diff --git a/read_count.py b/read_count.py
new file mode 100644
index 0000000000000000000000000000000000000000..11eca90f9c9bdc448257403edbfc333ace3871c9
--- /dev/null
+++ b/read_count.py
@@ -0,0 +1,310 @@
+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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