.npy 파일을 볼 때 오류가 발생했습니다.

Question

DICOM 파일로 저장된 CT 스캔 이미지에서 폐를 분류하는 방법을 안내했습니다. 그런 다음 세그먼트 화 된 이미지를 .npy 확장자로 저장하려고했습니다. 그러나 .npy 파일로 저장된 파일을 다시로드하고 보려고하면 다음 오류가 발생합니다.

TypeError: Image data can not convert to float 

이것은 내가 사용한 코드입니다.

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() 

전체 코드를 게시 할 수 없습니다. 그러나 이것은 이미지를 .npy로 저장하는 방법을 보여줍니다.

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) 

은 누군가가 힌트가 데이터를 생성하는 기능이 plot_3d라는 사실이 될 수 P.S에게 오류

에게

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

를 해결하기 위해 수행해야 할 걸 제안 할 수 있습니다. 로드 된 numpy 배열의 모양을 확인하십시오. 그것이 3d 줄거리라면, pyplot은 그것을 2 차원 이미지로 시각화하는 방법을 모른다.