查看.npy文件时出错

Question

I followed a tutorial to segment the lungs from CT scan images which saved as DICOM files. Then I tried to save the segmented images with .npy extension. But when I'm trying to load and view the files again which were saved as .npy files, I'm getting the following error.

TypeError: Image data can not convert to float

This is the code I used.

    import numpy as np
    from matplotlib import pyplot as plt

    img_array = np.load('../../PROCESSED_DATA/maskedimages_0.npy')
    plt.imshow(img_array, cmap='gray')
    plt.show()

I'm unable to post the whole code. But this shows the way how I saved the image as .npy.

for folder_index in range(folder_count):
    patient = load_scan(INPUT_FOLDER + patients[1])
    patient_pixels = get_pixels_hu(patient)
    plt.hist(patient_pixels.flatten(), bins=80, color='c')
    plt.xlabel("Hounsfield Units (HU)")
    plt.ylabel("Frequency")
    plt.show()
    pix_resampled, spacing = resample(patient_pixels, patient, [1,1,1])
    print("Shape before resampling\t", patient_pixels.shape)
    print("Shape after resampling\t", pix_resampled.shape)
    plot_3d(pix_resampled, 400)
    segmented_lungs = segment_lung_mask(pix_resampled, False)
    segmented_lungs_fill = segment_lung_mask(pix_resampled, True)
    plot_3d(segmented_lungs_fill, 0)
    imgs=plot_3d(segmented_lungs_fill - segmented_lungs, 0)
    np.save(output_path + "maskedimages_%d.npy" % (folder_index), imgs)

Can someone suggest me what need to be done to solve the error

PS

def plot_3d(image, threshold=-300):

    # Position the scan upright, 
    # so the head of the patient would be at the top facing the camera
    p = image.transpose(2,1,0)
   # p = p[:,:,::-1]

    verts, faces ,_,_= measure.marching_cubes(p, threshold)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111, projection='3d')

    # Fancy indexing: `verts[faces]` to generate a collection of triangles
    mesh = Poly3DCollection(verts[faces], alpha=0.70)
    face_color = [0.45, 0.45, 0.75]
    mesh.set_facecolor(face_color)
    ax.add_collection3d(mesh)

    ax.set_xlim(0, p.shape[0])
    ax.set_ylim(0, p.shape[1])
    ax.set_zlim(0, p.shape[2])

    plt.show()


def segment_lung_mask(image, fill_lung_structures=True):

    # not actually binary, but 1 and 2. 
    # 0 is treated as background, which we do not want
    binary_image = np.array(image > -320, dtype=np.int8)+1
    labels = measure.label(binary_image)

    # Pick the pixel in the very corner to determine which label is air.
    #   Improvement: Pick multiple background labels from around the patient
    #   More resistant to "trays" on which the patient lays cutting the air 
    #   around the person in half
    background_label = labels[0,0,0]

    #Fill the air around the person
    binary_image[background_label == labels] = 2


    # Method of filling the lung structures (that is superior to something like 
    # morphological closing)
    if fill_lung_structures:
        # For every slice we determine the largest solid structure
        for i, axial_slice in enumerate(binary_image):
            axial_slice = axial_slice - 1
            labeling = measure.label(axial_slice)
            l_max = largest_label_volume(labeling, bg=0)

            if l_max is not None: #This slice contains some lung
                binary_image[i][labeling != l_max] = 1


    binary_image -= 1 #Make the image actual binary
    binary_image = 1-binary_image # Invert it, lungs are now 1

    # Remove other air pockets insided body
    labels = measure.label(binary_image, background=0)
    l_max = largest_label_volume(labels, bg=0)
    if l_max is not None: # There are air pockets
        binary_image[labels != l_max] = 0

    return binary_image

Answer 1

A hint can be the fact that the function generating the data is named plot_3d . Try to check the shape of the loaded numpy array. If it is a 3d plot, pyplot doesn't know how to visualize it as a 2d image.