Python object_detection.utils.ops.native_crop_and_resize() Examples
The following are 4
code examples of object_detection.utils.ops.native_crop_and_resize().
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Example #1
| Source File: ops_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def testBatchCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]],
[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]],
[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
crop_output = self.execute_cpu(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
Example #2
| Source File: ops_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def testBatchCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]],
[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]],
[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
crop_output = self.execute_cpu(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
Example #3
| Source File: ops_test.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def testBatchCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]],
[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]],
[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
crop_output = self.execute_cpu(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
Example #4
| Source File: ops_test.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def testBatchCropAndResize3x3To2x2_2Channels(self):
def graph_fn(image, boxes):
return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
image = np.array([[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]],
[[[1, 0], [2, 1], [3, 2]],
[[4, 3], [5, 4], [6, 5]],
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
boxes = np.array([[[0, 0, 1, 1],
[0, 0, .5, .5]],
[[1, 1, 0, 0],
[.5, .5, 0, 0]]], dtype=np.float32)
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
crop_output = self.execute_cpu(graph_fn, [image, boxes])
self.assertAllClose(crop_output, expected_output)
