Merge pull request #202 from ChristopherSTAN/master

data/get_voc.sh

+start=`date +%s`
+
+# handle optional download dir
+if [ -z "$1" ]
+  then
+    # navigate to ~/data
+    echo "navigating to ../data/ ..." 
+    mkdir -p ../data
+    cd ../data/
+  else
+    # check if is valid directory
+    if [ ! -d $1 ]; then
+        echo $1 "is not a valid directory"
+        exit 0
+    fi
+    echo "navigating to" $1 "..."
+    cd $1
+fi
+
+echo "Downloading VOC2007 trainval ..."
+# Download the data.
+curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
+echo "Downloading VOC2007 test data ..."
+curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
+echo "Done downloading."
+
+# Extract data
+echo "Extracting trainval ..."
+tar -xvf VOCtrainval_06-Nov-2007.tar
+echo "Extracting test ..."
+tar -xvf VOCtest_06-Nov-2007.tar
+echo "removing tars ..."
+rm VOCtrainval_06-Nov-2007.tar
+rm VOCtest_06-Nov-2007.tar
+
+end=`date +%s`
+runtime=$((end-start))
+
+echo "Completed in" $runtime "seconds"
+
+start=`date +%s`
+
+# handle optional download dir
+if [ -z "$1" ]
+  then
+    # navigate to ~/data
+    echo "navigating to ../data/ ..." 
+    mkdir -p ../data
+    cd ../data/
+  else
+    # check if is valid directory
+    if [ ! -d $1 ]; then
+        echo $1 "is not a valid directory"
+        exit 0
+    fi
+    echo "navigating to" $1 "..."
+    cd $1
+fi
+
+echo "Downloading VOC2012 trainval ..."
+# Download the data.
+curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+echo "Done downloading."
+
+
+# Extract data
+echo "Extracting trainval ..."
+tar -xvf VOCtrainval_11-May-2012.tar
+echo "removing tar ..."
+rm VOCtrainval_11-May-2012.tar
+
+end=`date +%s`
+runtime=$((end-start))
+
+echo "Completed in" $runtime "seconds"
+
+cd ../data
+echo "Spliting dataset..."
+python3 - "$@" <<END
+import xml.etree.ElementTree as ET
+import pickle
+import os
+from os import listdir, getcwd
+from os.path import join
+
+sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
+
+classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+
+
+def convert(size, box):
+    dw = 1./(size[0])
+    dh = 1./(size[1])
+    x = (box[0] + box[1])/2.0 - 1
+    y = (box[2] + box[3])/2.0 - 1
+    w = box[1] - box[0]
+    h = box[3] - box[2]
+    x = x*dw
+    w = w*dw
+    y = y*dh
+    h = h*dh
+    return (x,y,w,h)
+
+def convert_annotation(year, image_id):
+    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
+    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
+    tree=ET.parse(in_file)
+    root = tree.getroot()
+    size = root.find('size')
+    w = int(size.find('width').text)
+    h = int(size.find('height').text)
+
+    for obj in root.iter('object'):
+        difficult = obj.find('difficult').text
+        cls = obj.find('name').text
+        if cls not in classes or int(difficult)==1:
+            continue
+        cls_id = classes.index(cls)
+        xmlbox = obj.find('bndbox')
+        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
+        bb = convert((w,h), b)
+        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
+
+wd = getcwd()
+
+for year, image_set in sets:
+    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
+        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
+    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
+    list_file = open('%s_%s.txt'%(year, image_set), 'w')
+    for image_id in image_ids:
+        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
+        convert_annotation(year, image_id)
+    list_file.close()
+
+END
+
+cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt
+cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt
+
+python3 - "$@" <<END
+
+import shutil
+import os
+os.system('mkdir ../VOC/')
+os.system('mkdir ../VOC/images')
+os.system('mkdir ../VOC/images/train')
+os.system('mkdir ../VOC/images/val')
+
+os.system('mkdir ../VOC/labels')
+os.system('mkdir ../VOC/labels/train')
+os.system('mkdir ../VOC/labels/val')
+
+import os
+print(os.path.exists('../data/train.txt'))
+f = open('../data/train.txt', 'r')
+lines = f.readlines()
+
+for line in lines:
+    #print(line.split('/')[-1][:-1])
+    line = "/".join(line.split('/')[2:])
+    #print(line)
+    if (os.path.exists("../" + line[:-1])):
+        os.system("cp ../"+ line[:-1] + " ../VOC/images/train")
+        
+print(os.path.exists('../data/train.txt'))
+f = open('../data/train.txt', 'r')
+lines = f.readlines()
+
+for line in lines:
+    #print(line.split('/')[-1][:-1])
+    line = "/".join(line.split('/')[2:])
+    line = line.replace('JPEGImages', 'labels')
+    line = line.replace('jpg', 'txt')
+    #print(line)
+    if (os.path.exists("../" + line[:-1])):
+        os.system("cp ../"+ line[:-1] + " ../VOC/labels/train")
+
+print(os.path.exists('../data/2007_test.txt'))
+f = open('../data/2007_test.txt', 'r')
+lines = f.readlines()
+
+for line in lines:
+    #print(line.split('/')[-1][:-1])
+    line = "/".join(line.split('/')[2:])
+    
+    if (os.path.exists("../" + line[:-1])):
+        os.system("cp ../"+ line[:-1] + " ../VOC/images/val")
+
+print(os.path.exists('../data/2007_test.txt'))
+f = open('../data/2007_test.txt', 'r')
+lines = f.readlines()
+
+for line in lines:
+    #print(line.split('/')[-1][:-1])
+    line = "/".join(line.split('/')[2:])
+    line = line.replace('JPEGImages', 'labels')
+    line = line.replace('jpg', 'txt')
+    #print(line)
+    if (os.path.exists("../" + line[:-1])):
+        os.system("cp ../"+ line[:-1] + " ../VOC/labels/val")
+
+END
+
+rm -rf ../data

data/voc.yaml

+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
+# Download command: bash yolov5/data/get_voc.sh
+# Train command: python train.py --data voc.yaml
+# Dataset should be placed next to yolov5 folder:
+#   /parent_folder
+#     /VOC
+#     /yolov5
+
+# train and val datasets (image directory or *.txt file with image paths)
+train: ../VOC/images/train/
+val: ../VOC/images/val/
+
+# number of classes
+nc: 20
+
+# class names
+names: ['aeroplane', 'bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse',
+        'motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']