add main_rearange before main

main_rearrange.py

+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
+
+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}/dont_save/{cat}/{f}")
+                        if not is_exist2:
+                            os.makedirs(f"{name}/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
+
+if __name__ == '__main__':    
+    # Create the parser
+    parser = argparse.ArgumentParser()
+    # Add an argument
+    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, required=True)
+    # Parse the argument
+    args = parser.parse_args()
+
+    scenario = "Worlds"
+
+    ### parameters ###
+    Categories = [] # to read
+    Nb_instance = 1
+    occ_target = 0.5
+
+    dataset_src = f"/gpfsscratch/rech/uli/ubn15wo/data{args.dataset_id}"
+    #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{args.dataset_id}/s2rg/Fruits_all_medium/GUIMOD_{choice}"
+    list_categories = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
+    Nb_camera = 15
+    #Nb_world = 10000
+
+    generate_folders(dataset_name, list_categories, scenario)
+
+    if choice == 'high':
+        camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
+                            [0.0, 1386.4138492513919, 540.5],
+                            [0.0, 0.0, 1.0]])
+        # (640/1920 = 1 / 3), (480/1080 = 4 / 9)
+        trans = np.matrix([[1 / 3, 0.0, 0.0],
+                        [0.0, (4 / 9), 0.0],
+                        [0.0, 0.0, 1.0]])
+    elif choice == 'low':
+        camera = np.matrix([[1086.5054444841007, 0.0, 640.5],
+                            [0.0, 1086.5054444841007, 360.5],
+                            [0.0, 0.0, 1.0]])
+        # 
+        trans = np.matrix([[0.5, 0.0, 0.0],
+                        [0.0, (2 / 3), 0.0],
+                        [0.0, 0.0, 1.0]])
+
+    new_size = (640, 480)
+
+    new_camera = trans @ camera
+
+    #np.savetxt(f'{dataset_name}/Generated/camera_{choice}.txt', camera)
+
+    reform_data(dataset_src, dataset_name, dataset_type, Nb_camera, args.World_begin, args.Nb_worlds)
+
+    list_categories = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
+    objs = {"banana1": [ 0.02949700132012367249, 0.1511049866676330566, 0.06059300713241100311 ],
+            "kiwi1": [ 0.04908600077033042908, 0.07206099480390548706, 0.04909799993038177490 ],
+            "pear2": [ 0.06601099669933319092, 0.1287339925765991211, 0.06739201396703720093 ],
+            "strawberry1": [0.01698100194334983826, 0.02203200198709964752, 0.01685700193047523499],
+            "apricot": [0.04213499650359153748, 0.05482299625873565674, 0.04333199933171272278],
+            "orange2": [ 0.07349500805139541626, 0.07585700601339340210, 0.07458199560642242432 ],
+            "peach1": [ 0.07397901266813278198, 0.07111301273107528687, 0.07657301425933837891 ],
+            "lemon2": [0.04686100035905838013, 0.04684200137853622437, 0.07244800776243209839],
+            "apple2": [0.05203099921345710754, 0.04766000062227249146, 0.05089000239968299866]}
+
+    for categories in list_categories:
+        point_cloud = f"Models/{categories}/{categories.lower()}.ply"
+        pcd = o3d.io.read_point_cloud(point_cloud)
+
+        fps_points = apply_fps(pcd, 8)
+
+        np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_fps_3d.txt', fps_points)
+
+        point_cloud_in_numpy = np.asarray(pcd.points)
+        dim = calc_pts_diameter2(point_cloud_in_numpy) * 100
+        np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_diameter.txt', np.array([dim]))
+
+        size_bb = objs[categories]
+        ext = [x / 2 for x in size_bb]
+        bbox = get_3D_bbox(ext)
+        np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_bbox_3d.txt', bbox)  # save
+
+    #process_compute(dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, False)
+