diff --git a/bbox_3d.py b/bbox_3d.py
index 7f0241b9cda2fb86e15eebb807005ae0a26a1525..d0e06048c5d8c190ed7e6f92759406282f3552ea 100644
--- a/bbox_3d.py
+++ b/bbox_3d.py
@@ -1,7 +1,6 @@
import numpy as np
-
-def get_bbox(ext):
+def get_3D_bbox(ext):
pcd_bbox = np.array([
[ext[0], ext[1], ext[2]],
[- ext[0], ext[1], ext[2]],
@@ -12,17 +11,15 @@ def get_bbox(ext):
[ext[0], - ext[1], - ext[2]],
[- ext[0], - ext[1], - ext[2]]
])
-
return pcd_bbox
-
def generate_3d_bbox(data_name):
objs = {"Banana": [0.029497003182768822, 0.15110498666763306, 0.060593008995056152],
"Orange": [0.073495000600814819, 0.075856998562812805, 0.074581995606422424],
"Pear": [0.066010989248752594, 0.12873399630188942, 0.06739199161529541]}
for key, val in objs.items():
ext = [x / 2 for x in val]
- bbox = get_bbox(ext)
+ bbox = get_3D_bbox(ext)
np.savetxt(f'{data_name}/Generated/Bbox/{key}_bbox_3d.txt', bbox)
# print(ext)
diff --git a/compute_features.py b/compute_features.py
index c048b49970da3da9354a9ee88fedf3ac30468e87..e153c5a90e9652bf70f98f10196bb7d3b3b637fb 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -1,7 +1,4 @@
-
-
-import math
import numpy as np
import json
from utils import compute_categories_id, compute_id_good_occ
@@ -15,29 +12,24 @@ from fps_alg import process2
import os
-def updateJsonFile():
- jsonFile = open("replayScript.json", "r") # Open the JSON file for reading
- data = json.load(jsonFile) # Read the JSON into the buffer
- jsonFile.close() # Close the JSON file
-
- ## Working with buffered content
- tmp = data["location"]
- data["location"] = path
- data["mode"] = "replay"
-
- ## Save our changes to JSON file
- jsonFile = open("replayScript.json", "w+")
- jsonFile.write(json.dumps(data))
- jsonFile.close()
-
-
-def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_categories, occ_target, vis):
+def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, World_begin, Nb_world, list_categories, occ_target, vis):
transformation = np.matrix([[0.0000000, -1.0000000, 0.0000000],
[0.0000000, 0.0000000, -1.0000000],
[1.0000000, 0.0000000, 0.0000000]])
list_count_categories = {}
+
+
for i in range(World_begin, World_begin + Nb_world): # worlds
+
+ scenario = "Worlds"
+
+ if i > 4 :
+ destination_folder = f"Generated_{scenario}_Testing"
+ elif i > 3 :
+ destination_folder = f"Generated_{scenario}_Evaluating"
+ else :
+ destination_folder = f"Generated_{scenario}_Training"
catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, catergories_label_to_id = compute_categories_id(data_name, i)
@@ -56,56 +48,56 @@ def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_ca
with open(f"{data_name}/Bbox_2d/{p}.json", 'r') as f:
data_Bbox_2d = json.load(f)
+ with open(f"{data_name}/Bbox_3d/{p}.json", 'r') as f:
+ data_Bbox_3d = json.load(f)
if len(data_Bbox_2d) != len(data_3D_pose) :
raise TypeError("size of datas are differents !!")
- if os.path.isfile(f'{data_name}/Generated/Count_{p-1}.json'):
- with open(f'{data_name}/Generated/Count_{p-1}.json') as f:
+ if os.path.isfile(f'{data_name}/{destination_folder}/Count_{p-1}.json'):
+ with open(f'{data_name}/{destination_folder}/Count_{p-1}.json') as f:
list_count_categories = json.load(f)
-
for categories in list_categories:
-
if categories in catergories_occ_array.keys():
-
- if len(catergories_occ_array[categories]) == 1 :
-
- meta = {}
-
- if categories in list_count_categories.keys():
- list_count_categories[categories] += 1
- else:
- list_count_categories[categories] = 1
-
- meta['id_generated'] = list_count_categories[categories]
- meta['id_original'] = p
- meta['id_category'] = catergories_label_to_id[categories]
- meta['id_instance'] = catergories_occ_array[categories][0]
- meta['id_dataset'] = 1
- meta['world'] = i
- meta['camera'] = f"grabber_{j}"
- meta['occlusion'] = occ_target
- meta['Nb_instance_category'] = 1
-
-
- if not os.path.isfile(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json'):
- with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', mode='w') as f:
- feeds = {}
- feeds[meta['id_generated']]=meta
- f.write(json.dumps(feeds, indent=2))
- else:
- with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json') as feedsjson:
- feeds = json.load(feedsjson)
+ Nb_instance = len(catergories_occ_array[categories])
+
+ meta = {}
+ if categories in list_count_categories.keys():
+ print("hello")
+ else:
+ #list_count_categories[categories] = {categories}
+ list_count_categories[categories] = {}
+
+ if Nb_instance in list_count_categories[categories].keys() :
+ list_count_categories[categories][Nb_instance] += 1
+ else :
+ list_count_categories[categories][Nb_instance] = 1
+
+ meta['id_generated'] = list_count_categories[categories][Nb_instance]
+ meta['id_original'] = p
+ meta['id_category'] = catergories_label_to_id[categories]
+ meta['id_instance'] = catergories_occ_array[categories]
+ meta['id_dataset'] = 1
+ meta['world'] = i
+ meta['camera'] = f"grabber_{j}"
+ meta['occlusion'] = occ_target
+ meta['Nb_instance_category'] = Nb_instance
+
+ if not os.path.isfile(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json'):
+ with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
+ feeds = {}
feeds[meta['id_generated']]=meta
- with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', mode='w') as f:
- f.write(json.dumps(feeds, indent=4))
-
- # with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', "a") as meta_file:
- # json.dump(meta, meta_file, indent=4)
+ f.write(json.dumps(feeds, indent=2))
+ else:
+ with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json') as feedsjson:
+ feeds = json.load(feedsjson)
+ feeds[meta['id_generated']]=meta
+ with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
+ f.write(json.dumps(feeds, indent=4))
+ if (Nb_instance == 1):
for k in range(len(data_3D_pose)):
-
if data_3D_pose[k]['id'] == catergories_occ_array[categories][0]:
rpy = data_3D_pose[k]['pose']['rpy']
rot = convert2(rpy)
@@ -116,13 +108,19 @@ def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_ca
T_exp = transformation @ xyz
T_exp = np.array(T_exp)
num_arr = np.c_[R_exp, T_exp[0]]
- np.save(f'{data_name}/Generated/Pose_transformed/{categories}/{p}.npy', num_arr) # save
+ np.save(f'{data_name}/{destination_folder}/{categories}/Pose_transformed/{p}.npy', num_arr) # save
else:
continue
if data_Bbox_2d[k]['id'] == catergories_occ_array[categories][0]:
bbox = bbox_2d(data_Bbox_2d[k])
- np.savetxt(f'{data_name}/Generated/Bbox/{categories}/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/Bbox/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ else:
+ continue
+
+ if data_Bbox_3d[k]['id'] == catergories_occ_array[categories][0]:
+ bbox3d_size = data_Bbox_3d[k]['bbox']['size']
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/Bbox_3d_Gen/{p}.txt', bbox3d_size) # save
else:
continue
@@ -130,38 +128,46 @@ def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_ca
img = cv2.imread(f"{data_name}/Instance_Segmentation/{p}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path)
instance_img = instance(img, id)
- cv2.imwrite(f"{data_name}/Generated/Instance_Mask/{categories}/{p}.png", 255*instance_img)
-
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/Instance_Mask/{p}.png", 255*instance_img)
+ instance_img_resized = cv2.resize(instance_img, new_size)
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/Instance_Mask_resized/{p}.png", 255*instance_img_resized)
- img = image.imread(f"{data_name}/RGB/{p}.png")
+ img = cv2.imread(f"{data_name}/RGB/{p}.png")
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/RGB_Gen/{p}.png", img)
+ img_resized = cv2.resize(img, new_size)
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/RGB_resized/{p}.png", img_resized)
np.set_printoptions(precision=15)
- pose = np.load(f'{data_name}/Generated/Pose_transformed/{categories}/{p}.npy')
- #print(pose)
+ pose = np.load(f'{data_name}/{destination_folder}/{categories}/Pose_transformed/{p}.npy')
R_exp = pose[0:3, 0:3]
tVec = pose[0:3, 3]
- #print(tVec)
- # camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
- # [0.0, 1386.4138492513919, 540.5],
- # [0.0, 0.0, 1.0]])
-
-
- fps_points = np.loadtxt(f'{data_name}/Generated/FPS/{categories}_fps_3d.txt')
- # process(pcd_bbox, pcd, R_exp, tVec, camera, img)
+ fps_points = np.loadtxt(f'{data_name}/Generated/{categories}/{categories}_fps_3d.txt')
+ center = fps_points.mean(0)
+ fps_points = np.append(fps_points, [center], axis=0)
points = process2(fps_points, R_exp, tVec, camera, img, vis)
- out = np.zeros((1, 401))
-
- out[0] = catergories_occ_array[categories] #obj_id #len have to be 1 !!
+ out = [int(catergories_occ_array[categories][0])] #len have to be 1 !!
ind = 1
for point in points:
- out[0][ind] = point[0][0] / img.shape[1]
- out[0][ind + 1] = point[0][1] / img.shape[0]
+ x = point[0][0] / img.shape[1]
+ y = point[0][1] / img.shape[0]
+ out.append(x)
+ out.append(y)
ind += 2
- np.savetxt(f'{data_name}/Generated/FPS/{categories}/{p}.txt', out)
- #print("stop")
-
- with open(f'{data_name}/Generated/Count_{p}.json', mode='w') as f:
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/FPS/{p}.txt', np.array(out).reshape(1, len(out)))
+
+ points_resized = process2(fps_points, R_exp, tVec, camera_resized, img_resized, vis)
+ out_resized = [int(catergories_occ_array[categories][0])] #len have to be 1 !
+ ind_resized = 1
+ for point_resized in points_resized:
+ x_resized = point_resized[0][0] / img_resized.shape[1]
+ y_resized = point_resized[0][1] / img_resized.shape[0]
+ out_resized.append(x_resized)
+ out_resized.append(y_resized)
+ ind_resized += 2
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/FPS_resized/{p}.txt', np.array(out_resized).reshape(1, len(out_resized)))
+
+ with open(f'{data_name}/{destination_folder}/Count_{p}.json', mode='w') as f:
f.write(json.dumps(list_count_categories, indent=4))
print(list_count_categories)
diff --git a/fps_alg.py b/fps_alg.py
index 3d5f14c1a5ddbc77e10babbd3bec7ab16ad67c83..fa2f93f64d93135cbf2cd3086aa82850a6401336 100644
--- a/fps_alg.py
+++ b/fps_alg.py
@@ -108,13 +108,13 @@ def process2(pcd, R_exp, tVec, camera, img, vis= True):
# print(pcd_fps_numpy[n], '==>', keypoint_2d[0][n])
if vis:
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
out = np.zeros((img.shape[0], img.shape[1], 16))
fig, ax = plt.subplots()
ax.imshow(img)
for n in range(len(pcd_fps_numpy)):
point = keypoint_2d[0][n]
ax.plot(point[0][0], point[0][1], marker='.', color="red")
-
plt.imshow(img)
plt.show()
return keypoint_2d[0]
diff --git a/main.py b/main.py
index cced98fe248e6fa9029e7ce80058911fe00e9bb2..d031724b04fce617f5cb677ce263fc5974ac19aa 100644
--- a/main.py
+++ b/main.py
@@ -1,33 +1,50 @@
import os
import numpy as np
-import json
from prepare_data import reform_data
-#from pose import transform_pose
-#from bbox_2d import generate_2d_bbox
-#from instance_mask import generate_instance_mask
from fps_alg import apply_fps
-from bbox_3d import generate_3d_bbox
+from bbox_3d import get_3D_bbox
from compute_features import process_compute
-import shutil
import open3d as o3d
-# Import the library
+from scipy.spatial import distance
import argparse
-def generate_folders(name, list_categories):
+def generate_folders(name, list_categories, scenario):
is_exist = os.path.exists(name)
if not is_exist:
os.mkdir(name)
- folders = ["RGB", "RGB_Gen", "Meta_Gen", "Depth", "Mask", "Meta", "Pose", "Bbox_2d", "Bbox_2d_loose", "Instance_Segmentation", "Semantic_Segmentation", "Instance_Mask", "Occlusion", "Models", "Pose_transformed", "Bbox", "FPS"]
+ 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", "Instance_Mask", "Meta_Gen", "Models", "Pose_transformed", "Bbox", "FPS"]:
+ 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/{f}/{cat}")
+ is_exist2 = os.path.exists(f"{name}/Generated/{cat}")
if not is_exist2:
- os.makedirs(f"{name}/Generated/{f}/{cat}")
+ os.makedirs(f"{name}/Generated/{cat}")
+ 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}")
+
+
+
+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
@@ -38,20 +55,23 @@ if __name__ == '__main__':
# 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 = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
- # frame = "1_600000000"
- #frame = "1_926000000"
Nb_camera = 15
#Nb_world = 10000
@@ -71,23 +91,60 @@ if __name__ == '__main__':
#print(fps_points)
np.savetxt(f'{dataset_name}/Generated/FPS/{categories}_fps_3d.txt', fps_points)
+ 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]])
- else:
+ # (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)
+ #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)
- process_compute(dataset_name, camera, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, False)
- #transform_pose(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
- #generate_2d_bbox(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
- #generate_instance_mask(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
- #generate_fps(dataset_name, camera, Nb_camera, Nb_world, list_categories, occ_target, True)
- #generate_3d_bbox(dataset_name)
+ 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)
+
diff --git a/prepare_data.py b/prepare_data.py
index 1641a783bd38f9426a17350c5f62cd2c6f1fca67..8830d80b2bb81de78255b57eb7ea04cdd77ea24c 100644
--- a/prepare_data.py
+++ b/prepare_data.py
@@ -48,6 +48,11 @@ def reform_data(src, data_name, data_option, Nb_camera, World_begin, Nb_world):
dst_bbox = f"{data_name}/Bbox_2d/{count}.json"
shutil.copy(src_bbox, dst_bbox)
+ files_3D_bbox = os.listdir(f"{src}/{i}/grabber_{j}/{data_option}/3d_detection/")
+ src_3D_bbox = f"{src}/{i}/grabber_{j}/{data_option}/3d_detection/{files_3D_bbox[0]}"
+ dst_3D_bbox = f"{data_name}/Bbox_3d/{count}.json"
+ shutil.copy(src_3D_bbox, dst_3D_bbox)
+
files_bbox_loose = os.listdir(f"{src}/{i}/grabber_{j}/{data_option}/2d_detection_loose/")
src_bbox_loose = f"{src}/{i}/grabber_{j}/{data_option}/2d_detection_loose/{files_bbox_loose[0]}"
dst_bbox_loose = f"{data_name}/Bbox_2d_loose/{count}.json"
diff --git a/resize.py b/resize.py
index 70cc608640bcb86bab7e2941ba3b7e98659a96c6..6174f8cb1398132192bb6f6d79f86cb5d92bf838 100644
--- a/resize.py
+++ b/resize.py
@@ -1,12 +1,26 @@
from matplotlib import image
import matplotlib.pyplot as plt
+from utils import compute_categories_id, compute_id_good_occ
import cv2
import numpy as np
-
+import argparse
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)
+ # Parse the argument
+ args = parser.parse_args()
+
+
choice = "low"
+ Nb_instance = 1
+ occ_target = 0.5
if choice == 'high':
camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
@@ -28,27 +42,49 @@ if __name__ == '__main__':
dataset_name = f"GUIMOD_{choice}"
new_size = (640, 480)
+ Nb_camera = 15
+ list_categories = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
+
+ for i in range(args.World_begin, args.World_begin + args.Nb_worlds): # worlds
+
+ catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, catergories_label_to_id = compute_categories_id(dataset_name, i)
+
+ for j in range(1, Nb_camera+1): # cameras
+ p = ((i-1)*Nb_camera) + j
+
+ catergories_occ_array = compute_id_good_occ(dataset_name, p, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, occ_target)
+
+ #depth = cv2.resize(cv2.imread(f"{dataset_name}/Depth/{i}.tiff"), new_size)
+
+ for categories in list_categories:
+ if categories in catergories_occ_array.keys():
+ if len(catergories_occ_array[categories]) == 1 :
+
+ print(f"{dataset_name}/Generated/{categories}/RGB_Gen/{p}.png")
+ rgb = cv2.resize(cv2.imread(f"{dataset_name}/Generated/{categories}/RGB_Gen/{p}.png"), new_size)
+ cv2.imwrite(f"{dataset_name}/Generated/{categories}/RGB_resized/{p}.png", rgb)
- for i in range(4995):
- rgb = cv2.resize(cv2.imread(f"{dataset_name}/RGB/{i}.png"), new_size)
- cv2.imwrite(f"rgb/{i}.png", rgb)
+ #mask = cv2.resize(cv2.imread(f"{dataset_name}//Instance_Mask/{i}.png"), new_size)
+ #cv2.imwrite(f"mask/{i}.png", mask*255)
- mask = cv2.resize(cv2.imread(f"{dataset_name}/Mask/{i}.png"), new_size)
- cv2.imwrite(f"mask/{i}.png", mask*255)
+ print(f"{dataset_name}/Generated/{categories}/Instance_Mask/{p}.png")
+ print("new_size",new_size)
+ cat_mask = cv2.resize(cv2.imread(f"{dataset_name}/Generated/{categories}/Instance_Mask/{p}.png"), new_size)
- depth = cv2.resize(cv2.imread(f"{dataset_name}/Depth/{i}.tiff"), new_size)
+ print(f"{dataset_name}/Generated/{categories}/Instance_Mask_resized/{p}.png")
+ cv2.imwrite(f"{dataset_name}/Generated/{categories}/Instance_Mask_resized/{p}.png", cat_mask)
- banana_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/banana1/{i}.png"), new_size)
- cv2.imwrite(f"banana1_mask/{i}.png", banana_mask*255)
+ # banana_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/banana1/{i}.png"), new_size)
+ # cv2.imwrite(f"banana1_mask/{i}.png", banana_mask*255)
- orange_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/orange2/{i}.png"), new_size)
- cv2.imwrite(f"orange2_mask/{i}.png", orange_mask*255)
+ # orange_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/orange2/{i}.png"), new_size)
+ # cv2.imwrite(f"orange2_mask/{i}.png", orange_mask*255)
- Pear_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/pear2/{i}.png"), new_size)
- cv2.imwrite(f"pear2_mask/{i}.png", Pear_mask*255)
+ # Pear_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/pear2/{i}.png"), new_size)
+ # cv2.imwrite(f"pear2_mask/{i}.png", Pear_mask*255)
- print("Done")
+ print("Done")
# Check 2d bbox to resize it
diff --git a/test_resize.py b/test_resize.py
index 58177e3b28c9f070922cbad2000c4f8b2bf8d505..c014c5975f1ef22a590f3e70f8476481789afea2 100644
--- a/test_resize.py
+++ b/test_resize.py
@@ -166,7 +166,6 @@ def generate_fps(data_name, camera, vis=False, resize=False):
# Read the point cloud
for obj in ["Banana"]:
- sfefcij
obj_id = 0
point_cloud = f'{data_name}/Models/{obj}/{obj.lower()}.ply'
pcd = o3d.io.read_point_cloud(point_cloud)
diff --git a/utils.py b/utils.py
index 96e49e33cdf33cce9058ef1e5029b4f1e09d8cad..2e09900181b6c4ba0515ce9fb69945c4b8af7759 100644
--- a/utils.py
+++ b/utils.py
@@ -57,8 +57,11 @@ def compute_id_good_occ(data_name, count, catergories_instance_array_id_to_cat,
#print(cat)
catergories_occ_array[cat] = []
+ print(data2)
+
for i in data2:
- if i['occlusion_value'] > 0.5 :
+ if i['occlusion_value'] >= Occ_wanted :
+ print(i['id'])
catergories_occ_array[catergories_instance_array_id_to_cat[i['id']]].append(i['id'])
print(catergories_occ_array)