def load_scenes():
try:
df = pd.read_pickle(data_dir(['scenes', 'origin', 'scenes.pkl']))
except:
df = pd.read_csv(data_dir(['scenes', 'origin', 'scenes.csv']))
df['Polygon'] = df['Polygon'].apply(lambda s: eval(s))
df.to_pickle(data_dir(['scenes', 'origin', 'scenes.pkl']))
return df
def main():
df = load_scenes()
df = df[df['Polygon'].map(is_interested)]
filepath = lambda s: data_dir(['scenes', 'archives', '{}.csv'.format(s)])
df.to_csv(filepath('total'), index=None)
df = df.sample(frac=1) # important, to shuffle the data
for i in [1000, 1500, 2000, 2500, 5000, 10000, 15000, 20000, 50000, 75000, 100000]:
if not os.path.isfile(filepath(str(i))):
df.head(i).to_csv(filepath(str(i)), index=None)
else:
print(i, 'existed')
def visualize(vinfo, tag):
roi = vinfo['roi']
selected = []
unselected = []
sorted_vinfo = sorted(vinfo['frames'].items(), key=lambda kv: float(kv[0]))
frame_no = 0
frames = []
for timestamp, frame in sorted_vinfo:
selected.append(frame['selected'])
unselected = frame['unselected'] or []
frames.append(draw_frame(roi, selected, unselected))
frame_no += 1
print(frame_no, 'frame')
writeGif(data_dir(['visualize', '{}.gif'.format(tag)]),
frames,
duration=0.7,
dither=0)
def main():
MIN_PRODUCT_AREA = 0.011
small = [2, 4, 6, 8, 10, 12, 14, 16]
median = [20, 40, 60, 80, 100]
large = []
for ratio in small + median + large:
path = data_dir(['rois', 'rect(x)_{}.csv'.format(ratio)])
size = ratio * MIN_PRODUCT_AREA
if not os.path.isfile(path):
rects = [[list(point) for point in rect]
for rect in gen_axis_aligned_rectangle(1000, size)]
# fig, ax = plt.subplots()
# plot_polygons(ax, rects)
# plt.show()
df = pd.DataFrame([rects]).transpose()
df.columns = ['Polygon']
df.to_csv(path, index=None)
else:
print(path, 'exists!')
import numpy as np
import pandas as pd
import json
from utils.path import data_dir, fig_dir
import matplotlib.pyplot as plt
import matplotlib
import os
import re
vinfo_path = data_dir(['visualize', 'bnb'])
fig_path = fig_dir(['bnb_visual'])
matplotlib.rcParams.update({'font.size': 18})
d = {}
for dirpath, dirnames, filenames in os.walk(vinfo_path):
for filename in filenames:
btype, s = re.match("(\[.+\])(.+)", filename).groups()
roi_ratio = s.strip().split('-')[2]
if roi_ratio not in d:
d[roi_ratio] = {}
reports = json.load(open(os.path.join(dirpath, filename), 'r'))
if len(reports) == 1: # old version
reports = reports[0].items()
else: # new version
_ = {}
for k in reports[0].keys():
_[k] = [report[k] for report in reports]
reports = _.items()
for k, v in reports:
def query_dir(l):
return data_dir(['experiment', 'results', 'query'] + l)
def load_vinfo(n):
discrete = json.load(
open(data_dir(['visualize', 'vinfo', 'd{}_vinfo.json'.format(n)])))
continuous = json.load(
open(data_dir(['visualize', 'vinfo', 'c{}_vinfo.json'.format(n)])))
return discrete, continuous
def main():
summary = json.load(open(data_dir(['experiment', 'results', 'summary.json']), 'r'))
for sub_summary in summary:
plot_summary(next(iter(sub_summary['variable'].keys())), sub_summary['reports'])
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils.path import data_dir
deltas = [0.01, 0.02, 0.05, 0.1, 0.2, 0.5]
n_scenes = []
for i in [0.01, 0.02, 0.05, 0.1, 0.2, 0.5]:
file_path = data_dir(
['experiment', 'results', 'query', '0.01-50-{}-15000.json'.format(i)])
reports = json.load(open(file_path, 'r'))
n_scenes.append(
np.mean([len(report['possible_scenes']) for report in reports]))
df = pd.DataFrame([deltas, n_scenes]).transpose().sort_values(0)
df.columns = ['roi_ratio', 'number_of_scenes']
df.plot(x='roi_ratio', y='number_of_scenes', marker='x')
plt.savefig('n_scenes')