add interval for occlusion

compute_features.py

@@ -12,7 +12,7 @@ from fps_alg import process2
 import os
 from PIL import Image
 
-def process_compute(data_path, full_path, camera, camera_resized, new_size, Nb_camera, World_begin, Nb_world, list_categories, occ_target, vis):
+def process_compute(data_path, full_path, camera, camera_resized, new_size, Nb_camera, World_begin, Nb_world, list_categories, occ_target_min, occ_target_max=1, vis=False):
     transformation = np.matrix([[0.0000000, -1.0000000, 0.0000000],
                                 [0.0000000, 0.0000000, -1.0000000],
                                 [1.0000000, 0.0000000, 0.0000000]])
@@ -68,7 +68,7 @@ def process_compute(data_path, full_path, camera, camera_resized, new_size, Nb_c
             else : 
                 destination_folders["Mix_all"] = f"Generated_Mix_all_dont_save"
 
-            categories_array_filtered, categories_array_filtered_occ, categories_array_all, categories_array_all_occ = compute_id_good_occ(data_path, p, categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, occ_target)
+            categories_array_filtered, categories_array_filtered_occ, categories_array_all, categories_array_all_occ = compute_id_good_occ(data_path, p, categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, occ_target_min, occ_target_max)
 
             ### 3D Poses ###
             with open(f'{data_path}/Pose/{p}.json', 'r') as f:
@@ -146,7 +146,8 @@ def process_compute(data_path, full_path, camera, camera_resized, new_size, Nb_c
                         meta['Nb_instance'] = Nb_instance_all
                         meta['all_id_instance'] = categories_array_all[categories] 
                         meta['all_id_instance_occlusion'] = categories_array_all_occ[categories] 
-                        meta['target_occlusion'] = occ_target
+                        meta['target_occlusion_min'] = occ_target_min
+                        meta['target_occlusion_max'] = occ_target_max
                         meta['Nb_instance_filtered'] = Nb_instance_occ
                         meta['id_instance_good'] = categories_array_filtered[categories]
                         meta['id_instance_good_occlusion'] = categories_array_filtered_occ[categories]

main.py

@@ -66,7 +66,8 @@ if __name__ == '__main__':
     parser.add_argument('--Nb_worlds', type=int, required=True)
     parser.add_argument('--World_begin', type=int, required=True)
     parser.add_argument('--dataset_id', type=str, default='', required=True)
-    parser.add_argument('--occlusion_target', type=float, default='', required=True)
+    parser.add_argument('--occlusion_target_min', type=float, default='', required=True)
+    parser.add_argument('--occlusion_target_max', type=float, default='', required=True)
     #parser.add_argument('--rearrange', dest='rearrange', default=False, action='store_true')
     #parser.add_argument('--compute', dest='compute', default=False, action='store_true')
     parser.add_argument('--rearrange', type=str, default='no', required=True)
@@ -79,7 +80,8 @@ if __name__ == '__main__':
     ### parameters ###
     Categories = [] # to read
     Nb_instance = 1
-    occ_target = args.occlusion_target
+    occ_target_min = args.occlusion_target_min
+    occ_target_max = args.occlusion_target_max
 
     dataset_src = f"/gpfsscratch/rech/uli/ubn15wo/DATA/data{args.dataset_id}"
     #dataset_src = "/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data"
@@ -90,7 +92,7 @@ if __name__ == '__main__':
     dataset_type = data_options[choice]
     dataset_path = f"/gpfsscratch/rech/uli/ubn15wo/FruitBin{args.dataset_id}" #GUIMOD_New_{choice}_{args.dataset_id}"
     #dataset_path = f"/home/gduret/Documents/FruitBin{args.dataset_id}/"
-    dataset_name = f"FruitBin_{choice}_{Nb_instance}_{occ_target}"
+    dataset_name = f"FruitBin_{choice}_{Nb_instance}_{occ_target_min}_{occ_target_max}"
     #dataset_name = f"/gpfsscratch/rech/uli/ubn15wo/dataset_new{args.dataset_id}/s2rg/Fruits_all_medium/GUIMOD_{choice}"
     list_categories = ["banana1", "kiwi1", "pear2", "apricot", "orange2", "peach1", "lemon2", "apple2"]
     Nb_camera = 15
@@ -155,5 +157,5 @@ if __name__ == '__main__':
         np.savetxt(f'{dataset_path}/{dataset_name}/Generated/{categories}/{categories}_bbox_3d.txt', bbox)  # save
 
     if args.compute == 'yes' : 
-        process_compute(dataset_path, dataset_path+'/'+dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, False)
+        process_compute(dataset_path, dataset_path+'/'+dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target_min, occ_target_max, False)
 

slurm/process_dataset.slurm

 #!/bin/bash
-#SBATCH --job-name=Software      # nom du job
+#SBATCH --job-name=Process      # nom du job
 #SBATCH -A uli@cpu
 #SBATCH --ntasks=1                    # Nombre total de processus MPI
 #SBATCH --ntasks-per-node=1           # Nombre de processus MPI par noeud
 #SBATCH --cpus-per-task=2
 #SBATCH --partition=cpu_p1
-#SBATCH --qos=qos_cpu-t4
+#SBATCH --qos=qos_cpu-t3
 # /!\ Attention, la ligne suivante est trompeuse mais dans le vocabulaire
 # de Slurm "multithread" fait bien référence à l'hyperthreading.
 #SBATCH --hint=nomultithread           # 1 processus MPI par coeur physique (pas d'hyperthreading)
-#SBATCH --time=35:00:00                # Temps d’exécution maximum demande (HH:MM:SS)
-#SBATCH --output=Output/postprocess%j.out  # Nom du fichier de sortie
-#SBATCH --error=Output/postprocess%j.out   # Nom du fichier d'erreur (ici commun avec la sortie)
+#SBATCH --time=20:00:00                # Temps d’exécution maximum demande (HH:MM:SS)
+#SBATCH --output=Output/qpostprocess%j.out  # Nom du fichier de sortie
+#SBATCH --error=Output/qpostprocess%j.out   # Nom du fichier d'erreur (ici commun avec la sortie)
  
 # on se place dans le répertoire de soumission
 cd ${SLURM_SUBMIT_DIR}
@@ -31,10 +31,12 @@ set -x
 id_begin=$1
 Nb=$2
 id_dataset=$3
-rearrange=$4
-compute=$5
+occlusion_min=$4
+occlusion_max=$5
+rearrange=$6
+compute=$7
 
 conda activate stvNet
 
-time(python main.py --World_begin="$id_begin" --Nb_world="$Nb" --dataset_id="$id_dataset" --rearrange=$rearrange --compute=$compute)
+time(python main.py --World_begin="$id_begin" --Nb_world="$Nb" --dataset_id="$id_dataset" --occlusion_target_min=$occlusion_min --occlusion_target_max=$occlusion_max --rearrange=$rearrange --compute=$compute)
 

utils.py

@@ -9,6 +9,7 @@ def compute_categories_id(data_name, world):
     
     # returns JSON object as 
     # a dictionary
+    print(f'{data_name}/Meta/{world}.json')
     data = json.load(f)
     
     # Iterating through the json
@@ -35,7 +36,7 @@ def compute_categories_id(data_name, world):
 
 
 
-def compute_id_good_occ(data_name, count, categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, Occ_wanted):
+def compute_id_good_occ(data_name, count, categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, occ_target_min, occ_target_max):
 
     f2 = open(f'{data_name}/Occlusion/{count}.json')
 
@@ -53,7 +54,7 @@ def compute_id_good_occ(data_name, count, categories_instance_array_id_to_cat, c
         categories_array_all_occ[cat] = []
 
     for i in data2:
-        if i['occlusion_value'] >= Occ_wanted :
+        if i['occlusion_value'] > occ_target_min and i['occlusion_value'] <= occ_target_max :
             categories_array_filtered[categories_instance_array_id_to_cat[i['id']]].append(i['id'])
             categories_array_filtered_occ[categories_instance_array_id_to_cat[i['id']]].append(i['occlusion_value'])
         if  i['occlusion_value'] >= 0.05 :
@@ -69,4 +70,4 @@ def compute_id_good_occ(data_name, count, categories_instance_array_id_to_cat, c
     #print(categories_array_all_occ)
 
 
-    return categories_array_filtered, categories_array_filtered_occ, categories_array_all, categories_array_all_occ
+    return categories_array_filtered, categories_array_filtered_occ, categories_array_all, categories_array_all_occ
\ No newline at end of file