-
Notifications
You must be signed in to change notification settings - Fork 0
/
analyze_po.py
36 lines (29 loc) · 1.38 KB
/
analyze_po.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
import pandas as pd
import numpy as np
import json
from PIL import Image
import os
DIR = os.environ['RESULTS_FOLDER']
log_lines = open(os.path.join(DIR, 'details.log')).readlines()
df = pd.DataFrame.from_records(list(map(json.loads, log_lines)))
# From class index to class name (for readability)
class_map = json.load(open(os.path.join(DIR, 'class_maps.json')))
df['prediction'] = df['prediction'].apply(lambda x: class_map[x[0]])
# We'll be a little lenient here to get a more interesting heatmap
df['is_correct'] = df['prediction'].isin(['cup', 'coffee mug'])
uv_num_correct = np.zeros((256, 256))
uv_num_visible = np.zeros((256, 256))
for imid in df["id"].unique().tolist():
is_correct = float(df.set_index('id').loc[imid]['is_correct'])
vis_coords_im = Image.open(os.path.join(DIR, f'images/{imid}_uv.png'))
vis_coords = np.array(vis_coords_im).reshape(-1, 3)
# R and G channels encode texture coordinates (x, y),
# B channel is 255 for object and 0 for background
# So we will filter by B then only look at R and G.
vis_coords = vis_coords[vis_coords[:,2] > 0][:,:2]
uv_num_visible[vis_coords[:,0], vis_coords[:,1]] += 1.
uv_num_correct[vis_coords[:,0], vis_coords[:,1]] += is_correct
# Accuracy = # correct / # visible
uv_accuracy = uv_num_correct / (uv_num_visible + 1e-4)
# Saves a black-and-white heatmap
Image.fromarray((255 * uv_accuracy).astype('uint8'))