使用3D阵列的3D箭图视频

Question

我试图创建一个3d quiver plot的速度向量，使用3个数组，包含x，y，z空间中关于时间的向量。也就是说，一段箭袋情节的视频。有人能帮忙吗？我已经在下面显示了按原样运行代码的错误消息

例如，输出的一帧应该有点像这样：

主代码：part 1和part 2。这里也有：

（注意：此代码已成功用于现在升级到3D的2D版本）

    print("Rendering 'Nucleation and Motion in G gradient in 3D'")
    print("Lattice constant dx = {}, time step dt = {}".format(fluid_model_g.dx, fluid_model_g.dt))
    for n in progressbar.progressbar(range(args.num_frames)):
        fluid_model_g.step()
        for _ in range(20):
            indices = tf.cast(flow_particles, 'int32')
            for index in indices.numpy():
                flow_streaks[index[0], index[1]] += 0.15 / args.oversampling
            dx = tf.gather_nd(fluid_model_g.u, indices)
            dy = tf.gather_nd(fluid_model_g.v, indices)
            dz = tf.gather_nd(fluid_model_g.w, indices)
            flow_particles = (flow_particles + tf.stack([dx, dy, dz], axis=1) * 400) % x.shape
        if n % args.oversampling == 0:
            rgb = [
                tf.reduce_mean((7*fluid_model_g.G)**2, axis=2) + flow_streaks,
                tf.reduce_mean((4*fluid_model_g.Y)**2, axis=2),
                tf.reduce_mean((2*fluid_model_g.X)**2, axis=2),
            ]
            frame = make_video_frame(rgb)
            writer.append_data(frame)
            flow_streaks *= 0
            flow_particles = tf.constant(flow_particle_origins, dtype='float64')
            
#-------------------BJD 22.4.2021----additional rough code at present-----------------------------------------        
            if n == 200:
                print("n = ", n)
                break

            c1 = c1 + 1

            nx, ny, nz = 240, 426, 426

            x1 = range(nx)
            y1 = range(ny)
            z1 = range(nz)

            U = np.loadtxt("/home/brendan/runs/tf2-model-g_3d_vel_vector/tf2-model-g/arrays/quiver3D_array31/u.txt")
            V = np.loadtxt("/home/brendan/runs/tf2-model-g_3d_vel_vector/tf2-model-g/arrays/quiver3D_array31/v.txt")
            W = np.loadtxt("/home/brendan/runs/tf2-model-g_3d_vel_vector/tf2-model-g/arrays/quiver3D_array31/w.txt")

            X1, Y1, Z1 = np.meshgrid(x1, y1, z1)

            fig = plt.figure(figsize=(10,10))
            ax = fig.gca(projection='3d')
            ax.set_title("pivot='mid'; every 10th arrow; units='velocity vector' time=" + str(c1))
            Q = ax.quiver(X1[::10, ::10, ::10], Y1[::10, ::10, ::10], Z1[::10, ::10, ::10], U[::10, ::10, ::10],
                V[::10, ::10, ::10], W[::10, ::10, ::10], pivot='mid', units='inches')
            Q.set_array(np.random.rand(np.prod(x.shape)))  # may need this? BJD 22.4.2021
            ax.scatter(X1[::10, ::10, ::10], Y1[::10, ::10, ::10], Z1[::10, ::10, ::10], color='c', s=0)
            plt.tight_layout()
            plt.savefig('/home/brendan/runs/tf2-model-g_3d_vel_vector/tf2-model-g/plots/3D_video37/3D_video_velocity_' + str(c1) + '.png')

运行代码时出现错误消息：

brendan@DL380pGen8:~/runs/tf2-model-g_3d_vel_vector/tf2-model-g$ python3 render_video.py /home/brendan/runs/tf2-model-g_2/tf2-model-g/nucleation_3D___3d_velocity_vector__1_seed__y0_x0_____R12_res160pVD20_OS1_ST20___TEST_8th_160.mp4 --params params/nucleation_3D.yaml
2021-04-22 16:44:29.517603: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-04-22 16:44:29.517657: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-04-22 16:44:43.078155: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-22 16:44:43.079068: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2021-04-22 16:44:43.079095: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-04-22 16:44:43.079164: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (DL380pGen8): /proc/driver/nvidia/version does not exist
2021-04-22 16:44:43.081520: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
Rendering 'Nucleation and Motion in G gradient in 3D'
Lattice constant dx = 0.15, time step dt = 0.041666666666666664
N/A% (0 of 480) |                                                                                             | Elapsed Time: 0:00:00 ETA:  --:--:--2021-04-22 16:44:46.312362: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:126] None of the MLIR optimization passes are enabled (registered 2)
2021-04-22 16:44:46.335468: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 1994870000 Hz
Traceback (most recent call last):
  File "render_video.py", line 855, in <module>
    episodes[args.episode](writer, args)
  File "render_video.py", line 647, in nucleation_3D
    fluid_model_g.step()
  File "/home/brendan/runs/tf2-model-g_3d_vel_vector/tf2-model-g/fluid_model_g.py", line 284, in step
    np.savetxt("/home/brendan/runs/tf2-model-g_3d_vel_vector/tf2-model-g/arrays/quiver3D_array31/u.txt", self.u) # BJD 22.4.2021
  File "<__array_function__ internals>", line 5, in savetxt
  File "/usr/local/lib/python3.8/dist-packages/numpy/lib/npyio.py", line 1371, in savetxt
    raise ValueError(
ValueError: Expected 1D or 2D array, got 3D array instead
brendan@DL380pGen8:~/runs/tf2-model-g_3d_vel_vector/tf2-model-g$ 

Answer 1

不久前，我不得不录制一个2D箭袋图

我采用的方法是：

1. 创建要使用的图形
2. 将fig转换为图像（numpy数组）
3. 使用opencv-python（cv2）写入输出

图至arr

import matplotlib.pyplot as plt
import numpy as np
import io


def fig_to_numpy(fig, dpi=180):
    """
    Converts an input Figure, to a numpy array.
    If used by Matplotlib, this will close the figure.

    :param fig: plt.figure
        The input figure, with all items drawn onto it.
    :param dpi: int
        The resolution of the output image, keep in mind that larger
        takes longer.
    :return: np.ndarray
        Return a numpy array containing the figure images.
    """

    buf = io.BytesIO()
    fig.tight_layout()
    fig.savefig(buf, format="png", dpi=dpi)
    plt.close(fig=fig)

    buf.seek(0)
    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
    buf.close()
    img = cv2.imdecode(img_arr, 1)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img

录制

这是一个示例录制类，有许多可选选项（examples）

class Record:
    def __init__(self, output, size, fps=20., format='mp4v'):
        fourcc = cv2.VideoWriter_fourcc(*format)
        self.out = cv2.VideoWriter(output, fourcc, fps, size[::-1])

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.close()

    def add(self, frame):
        if not self.out.isOpened():
            raise RuntimeError(f"Video is already closed.")
        self.out.write(frame)

    def show(self, frame, delay=1):
        cv2.imshow('Recording', frame)
        cv2.waitKey(delay)

    def close(self):
        self.out.release()
        cv2.destroyWindow('Recording')

测试用例

这将生成一个（假）测试图像

def quiver_plot(data, time: int):
    """ Create a fake quiver plot.  """
    fig = plt.figure(figsize=(10, 10))
    ax = fig.gca(projection='3d')
    ax.set_title("pivot='mid'; every 10th arrow; units='velocity vector' time=" + str(time))
    ax.scatter(*data, color='c', s=0)
    return fig

def quiver_data(time):
    x, y, z = np.meshgrid(np.arange(0.2, 1, 0.2 * time), np.arange(0.2, 1, .2 * time), np.arange(0.2, 1, .8))
    u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)
    v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)
    w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) * np.sin(np.pi * z))
    return u, v, w

测试代码

if __name__ == '__main__':
    # Get size of figure
    data = quiver_data(time=1)
    fig = quiver_plot(data, time=1)
    arr = fig_to_numpy(fig)

    with Record(output='test.mp4', size=arr.shape[:2], fps=1) as writer:
        for time in tqdm.trange(1, 11):
            data = quiver_data(time)
            fig = quiver_plot(data, time)
            arr = fig_to_numpy(fig)
            writer.add(arr)
            writer.show(arr, delay=1)