def testNoOp(self):
x_shape = [13, 9, 3]
x_np = np.ones(x_shape, dtype=np.float32)
with self.test_session():
x = constant_op.constant(x_np, shape=x_shape)
target_height = x_shape[0]
target_width = x_shape[1]
y = image_ops.crop_to_bounding_box(x, 0, 0, target_height, target_width)
y_tf = y.eval()
self.assertAllEqual(y_tf, x_np)
def testNoOp(self):
x_shape = [13, 9, 3]
x_np = np.ones(x_shape, dtype=np.float32)
with self.test_session():
x = constant_op.constant(x_np, shape=x_shape)
target_height = x_shape[0]
target_width = x_shape[1]
y = image_ops.crop_to_bounding_box(x, 0, 0, target_height, target_width)
y_tf = y.eval()
self.assertAllEqual(y_tf, x_np)
def testCropping(self):
x_np = np.arange(0, 30, dtype=np.int32).reshape([6, 5, 1])
offset_height = 1
after_height = 2
offset_width = 0
after_width = 3
target_height = x_np.shape[0] - offset_height - after_height
target_width = x_np.shape[1] - offset_width - after_width
y_np = x_np[offset_height : offset_height + target_height, offset_width : offset_width + target_width, :]
with self.test_session():
x = constant_op.constant(x_np, shape=x_np.shape)
y = image_ops.crop_to_bounding_box(x, offset_height, offset_width, target_height, target_width)
y_tf = y.eval()
self.assertAllEqual(y_tf.flatten(), y_np.flatten())
def testCropping(self):
x_np = np.arange(0, 30, dtype=np.int32).reshape([6, 5, 1])
offset_height = 1
after_height = 2
offset_width = 0
after_width = 3
target_height = x_np.shape[0] - offset_height - after_height
target_width = x_np.shape[1] - offset_width - after_width
y_np = x_np[offset_height:offset_height + target_height,
offset_width:offset_width + target_width, :]
with self.test_session():
x = constant_op.constant(x_np, shape=x_np.shape)
y = image_ops.crop_to_bounding_box(x, offset_height, offset_width,
target_height, target_width)
y_tf = y.eval()
self.assertAllEqual(y_tf.flatten(), y_np.flatten())
def _evalDecodeJpeg(self,
image_name,
parallelism,
num_iters,
crop_during_decode=None,
crop_window=None,
tile=None):
"""Evaluate DecodeJpegOp for the given image.
TODO(tanmingxing): add decoding+cropping as well.
Args:
image_name: a string of image file name (without suffix).
parallelism: the number of concurrent decode_jpeg ops to be run.
num_iters: number of iterations for evaluation.
crop_during_decode: If true, use fused DecodeAndCropJpeg instead of
separate decode and crop ops. It is ignored if crop_window is None.
crop_window: if not None, crop the decoded image. Depending on
crop_during_decode, cropping could happen during or after decoding.
tile: if not None, tile the image to composite a larger fake image.
Returns:
The duration of the run in seconds.
"""
ops.reset_default_graph()
image_file_path = resource_loader.get_path_to_datafile(
os.path.join('core', 'lib', 'jpeg', 'testdata', image_name))
if tile is None:
image_content = variable_scope.get_variable(
'image_%s' % image_name,
initializer=io_ops.read_file(image_file_path))
else:
single_image = image_ops.decode_jpeg(
io_ops.read_file(image_file_path),
channels=3,
name='single_image')
# Tile the image to composite a new larger image.
tiled_image = array_ops.tile(single_image, tile)
image_content = variable_scope.get_variable(
'tiled_image_%s' % image_name,
initializer=image_ops.encode_jpeg(tiled_image))
with session.Session() as sess:
self.evaluate(variables.global_variables_initializer())
images = []
for _ in xrange(parallelism):
if crop_window is None:
# No crop.
image = image_ops.decode_jpeg(image_content, channels=3)
elif crop_during_decode:
# combined decode and crop.
image = image_ops.decode_and_crop_jpeg(image_content,
crop_window,
channels=3)
else:
# separate decode and crop.
image = image_ops.decode_jpeg(image_content, channels=3)
image = image_ops.crop_to_bounding_box(
image,
offset_height=crop_window[0],
offset_width=crop_window[1],
target_height=crop_window[2],
target_width=crop_window[3])
images.append(image)
r = control_flow_ops.group(*images)
for _ in xrange(3):
# Skip warm up time.
self.evaluate(r)
start_time = time.time()
for _ in xrange(num_iters):
self.evaluate(r)
end_time = time.time()
return end_time - start_time
def _evalDecodeJpeg(self,
image_name,
parallelism,
num_iters,
crop_during_decode=None,
crop_window=None,
tile=None):
"""Evaluate DecodeJpegOp for the given image.
TODO(tanmingxing): add decoding+cropping as well.
Args:
image_name: a string of image file name (without suffix).
parallelism: the number of concurrent decode_jpeg ops to be run.
num_iters: number of iterations for evaluation.
crop_during_decode: If true, use fused DecodeAndCropJpeg instead of
separate decode and crop ops. It is ignored if crop_window is None.
crop_window: if not None, crop the decoded image. Depending on
crop_during_decode, cropping could happen during or after decoding.
tile: if not None, tile the image to composite a larger fake image.
Returns:
The duration of the run in seconds.
"""
ops.reset_default_graph()
image_file_path = os.path.join(prefix_path, image_name)
if tile is None:
image_content = variable_scope.get_variable(
'image_%s' % image_name,
initializer=io_ops.read_file(image_file_path))
else:
single_image = image_ops.decode_jpeg(
io_ops.read_file(image_file_path), channels=3, name='single_image')
# Tile the image to composite a new larger image.
tiled_image = array_ops.tile(single_image, tile)
image_content = variable_scope.get_variable(
'tiled_image_%s' % image_name,
initializer=image_ops.encode_jpeg(tiled_image))
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
images = []
for _ in xrange(parallelism):
if crop_window is None:
# No crop.
image = image_ops.decode_jpeg(image_content, channels=3)
elif crop_during_decode:
# combined decode and crop.
image = image_ops.decode_and_crop_jpeg(
image_content, crop_window, channels=3)
else:
# separate decode and crop.
image = image_ops.decode_jpeg(image_content, channels=3)
image = image_ops.crop_to_bounding_box(
image,
offset_height=crop_window[0],
offset_width=crop_window[1],
target_height=crop_window[2],
target_width=crop_window[3])
images.append(image)
r = control_flow_ops.group(*images)
for _ in xrange(3):
# Skip warm up time.
sess.run(r)
start_time = time.time()
for _ in xrange(num_iters):
sess.run(r)
return time.time() - start_time