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import numpy as np
# Functions for converting
def figure_to_image(figures, close=True):
"""Render matplotlib figure to numpy format.
Note that this requires the ``matplotlib`` package.
Args:
figure (matplotlib.pyplot.figure) or list of figures: figure or a list of figures
close (bool): Flag to automatically close the figure
Returns:
numpy.array: image in [CHW] order
"""
import matplotlib.pyplot as plt
import matplotlib.backends.backend_agg as plt_backend_agg
def render_to_rgb(figure):
canvas = plt_backend_agg.FigureCanvasAgg(figure)
canvas.draw()
data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8)
w, h = figure.canvas.get_width_height()
image_hwc = data.reshape([h, w, 4])[:, :, 0:3]
image_chw = np.moveaxis(image_hwc, source=2, destination=0)
if close:
plt.close(figure)
return image_chw
if isinstance(figures, list):
images = [render_to_rgb(figure) for figure in figures]
return np.stack(images)
else:
image = render_to_rgb(figures)
return image
def _prepare_video(V):
"""
Converts a 5D tensor [batchsize, time(frame), channel(color), height, width]
into 4D tensor with dimension [time(frame), new_width, new_height, channel].
A batch of images are spreaded to a grid, which forms a frame.
e.g. Video with batchsize 16 will have a 4x4 grid.
"""
b, t, c, h, w = V.shape
if V.dtype == np.uint8:
V = np.float32(V) / 255.0
def is_power2(num):
return num != 0 and ((num & (num - 1)) == 0)
# pad to nearest power of 2, all at once
if not is_power2(V.shape[0]):
len_addition = int(2 ** V.shape[0].bit_length() - V.shape[0])
V = np.concatenate((V, np.zeros(shape=(len_addition, t, c, h, w))), axis=0)
n_rows = 2 ** ((b.bit_length() - 1) // 2)
n_cols = V.shape[0] // n_rows
V = np.reshape(V, newshape=(n_rows, n_cols, t, c, h, w))
V = np.transpose(V, axes=(2, 0, 4, 1, 5, 3))
V = np.reshape(V, newshape=(t, n_rows * h, n_cols * w, c))
return V
def make_grid(I, ncols=8):
# I: N1HW or N3HW
assert isinstance(I, np.ndarray), "plugin error, should pass numpy array here"
if I.shape[1] == 1:
I = np.concatenate([I, I, I], 1)
assert I.ndim == 4 and I.shape[1] == 3
nimg = I.shape[0]
H = I.shape[2]
W = I.shape[3]
ncols = min(nimg, ncols)
nrows = int(np.ceil(float(nimg) / ncols))
canvas = np.zeros((3, H * nrows, W * ncols), dtype=I.dtype)
i = 0
for y in range(nrows):
for x in range(ncols):
if i >= nimg:
break
canvas[:, y * H : (y + 1) * H, x * W : (x + 1) * W] = I[i]
i = i + 1
return canvas
# if modality == 'IMG':
# if x.dtype == np.uint8:
# x = x.astype(np.float32) / 255.0
def convert_to_HWC(tensor, input_format): # tensor: numpy array
assert len(set(input_format)) == len(
input_format
), "You can not use the same dimension shordhand twice. \
input_format: {}".format(
input_format
)
assert len(tensor.shape) == len(
input_format
), "size of input tensor and input format are different. \
tensor shape: {}, input_format: {}".format(
tensor.shape, input_format
)
input_format = input_format.upper()
if len(input_format) == 4:
index = [input_format.find(c) for c in "NCHW"]
tensor_NCHW = tensor.transpose(index)
tensor_CHW = make_grid(tensor_NCHW)
return tensor_CHW.transpose(1, 2, 0)
if len(input_format) == 3:
index = [input_format.find(c) for c in "HWC"]
tensor_HWC = tensor.transpose(index)
if tensor_HWC.shape[2] == 1:
tensor_HWC = np.concatenate([tensor_HWC, tensor_HWC, tensor_HWC], 2)
return tensor_HWC
if len(input_format) == 2:
index = [input_format.find(c) for c in "HW"]
tensor = tensor.transpose(index)
tensor = np.stack([tensor, tensor, tensor], 2)
return tensor
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