163 lines
4.5 KiB
Python
163 lines
4.5 KiB
Python
import scipy.io as sio
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import os
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from PIL import Image
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import time
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import numpy as np
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import skimage
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from skimage import draw
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#import cv2
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IMAGE_SHAPE = (512, 512, 1)
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WINDOW_SHAPE = (50, 50)
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STEP_SIZE = (7, 7)
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def correct_image_path(path_imageBF):
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pl = path_imageBF.split(':')
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correct_path = 'I:' + pl[1]
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return correct_path
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def load_csg(filename_csg):
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mat_contents = sio.loadmat(filename_csg)
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hh = mat_contents['hh']
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val = hh[0, 0]
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seg_data = dict()
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seg_data['cellsegperim'] = val['cellsegperim']
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seg_data['filenameBF'] = val['filenameBF']
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seg_data['path_imageBF'] = str(val['pathnameBF'][0])
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# 下步仅用于矫正不同机器上驱动器名称的误差
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seg_data['path_imageBF'] = correct_image_path(seg_data['path_imageBF'])
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return seg_data
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def transform_cellseg(cellseg):
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cellsegs = list()
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for i in range(cellseg.shape[0]):
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seg = cellseg[i, 0]
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if(seg.shape[1]==2):
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cellsegs.append(seg)
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return cellsegs
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def get_seg_im(seg_data, idx):
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seg_im = dict()
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seg_im['cellseg'] = transform_cellseg(seg_data['cellsegperim'][0, idx])
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seg_im['filenameBF'] = str(seg_data['filenameBF'][0, idx][0])
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image_file = os.path.join(seg_data['path_imageBF'], seg_im['filenameBF'])
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seg_im['imageBF'] = np.array(Image.open(image_file))
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return seg_im
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def segperim_generator(seg_data):
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for i in range(seg_data['cellsegperim'].shape[1]):
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seg_im = get_seg_im(seg_data, i)
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yield seg_im
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def plot_cellseg(seg_im):
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colormap = mpl.cm.gray
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plt.imshow(seg_im['imageBF'], cmap=colormap)
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for idx in range(len(seg_im['cellseg'])):
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seg = seg_im['cellseg'][idx, 0]
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plt.plot(seg[:, 1], seg[:, 0], 'r')
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plt.plot(find_center(seg)[1], find_center(seg)[0], 'r*')
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plt.show()
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def find_center(seg):
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c_mean = np.mean(seg[:, 1])
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r_mean = np.mean(seg[:, 0])
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return (int(r_mean), int(c_mean))
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"""
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def get_circle(seg):
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circle = np.zeros(IMAGE_SHAPE)
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circle[seg[:, 0], seg[:, 1]] = 1
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return circle
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def get_circles(seg_im):
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circles = np.zeros(IMAGE_SHAPE)
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for idx in range(len(seg_im['cellseg'])):
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seg = seg_im['cellseg'][idx]
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circles[seg[:, 0], seg[:, 1]] = 1
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return circles
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"""
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"""
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seg_data = load_csg('E:/LTQ work/tanglab/deepYeast/xy01 1-120.csg')
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seg_im = next(segperim_generator(seg_data))
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plot_cellseg(seg_im)
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"""
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"""
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# 看来还是不能保存为.mat， 不然再次打开内部结构和数据类型就乱了
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def load_seg_im_data(filename):
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mat_contents = sio.loadmat(filename)
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data = mat_contents['data']
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"""
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def load_data(filename_list):
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Xy_list = list()
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for filename in filename_list:
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Xy_list.append(np.loadtxt(filename, dtype=np.int32, comments='#', delimiter=' '))
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Xy = np.vstack(tuple(Xy_list))
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np.random.shuffle(Xy)
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X = Xy[:, 1:]
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y = Xy[:, 0]
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X = X.reshape(X.shape[0], WINDOW_SHAPE[0], WINDOW_SHAPE[1])
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return X, y
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def load_rect_data(filename_list):
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Xy_list = list()
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for filename in filename_list:
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Xy_list.append(np.loadtxt(filename, dtype=np.int32, comments='#', delimiter=' '))
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Xy = np.vstack(tuple(Xy_list))
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np.random.shuffle(Xy)
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X = Xy[:, 4:]
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y = Xy[:, 0:4]
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X = X.reshape(X.shape[0], WINDOW_SHAPE[0], WINDOW_SHAPE[1])
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return X, y
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def save_image(data, path):
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for idx in range(data.shape[0]):
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im = data[idx]
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img = Image.fromarray(np.uint16(im))
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img.save(os.path.join(path, '%s.tif'%idx))
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def plot_rect(imageBF, vertex):
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colormap = mpl.cm.gray
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plt.imshow(imageBF, cmap=colormap)
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(r1, r2, c1, c2) = vertex
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plt.plot(np.ones(r2-r1)*c1, np.array(range(r1, r2)), 'r')
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plt.plot(np.ones(r2-r1)*c2, np.array(range(r1, r2)), 'r')
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plt.plot(np.array(range(c1, c2)), np.ones(c2-c1)*r1, 'r')
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plt.plot(np.array(range(c1, c2)), np.ones(c2-c1)*r2, 'r')
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plt.xlim(0, WINDOW_SHAPE[1])
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plt.ylim(WINDOW_SHAPE[0], 0)
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plt.show()
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def poly2mask(vertex_row_coords, vertex_col_coords, shape):
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fill_row_coords, fill_col_coords = draw.polygon(vertex_row_coords, vertex_col_coords, shape)
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mask = np.zeros(shape, dtype=np.bool)
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mask[fill_row_coords, fill_col_coords] = True
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return mask
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def pol2cart(phi, rho):
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x = rho * np.cos(phi)
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y = rho * np.sin(phi)
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return (x, y)
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