knee_seg/archive/read_data.py

import os
import numpy as np
import matplotlib.pyplot as plt
import pydicom
import cv2
from skimage.filters import gaussian
from skimage.segmentation import active_contour
from skimage.measure import find_contours


def get_mask_contour(mask):
    mskray = mask.astype(np.uint8)
    edged_mask = cv2.Canny(mskray, np.min(mskray), np.max(mskray))
    return edged_mask


folder_pth = r'C:\Users\user\Desktop\knee_seg\LITVYAK_D.I\LITVYAK_D.I\2025-01-22 181006'
# folder_pth = r'E:\projects\knee_seg\Юшкевич_Оба колена\Yushkevich_L\YUSHKEVICH_A.V. 172\2025-01-31 090519'
# folder_pth = r'E:\projects\knee_seg\LITVYAK_D.I\2025-01-22 181006'
# folder_pth = r'E:\projects\knee_seg\data_tamplet\PA0\ST0\SE4'
mx = []
list_files = os.listdir(folder_pth)

pd_fils = []
for i, fname in enumerate(list_files):
    file_pth = os.path.join(folder_pth, list_files[i])
    file_dcm = pydicom.dcmread(file_pth)
    print(file_dcm['0008', '103e'][0:9])
    if 'Sag PD' in file_dcm['0008', '103e'][0:9]:
        pd_fils.append(file_pth)


for f in range (2, len(pd_fils) - 2):
    file_dcm = pydicom.dcmread(pd_fils[f])
    im = file_dcm.pixel_array
    counts, bins = np.histogram(im, 100)
    threshold = 0.50 * (np.max(im) - 0.05 * np.max(im))
    segmented_im = np.copy(im)
    segmented_im[segmented_im < threshold] = 0
    contours = find_contours(segmented_im, level=0.5)

    plt.subplot(1, 3, 1)
    plt.imshow(im, cmap='gray')
    plt.title ('Исходное изображение')
    plt.subplot(1, 3, 2)
    plt.imshow(im, 'gray')
    plt.title ('Фильтрация по яркости')
    contours_square = []
    for c in range(len(contours)):
        contours_square.append((contours[c].shape[0]*contours[c].shape[1]))
    contours_thr = 0.3 * np.max (contours_square)
    # plot all
    for cn in range(len(contours)):

        plt.plot(contours[cn][:, 1], contours[cn][:, 0], 'r')

    plt.subplot(1, 3, 3)
    plt.imshow(im, 'gray')
    plt.title ('Фильтрация по размеру объекта')

    for cn in range(len(contours)):
        if contours[cn].shape[0]*contours[cn].shape[1]> contours_thr:
            # contours[cn] = 0
            plt.plot(contours[cn][:, 1], contours[cn][:, 0], 'r')

    plt.show()