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util.py

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