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Commit c1fbacf6 authored by Guillaume Duret's avatar Guillaume Duret
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reading script of json file stat from compute statistics

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import os
import numpy as np
from prepare_data import reform_data
from fps_alg import apply_fps
from bbox_3d import get_3D_bbox
from compute_features import process_compute
import open3d as o3d
from scipy.spatial import distance
import argparse
import json
import pandas as pd
import altair as alt
def generate_folders(name, list_categories, scenario):
is_exist = os.path.exists(name)
if not is_exist:
os.mkdir(name)
folders = ["RGB", "RGB_Gen", "RGB_resized", "Meta_Gen", "Depth", "Mask", "Meta", "Pose", "Bbox_2d", "Bbox_2d_loose", "Bbox_3d", "Bbox_3d_Gen", "Instance_Segmentation", "Semantic_Segmentation", "Instance_Mask", "Instance_Mask_resized", "Occlusion", "Models", "Pose_transformed", "Bbox", "FPS", "FPS_resized"]
for f in folders:
is_exist = os.path.exists(f"{name}/{f}")
if not is_exist:
if f not in ["RGB_Gen", "RGB_resized", "Instance_Mask", "Instance_Mask_resized", "Meta_Gen", "Models", "Pose_transformed", "Bbox", "Bbox_3d_Gen", "FPS" , "FPS_resized"]:
os.mkdir(f"{name}/{f}")
else:
for cat in list_categories:
is_exist2 = os.path.exists(f"{name}/Generated/{cat}")
if not is_exist2:
os.makedirs(f"{name}/Generated/{cat}")
is_exist2 = os.path.exists(f"{name}/Generated/{cat}/Pose_transformed")
if not is_exist2:
os.makedirs(f"{name}/Generated/{cat}/Pose_transformed")
for scenario in ["Worlds", "Cameras", "Mix_all"] :
is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Training/{cat}/{f}")
if not is_exist2:
os.makedirs(f"{name}/Generated_{scenario}_Training/{cat}/{f}")
is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Evaluating/{cat}/{f}")
if not is_exist2:
os.makedirs(f"{name}/Generated_{scenario}_Evaluating/{cat}/{f}")
is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Testing/{cat}/{f}")
if not is_exist2:
os.makedirs(f"{name}/Generated_{scenario}_Testing/{cat}/{f}")
is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_dont_save/{cat}/{f}")
if not is_exist2:
os.makedirs(f"{name}/Generated_{scenario}_dont_save/{cat}/{f}")
def calc_pts_diameter2(pts):
"""Calculates the diameter of a set of 3D points (i.e. the maximum distance
between any two points in the set). Faster but requires more memory than
calc_pts_diameter.
:param pts: nx3 ndarray with 3D points.
:return: The calculated diameter.
"""
dists = distance.cdist(pts, pts, 'euclidean')
diameter = np.max(dists)
return diameter
def prep_df(df, name):
df = df.stack().reset_index()
df.columns = ['c1', 'c2', 'values']
df['DF'] = name
return df
if __name__ == '__main__':
# Create the parser
parser = argparse.ArgumentParser()
# Parse the argument
args = parser.parse_args()
scenario = "Worlds"
### parameters ###
Categories = [] # to read
Nb_instance = 1
occ_target = 0.5
dataset_src = "/gpfsscratch/rech/uli/ubn15wo/dataset/s2rg/Fruits_all_medium/data/"
#dataset_src = "/media/mahmoud/E/Fruits_easy/data"
#dataset_src = "/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data"
choice = "low" # depth of rgb resolution datas
data_options = {"high": "ground_truth_rgb",
"low": "ground_truth_depth"}
dataset_type = data_options[choice]
dataset_name = f"/gpfsscratch/rech/uli/ubn15wo/dataset/s2rg/Fruits_all_medium/GUIMOD_{choice}"
list_categories = [ "apple2" , "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
#list_Nb_instance = [ "apple2" , "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
path_json = "Count_150000.json"
if os.path.isfile(path_json):
with open(path_json) as f:
list_count_categories = json.load(f)
scenarios = ["Worlds", "Cameras", "Mix_all"]
destination_folders_list = {}
for scenario_loop in scenarios:
destination_folders_list[scenario_loop] = [f"Generated_{scenario_loop}_Testing", f"Generated_{scenario_loop}_Evaluating", f"Generated_{scenario_loop}_Training", f"Generated_{scenario_loop}_dont_save" ]
print(list_count_categories)
stat_cat = {}
worlds_train=[]
worlds_eval=[]
worlds_test=[]
cameras_train=[]
cameras_eval=[]
cameras_test=[]
mix_train=[]
mix_eval=[]
mix_test=[]
array_apple=[]
array_apricot=[]
array_banana=[]
array_kiwi=[]
array_lemon=[]
array_orange=[]
array_peach=[]
array_pear=[]
stat_cat_inst = {}
for scenario_loop in scenarios :
stat_cat_inst[scenario_loop] = {}
for cat in list_categories :
stat_cat_inst[scenario_loop][cat] = {}
for cat in list_categories :
stat_cat[cat] = {}
for scenario_loop in scenarios :
stat_cat[cat][scenario_loop] = {}
for destination_folder_loop in destination_folders_list[scenario_loop] : # [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training"] :
#print("scenario_loop : " , scenario_loop)
#print("destination_folder_loop : " , destination_folder_loop)
#print("cat : " , cat)
#print("list_count_categories[scenario_loop][destination_folder_loop][cat][1_instances] : " , list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
#print(list_count_categories[scenario_loop][destination_folder_loop])
if destination_folder_loop == "Generated_Worlds_Testing" :
print("\nGenerated_Wolrds_Testing1")
worlds_test.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Cameras_Testing" :
print("\nGenerated_Wolrds_Testing2")
cameras_test.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Mix_all_Testing" :
print("\nGenerated_Wolrds_Testing3")
mix_test.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Worlds_Evaluating" :
print("\nGenerated_Wolrds_Testing4")
worlds_eval.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Cameras_Evaluating" :
print("\nGenerated_Wolrds_Testing5")
cameras_eval.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Mix_all_Evaluating" :
print("\nGenerated_Wolrds_Testing6")
mix_eval.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Worlds_Training" :
print("\nGenerated_Wolrds_Testing7")
worlds_train.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Cameras_Training" :
print("\nGenerated_Wolrds_Testing8")
cameras_train.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if destination_folder_loop == "Generated_Mix_all_Training" :
print("\nGenerated_Wolrds_Testing9")
mix_train.append(list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"])
if cat in list_count_categories[scenario_loop][destination_folder_loop].keys() :
stat_cat[cat][scenario_loop][destination_folder_loop] = list_count_categories[scenario_loop][destination_folder_loop][cat]["1_instances"]
#print(list_count_categories[scenario_loop][destination_folder_loop].keys())
if cat in list_count_categories[scenario_loop][destination_folder_loop].keys() : # because dont save empty for world and camera scenario
for nb_inst in list_count_categories[scenario_loop][destination_folder_loop][cat].keys() :
if nb_inst in stat_cat_inst[scenario_loop][cat].keys() :
stat_cat_inst[scenario_loop][cat][nb_inst] = stat_cat_inst[scenario_loop][cat][nb_inst] + list_count_categories[scenario_loop][destination_folder_loop][cat][nb_inst]
else:
stat_cat_inst[scenario_loop][cat][nb_inst] = list_count_categories[scenario_loop][destination_folder_loop][cat][nb_inst]
#print(stat_cat)
print(stat_cat_inst)
print(worlds_train)
print(worlds_eval)
print(worlds_test)
print(cameras_train)
print(cameras_eval)
print(cameras_test)
print(mix_train)
print(mix_eval)
print(mix_test)
# for scenario_loop in scenarios :
# for cat in list_categories :
# for destination_folder_loop in destination_folders_list[scenario_loop] :
# stat_cat_inst[scenario_loop][cat][nb_inst]
df1_train=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_train), np.array(cameras_train), np.array(mix_train)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
df2_eval=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_eval), np.array(cameras_eval), np.array(mix_eval)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
df3_test=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_test), np.array(cameras_test), np.array(mix_test)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
df1 = prep_df(df1_train, 'Train')
df2 = prep_df(df2_eval, 'Eval')
df3 = prep_df(df3_test, 'Test')
df = pd.concat([df1, df2, df3])
# print(np.resize(np.concatenate((np.array(worlds_train), np.array(cameras_train), np.array(mix_train)) ), (3,8)))
# print(np.random.rand(4,3))
# df1=pd.DataFrame(10*np.random.rand(4,3),index=["A","B","C","D"],columns=["I","J","K"])
# df2=pd.DataFrame(10*np.random.rand(4,3),index=["A","B","C","D"],columns=["I","J","K"])
# df3=pd.DataFrame(10*np.random.rand(4,3),index=["A","B","C","D"],columns=["I","J","K"])
# def prep_df(df, name):
# df = df.stack().reset_index()
# df.columns = ['c1', 'c2', 'values']
# df['DF'] = name
# return df
# df1 = prep_df(df1, 'DF1')
# df2 = prep_df(df2, 'DF2')
# df3 = prep_df(df3, 'DF3')
# df = pd.concat([df1, df2, df3])
alt.renderers.enable('altair_viewer')
chart = alt.Chart(df).mark_bar().encode(
# tell Altair which field to group columns on
x=alt.X('c2:N', title=None),
# tell Altair which field to use as Y values and how to calculate
y=alt.Y('sum(values):Q',
axis=alt.Axis(
grid=False,
title=None)),
# tell Altair which field to use to use as the set of columns to be represented in each group
column=alt.Column('c1:N', title=None),
# tell Altair which field to use for color segmentation
color=alt.Color('DF:N',
scale=alt.Scale(
# make it look pretty with an enjoyable color pallet
range=['#96ceb4', '#ffcc5c','#ff6f69'],
),
))\
.configure_view(
# remove grid lines around column clusters
strokeOpacity=0
)
chart.show()
with open(f'Count_Stat.json', mode='w') as f:
f.write(json.dumps(stat_cat, indent=4))
with open(f'Count_Stat_instance.json', mode='w') as f:
f.write(json.dumps(stat_cat_inst, indent=4))
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