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