Still trying to understand how it is split

understand.py

@@ -4,6 +4,7 @@ import torch
 import pathlib
 import numpy as np
 from PIL import Image
+import cv2 as cv
 
 from transformers import DetrFeatureExtractor, DetrForSegmentation, MaskFormerImageProcessor, MaskFormerForInstanceSegmentation
 # from transformers.models.detr.feature_extraction_detr import rgb_to_id
@@ -214,6 +215,53 @@ array([[False, False, False, ..., False, False, False],
 """
 
 
+def contour_map(map_to_use, label_id):
+    """
+    map_to_use: You have to pass in `results["segmentation"]`
+    """
+    if torch.cuda.is_available():
+        mask = (map_to_use.cpu().numpy() == label_id)
+    else:
+        mask = (map_to_use.numpy() == label_id)
+    
+    visual_mask = (mask* 255).astype(np.uint8)
+
+"""
+>>> mask = (results["segmentation"].cpu().numpy() == 1)
+>>> visual_mask = (mask* 255).astype(np.uint8)
+>>> import cv2 as cv
+>>> contours, hierarchy = cv.findContours(visual_mask, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
+>>> contours.shape
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+AttributeError: 'tuple' object has no attribute 'shape'
+>>> contours[0].shape
+(7, 1, 2)
+>>> shrunk = contours[0][:, 0, :]
+>>> shrunk
+array([[400, 340],
+       [399, 341],
+       [400, 342],
+       [401, 342],
+       [402, 341],
+       [403, 341],
+       [402, 340]], dtype=int32)
+>>> get_coordinates_for_bb_simple(results["segmentation"], 1)
+((300, 399), (392, 538))
+>>> shrunk = contours[1][:, 0, :]
+>>> max(shrunk[:, 0])
+538
+>>> min(shrunk[:, 0])
+409
+>>> min(shrunk[:, 1])
+300
+>>> max(shrunk[:, 1])
+392
+>>> 
+"""
+
+
+
 """
 import cv2 as cv
 contours, hierarchy = cv.findContours(visual_mask, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)