Moved functions and comments around

interpretter_notes.py

@@ -0,0 +1,128 @@
+
+
+"""
+# 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)
+"""

understand.py

@@ -147,89 +147,6 @@ def test(map_to_use, label_id):
     plt.imshow(visual_mask)
     plt.show()
 
-
-
-# From Tutorial (Box 79)
-# def get_mask(segment_idx):
-#     segment = results['segments_info'][segment_idx]
-#     print("Visualizing mask for:", id2label[segment['label_id']])
-#     mask = (predicted_panoptic_seg == segment['id'])
-#     visual_mask = (mask * 255).astype(np.uint8)
-#     return Image.fromarray(visual_mask)
-
-# 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
-"""
-
-
 def contour_map(map_to_use, label_id):
     """
     map_to_use: You have to pass in `results["segmentation"]`
@@ -243,57 +160,4 @@ def contour_map(map_to_use, label_id):
     contours, hierarchy = cv.findContours(visual_mask, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
     return contours, hierarchy
 
-"""
->>> 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)
-"""