interpretter_notes.py

"""
# How to get ID
>>> model.config.id2label
{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 
13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 
27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 
39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 
54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 
68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush', 80: 'banner', 81: 'blanket', 
82: 'bridge', 83: 'cardboard', 84: 'counter', 85: 'curtain', 86: 'door-stuff', 87: 'floor-wood', 88: 'flower', 89: 'fruit', 90: 'gravel', 91: 'house', 92: 'light', 93: 'mirror-stuff', 94: 'net', 95: 'pillow', 
96: 'platform', 97: 'playingfield', 98: 'railroad', 99: 'river', 100: 'road', 101: 'roof', 102: 'sand', 103: 'sea', 104: 'shelf', 105: 'snow', 106: 'stairs', 107: 'tent', 108: 'towel', 109: 'wall-brick', 
110: 'wall-stone', 111: 'wall-tile', 112: 'wall-wood', 113: 'water-other', 114: 'window-blind', 115: 'window-other', 116: 'tree-merged', 117: 'fence-merged', 118: 'ceiling-merged', 119: 'sky-other-merged', 
120: 'cabinet-merged', 121: 'table-merged', 122: 'floor-other-merged', 123: 'pavement-merged', 124: 'mountain-merged', 125: 'grass-merged', 126: 'dirt-merged', 127: 'paper-merged', 128: 'food-other-merged', 
129: 'building-other-merged', 130: 'rock-merged', 131: 'wall-other-merged', 132: 'rug-merged'}
>>> model.config.id2label[123]
'pavement-merged'
>>> results["segments_info"][1]
{'id': 2, 'label_id': 123, 'was_fused': False, 'score': 0.995813}
""" 
# Above labels don't correspond to anything ... https://github.com/nightrome/cocostuff/blob/master/labels.md
# This one was closest to helping: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/MaskFormer/Inference/Inference_with_MaskFormer_for_semantic_%2B_panoptic_segmentation.ipynb

"""
>>> Image.fromarray((mask * 255).cpu().numpy().astype(np.uint8))
<PIL.Image.Image image mode=L size=2000x1500 at 0x7F07773691C0>
>>> temp = Image.fromarray((mask * 255).cpu().numpy().astype(np.uint8))
"""

"""
>>> mask = (results["segmentation"].cpu().numpy == 4)
>>> mask = (results["segmentation"].cpu().numpy() == 4)
>>> mask
array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]])
>>> visual_mask = (mask * 255).astype(np.uint8)
>>> visual_mask = Image.fromarray(visual_mask)
>>> plt.imshow(visual_mask)
<matplotlib.image.AxesImage object at 0x7f0761e78040>
>>> plt.show()
"""

"""
>>> mask = (results["segmentation"].cpu().numpy() == 1)
>>> visual_mask = (mask*255).astype(np.uint8)
>>> visual_mask = Image.fromarray(visual_mask)
>>> plt.imshow(visual_mask)
<matplotlib.image.AxesImage object at 0x7f0760298550>
>>> plt.show()
>>> results["segments_info"][0]
{'id': 1, 'label_id': 25, 'was_fused': False, 'score': 0.998022}
>>> 
"""

"""
>>> np.where(mask==True)
(array([300, 300, 300, ..., 392, 392, 392]), array([452, 453, 454, ..., 473, 474, 475]))
>>> max(np.where(mask==True)[0])
392
>>> min(np.where(mask==True)[0])
300
>>> max(np.where(mask==True)[1])
538
>>> min(np.where(mask==True)[1])
399
"""


"""
>>> 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)
shrunk = contours[0][:, 0, :]

>>> shrunk[0, :]
array([1907,  887], dtype=int32)
>>> shrunk[:, 0]
array([1907, 1907, 1908, 1908, 1908], dtype=int32)
>>> shrunk[:, 1]
array([887, 888, 889, 890, 888], dtype=int32)
>>> shrunk
array([[1907,  887],
       [1907,  888],
       [1908,  889],
       [1908,  890],
       [1908,  888]], dtype=int32)
"""

"""
>>> cv.boundingRect(c[0])
(399, 340, 5, 3)

>>> get_coordinates_for_bb_simple(results["segmentation"], 1)
((399, 300), (538, 392))
>>> make_new_bounding_box(cv.boundingRect(c[0]), cv.boundingRect(c[1]))
(399, 300, 140, 93)
>>> cv.boundingRect(c[0])
(399, 340, 5, 3)
>>> cv.boundingRect(c[1])
(409, 300, 130, 93)
"""

"""
for r in results["segments_info"]:
...     current_id = r["id"]
...     c, _ = contour_map(results["segmentation"], current_id)
...     print(f"id {current_id}, label = {model.config.id2label[r['label_id']]}({r['label_id']}) -- {len(c)}")
"""

"""
def quick_function(id_number):
...     c, _ = contour_map(results["segmentation"], id_number)
...     print(f'{model.config.id2label[results["segments_info"][id_number-1]["label_id"]]}, {results["segments_info"][id_number -1]["score"]}, Contour Count: {len(c)}')
...     show_mask_for_number_over_image(results["segmentation"],id_number, TEST_IMAGE)
... 
"""

"""
>>> m = results["segmentation"].cpu().numpy()
>>> new_dim = (m[0], m[1], 3)
>>> new_dim
(array([43, 43, 43, ..., 21, 21, 21], dtype=int32), array([43, 43, 43, ..., 21, 21, 21], dtype=int32), 3)
>>> new_dim = (m.shape[0], m.shape[1], 3)
>>> all_z = np.zeros(new_dim, dtype=np.uint8)

>>> z = np.zeros((m.shape[0], m.shape[1], 3), dtype=np.uint8)
>>> z[:, :, 0] = m[:, :]
>>> z[0,0]
array([43,  0,  0], dtype=uint8)
>>> z[0, 0]
array([43,  0,  0], dtype=uint8)
>>> m[0, 0]
43

>>> z[:, :, 1] = m[:, :]*4 %256
>>> z[:, :, 2] = m[:, :]*5 %256
>>> plt.imshow(z)
<matplotlib.image.AxesImage object at 0x7f8da4ad7d30>
>>> plt.show()
"""