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mxnet data IO im2rec tutorial

update 2018/8/21
Prepare custom datasets for gluonCV and mxnet
https://gluon-cv.mxnet.io/build/examples_datasets/detection_custom.html

this simple tutorial will introduce how to use im2rec for mx.image.ImageIter and ImageDetIter and how to use im2rec for COCO DataSet ok let's start but prepare your im2rec first https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py

im2rec

step 1. you should make a .lst file run in terminal

python im2rec.py --list testlst Dataset

--list     => make .lst file 
              you should set True when your folder didnt have any .lst file
              
test       => name of your .lst  name what you want 
DataSet    => your image dataset folder

check your folder path correct

use .lst for imageIter

data_iter = mx.image.ImageIter(
    batch_size=4, 
    data_shape=(3,816, 1232),
    label_width=1,
    path_imglist='test.lst',
    path_root='DataSet')  


for data in data_iter:  
    d = data.data[0]  
    break  
  
img =nd.transpose(d,(0,2,3,1))  
print(img.shape)  
io.imshow(img[0].asnumpy().astype(np.uint8))  
io.show()

step 2. make .rec file

python im2rec.py testlst.lst Dataset

testlst     => name of your .rec  name what you want
Dataset     => your image dataset folder

use .rec for imageiter

train_iter = mx.image.ImageIter(  
    batch_size=32,  
   data_shape=(3, 816, 1232),  
   path_imgrec='data.rec',  
   path_imgidx='data.idx',  #help shuffle performance
   shuffle=True,  
   aug_list=[mx.image.HorizontalFlipAug(0.5)]  
)

  
train_iter.reset()  
for batch in train_iter:  
    x = batch.data[0]  
    break  
  
img =nd.transpose(x,(0,2,3,1))  
print(img.shape)  
io.imshow(img[0].asnumpy().astype(np.uint8))  
io.show()

other feature

'--list' 
If this is set im2rec will create image list(s) by traversing root folder 
and output to .lst
Otherwise im2rec will read .lst and create a database at .rec

'--exts', default=['.jpeg', '.jpg', '.png'] 
list of acceptable image extensions.


'--train-ratio', default=1.0
Ratio of images to use for training.

'--test-ratio', default=0,  
Ratio of images to use for testing.

'--no-shuffle', dest='shuffle', action='store_false',  
If this is passed,im2rec will not randomize the image order in <prefix>.lst'


'--resize', type=int, default=0,  
resize the shorter edge of image to the newsize, original images will 
 be packed by default'

'--center-crop',  
specify whether to crop the center image to make it rectangular.

'--quality', default=95,  
JPEG quality for encoding, 1-100; or PNG compression for encoding, 1-9

'--num-thread',default=1,  
number of thread to use for encoding. order of images will be different


'--color', default=1, choices=[-1, 0, 1],  
specify the color mode of the loaded image.
 1: Loads a color image. Any transparency of image will be neglected. 
    It is the default flag 
 0: Loads image in grayscale mode.
-1:Loads image as such including alpha channel. 
    


ImageDetIter

make own .lst for detection

A B [extra header] [(object0), (object1), ... (objectN)]

  1. you should write the .lst with your image information Where A is the width of header (2 + length of extra header), B is the width of each object. Extra header is optional and used for inserting helper information such as (width, height). Each object is usually 5 or 6 numbers describing the object properties, for example: [id, xmin, ymin, xmax, ymax, difficulty] Putting all together, we have a lst file for object detection:

ref: https://mxnet.incubator.apache.org/api/python/image/image.html

with open('dataset.lst', 'w+') as f:  
    for i in range(3):  
        f.write(  
            str(i) + '\t' +  
            # idx  
            str(4) + '\t' + str(5) + '\t' +  
            # width of header and width of each object.  
            str(256) + '\t' + str(256) + '\t' +  
            # (width, height)  
            str(1) + '\t' +  
            # class  
            str((i / 10)) + '\t' + str((i / 10)) + '\t' + str(((i + 3) / 10)) + '\t' +str(((i + 3) / 10)) + '\t' +  
            # xmin, ymin, xmax, ymax  
           str(i) + '.jpg\n'
        )


you will see like this format in .lst

idx  A    B    [extra header]   [(object0), (object1), ... (objectN)]
0    4  5      256    256       1  0.1    0.1    0.3    0.3    0.jpg  
1    4  5      256    256       1  1.1    1.1    1.3    1.3    1.jpg  
2    4  5      256    256       1  2.1    2.1    2.3    2.3    2.jpg

 

must '\t' between data information

make own dataset .rec for ImageDetIter

python im2rec.py --pack-label dataset.lst Dataset

--pack-label  => when you have label
dataset.lst     => name for .rec  type for your self in .lst
Dataset       => DataSet image folder
import mxnet as mx  
import mxnet.ndarray as nd  
from skimage import io  
import numpy as np

train_iter = mx.image.ImageDetIter(  
   batch_size=3,  
   data_shape=(3, 256, 256),  
   path_imgrec='dataset.rec',  
   path_imgidx='dataset.idx',
   shuffle=True,  
   rand_crop=1,  
   min_object_covered=0.95,  
   max_attempts=200  
)  # you can aug your data in ImageDetIter
  
  
train_iter.reset()  
  
for batch in train_iter:  
    x = batch.data[0]  
    y = batch.label[0]  
    break  
  
print(y[0])  
  
  
img =nd.transpose(x,(0,2,3,1))  
print(img.shape)  
io.imshow(img[0].asnumpy().astype(np.uint8))  
io.show()

How to use im2rec on COCO DataSet

1. download cococ dataset first!!! 2. make own .json or use COCO.json
3. .json => .lst
4. .lst => .rec 5. .rec for ImageDetIter

how to load COCO DataSet from .json

import json  

with open('annotations/instances_train2017.json', 'r') as f:  
    DataSets = json.load(f)  
print(DataSets['annotations'][0])

>>{'segmentation': [[239.97, 260.24, 222.04, 270.49, 199.84, 253.41, 213.5, 227.79, 259.62, 200.46, 274.13, 202.17, 277.55, 210.71, 249.37, 253.41, 237.41, 264.51, 242.54, 261.95, 228.87, 271.34]],
 'area': 2765.1486500000005, 
 'iscrowd': 0, 
 'image_id': 558840, 
 'bbox': [199.84, 200.46, 77.71, 70.88], 
 'category_id': 58, 'id': 156}

as you see, these annotations about image 558840 , boundingbox,class.... in detail you can check coco website

COCO.json => own.json

make own dataset (class:bird) from COCO dataset

import json  
from mxnet import image  
from skimage import io  
import os  
  
  
  
## load COCO annotations  
with open('annotations/instances_train2017.json', 'r') as f:  
    DataSets = json.load(f)  
print(DataSets['annotations'][0])  
  
  
  
## save class and own dataset .json  
jsonName = 'ownset.json'  
directory = 'ownSet/'  
data = {}  
data['DataSet] = []  
with open(jsonName, 'w') as outfile:  
    if not os.path.exists(directory):  
        os.makedirs(directory)  
    for DataSet in DataSets['annotations']:  
        box = DataSet['bbox']  
        default_name = "000000000000"  
   img_id = str(DataSet['image_id'])  
        img_name = default_name[:len(default_name) - len(img_id)] + str(img_id) + '.jpg'  
   coco_name = 'train2017/' + img_name  
        if DataSet['category_id'] == 16:  #bird
  
            with open(coco_name, 'rb') as f:  
                img = image.imdecode(f.read())  
                height = img.shape[0]  
                width  = img.shape[1]  
                box[0] = box[0]/width  #normalize
                box[2] = box[2]/width  
                box[1] = box[1]/height  
                box[3] = box[3]/height  
            io.imsave(directory + img_name, img.asnumpy())  
            data['DataSet'].append({  
                'img_name': img_name,  
                'height': height,  
                'width': width,  
                'bbox': box,  
                'class':DataSet['category_id']  
            })  
    json.dump(data, outfile)  
print('save ok')  
  
  
  
with open(jsonName, 'r') as f:  
    Sets = json.load(f)  
print(Sets['DataSet'][0])

{'img_name': '000000202273.jpg', 'height': 640, 'width': 480, 'bbox': [0.6530625000000001, 0.089296875, 0.33064583333333336, 0.075390625], 'class': 16}

as you see this is your own dataset annotations

own data (.json) to .lst format

1.How to use own own dataset.json and make own dataset .lst

import json  
import mxnet as mx  
from skimage import io  
  
jsonName = 'ownset.json'  
directory = 'ownSet/'  
with open(jsonName, 'r') as f:  
    DataSet = json.load(f)  
  
print(DataSet['DataSet'][0]['img_name'])  
  
img_idx = 0  
with open('ownSet.lst', 'w+') as f:  
    for Data in DataSet['DataSet']:  
  
        x_min = Data['bbox'][0]  
        y_min = Data['bbox'][1]  
        x_max = Data['bbox'][0]+ Data['bbox'][2]  
        y_max = Data['bbox'][1]+ Data['bbox'][3] 
        f.write(  
                str(img_idx) + '\t' +  # idx  
                str(4) + '\t' + str(5) + '\t' +  # width of header and width of each object.  
                str(int(Data['height'])) + '\t' + str(Data['width']) + '\t' +  # (width, height)  
                str(1) + '\t' +  # class  
                str(x_min) + '\t' + str(y_min) + '\t' + str(x_max) + '\t' + str(y_max) + '\t' +  # xmin, ymin, xmax, ymax  
                str(Data['img_name'])+'\n')  
        img_idx += 1



you will see your .lst like this format

use im2rec for COCO dataSet

this step just use 'bird' class (Previous) to show you, .lst => .rec

python im2rec.py --pack-label ownSet.lst ownSet

ownSet.lst => last step you make
ownSet     => image folder

OK now we can use own dataSet ownSet.rec for ImageDetIter

import mxnet as mx  
shape = 800  
train_iter = mx.image.ImageDetIter(  
    batch_size=32,  
   data_shape=(3, shape, shape),  
   path_imgrec='ownSet.rec',  
   path_imgidx='ownSet.idx',  
   shuffle=False,  
  
)  # you can aug your data in ImageDetIter  
  
import matplotlib.pyplot as plt  
def box_to_rect(box, color, linewidth=3):  
  
    box = box.asnumpy()  
    print((box[0], box[1]), box[2] - box[0], box[3]-box[1])  
    return plt.Rectangle(  
        (box[0], box[1]), box[2] - box[0], box[3]-box[1],  
   fill=False, edgecolor=color, linewidth=linewidth  
    )  
  
  
train_iter.reset()  
  
batch = train_iter.next()  
  
img, labels = batch.data[0], batch.label[0]  
  
print(labels.shape)  
  
img = img.transpose((0,2,3,1))  
img = img.clip(0,255).asnumpy()/255  
  
  
for i in range(32):  
    _, fig = plt.subplots()  
    plt.imshow(img[i])  
  
  
    rect = box_to_rect(labels[i][0][1:5]*shape,'red',2)  
    fig.add_patch(rect)  
    fig.axes.get_xaxis().set_visible(False)  
    fig.axes.get_yaxis().set_visible(False)  
    plt.show()



result

make very very example for multilabeling

will generate dataset.lst

with open('dataset.lst', 'w+') as f:
    for i in range(12):
        f.write(
            str(i) + '\t' +
            # idx
            str(4) + '\t' + str(5) + '\t' +
            # width of header and width of each object.
            str(256) + '\t' + str(256) + '\t' +
            # (width, height)
            str(1) + '\t' +
            # class
            str((i / 15)) + '\t' + str((i / 15)) + '\t' + str(((i + 3) / 15)) + '\t' +str(((i + 3) / 15)) + '\t' +

            str(2) + '\t' +
            # class
            str((i / 50)) + '\t' + str((i / 50)) + '\t' + str(((i + 3) / 50)) + '\t' + str(((i + 3) / 50)) + '\t' +

            str(3) + '\t' +
            # class
            str((i / 100)) + '\t' + str((i / 100)) + '\t' + str(((i + 3) / 100)) + '\t' + str(((i + 3) / 100)) + '\t' +
            # xmin, ymin, xmax, ymax
           str(i) + '.jpg\n'
        )

check dataset.lst

step2: use im2rec

python im2rec.py --pack-label dataset.lst data

step3: use ImageDetIter show our multilabel

import mxnet as mx
shape = 800
train_iter = mx.image.ImageDetIter(
    batch_size=32,
    data_shape=(3, shape, shape),
    path_imgrec='dataset.rec',
    path_imgidx='dataset.idx',
    shuffle=False,

)  # you can aug your data in ImageDetIter

import matplotlib.pyplot as plt
def box_to_rect(box, color, linewidth=3):

    box = box.asnumpy()
    print((box[0], box[1]), box[2] - box[0], box[3]-box[1])
    return plt.Rectangle(
        (box[0], box[1]), box[2] - box[0], box[3]-box[1],
        fill=False, edgecolor=color, linewidth=linewidth
    )


train_iter.reset()

batch = train_iter.next()

img, labels = batch.data[0], batch.label[0]

print(labels.shape)


img = img.transpose((0,2,3,1))
img = img.clip(0,255).asnumpy()/255


for i in range(12):
    _, fig = plt.subplots()
    plt.imshow(img[i])


    color_list = ['red','blue','black']
    for k in range(labels[i].shape[0]):  # how many object in your label
        rect = box_to_rect(labels[i][k][1:5]*shape,color_list[k],2)
        fig.add_patch(rect)
    fig.axes.get_xaxis().set_visible(False)

if you feel useful pls give a star!!!


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