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
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 testrec Dataset
testrec => 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.
A B [extra header] [(object0), (object1), ... (objectN)]
- 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
lstfile 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
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()1. download cococ dataset first!!!
2. make own .json or use COCO.json
3. .json => .lst
4. .lst => .rec
5. .rec for ImageDetIter
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
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
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
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
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
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
python im2rec.py --pack-label dataset.lst data
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!!!