, res=256):
'''
Function to plot the directed graph
Args:
- info_dict (dict): output json containing the graph information
- res (int): resolution of the image
Returns:
- img_arr (np.array): image array
'''
# build tree
tree = info_dict['diffuse_tree']
edges = []
for node in tree:
edges += [(node['id'], child) for child in node['children']]
G = nx.DiGraph()
G.add_edges_from(edges)
# plot tree
plt.figure(figsize=(res/100, res/100))
colors = get_color(graph_color_ref, len(tree))
pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
node_order = sorted(G.nodes())
nx.draw(G, pos, node_color=colors, nodelist=node_order, edge_color='k', with_labels=False)
buf = BytesIO()
plt.savefig(buf, format="png", dpi=100)
buf.seek(0)
img = Image.open(buf)
img_arr = np.asarray(img)
buf.close()
plt.clf()
plt.close()
return img_arr[:, :, :3]
def viz_patch_feat_pca(feat):
pca = PCA(n_components=3)
pca.fit(feat)
feat_pca = pca.transform(feat)
t = np.array(feat_pca)
t_min = t.min(axis=0, keepdims=True)
t_max = t.max(axis=0, keepdims=True)
normalized_t = (t - t_min) / (t_max - t_min)
array = (normalized_t * 255).astype(np.uint8)
img_array = array.reshape(16, 16, 3)
return img_array |