when running data.py,a problem occured.

[zhangbanxian123]

Creating training images...

Done: 0/5635 images Done: 100/5635 images Traceback (most recent call last): File "data.py", line 186, in create_train_data() File "data.py", line 51, in create_train_data imgs_mask[i] = img_mask TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

I have tryed 'int(total)',then this problem occured. "imgs = np.ndarray((int(total), 1, image_rows, image_cols), dtype=np.uint8) imgs_mask = np.ndarray((int(total), 1, image_rows, image_cols), dtype=np.uint8) "

[ethanhe42]

Seems that total is None, why?

[zhangbanxian123]

This is the source of 'total',i can not find why it is None. Can you give me some suggests?

def create_train_data(): train_data_path = os.path.join(data_path, 'train') images = os.listdir(train_data_path) total = len(images) / 2 imgs = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8) imgs_mask = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8)

i = 0
print('-'*30)
print('Creating training images...')
print('-'*30)
for image_name in images:
    if 'mask' in image_name:
        continue
    image_mask_name = image_name.split('.')[0] + '_mask.tif'
    img = cv2.imread(os.path.join(train_data_path, image_name), cv2.IMREAD_GRAYSCALE)
    img_mask = cv2.imread(os.path.join(train_data_path, image_mask_name), cv2.IMREAD_GRAYSCALE)

    img = np.array([img])
    img_mask = np.array([img_mask])

    imgs[i] = img
    imgs_mask[i] = img_mask

    if i % 100 == 0:
        print('Done: {0}/{1} images'.format(i, total))
    i += 1
print('Loading done.')

[ethanhe42]

Maybe there's no image under train_data_path ‌

On Thu, Jan 18, 2018 at 9:08 PM dong-zhang1 [email protected] wrote:

This is the source of 'total',i can not find why it is None. Can you give me some suggests?

def create_train_data(): train_data_path = os.path.join(data_path, 'train') images = os.listdir(train_data_path) total = len(images) / 2 imgs = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8) imgs_mask = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8)

i = 0 print('-'*30) print('Creating training images...') print('-'*30) for image_name in images: if 'mask' in image_name: continue image_mask_name = image_name.split('.')[0] + '_mask.tif' img = cv2.imread(os.path.join(train_data_path, image_name), cv2.IMREAD_GRAYSCALE) img_mask = cv2.imread(os.path.join(train_data_path, image_mask_name), cv2.IMREAD_GRAYSCALE)

img = np.array([img])
img_mask = np.array([img_mask])

imgs[i] = img
imgs_mask[i] = img_mask

if i % 100 == 0:
    print('Done: {0}/{1} images'.format(i, total))
i += 1

print('Loading done.')

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[zhangbanxian123]

The path has images on it. If there's no images, it will not show the number of the images . See my first comment , the 1/2 number of total is 5635.

[zhangbanxian123]

I have changed all 'total' as '5635',but the same error still occurs...... And there is no 'int()' in 'data.py'

Creating training images...

Done: 0/5635 images Done: 100/5635 images Traceback (most recent call last): File "data.py", line 186, in create_train_data() File "data.py", line 51, in create_train_data imgs_mask[i] = img_mask TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

[zhangbanxian123]

Here is my 'data.py'.

from future import print_function

import os import numpy as np

import cv2 import pandas as pd import sys import os import os.path import string import scipy.io import pdb import PIL.Image as Image import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib.font_manager import FontProperties import skimage import skimage.measure

data_path = 'raw/' save_path = 'mnt/' image_rows = 420 image_cols = 580

def create_train_data(): train_data_path = os.path.join(data_path, 'train') images = os.listdir(train_data_path) total =len(images)/2

imgs = np.ndarray((5635, 1, image_rows, image_cols), dtype=np.uint8)
imgs_mask = np.ndarray((5635, 1, image_rows, image_cols), dtype=np.uint8)

i = 0
print('-'*30)
print('Creating training images...')
print('-'*30)
for image_name in images:
    if 'mask' in image_name:
        continue
    image_mask_name = image_name.split('.')[0] + '_mask.tif'
    img = cv2.imread(os.path.join(train_data_path, image_name), cv2.IMREAD_GRAYSCALE)
    img_mask = cv2.imread(os.path.join(train_data_path, image_mask_name), cv2.IMREAD_GRAYSCALE)

    img = np.array([img])
    img_mask = np.array([img_mask])

    imgs[i] = img
    imgs_mask[i] = img_mask

    if i % 100 == 0:
        print('Done: {0}/{1} images'.format(i, 5635))
    i += 1
print('Loading done.')

np.save('imgs_train.npy', imgs)
np.save('imgs_mask_train.npy', imgs_mask)
print('Saving to .npy files done.')

def load_train_data(): imgs_train = np.load('imgs_train.npy') imgs_mask_train = np.load('imgs_mask_train.npy') return imgs_train, imgs_mask_train

def preprocess(imgs, img_rows,img_cols): imgs_p = np.ndarray((imgs.shape[0],imgs.shape[1],img_rows,img_cols),dtype=np.uint8) for i in range(imgs.shape[0]): imgs_p[i, 0 ] = cv2.resize(imgs[i,0],(img_cols,img_rows),interpolation=cv2.INTER_CUBIC) return imgs_p

def detseg(): # out_rows=240 # out_cols=320 out_rows=160 out_cols=224 imgs_train = np.load('imgs_train.npy') imgs_train=preprocess(imgs_train, out_rows,out_cols).astype(np.float32) mean_image=imgs_train.mean(0)[np.newaxis,] imgs_train -=mean_image print(np.histogram(imgs_train)) std_image=imgs_train.std(0)[np.newaxis,] imgs_train /=std_image print(np.histogram(imgs_train))

imgs_mask_train = np.load('imgs_mask_train.npy')
imgs_mask_train=preprocess(imgs_mask_train, out_rows,out_cols)
imgs_mask_train[imgs_mask_train<=50]=False
imgs_mask_train[imgs_mask_train>50]=True
print(np.histogram(imgs_mask_train))

# if os.path.exists(save_path+'data.npy')==False:
np.save(save_path+'mean.npy',mean_image)
np.save(save_path+'std.npy',std_image)
np.save(save_path+'data.npy',imgs_train.astype(np.float32))
print('save data')
np.save(save_path+'mask.npy',imgs_mask_train.astype(np.bool))
print('save mask')
del imgs_train

bboxes=[]
masks=[]

acc_width=[]
acc_height=[]
max_width=0

for percent,label in enumerate(imgs_mask_train):
    if percent % 100==0:
        print(percent)
    label=label[0]
    CCMap,CCNum = skimage.measure.label(label,connectivity=1,background=0,return_num=True)
    gt_boxes=[]
    instance_masks=[]
    for ins in range(CCNum):
        foregroundIdx=CCMap==ins
        # plt.imshow(foregroundIdx)
        # plt.show()
        area=np.sum(foregroundIdx)
        if area<10:
            CCMap[foregroundIdx]=-1
            continue
        idx_map=np.where(foregroundIdx==True)
        ymin=idx_map[0].min()
        ymax=idx_map[0].max()
        xmin=idx_map[1].min()
        xmax=idx_map[1].max()

        max_width=foregroundIdx.shape[1]
        acc_width.append(xmax-xmin)
        acc_height.append(ymax-ymin)
        # print(xmin,ymin,xmax,ymax)
        instance_masks.append(label[ymin:ymax,xmin:xmax])
        gt_boxes.append([xmin,ymin,xmax,ymax,1])
    bboxes.append(gt_boxes)
    masks.append(instance_masks)

print("xmax", max_width, max(acc_width))
H, xedges, yedges=np.histogram2d(acc_width,acc_height, bins=50)
plt.imshow(H, interpolation='nearest', origin='low',
            extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
plt.show()

np.save(save_path+'roidb.npy',np.array(bboxes))
np.save(save_path+'maskdb.npy',np.array(masks))

def create_test_data(): train_data_path = os.path.join(data_path, 'test') images = os.listdir(train_data_path) total = len(images)

imgs = np.ndarray((total, 1, image_rows, image_cols), dtype=np.uint8)
imgs_id = np.ndarray((total, ), dtype=np.int32)

i = 0
print('-'*30)
print('Creating test images...')
print('-'*30)
for image_name in images:
    img_id = image_name.split('.')[0]
    img = cv2.imread(os.path.join(train_data_path, image_name), cv2.IMREAD_GRAYSCALE)

    img = np.array([img])

    imgs[i] = img
    imgs_id[i] = img_id

    if i % 100 == 0:
        print('Done: {0}/{1} images'.format(i, 5635))
    i += 1
print('Loading done.')

np.save('imgs_test.npy', imgs)
np.save('imgs_id_test.npy', imgs_id)
print('Saving to .npy files done.')

def load_test_data(): imgs_test = np.load('imgs_test.npy') imgs_id = np.load('imgs_id_test.npy') return imgs_test, imgs_id

if name == 'main': create_train_data() create_test_data()

detseg()