如何使用mxnet的im2rec函数创建自己的对象检测数据集.zip

# MXNet-im2rec_tutorial **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** ```python 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** ```python 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** ```python '--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 ```python 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 ``` ```python 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 ## ```python 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** ```python 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