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Unverified Commit 11c043d2 authored by Mishig Davaadorj's avatar Mishig Davaadorj Committed by GitHub
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Specify im-seg mask greyscole mode (#13974)

parent 85d69a7d
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Showing with 30 additions and 30 deletions
src/transformers/pipelines/image_segmentation.py
View file @ 11c043d2
......@@ -107,9 +107,9 @@ class ImageSegmentationPipeline(Pipeline):
- **label** (:obj:`str`) -- The class label identified by the model.
- **score** (:obj:`float`) -- The score attributed by the model for that label.
- **mask** (:obj:`str`) -- base64 string of a single-channel PNG image that contain masks information. The
PNG image has size (heigth, width) of the original image. Pixel values in the image are either 0 or 255
(i.e. mask is absent VS mask is present).
- **mask** (:obj:`str`) -- base64 string of a grayscale (single-channel) PNG image that contain masks
information. The PNG image has size (heigth, width) of the original image. Pixel values in the image are
either 0 or 255 (i.e. mask is absent VS mask is present).
"""
return super().__call__(*args, **kwargs)
......@@ -158,7 +158,7 @@ class ImageSegmentationPipeline(Pipeline):
Returns:
A base64 string of a single-channel PNG image that contain masks information.
"""
img = Image.fromarray(mask.astype(np.int8))
img = Image.fromarray(mask.astype(np.int8), mode="L")
with io.BytesIO() as out:
img.save(out, format="PNG")
png_string = out.getvalue()
......
tests/test_pipelines_image_segmentation.py
View file @ 11c043d2
......@@ -112,12 +112,12 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
{
"score": 0.004,
"label": "LABEL_0",
"mask": "8423ef82b9a8e8790346bc452b557aa78ea997bc",
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
},
{
"score": 0.004,
"label": "LABEL_0",
"mask": "8423ef82b9a8e8790346bc452b557aa78ea997bc",
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
},
],
)
......@@ -140,24 +140,24 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
{
"score": 0.004,
"label": "LABEL_0",
"mask": "8423ef82b9a8e8790346bc452b557aa78ea997bc",
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
},
{
"score": 0.004,
"label": "LABEL_0",
"mask": "8423ef82b9a8e8790346bc452b557aa78ea997bc",
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
},
],
[
{
"score": 0.004,
"label": "LABEL_0",
"mask": "8423ef82b9a8e8790346bc452b557aa78ea997bc",
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
},
{
"score": 0.004,
"label": "LABEL_0",
"mask": "8423ef82b9a8e8790346bc452b557aa78ea997bc",
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
},
],
],
......@@ -177,12 +177,12 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.9094, "label": "blanket", "mask": "f939d943609821ad27cdb92844f2754ad3735b52"},
{"score": 0.9941, "label": "cat", "mask": "32913606de3958812ced0090df7b699abb6e2644"},
{"score": 0.9987, "label": "remote", "mask": "f3988d35f3065f591fa6a0a9414614d98a9ca13e"},
{"score": 0.9995, "label": "remote", "mask": "ff0d541ace4fe386fc14ced0c546490a8e7001d7"},
{"score": 0.9722, "label": "couch", "mask": "543c3244b291c4aec134f1d8f92af553da795529"},
{"score": 0.9994, "label": "cat", "mask": "891313e21290200e6169613e6a9cb7aff9e7b22f"},
{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
],
)
......@@ -201,20 +201,20 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.9094, "label": "blanket", "mask": "f939d943609821ad27cdb92844f2754ad3735b52"},
{"score": 0.9941, "label": "cat", "mask": "32913606de3958812ced0090df7b699abb6e2644"},
{"score": 0.9987, "label": "remote", "mask": "f3988d35f3065f591fa6a0a9414614d98a9ca13e"},
{"score": 0.9995, "label": "remote", "mask": "ff0d541ace4fe386fc14ced0c546490a8e7001d7"},
{"score": 0.9722, "label": "couch", "mask": "543c3244b291c4aec134f1d8f92af553da795529"},
{"score": 0.9994, "label": "cat", "mask": "891313e21290200e6169613e6a9cb7aff9e7b22f"},
{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
],
[
{"score": 0.9094, "label": "blanket", "mask": "f939d943609821ad27cdb92844f2754ad3735b52"},
{"score": 0.9941, "label": "cat", "mask": "32913606de3958812ced0090df7b699abb6e2644"},
{"score": 0.9987, "label": "remote", "mask": "f3988d35f3065f591fa6a0a9414614d98a9ca13e"},
{"score": 0.9995, "label": "remote", "mask": "ff0d541ace4fe386fc14ced0c546490a8e7001d7"},
{"score": 0.9722, "label": "couch", "mask": "543c3244b291c4aec134f1d8f92af553da795529"},
{"score": 0.9994, "label": "cat", "mask": "891313e21290200e6169613e6a9cb7aff9e7b22f"},
{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
],
],
)
......@@ -235,7 +235,7 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.9995, "label": "remote", "mask": "ff0d541ace4fe386fc14ced0c546490a8e7001d7"},
{"score": 0.9994, "label": "cat", "mask": "891313e21290200e6169613e6a9cb7aff9e7b22f"},
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
],
)
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