VisDrone2019-DET Dataset Auto-Download (#2882)

data/argoverse_hd.yaml

 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
 # Train command: python train.py --data argoverse_hd.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /argoverse
 #     /yolov5

data/coco.yaml

 # COCO 2017 dataset http://cocodataset.org
 # Train command: python train.py --data coco.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /coco
 #     /yolov5

data/coco128.yaml

 # COCO 2017 dataset http://cocodataset.org - first 128 training images
 # Train command: python train.py --data coco128.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /coco128
 #     /yolov5

data/scripts/get_argoverse_hd.sh

@@ -2,7 +2,7 @@
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
 # Download command: bash data/scripts/get_argoverse_hd.sh
 # Train command: python train.py --data argoverse_hd.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /argoverse
 #     /yolov5

data/scripts/get_coco.sh

@@ -2,7 +2,7 @@
 # COCO 2017 dataset http://cocodataset.org
 # Download command: bash data/scripts/get_coco.sh
 # Train command: python train.py --data coco.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /coco
 #     /yolov5

data/scripts/get_voc.sh

@@ -2,7 +2,7 @@
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
 # Download command: bash data/scripts/get_voc.sh
 # Train command: python train.py --data voc.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /VOC
 #     /yolov5
@@ -29,34 +29,27 @@ echo "Completed in" $runtime "seconds"
 
 echo "Splitting dataset..."
 python3 - "$@" <<END
-import xml.etree.ElementTree as ET
-import pickle
 import os
-from os import listdir, getcwd
-from os.path import join
+import xml.etree.ElementTree as ET
+from os import getcwd
+
+sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
 
-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"]
 
-classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
 
+def convert_box(size, box):
+    dw = 1. / (size[0])
+    dh = 1. / (size[1])
+    x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+    return x * dw, y * dh, w * dw, h * dh
 
-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)
+    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)
@@ -65,74 +58,58 @@ def convert_annotation(year, image_id):
     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:
+        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)
+        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
+             float(xmlbox.find('ymax').text))
+        bb = convert_box((w, h), b)
         out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
 
-wd = getcwd()
 
+cwd = 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')
+    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))
+        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (cwd, 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
+mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val
+mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val
 
-import shutil
+python3 - "$@" <<END
 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('../tmp/train.txt'))
-f = open('../tmp/train.txt', 'r')
-lines = f.readlines()
-
-for line in lines:
-    line = "/".join(line.split('/')[-5:]).strip()
-    if (os.path.exists("../" + line)):
-        os.system("cp ../"+ line + " ../VOC/images/train")
-        
-    line = line.replace('JPEGImages', 'labels')
-    line = line.replace('jpg', 'txt')
-    if (os.path.exists("../" + line)):
-        os.system("cp ../"+ line + " ../VOC/labels/train")
+with open('../tmp/train.txt', 'r') as f:
+    for line in f.readlines():
+        line = "/".join(line.split('/')[-5:]).strip()
+        if os.path.exists("../" + line):
+            os.system("cp ../" + line + " ../VOC/images/train")
 
+        line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
+        if os.path.exists("../" + line):
+            os.system("cp ../" + line + " ../VOC/labels/train")
 
 print(os.path.exists('../tmp/2007_test.txt'))
-f = open('../tmp/2007_test.txt', 'r')
-lines = f.readlines()
-
-for line in lines:
-    line = "/".join(line.split('/')[-5:]).strip()
-    if (os.path.exists("../" + line)):
-        os.system("cp ../"+ line + " ../VOC/images/val")
-        
-    line = line.replace('JPEGImages', 'labels')
-    line = line.replace('jpg', 'txt')
-    if (os.path.exists("../" + line)):
-        os.system("cp ../"+ line + " ../VOC/labels/val")
-
+with open('../tmp/2007_test.txt', 'r') as f:
+    for line in f.readlines():
+        line = "/".join(line.split('/')[-5:]).strip()
+        if os.path.exists("../" + line):
+            os.system("cp ../" + line + " ../VOC/images/val")
+
+        line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
+        if os.path.exists("../" + line):
+            os.system("cp ../" + line + " ../VOC/labels/val")
 END
 
 rm -rf ../tmp # remove temporary directory

data/visdrone.yaml

+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
+# Train command: python train.py --data visdrone.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /VisDrone
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../VisDrone/VisDrone2019-DET-train/images  # 6471 images
+val: ../VisDrone/VisDrone2019-DET-val/images  # 548 images
+test: ../VisDrone/VisDrone2019-DET-test-dev/images  # 1610 images
+
+# number of classes
+nc: 10
+
+# class names
+names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  import os
+  from pathlib import Path
+
+  from utils.general import download
+
+
+  def visdrone2yolo(dir):
+      from PIL import Image
+      from tqdm import tqdm
+
+      def convert_box(size, box):
+          # Convert VisDrone box to YOLO xywh box
+          dw = 1. / size[0]
+          dh = 1. / size[1]
+          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
+      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+      for f in pbar:
+          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+          lines = []
+          with open(f, 'r') as file:  # read annotation.txt
+              for row in [x.split(',') for x in file.read().strip().splitlines()]:
+                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
+                      continue
+                  cls = int(row[5]) - 1
+                  box = convert_box(img_size, tuple(map(int, row[:4])))
+                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                      fl.writelines(lines)  # write label.txt
+
+
+  # Download
+  dir = Path('../VisDrone')  # dataset directory
+  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip'
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+  download(urls, dir=dir)
+
+  # Convert
+  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

data/voc.yaml

 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
 # Train command: python train.py --data voc.yaml
-# Default dataset location is next to /yolov5:
+# Default dataset location is next to YOLOv5:
 #   /parent_folder
 #     /VOC
 #     /yolov5

utils/general.py

@@ -9,6 +9,8 @@ import random
 import re
 import subprocess
 import time
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
 from pathlib import Path
 
 import cv2
@@ -161,18 +163,40 @@ def check_dataset(dict):
         if not all(x.exists() for x in val):
             print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
             if s and len(s):  # download script
-                print('Downloading %s ...' % s)
                 if s.startswith('http') and s.endswith('.zip'):  # URL
                     f = Path(s).name  # filename
+                    print(f'Downloading {s} ...')
                     torch.hub.download_url_to_file(s, f)
-                    r = os.system('unzip -q %s -d ../ && rm %s' % (f, f))  # unzip
-                else:  # bash script
+                    r = os.system(f'unzip -q {f} -d ../ && rm {f}')  # unzip
+                elif s.startswith('bash '):  # bash script
+                    print(f'Running {s} ...')
                     r = os.system(s)
-                print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure'))  # analyze return value
+                else:  # python script
+                    r = exec(s)  # return None
+                print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure'))  # print result
             else:
                 raise Exception('Dataset not found.')
 
 
+def download(url, dir='.', multi_thread=False):
+    # Multi-threaded file download function
+    def download_one(url, dir):
+        # Download 1 file
+        f = dir / Path(url).name  # filename
+        print(f'Downloading {url} to {f}...')
+        torch.hub.download_url_to_file(url, f, progress=True)  # download
+        if f.suffix == '.zip':
+            os.system(f'unzip -qo {f} -d {dir} && rm {f}')  # unzip -quiet -overwrite
+
+    dir = Path(dir)
+    dir.mkdir(parents=True, exist_ok=True)  # make directory
+    if multi_thread:
+        ThreadPool(8).imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # 8 threads
+    else:
+        for u in tuple(url) if isinstance(url, str) else url:
+            download_one(u, dir)
+
+
 def make_divisible(x, divisor):
     # Returns x evenly divisible by divisor
     return math.ceil(x / divisor) * divisor