diff --git a/compute_features.py b/compute_features.py
index f2473af779e6b99f5150565b12121865a123fa59..af054034c27ac94d1f9f8f600020b854f5306850 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -31,7 +31,7 @@ def updateJsonFile():
     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]])
@@ -66,9 +66,7 @@ def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_ca
 
 
             for categories in list_categories:
-
                 if categories in catergories_occ_array.keys():
-
                     if len(catergories_occ_array[categories]) == 1 :
 
                         meta = {}
@@ -131,11 +129,15 @@ def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_ca
 
                         instance_img = instance(img, id)
                         cv2.imwrite(f"{data_name}/Generated/{categories}/Instance_Mask/{p}.png", 255*instance_img)
-
+                        cat_mask = cv2.resize(instance_img, new_size)
+                        cv2.imwrite(f"{data_name}/Generated/{categories}/Instance_Mask_resized/{p}.png", 255*cat_mask)
 
                         img = cv2.imread(f"{data_name}/RGB/{p}.png")
                         cv2.imwrite(f"{data_name}/Generated/{categories}/RGB_Gen/{p}.png", img)
 
+                        img_resized = cv2.resize(img, new_size)
+                        cv2.imwrite(f"{data_name}/Generated/{categories}/RGB_resized/{p}.png", img_resized)
+
                         np.set_printoptions(precision=15)
                         pose = np.load(f'{data_name}/Generated/{categories}/Pose_transformed/{p}.npy')
                         #print(pose)
@@ -170,12 +172,30 @@ def process_compute(data_name, camera, Nb_camera, World_begin, Nb_world, list_ca
                             out.append(x)
                             out.append(y)
                             ind += 2
-                        print("points" , points)
-                        print("out" , out)
-                        print("np.array(out).reshape(1, len(out))", np.array(out).reshape(1, len(out)))
+
                         np.savetxt(f'{data_name}/Generated/{categories}/FPS/{p}.txt',  np.array(out).reshape(1, len(out)))
                         #print("stop")
 
+                        points_resized = process2(fps_points, R_exp, tVec, camera_resized, img_resized, vis)
+                        #out = np.zeros((1, ))
+
+                        out_resized = [int(catergories_occ_array[categories][0])]# [catergories_occ_array[categories][0]] #obj_id #len have to be 1 !!
+                        print(out)
+
+
+                        ind_resized = 1
+                        for point_resized in points_resized:
+                            #out[0][ind] = point[0][0] / img.shape[1]
+                            #[0][ind + 1] = point[0][1] / img.shape[0]
+                            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}/Generated/{categories}/FPS_resized/{p}.txt',  np.array(out_resized).reshape(1, len(out_resized)))
+                        #print("stop")         
+
     with open(f'{data_name}/Generated/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 ab43f89ea2fa850bb1c7dd666b9177be43f4f9b6..421af8466c169ee345cd60ad293bed629c890a95 100644
--- a/main.py
+++ b/main.py
@@ -17,11 +17,11 @@ def generate_folders(name, list_categories):
     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", "Instance_Segmentation", "Semantic_Segmentation", "Instance_Mask", "Instance_Mask_resized", "Occlusion", "Models", "Pose_transformed", "Bbox", "FPS"]
+    folders = ["RGB", "RGB_Gen", "RGB_resized", "Meta_Gen", "Depth", "Mask", "Meta", "Pose", "Bbox_2d", "Bbox_2d_loose", "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", "FPS"]:
+            if f not in ["RGB_Gen", "RGB_resized",  "Instance_Mask", "Instance_Mask_resized", "Meta_Gen", "Models", "Pose_transformed", "Bbox", "FPS" , "FPS_resized"]:
                 os.mkdir(f"{name}/{f}")
             else:
                 for cat in list_categories:
@@ -74,6 +74,9 @@ if __name__ == '__main__':
         np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_fps_3d.txt', fps_points)
 
 
+
+
+
     if choice == 'high':
         camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
                             [0.0, 1386.4138492513919, 540.5],
@@ -85,11 +88,17 @@ if __name__ == '__main__':
 
     new_size = (640, 480)
 
+    trans = np.matrix([[0.5, 0.0, 0.0],
+                       [0.0, (2 / 3), 0.0],
+                       [0.0, 0.0, 1.0]])
+
+    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)
 
-    process_compute(dataset_name, camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, False)
+    process_compute(dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, True)
     #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)